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Terminator: A Software Package for Fast and Local Optimization of His-Tag Placement for Protein Affinity Purification
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  • Rokas Gerulskis
    Rokas Gerulskis
    Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
    More by Rokas Gerulskis
  • Shelley D. Minteer*
    Shelley D. Minteer
    Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
    Kummer Institute Center for Resource Sustainability, Missouri University of Science and Technology, Rolla, Missouri 65409, United States
    *Email: [email protected]
    More by Shelley D. Minteer
    Orcidhttps://orcid.org/0000-0002-5788-2249
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ACS Bio & Med Chem Au

Cite this: ACS Bio Med Chem Au 2025, XXXX, XXX, XXX-XXX
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https://doi.org/10.1021/acsbiomedchemau.4c00055
Published January 3, 2025

© 2025 The Authors. Published by American Chemical Society. This publication is licensed under

CC-BY-NC-ND 4.0 .

Abstract

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Although the use of affinity tags can greatly improve purification of expressed enzymes, the placement of affinity tags can significantly impact the expression, solubility, and function of recombinant proteins. To facilitate the optimal design of 6xHis-tagged constructs for protein purification, we developed Terminator, a Python-based software package, which takes a UniProt ID or existing protein sequence as input, identifies related sequences, maps sequence conservation retrieved from ConSurf onto protein 3D structures retrieved from the PDB and SWISS-MODEL, and analyzes proximity to cavities and functional sites to recommend the N- or C-terminus for placement of 6xHis fusion tags <15 residues in length. The package also outputs a document with available purification and activity literature for the target and closely related proteins organized by year. Comparative analysis of Terminator predictions against published experimental tag behavior for 6xHis fusion tags <15 residues in length demonstrates an 86–100% accuracy in predicting the relative risk of ill effects between termini and a 92–93% accuracy in predicting the absolute risk of modifying individual termini. This reliability of Terminator’s analysis suggests that proximity to surface cavities, not burial of wild-type termini, is the most reliable predictor of ill effects arising from short 6xHis fusion tags. This tool aims to expedite construct design and enhance the successful production of well-behaved proteins for studies in enzymology and biocatalysis with minimal need for computational resources, programming knowledge, or familiarity with protein-tag interference mechanisms.

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Introduction

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The last three decades have seen a significant acceleration in the research and industrial application of biocatalytic methods. (1) This growth is due, in part, to method developments significantly improving the ease of obtaining large amounts of target proteins for catalytic research. Early protein research was hampered by the need to purify target proteins from their original, poorly characterized host organisms. Developments in gene regulatory architectures and genetically tuned host organisms then refocused the protein procurement workflow to growing protein in foreign organisms (recombinant expression), chiefly in workhorse strains of Escherichia coli and other species. (2,3) Although protein yields saw significant improvement, procedures were still bottlenecked by the need to purify target genes from the original host organisms and package them into DNA architectures for recombinant expression. This limitation was addressed in the past decade with significant advances in commercial gene synthesis and vector construction services, which reliably and inexpensively convert digital DNA sequences into codon-optimized, physical plasmids ready for expression in host organisms. (4,5) All these advances have narrowed the labor of protein scientists to selecting a target sequence from a protein database such as UniProt, (6) ordering the selected gene from a commercial gene synthesizer, and performing subsequent expression and purification procedures with the aid of available literature.
In parallel to these developments, affinity tags quickly became an indispensable tool for the purification of recombinantly expressed proteins by enabling specific affinity between target proteins and purification matrices. (7) Affinity tags range in size and mechanism, from small 6xHis tags binding nickel-containing polymers to 400-residue maltose-binding protein, offering additional benefits to solubility. (8,9) An affinity tag is added to a protein by extension of the start or end of its genetic sequence and consequently its protein sequence, typically through site-directed mutagenesis, targeted positioning in a DNA vector, or digital modification prior to gene synthesis.
Carefully selecting which protein terminus should be modified with an affinity tag is critical to the successful expression and purification of a target protein, as protein sequence modification with affinity tags can have radical effects on solubility, (10−12) stability, (13) and catalytic efficiency through structural interference near the site of modification. A long linker sequence is sometimes added between the protein and affinity tag in an attempt to minimize the risk of structural issues (the combination of an affinity tag with linker or cleavage sequences, etc., is known as a fusion tag), but this may simply introduce longer-range or even interassembly tag interference. (14) Simply replicating tag architecture established in the literature for a target can be a reliable option for obtaining functional proteins; however, experimental verification of tag noninterference is uncommon, while the rationale for choosing one terminus versus the other is exceptionally rare.
If purification protocols for a target protein are not established, researchers typically evaluate the suitability of a protein terminus for tag addition by loading the protein’s crystal structure, when available, into molecular visualization software. One can then visually assess how buried the terminus is within the surrounding structure and its proximity to the enzyme’s active site, as informed by published experimental data. However, this approach may be hindered by the researcher’s familiarity with the visualization software, including difficulty in identifying catalytic residues or interpreting terminal coordinate assignment due to nonstandard sequence numbering and knowing to convert the default asymmetrical unit stored in a PDB file to the biologically relevant quaternary assembly. Furthermore, conclusive identification of an enzyme active site can take several years of experimental deliberation. (15,16) More rigorous prediction of tag behavior via molecular modeling (MM) and molecular dynamic simulation requires familiarity with computational tools and a thorough understanding of complex modeling outputs, a time investment unavailable to many experimentalists, particularly in biocatalysis fields only tangential to enzymology. (17)
Research in enzymology exploring natural protein behavior requires rigorous investigation of tag–protein interaction and often the removal of tags postpurification to better represent wild-type behavior. In contrast, biocatalytic research areas such as brewing, textile manufacture, and bioelectrochemistry need to devote minimal time to natural protein behavior, devoting attention to downstream optimizations, such as protein operational stability, reaction vessel architecture, immobilization strategy, or interaction in catalytic cascades. In short, many protein-employing research areas merely need to quickly obtain a functioning protein to focus on other optimizations in more complex systems.
This work introduces Terminator, a Python package that automates the collection of literature and protein structure and residue conservation data to rapidly predict structural and catalytic issues arising from the addition of a 6x-His tag to either protein terminus, completing in a few minutes on a personal computer starting with only a UniProt ID or partial protein sequence. Comparative analysis of Terminator’s predictions with published data demonstrates an 86–100% success rate in predicting relative suitability between termini when short (<14 residues) tags are employed, with a 92–93% accuracy in predicting the absolute suitability of individual termini for the addition of 6x-His tags.

Methods

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Structure Selection

Terminator begins with an input of either a UniProt ID or, for convenience, a partial protein sequence of an existing protein. In either case, it then performs a BLAST multiple-sequence alignment (18) against the UniProtKB database to find synonymous UniProt entries, well-characterized proteins with >80% sequence identity to the target protein. This operation is especially useful in cases where a manual search for protein structures fails despite structures merely being categorized under separate UniProt entries with identical or very closely related protein sequences. The protein sequence is then retrieved from the input UniProt entry, or the highest-scoring entry is retrieved using the input sequence. This sequence is then modified by subtracting any residues belonging to a transit, localization, or propeptide sequence as cataloged in the UniProt entry, as these are typically removed in vivo in the process of protein maturation. The SWISS-MODEL database (19) is subsequently queried for entries using each synonymous ID until a model is retrieved. The SWISS-MODEL server not only quantifies the relative quality of available structures by discriminating crystal artifacts from biological contacts and can even offer model crystal structures based on similarly folding sequences belonging to unrelated proteins. Consequently, the SWISS-MODEL query serves a similar function as synonymous entry retrieval, as it can retrieve a crystal structure when no structure is available across any synonymous entries.

Literature Retrieval

The tangential utility of the Terminator package is the literature retrieval tool, which provides an Excel document with a significant number of structure, purification, and activity literature, with links, retrieved from UniProt and a Boolean PubMed (20) query, organized by year. The PubMed query functions by submitting every name for the target protein and species of origin collected from synonymous UniProt entries, with the narrowing terms “expression”, “purification”, “mechanism”, and “activity”. The user is advised to search for additional literature, nevertheless, due to occasional inconsistencies in enzyme naming in the literature versus names assigned in UniProt and because both databases may exclude literature in biochemistry-adjacent fields such as bioelectrochemistry. However, test runs for enzymes with which we are rigorously familiar did retrieve additional papers that we had missed in traditional searches. Even if the user judges that crystal structure data are insufficient or their analysis by Terminator is unnecessary, the literature retrieval function is an invaluable time-saving tool for collecting data to write up a purification or assay protocol.

ConSurf

The ConSurf server is instrumental in the determination of protein functional regions from sequence-evolutionary data alone. (21) The tool functions by clustering related protein sequences into a phylogenetic tree and calculating residue-specific conservation scores as a function of evolutionary rate. In short, ConSurf estimates structurally and catalytically important residues in a protein sequence without information from wet-lab experiments, providing insights that are rarely determined experimentally due to the exponential complexity of sequence-space-dependent experiments. One interesting example is W274 in oxalate decarboxylase (OxDC) from Bacillus subtilis, which has the ninth-highest conservation score of OxDC's 385 residues. This residue was shown to participate in interchain electron–hole hopping to protect the protein from oxidative damage in 2021. (22) The importance of this residue to OxDC function could have been demonstrated by ConSurf possibly as early as 2001, 20 years before it was demonstrated experimentally. (23) The specific way in which Terminator employs ConSurf outputs is discussed further in the following section and in detail in the Supporting Information (SI).

Terminus Quality Metric Calculation

A detailed discussion of how Terminator analyzes a crystal structure and compares protein termini is provided in the Supporting Information; however, a simplified explanation is provided below. Figure 1 shows a simple representation of Terminator analyzing a terminus on a hypothetical, 2-dimensional protein.

Figure 1

Figure 1. Simplified representation of Terminator’s structure–analysis algorithm on a hypothetical two-dimensional protein. Light and dark gray regions represent separate protein chains. Beige to purple circles represent simplified protein residues colored by their ConSurf-derived color value and numbered according to their ConSurf-derived conservation value. The N-terminus is represented by the cyan “N” circle, with its cavity–assignment radius as a large light-blue region. An ActiveSite and ConservedSite are represented as navy and purple regions, respectively. A cavity is represented as the orange region with its assigned residues outlined in orange. To the right, a flowchart illustrating how each object recognized by Terminator (ActiveSite, ConservedSite, cavity, and terminus) defines its own properties and aggregates the properties of subordinate objects, leading to an evaluation of the status of a terminus.

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First, Terminator searches the crystal structure for collections of proximal residues (Figure 1, beige/purple/navy circles) having the same ConSurf-provided color score, implying a similar conservation value among protein homologs and, consequently, a similar level of functional importance. These collections are defined as a new object called a “Site,” which comes in two forms: an “ActiveSite,” composed of residues with the highest possible color score of 9, and a “ConservedSite,” whose residues each have a color score of 8. These Site objects are assigned a conservation “site score” equal to the sum of the ConSurf-provided conservation values of their residues. This score will be employed again later in quantifying the functional importance of protein cavities (Figure 1, box 3). Commencing the analysis of termini (Figure 1, box 1), the residue-level properties of the N-terminus are determined: its burial value normalized against other residues in the structure, representing the likelihood of a tag added to this terminus disturbing the tertiary structure, and its interface proximity, representing the likelihood that a tag may disturb the quaternary assembly of the protein (represented in Figure 1 by proximity to a foreign chain, light gray). Next, Terminator floods an extended area surrounding the terminus with pseudoatoms (not pictured; see Example 1 in Discussion section III) to determine the location and volume of each nearby cavity. When a cavity is found (Figure 1, box 2), a “cavity” object is created and assigned a tag–cavity interference probability based on the cavity–terminus distance and the radius of the cavity–assignment area (cyan dashed region). A volume is also assigned to the cavity based on the number of pseudoatoms assigned to it during its initial detection. Then (Figure 1, box 3), each cavity is further assigned a broad conservation score “ConsfScore” as a modified sum of the ConSurf-derived conservation values of the residues assigned to the cavity (orange outlined circles). Similarly, the cavity is also assigned ConservedSites and ActiveSites based on the cavity and the Site sharing residues, and the “site scores” of these Sites are transferred directly to the cavity’s “ConservedSite score” and “ActiveSite score”. Thus, the cavity is assigned four properties to represent its functional importance: a volume, a general “ConsfScore” encompassing all of its residues, an “ActiveSite score” from the site score of its assigned ActiveSites, and a “ConservedSite score” from the site score of its assigned ConservedSites.
These cavity properties are transferred to the proximal terminus so that its overall suitability for tag addition may be evaluated (box 4). The ConsfScore, ConservedSite score, and ActiveSite score of the cavity are normalized by the cavity–tag interference probability, while the volume is modified using a pseudoatom searching algorithm (“f1()”). The terminus is then assigned homonymous properties using the sum of these modified values from each assigned cavity (box 5). When the eligibility of a protein’s N- and C-termini for tag addition is compared, these 6 terminus-assigned properties (in boxes 1 and 5) are compared directly between the termini, as they collectively represent the functional importance of regions near the protein terminus, which might be disturbed if the termini were modified with a His-tag. When a risk of modification is calculated for the terminus (box 6), these values are instead normalized with separate integer-division functions (“f3–8()”) and summed to a “risk” score for each terminus, intended to suggest to the user the likelihood that a 6xHis fusion tag added to this terminus may cause structural interference leading to a change in catalytic activity (an activity altering terminus, AT) or not cause structural interference (a safe terminus, ST). Readers are directed to the Supporting Information for a guide on how to download and operate Terminator (github.com/MinteerLab).

Selection of Literature to Test Package Efficacy

An initial literature search for studies that explicitly discuss His-tag-related enzyme performance was conducted with a Boolean search on PubMed, with additional literature collected by parsing a JSON export from the Braunschweig Enzyme Database (BRENDA). (24) This resulted in 43 papers explicitly discussing His-tag performance. Of these, 14 were omitted due to a lack of available structures or a lack of clarity regarding the length of fusion tags employed in experiments. This left 29 papers discussing 31 proteins (Table 1), 16 of which investigated His-tags at both termini independently (p1, p4–5, p7–12, p16–17, p24–26, and p29–30), while the remaining 15 proteins were experimentally investigated with a His-tag at only one terminus relative to the wild-type protein (p2–3, p6, p13–15, p18–23, p27–28, and p31). Although AlphaFold structures are available for the majority of UniProt entries, these could not be employed for the structure-lacking publications because AlphaFold does not yet identify the oligomeric state of the protein or determine the structure of the quaternary assembly, which is critical to an accurate evaluation of tag–structure interference.
Table 1. Complete Set of Proteins Analyzed by Terminator in This Work: The Number of Terminal Residues Without Coordinates in Their Best Crystal Structure, the Predictions Made by Terminator Regarding the Effect of Adding an Affinity Tag to Each Terminus, and the Experimental Performance of Each Protein With and Without Affinity Tags in Published Worka
abbreviationUniProt IDnumber of terminal residues without coordinates in best crystal structureterminator outputspublished experimental data
N-terminusC-terminusselection 
N-terminusC-terminusrisk of structure alterationAT/ST statuscorrect predictionrisk of structure alterationAT/ST statuscorrect predictionpreferred terminusCorrect predictionrelative experimental performance of tagged proteinslength of N-terminal tag usedlength of C-terminal tag usedreference
p1Q96EY824113STNo15ATNoNNoN < C = WT66 (25)
p2Q9BHM6102STb0STYesCbC = WTc8 (26)
p3P81453105STb5STYesCbC = WTc6 (27)
p4P222561112ATYes9ATNoCYesN < C = WT66 (28)
p5P02787200STYes26ATYesNYesC < N = WT1411 (29,30)
p6P07320102STYes2STbCbN = WT11c (31)
p7G9F1Y9337STb20ATbNYesC < N209 (11)
p8Q47PU310011ATb2STbCYesN < C108 (32)
p9B1VK30202STb14ATbNYesC < N109 (33)
p10D0VWQ0007STb38ATbNYesC < N66 (34,35)
p11G8LK721310ATb11ATbNYesC < N206 (36)
p12H7C697309ATb86ATbNYesC < N66 (37)
p13P007223015ATb9ATYesCbC < WTc8 (38)
p14Q6QGP7003STb9ATYesNbC < WTc6 (39)
p15Q8VNN20033ATb11ATYesCbC < WTc10 (40)
p16P537041051ATYes67ATYesNAmbiguousC < WT > N98 (41)
p17P045370214ATYes12ATYesCAmbiguousC < WT < N1410 (42)
p18Q93VR312215ATYes27ATbNbN < WT21c (43,44)
p19Q9HV140113ATYes9ATbCbN < WT22c (14)
p20Q5HH30039ATYes9ATbCbN < WT24c (14)
p21Q9KL032014ATYes10ATbCbN < WT24c (14)
p22A0A089Q240100STNo2STbNbN < WT21c (45)
p23A0A0D8BQX7200STNo4STbNbN < WT21c (46)
p24A0A892IHP6112STb19ATbNNoN < C219 (12)
p25G0SGU4012STb11ATbNNoN < C326 (47)
p26O46414549ATb21ATbNNoN < C5013 (10)
p27P4001372202STNo2STbEqualbWT < N19c (48)
p28P59807002STNo0STbCbN < WT19c (49)
p29Q550800112ATYes0STNoCNoC < N < WT2016 (50)
p30Q816 V1009ATYes28ATYesNYesC < N < WT2022 (51)
p31Q84EK3590STNo2STbNbN < WT20c (52−55)d
a

A large risk value (>8) for a terminus is predictive of AT status, while values below 9 are predictive of ST.

b

Experimental data is insufficient to verify Terminator’s predictions for this category. AT/ST verification requires comparing the performance of a protein tagged at the relevant terminus to wild-type, while verification of terminus preference requires comparison of proteins tagged at each terminus. Ambiguous: both tags alter activity differently depending on the terminus.

c

Terminus was not modified in publication.

d

Publication discusses three identically tagged proteins identified by Terminator as sibling entries (>99.4% identity), therefore sharing a single analysis. p1 is included as an example of poor terminal coordinate assignment producing incorrect predictions but is omitted from further statistical analysis. A file with complete Terminator outputs for this set of proteins is available on our GitHub (github.com/MinteerLab/Terminator).

Results and Discussion

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I. On the Applicability of Available Experimental Data

Because Terminator predicts 6xHis fusion tag effects by searching for cavities within a 20 Å radius of a protein terminus as it appears in the crystal structure, there are two caveats to consider when comparing Terminator’s prediction of fusion tag behavior to published kinetic data of 6xHis fusion-tagged proteins: the issue of terminus coordinates assignment in the available structural data employed by Terminator and the issue of fusion tag lengths experimentally employed in the published literature.
In the first case, it is not uncommon for several residues at a terminus in a crystal structure to be too labile to demonstrate a well-defined position resulting in a lack of assigned coordinates in the final structure. The position of a fusion tag added to this terminus may then be displaced from the apparent location of the terminus on the crystal structure (the first residue with successfully assigned coordinates) by up to 3.8 Å per unassigned residue. Terminator may therefore erroneously predict AT status when the apparent terminus is closer to a cavity than the actual wild-type terminus (false positive) or erroneously predict ST status when the apparent terminus is erroneously further away (false negative). Terminator identifies the number of residues with unassigned coordinates at both termini to guide the user in selecting only high-quality structures for in-depth analysis by automatically parsing structural data against the associated UniProt entry. Although any protein that failed to demonstrate a structure with sufficient terminal coordinate assignment was largely omitted from analysis in this work, Terminator users must carefully consider this point when interpreting Terminator’s predictions regarding their protein of interest.
The other caveat to consider when comparing Terminator’s predictions against published kinetic data for tagged proteins is the tag length in the published data. A minimal 6x-His tag, as offered C-terminally in many pET plasmids, has a significantly shorter interaction range with protein surface elements than would a tag containing spacers or cleavage sequences, such as the N-terminal tags of pET15b (20 residues) or pET30a (44 residues). Because Terminator assigns cavities only within 20 Å of a protein terminus, it often fails to recognize every avenue of interference afforded to longer fusion tags. This can be expected to increase false negative rates when the AT status was experimentally determined using long fusion tags. A high false positive rate might be expected if longer tags are denied interference with proximal cavities due to the formation of rigid secondary structures, high entropic costs for local interference, or formation of stable interactions with distal, benign cavities in a catalytically favorable orientation. In the following section, we introduce a set of proteins with published data concerning the effect of 6x-His tags on protein activity, process these proteins using Terminator, and analyze the relationship between Terminator accuracy and the length of fusion tags employed in these publications.

II. Evaluating Terminator’s Accuracy against Published Data

Thirty proteins from 28 publications were analyzed with Terminator to test its capacity to assign AT or ST status and capacity to accurately predict which terminus would exhibit a less severe effect on enzyme activity when modified with a 6x-His tag (Table 1). All proteins were analyzed with only their UniProt ID as input, targeting the structure retrieved from SWISS-MODEL, with exception to p3, whose SWISS-MODEL-retrieved structure contained an N-terminal propeptide sequence as identified in its UniProt entry. These 28 papers provide experimental evidence for 21 ATs (terminus < WT or terminus > WT in the “relative experimental performance of tagged proteins” column, where N and C refer to the N- or C-terminus of the preceding protein, p4N, p16–21N, p29–30N, p13–17C, p22–23N, p27–28N, p31N, p29–30C) and 5 STs (terminus = WT, p2–4C, p5–6N), and 14 selections of a safer terminus (N < C or C < N, proteins p4–5, p7–p12, p17, p24–26, p29–30). p1 is provided as an example of a lack of terminal coordinate assignment demonstrating erroneous predictions to emphasize the warnings discussed in Discussion Section I but is omitted from statistical analysis. Evaluation of Terminator’s predictions is discussed below.

Interpretation of Predicted Risk Values

A risk cutoff of 8 was chosen as the upper limit for termini to be assigned ST status, employing termini experimentally characterized with fusion tags less than 15 residues in length in published experiments (Table 1) as this risk cutoff value minimizes the false positive rate to 20% (1/5 termini; the incorrect prediction of p4C is discussed in detail in Discussion Section III, example 3) while maintaining a false negative rate of 0% (0/9 termini) (Figure 2). The selection of 14 residues as the upper limit for affinity tag length is elaborated in the following section. False positive refers to incorrectly predicting AT status for an experimentally determined ST, and false negative refers to incorrectly predicting ST status for an experimentally determined AT.

Figure 2

Figure 2. Comparison of false positive and false negative rates at a variety of risk cutoffs for 14 proteins, which were experimentally tested with His-tags shorter than 15 residues in published works. Risk cutoff referring to the highest value of risk for a terminus with which it is still assigned ST status. False positive refers to predicting AT for an experimentally demonstrated ST, while false negative refers to predicting ST for an experimentally demonstrated AT. At the selected cutoff value of 8, true positives: [p4N, p5C, p13C, p14C, p15C, p16N, p16C, p17N, p17C]; no false positives; true negatives: [p3C, p5N, p2C, p6N]; false negatives: [p4C].

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Effect of Affinity Tag Length on Prediction Accuracy

When including all tested proteins regardless of the length of the fusion tag employed in published experimental data, Terminator demonstrates only a 64% (9/14 proteins) accuracy in selecting the safer terminus on modification (Figure 3A, protein-specific result provided in the legend) and a 72% (18/25 termini) accuracy in categorizing individual termini as AT or ST (Figure 3B, terminus-specific results in the legend). As discussed in Discussion Section I above, the accuracy of Terminator’s predictions depends on the length of the fusion tag employed in experiments. Accordingly, excluding proteins and termini that were tested with fusion tags longer than 14 residues in published work increases accuracy in selecting the safer terminus to 86% (6/7 proteins, p17 contradicting experimental data) and increases the accuracy in assigning individual AT/ST status to 93% (13/14 termini, p4C contradicting experimental data, as discussed in greater detail in Discussion Section III, example 3). p17 can be categorized as a different structural class from the other proteins, as discussed in greater detail in Discussion Section III, example 4. If users refrain from analyzing this category of proteins with Terminator, this data set demonstrates a 100% accuracy in selecting the safer terminus (6/6 proteins) and a 92% (11/12 termini) accuracy in assigning individual AT/ST status to termini for proteins with fusion tags shorter than 15 residues. Although it may appear that this refers to only 6 proteins and both of their termini, the selection and AT/ST assignment data sets share only one protein (p5), with the latter set representing 13 termini from 10 proteins (Figure 3, legend).

Figure 3

Figure 3. (A) Comparison of Terminator’s selection of the safer terminus versus experimentally determined preference, organized by the length of the longer affinity tag (between the N- and C-terminus) employed in experiments. (B) Comparison of Terminator’s predicted AT/ST status for individual termini versus experimentally determined status. Data points refer to (listed as length: protein) for agreeing with experimental data in A [6: p4, p10, p12; 10: p8, p9; 14: p5; 20: p7, p11; 22: p30]. For disagreeing with experimental data in A [14: p17; 20: p29; 21: p24; 32: p25; 50: p26]. Termini agreeing with experimental data in B [6: p3C, p4N, p14C; 8: p2C, p13C, p16C; 9: p16N; 10: p15C, p17C; 11: p5C, p6N; 14: p5N, p17N; 20: p29N, p30N; 21: p18N; 22: p19N, p30C; 24: p20N, p21N]. Disagreeing with experimental data in B [6: p4C; 16: p29C; 19: p27N, p28N; 20: p31N; 21: p22N, p23N].

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III. Selection of Detailed Examples

Several detailed examples of Terminator analysis are provided in this section to help the reader understand the calculation of structural properties by Terminator and the correlation between Terminator outputs and the manual analysis of crystal structures.

Example 1: A Well-Studied Case

Tropinone reductase II (TrII, p9, UniProt B1VK30) from Solanum dulcamara is an insightful protein to study from the perspective of terminal modification, as its terminal tag effects were investigated both experimentally and with MM. (33) The MM (and the SWISS-MODEL structure analysis performed by Terminator) was conducted on a close ortholog TrII from Datura stramonium with a sequence identity of 92%. The experimental results demonstrate that an N-terminal modification results in catalytic activity close to wild-type, while MM demonstrates that a C-terminal His-tag fills the active site-adjacent cavity, preventing substrate entry. Terminator provides the same cavity interference prediction, as well as the convenient automatic detection of the catalytic significance of this cavity without the need to manually parse literature as may be necessary in the case of manual structure analysis, all in less than 10 min of automatic calculation with minimal computational resources and user input beyond providing a UniProt ID. Weighing the relative magnitude of each property assigned to each terminus, Terminator correctly predicts a significantly higher risk for C-terminal modification (risk values of 0 and 13 for the N- and C-termini, respectively, Table 1).
Figure 4 demonstrates a recolored output of the label_cavities script, which employs PyMOL (56) to visualize the cavities detected on this protein by Terminator. Cavities are detected by flood-filling the protein surface with pseudoatoms (as described quantitatively in the Supporting Information). The orange spheres in this figure represent the pseudoatoms assigned to a single wide cavity detected on this protein. The conservation properties for this cavity are derived from pseudoatom adjacent residues, and these properties are then weighed by the (short) distance between the C-terminus and this cavity and transferred to the C-terminus. These terminus-assigned values are used to compare the suitability of the two termini for tag addition and calculate their modification risks to determine the AT/ST status. Because no cavities were detected near the N-terminus, it is far from any chain interfaces in the quaternary structure, and its terminal residue is relatively unburied; this terminus is assigned a modification risk of 0, correctly predicting that a 6xHis tag added to this terminus would have no negative effect on catalytic activity.

Figure 4

Figure 4. Crystal structure of trII from Datura stramonium (p9, UniProt B1VK30) (A) focused on C-terminus and identified cavity and (B) tilted down 90° and rotated left 90° to focus on the N-terminus. The structure is colored by chain (shades of gray), N-terminus (blue), and C-terminus (red). Pseudoatoms generated by Terminator to detect cavities (orange spheres), representing a single, structure spanning cleft, with the protein’s active site located in the dense collection immediately below the C-terminus.

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Example 2: On the Weighing of Cavities

3-Hydroxybutyrate dehydrogenase from Alcaligenes faecalis (p10, UniProt D0VWQ0) is reported as having a kcat/Km value 1000× higher with an N-terminal 6xHis tag compared to a C-terminal 6xHis tag. (34) A cursory glance at a PDB structure for the protein (Figure 5) reveals that the C-terminus lies at the center of several cavities and at the junction of all four protein chains in the quaternary structure (although a new user of protein visualization software may neglect to generate the quaternary assembly required to make these observations). Terminator detects a single, small cavity only a few angstroms from the N-terminus (Table 1, cavity N1; Figure 5B, yellow spheres). The C-terminus in contrast is adjacent to two large, rotationally symmetrical clefts across independent chains (cavities C1 and C3), as well as lying just at the opening of a very large cavity at the interior of the assembly (cavity C2).
Table 2. Cavity Data Calculated by Terminator for 3-Hydroxybutyrate Dehydrogenase from A. faecalisa
a

Rows are colored according to pseudoatom coloration in Figure 5.

Figure 5

Figure 5. Crystal structure of 3-hydroxybutyrate dehydrogenase from Alcaligenes faecalis (p10, UniProt D0VWQ0) (A) focused on the analyzed C-terminus (red) and other adjacent C-terminus (magenta) and (B) tilted down 90° to focus on analyzed N-terminus (blue) and other adjacent N-terminus (cyan). Cavity pseudoatoms (small spheres) for the C-terminus assigned cavities C1 (dark green), C2 (lime green), and C3 (pale green). N-terminus assigned cavity N1 (yellow spheres). Protein chains are shown in shades of gray. Although only a small opening is visualized here, cavity C2 is rather large at the interior of the quaternary structure (as suggested by a large volume in Table 2). With so many avenues for structural interference at the C-terminus, it may be difficult to imagine what orientation of C-terminal 6xHis tags produces the nonzero experimental activity reported for this protein.

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Although score assignment is described in more detail in the Supporting Information, in short, the properties of assigned cavities are transferred to the relevant terminus, weighted by a tag–cavity interference probability calculated as a function of the linear distance between the terminus and the nearest cavity-assigned surface atom. Accordingly, because Terminator analyzed the red C-terminus pictured in Figure 5, the more distant cavity C1 (dark green) is assigned a lower interference probability compared to C2 (lime green) and C3 (pale green). A terminus is assigned a homonymous value for each cavity-derived property (e.g., ConsfScore), as the interference probability-weighed sum from each of its assigned cavities. These terminus-assigned properties are compared directly between termini for selecting the terminus with a lower AT character and are modified with independent normalization functions to calculate an overall risk of modification used to assign an AT/ST character to the terminus. In accordance with this C-terminus experimentally demonstrating catastrophic effects on activity when modified with a 6xHis tag, Terminator calculates an unusually high modification risk value of 38, four times higher than the minimum value corresponding to an AT (risk = 9).

Example 3: A More Subtle Case

4-Aminobutyrate aminotransferase (GabT, p4, UniProt P22256) from Escherichia coli catalyzes the deamination of 4-aminobutyrate to succinic semialdehyde and subsequent amination of α-ketoglutarate to l-glutamate. (57) This enzyme was shown to have near-wild-type activity with a C-terminal 6xHis tag and close to half of this activity with an N-terminal 6xHis tag. (28) The behavior of this protein is difficult to predict based on manual structural analysis because both termini are nearly equidistant from the active site cavity (Figure 6). Terminator correctly predicts that the N-terminal His-tag produces lower activity than the C-terminal (Table 1) based on a marginally smaller distance (3 Å) between the N-terminus and the active site cavity compared to the C-terminus and the same cavity, as well as a higher quaternary interface proximity. However, due to its proximity to the active site cavity, Terminator calculates a risk value of 9 for this C-terminus, the only experimentally demonstrated ST predicted to be an AT by Terminator (the only false-positive result).

Figure 6

Figure 6. GabT from E. coli (p4, UniProt P22256) colored by the B-factor. N- and C-termini outlined in blue and red, respectively, and pointed to with arrows. Cavity pseudoatoms generated by Terminator indicating the location of the active site (purple spheres). Notice the generally lower B-factor (blue to cyan surfaces) surrounding the N-terminus, compared to the higher B-factors (green to orange) surrounding the C-terminus.

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It is unclear from structural analysis what may be responsible for the significant difference in impact on catalytic activity between tags at the two termini. One hypothesis is that the shallow cavity area closer to the N-terminus is instrumental in stabilizing a cavity-interfering conformer of an N-terminal tag. Another option is that the quaternary interface proximity of the N-terminus is significant enough to damage the quaternary assembly, leading to catalytic issues. Finally, the issue could lie in the higher B-factor of the C-terminus (Figure 6), such that a tag attached there would simply have a significantly higher entropic cost of approaching or depositing near the cavity. Higher B-factors correspond to higher variance in atom position, with very high B-factors often denoting labile regions, such as hinges and motile lid structures. (58) B-factor is not considered in the current version of Terminator but is an important factor to incorporate in future versions. This protein serves as a warning to parse the Terminator output more rigorously on the rare occasion when termini receive very similar risk counts, especially when the “cavity” row reveals that the script has identified the same cavity for both termini with only a small difference in interference probability.

Example 4: An Unusually Complex Case

Recombinase uvsY from bacteriophage T4 (p17, UniProt P04537) was demonstrated experimentally to have higher DNA amplification activity with a 14 residue N-terminal 6xHis tag than either C-terminally tagged (12 residues) or untagged protein. (42) The analysis of p17 with Terminator is problematic for three reasons. First, because Terminator predicts structural interference and assumes that this will lead to negative effects on activity (the only other activity-improving tag being p27N) when Terminator recognizes more significant avenues for interference at the N-terminus of p17, it incorrectly selects a preference for the C-terminus, whose structural interference incidentally leads to a loss rather than an increase in activity. Second, the analysis of p17 is further complicated by the asymmetrical quaternary structure of this protein, which can produce radically different results based on which N-terminus in the quaternary structure is analyzed (Figure 7). While Terminator selects for analysis whichever chain has the most terminal residue assigned coordinates, it does not currently analyze multiple sequence-equivalent chains for different results. Finally, the third reason p17 may not be appropriate to analyze with Terminator is that this protein’s active functional form involves a heteromeric assembly with DNA and a single-stranded DNA-binding protein gp32, replaced in the course of activity with recombinase uvsX. (59,60) This is a complex, apparently cavity-independent mechanism outside the scope of Terminator’s capacity to analyze, particularly with only the homoheptameric p17 structure available at the time of this work’s publication. Thus, it may be argued that p17 does not fairly represent Terminator’s capacity to predict the behavior of more common and simpler enzyme structures, and users are warned to be cautious when employing Terminator to analyze heteromeric proteins or those that otherwise do not employ traditional active site cavities for activity.

Figure 7

Figure 7. Structure of uvsY from bacteriophage T4 (p17, UniProt P04537, PDB entry 4ZWQ) (A) focused on C-termini and (B) tilted down 90° (C-termini now on bottom of structure) to focus on N-termini. N- and C-termini selected for analysis by Terminator in blue and red, respectively. Other N- and C-termini in cyan and magenta, respectively. Alternating protein chains in shades of gray. Notice the significantly different local environment of the “other” termini relative to those analyzed, being further away from the lengthwise opening of the quaternary structure. The hypothesized catalytic mechanism involves gp32-bound DNA wrapping around the top end of the barrel structure in panel (B), opposite of the bottom region dense with C-termini.

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Limitations

Besides the limited range of Terminator’s search algorithm and requirement for thorough coordinate assignment of terminal residues in available crystal structures as described in Discussion Section I, Terminator has several other limitations that users must carefully consider before employing the program to analyze a protein of interest. First, the calculation of property inheritance from cavities: In the case of small, deep cavities appearing in many proteins, any transient interaction between the tag and the surface of an active site cavity can be expected to be detrimental to substrate–active site interaction. However, when Terminator detects a very large cleft in proximity to a terminus, with the active site tunnel far outside the reach of said terminus, the terminus nevertheless inherits the properties of the entire cleft and tunnel weighed only by the cleft–terminus distance, even when the highly conserved tunnel is far outside the reach of a fusion tag. This can be addressed in a future update by placing an upper limit on cavity volume, thus subdividing a cavity into independent, interacting objects. Users are thus advised to more carefully consider Terminator-calculated properties and supplement analysis with manual examination of the crystal structure when very large cavity volumes (>700) are presented in the cavities row of the output file. Second, Terminator struggles when evaluating rare crystal structures that encode multiple chains in separate quaternary assemblies presented as a single asymmetrical unit, which may be verified by simply glancing at the relevant structure in visualization software. Third, in certain proteins, active site cavities may alternate between open and closed states (16); hence, Terminator may fail to accurately predict tag behavior in proteins if no open-state structures have been determined. Fourth, as discussed in Example 4 above, because Terminator predicts structural interference, it is not able to distinguish more common detrimental structural interactions from very rare, beneficial interactions. Furthermore, because Terminator relies mostly on cavity detection, it is not likely to accurately analyze cavity-independent mechanisms, such as those employing certain heteromeric complexes, particularly in enzymes that catalyze reactions with DNA or polypeptide chains. Expanding further on this example, because Terminator only performs conservation analysis on a single protein, it is not currently able to calculate conservation-based properties for heterocomplexes of different proteins even when such crystal structures are available.

Conclusions

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This work introduces a fast and accessible tool integrating protein sequence conservation analysis with structural data to predict the most suitable terminus for placement of short (<15 residues) 6xHis fusion tags. When comparing Terminator predictions to published experimental data for proteins modified with 6xHis tag sequences totaling less than 14 residues, Terminator demonstrates an 86–100% accuracy in predicting the safer terminus for tag addition and a 92–93% accuracy in predicting the risk of activity-modifying structural interactions, resulting from the addition of fusion tags to specific termini. Users are advised to avoid analyzing crystal structures with a significant number of terminal residues lacking coordinate assignment (>3 residues) and to supplement Terminator with manual analysis if detected cavities demonstrate volumes above 700 Å3 or when termini are assigned the same cavity with small variation in interaction probability. The minimal need for time investment, domain knowledge, and computational resources is the main advantage of Terminator over manual crystal structure interpretation and molecular modeling of tag effects. Critically, Terminator can be retroactively employed to analyze enzymes in published biocatalytic systems to easily improve the performance of these systems by employing proteins with better-behaved tags. The speed and ease of employing Terminator will expedite construct design and enhance the successful production of well-behaved proteins. Terminator eliminates the labor and time required to purify and compare the relative performance of a protein tagged at either terminus, thus accelerating the publication of valuable research in the fields of biocatalysis and enzymology.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsbiomedchemau.4c00055.

  • User guide covering installation, implementation, and proper use of Terminator and expanded quantitative description of the algorithms involved in terminus selection and evaluation (PDF)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Author
    • Shelley D. Minteer - Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States;  Kummer Institute Center for Resource Sustainability, Missouri University of Science and Technology, Rolla, Missouri 65409, United States;  Orcidhttps://orcid.org/0000-0002-5788-2249;  Email: [email protected]
  • Author
    • Rokas Gerulskis - Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
  • Author Contributions

    CRediT: Rokas Gerulskis conceptualization, data curation, formal analysis, validation, writing - original draft; Shelley D. Minteer funding acquisition, project administration, supervision, writing - review & editing.

  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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The authors would like to thank the Office of Naval Research (ONR) for funding this research (Grant No.: N000142114008).

References

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This article references 60 other publications.

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    Basic local alignment search tool
    Altschul, Stephen F.; Gish, Warren; Miller, Webb; Myers, Eugene W.; Lipman, David J.
    Journal of Molecular Biology (1990), 215 (3), 403-10CODEN: JMOBAK; ISSN:0022-2836.
    A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent math. results on the stochastic properties of MSP scores allow an anal. of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a no. of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the anal. of multiple regions of similarity in long DNA sequences. In addn. to its flexibility and tractability to math. anal., BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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    Waterhouse, A. SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Res. 2018, 46, W296– W303,  DOI: 10.1093/nar/gky427
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    19
    SWISS-MODEL: homology modelling of protein structures and complexes
    Waterhouse, Andrew; Bertoni, Martino; Bienert, Stefan; Studer, Gabriel; Tauriello, Gerardo; Gumienny, Rafal; Heer, Florian T.; de Beer, Tjaart A. P.; Rempfer, Christine; Bordoli, Lorenza; Lepore, Rosalba; Schwede, Torsten
    Nucleic Acids Research (2018), 46 (W1), W296-W303CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)
    Homol. modeling has matured into an important technique in structural biol., significantly contributing to narrowing the gap between known protein sequences and exptl. detd. structures. Fully automated workflows and servers simplify and streamline the homol. modeling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modeling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modeling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modeling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homol. modeling. Other major improvements include the implementation of a new modeling engine, ProMod3 and the introduction a new local model quality estn. method, QMEANDisCo.
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    Sayers, E. W. Database resources of the national center for biotechnology information. Nucleic Acids Res. 2022, 50, D20– D26,  DOI: 10.1093/nar/gkab1112
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    Database resources of the national center for biotechnology information
    Sayers, Eric W.; Bolton, Evan E.; Brister, J. Rodney; Canese, Kathi; Chan, Jessica; Comeau, Donald C.; Connor, Ryan; Funk, Kathryn; Kelly, Chris; Kim, Sunghwan; Madej, Tom; Marchler-Bauer, Aron; Lanczycki, Christopher; Lathrop, Stacy; Lu, Zhiyong; Thibaud-Nissen, Francoise; Murphy, Terence; Phan, Lon; Skripchenko, Yuri; Tse, Tony; Wang, Jiyao; Williams, Rebecca; Trawick, Barton W.; Pruitt, Kim D.; Sherry, Stephen T.
    Nucleic Acids Research (2022), 50 (D1), D20-D26CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)
    A review. The National Center for Biotechnol. Information (NCBI) produces a variety of online information resources for biol., including the GenBank nucleic acid sequence database and the PubMed database of citations and abstrs. published in life science journals. NCBI provides search and retrieval operations for most of these data from 35 distinct databases. The E-utilities serve as the programming interface for the most of these databases. Resources receiving significant updates in the past year include PubMed, PMC, Bookshelf, RefSeq, SRA, Virus, dbSNP, dbVar, ClinicalTrials.gov, MMDB, iCn3D and PubChem.
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  21. 21
    Celniker, G. ConSurf: Using evolutionary data to raise testable hypotheses about protein function. Isr. J. Chem. 2013, 53, 199– 206,  DOI: 10.1002/ijch.201200096
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    ConSurf: Using Evolutionary Data to Raise Testable Hypotheses about Protein Function
    Celniker, Gershon; Nimrod, Guy; Ashkenazy, Haim; Glaser, Fabian; Martz, Eric; Mayrose, Itay; Pupko, Tal; Ben-Tal, Nir
    Israel Journal of Chemistry (2013), 53 (3-4), 199-206CODEN: ISJCAT; ISSN:0021-2148. (Wiley-VCH Verlag GmbH & Co. KGaA)
    A review. Many mutations disappear from the population because they impair protein function and/or stability. Thus, amino acid positions that are essential for proper function evolve more slowly than others, or in other words, the slow evolutionary rate of a position reflects its importance. ConSurf (http://consurfτac.il), reviewed in this manuscript, exploits this to reveal key amino acid positions that are important for maintaining the native conformation(s) of the protein and its function, be it binding, catalysis, transport, etc. Given the sequence or 3D structure of the query protein as input, a search for similar sequences is conducted and the sequences are aligned. The multiple sequence alignment is subsequently used to calc. the evolutionary rates of each amino acid site, using Bayesian or max.-likelihood algorithms. Both algorithms take into account the evolutionary relationships between the sequences, reflected in phylogenetic trees, to alleviate problems due to uneven (biased) sampling in sequence space. This is particularly important when the no. of sequences is low. The ConSurf-DB, a new release of which is presented here, provides precalcd. ConSurf conservation anal. of nearly all available structures in the Protein DataBank (PDB). The usefulness of ConSurf for the study of individual proteins and mutations, as well as a range of large-scale, genome-wide applications, is reviewed.
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  22. 22
    Pastore, A. J. Oxalate decarboxylase uses electron hole hopping for catalysis. J. Biol. Chem. 2021, 297, 100857  DOI: 10.1016/j.jbc.2021.100857
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    Armon, A.; Graur, D.; Ben-Tal, N. ConSurf: An algorithmic tool for the identification of functional regions in proteins by surface mapping of phylogenetic information. J. Mol. Biol. 2001, 307, 447– 463,  DOI: 10.1006/jmbi.2000.4474
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    ConSurf: An algorithmic tool for the identification of functional regions in proteins by surface mapping of phylogenetic information
    Armon, Aharon; Graur, Dan; Ben-Tal, Nir
    Journal of Molecular Biology (2001), 307 (1), 447-463CODEN: JMOBAK; ISSN:0022-2836. (Academic Press)
    Exptl. approaches for the identification of functionally important regions on the surface of a protein involve mutagenesis, in which exposed residues are replaced one after another while the change in binding to other proteins or changes in activity are recorded. However, practical considerations limit the use of these methods to small-scale studies, precluding a full mapping of all the functionally important residues on the surface of a protein. We present here an alternative approach involving the use of evolutionary data in the form of multiple-sequence alignment for a protein family to identify hot spots and surface patches that are likely to be in contact with other proteins, domains, peptides, DNA, RNA or ligands. The underlying assumption in this approach is that key residues that are important for binding should be conserved throughout evolution, just like residues that are crucial for maintaining the protein fold, i.e. buried residues. A main limitation in the implementation of this approach is that the sequence space of a protein family may be unevenly sampled, e.g. mammals may be overly represented. Thus, a seemingly conserved position in the alignment may reflect a taxonomically uneven sampling, rather than being indicative of structural or functional importance. To avoid this problem, we present here a novel methodol. based on evolutionary relations among proteins as revealed by inferred phylogenetic trees, and demonstrate its capabilities for mapping binding sites in SH2 and PTB signaling domains. A computer program that implements these ideas is available freely at: http://ashtoretτac.il/∼rony. (c) 2001 Academic Press.
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    Chang, A. BRENDA, the ELIXIR core data resource in 2021: New developments and updates. Nucleic Acids Res. 2021, 49, D498– D508,  DOI: 10.1093/nar/gkaa1025
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    BRENDA, the ELIXIR core data resource in 2021: new developments and updates
    Chang, Antje; Jeske, Lisa; Ulbrich, Sandra; Hofmann, Julia; Koblitz, Julia; Schomburg, Ida; Neumann-Schaal, Meina; Jahn, Dieter; Schomburg, Dietmar
    Nucleic Acids Research (2021), 49 (D1), D498-D508CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)
    A review. The BRENDA enzyme database, established in 1987, has evolved into the main collection of functional enzyme and metab. data. In 2018, BRENDA was selected as an ELIXIR Core Data Resource. BRENDA provides reliable data, continuous curation and updates of classified enzymes, and the integration of newly discovered enzymes. The main part contains >5 million data for ~ 90 000 enzymes from ~ 13 000 organisms, manually extd. from ~ 157 000 primary literature refs., combined with information of text and data mining, data integration, and prediction algorithms. Supplements comprise disease-related data, protein sequences, 3D structures, genome annotations, ligand information, taxonomic, bibliog., and kinetic data. BRENDA offers an easy access to enzyme information from quick to advanced searches, text- and structured-based queries for enzyme-ligand interactions, word maps, and visualization of enzyme data. The BRENDA Pathway Maps are completely revised and updated for an enhanced interactive and intuitive usability. The new design of the Enzyme Summary Page provides an improved access to each individual enzyme. A new protein structure 3D viewer was integrated. The prediction of the intracellular localization of eukaryotic enzymes has been implemented. The new EnzymeDetector combines BRENDA enzyme annotations with protein and genome databases for the detection of eukaryotic and prokaryotic enzymes.
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  25. 25
    Fan, C.; Bobik, T. A. Functional characterization and mutation analysis of human ATP:Cob(I)alamin adenosyltransferase. Biochemistry 2008, 47, 2806– 2813,  DOI: 10.1021/bi800084a
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    Functional characterization and mutation analysis of human ATP:cob(I)alamin adenosyltransferase
    Fan, Chenguang; Bobik, Thomas A.
    Biochemistry (2008), 47 (9), 2806-2813CODEN: BICHAW; ISSN:0006-2960. (American Chemical Society)
    ATP:cob(I)alamin adenosyltransferase (ATR) catalyzes the final step in the conversion of vitamin B12 into the active coenzyme, adenosylcobalamin. Inherited defects in the gene for the human adenosyltransferase (hATR) result in methylmalonyl aciduria (MMA), a rare but life-threatening illness. In this study, we conducted a random mutagenesis of the hATR coding sequence. An ATR-deficient strain of Salmonella was used as a surrogate host to screen for mutations that impaired hATR activity in vivo. Fifty-seven missense mutations were isolated. These mapped to 30 positions of the hATR, 25 of which had not previously been shown to impair enzyme activity. Kinetic anal. and in vivo tests for enzyme activity were performed on the hATR variants, and mutations were mapped onto a hATR structural model. These studies functionally defined the hATR active site and tentatively implicated three amino acid residues in facilitating the redn. of cob(II)alamin to cob(I)alamin which is a prerequisite to adenosylation.
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    Smits, S. H. J.; Mueller, A.; Grieshaber, M. K.; Schmitt, L. Coenzyme- and His-tag-induced crystallization of octopine dehydrogenase. Acta Crystallogr. Sect. F Struct. Biol. Cryst. Commun. 2008, 64, 836– 839,  DOI: 10.1107/S1744309108025487
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    Marx, C. K.; Hertel, T. C.; Pietzsch, M. Purification and activation of a recombinant histidine-tagged pro-transglutaminase after soluble expression in Escherichia coli and partial characterization of the active enzyme. Enzyme Microb. Technol. 2008, 42, 568– 575,  DOI: 10.1016/j.enzmictec.2008.03.003
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    Purification and activation of a recombinant histidine-tagged pro-transglutaminase after soluble expression in Escherichia coli and partial characterization of the active enzyme
    Marx, Christian K.; Hertel, Thomas C.; Pietzsch, Markus
    Enzyme and Microbial Technology (2008), 42 (7), 568-575CODEN: EMTED2; ISSN:0141-0229. (Elsevier Inc.)
    Pro-transglutaminase from Streptomyces mobaraensis was expressed in Escherichia coli as a fusion protein carrying a C-terminal histidine tag (pro-MTG-His6). The recombinant organism was cultivated in 15 L bioreactor scale and pro-MTG-His6 was purified by immobilized metal affinity chromatog. Activation of the inactive pro-enzyme using trypsin resulted in an unexpected degrdn. of the transglutaminase and a concomitant loss of activity. Therefore, a set of com. available proteases was investigated for their activation potential without destroying the target enzyme. Besides trypsin, chymotrypsin and proteinase K were found to activate but hydrolyze the (pro-MTG-His6). Cathepsin B, dispase I, and thrombin were shown to specifically hydrolyze pro-MTG-His6 without deactivation. TAMEP, the endogenous protease from S. mobaraensis was purified for comparison and also found to activate the recombinant histidine-tagged transglutaminase without degrdn. The TAMEP activated MTG-His6 was purified and characterized. The specific activity (23 U/mg) of the recombinant histidine-tagged transglutaminase, the temp. optimum (50 °C), and the temp. stability (t 1/2 at 60 °C = 1.7 min) were comparable to the wild-type enzyme. A C-terminal peptide tag did neither affect the activity nor the stability but facilitated the purifn. The purifn. of the histidine-tagged protein is possible before or after activation.
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  28. 28
    Meng, L. Effects of His-tag on Catalytic Activity and Enantioselectivity of Recombinant Transaminases. Appl. Biochem. Biotechnol. 2020, 190, 880– 895,  DOI: 10.1007/s12010-019-03117-8
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    Mason, A. B. Expression, purification, and characterization of recombinant nonglycosylated human serum transferrin containing a C-terminal hexahistidine tag. Protein Expr. Purif. 2001, 23, 142– 150,  DOI: 10.1006/prep.2001.1480
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    Expression, Purification, and Characterization of Recombinant Nonglycosylated Human Serum Transferrin Containing a C-Terminal Hexahistidine Tag
    Mason, Anne B.; He, Qing-Yu; Adams, Ty E.; Gumerov, Dmitry R.; Kaltashov, Igor A.; Nguyen, Vinh; MacGillivray, Ross T. A.
    Protein Expression and Purification (2001), 23 (1), 142-150CODEN: PEXPEJ; ISSN:1046-5928. (Academic Press)
    Attachment of a hexa-His tag is a common strategy in recombinant protein prodn. The use of such a tag greatly simplifies the purifn. of the protein from the complex mixt. of other proteins in the media or cell ext. We describe the prodn. of two recombinant nonglycosylated human serum transferrins (hTF-NG), contg. a factor Xa cleavage site and a hexa-His tag at their carboxyl-terminal ends. One of the constructs comprises the entire coding region for hTF (residues 1-679), while the other lacks the final three carboxyl-terminal amino acids. After insertion of the His-tagged hTFs into the pNUT vector, transfection into baby hamster kidney (BHK) cells, and selection with methotrexate, the secreted recombinant proteins were isolated from the tissue culture medium. Av. max. expression levels of the His-tagged hTFs were about 40 mg/L compared to an av. max. of 50 mg/L for hTF-NG. The first step of purifn. involved an anion exchange column. The second step utilized a Poros metal chelate column preloaded with copper from which the His-tagged sample was eluted with a linear imidazole gradient. The His-tagged hTFs were characterized and compared to both recombinant hTF-NG and glycosylated hTF from human serum. The identity of each of the His-tagged hTFs constructs was verified by electrospray mass spectroscopy. In summary, the His-tagged hTF constructs simplify the purifn. of these metal-binding proteins with minimal effects on many of their phys. properties. The His-tagged hTFs share many features common to hTF, including reversible iron binding, reactivity with a monoclonal antibody, and presence as a monomer in soln. (c) 2001 Academic Press.
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  30. 30
    Mason, A. B. Differential effect of a His tag at the N- and C-termini: Functional studies with recombinant human serum transferrin. Biochemistry 2002, 41, 9448– 9454,  DOI: 10.1021/bi025927l
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    Differential Effect of a His Tag at the N- and C-Termini: Functional Studies with Recombinant Human Serum Transferrin
    Mason, Anne B.; He, Qing-Yu; Halbrooks, Peter J.; Everse, Stephen J.; Gumerov, Dmitry R.; Kaltashov, Igor A.; Smith, Valerie C.; Hewitt, Jeff; MacGillivray, Ross T. A.
    Biochemistry (2002), 41 (30), 9448-9454CODEN: BICHAW; ISSN:0006-2960. (American Chemical Society)
    Attachment of a cleavable hexa His tag is a common strategy for the prodn. of recombinant proteins. Prodn. of two recombinant nonglycosylated human serum transferrins (hTF-NG), contg. a factor Xa cleavage site and a hexa His tag at the carboxyl terminus, has been described [Mason et al. (2001) Prot. Exp. Purif 23, 142-150]. More recently, hTF-NG with an amino-terminal His tag and a factor Xa cleavage site has been expressed (>30 mg/L) in baby hamster kidney cells and purified from the tissue culture medium. Although it is frequently assumed that addn. of a His tag has little or no effect on function, this is not always confirmed exptl. In the present study, in vitro quant. data clearly shows that the presence of the C-terminal His tag has an effect on the release of iron from recombinant hTF at pH 7.4 and 5.6. Measurement of the rate of release from both the N- and C-lobes is reduced 2-4-fold. These findings provide further compelling evidence that the two lobes communicate with each other and highlight the importance of the C-terminal portion of the C-terminal lobe in this interaction. In contrast to these results, we demonstrate that the presence of a His tag at the N-terminus of hTF has no effect on the rate of iron release from either lobe. In a competition expt., both unlabeled N- and C-terminal His-tagged constructs were equally effective at inhibiting the binding of radio-iodinated diferric glycosylated hTF from a com. source to receptors on HeLa cells as the unlabeled recombinant diferric hTF-NG control. Thus, the presence of a His tag at either the N- or C-terminus of hTF-NG has no apparent effect on the ability of these hTF species to bind to transferrin receptors.
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    Lin, Y. W.; Ying, T. L.; Liao, L. F. Molecular modeling and dynamics simulation of a histidine-tagged cytochrome b 5. J. Mol. Model. 2011, 17, 971– 978,  DOI: 10.1007/s00894-010-0795-4
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    Molecular modeling and dynamics simulation of a histidine-tagged cytochrome b 5
    Lin, Ying-Wu; Ying, Tian-Lei; Liao, Li-Fu
    Journal of Molecular Modeling (2011), 17 (5), 971-978CODEN: JMMOFK; ISSN:0948-5023. (Springer)
    Although an affinity tag such as six consecutive histidines, (His)6-tag, has been widely used to obtain high quantity of recombinant proteins, little is known about its influences on heme proteins for lack of structural information. When (His)6-tag was introduced to the N-terminus of a small heme protein, cytochrome b 5, exptl. results showed the resultant protein, (His)6-cyt b 5, has similar property and function to that of isolated cyt b 5. To provide structural information for this observation, we herein performed a structural prediction of (His)6-cyt b 5 by mol. modeling in combination with mol. dynamics simulation. The predicted structure, as assessed by a series of criteria with good quality, reveals that the (His)6-tag adopts a helical conformation and packs against the hydrophobic core 2 of cyt b 5 through salt bridges, hydrogen bonding and hydrophobic interactions. The heme group, with the axial His ligands slightly rotated, was found to have similar conformation as in isolated cyt b 5, which indicates that the N-terminal (His)6-tag does not alter the heme active site, resulting in similar dynamics properties for core 1. This study provides valuable information of interactions between (His)6-tag and the rest of the protein, aiding in rational design and application of functional His-tagged proteins.
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    Parshin, P. D. Effect of His6-tag Position on the Expression and Properties of Phenylacetone Monooxygenase from Thermobifida fusca. Biochem. 2020, 85, 575– 582,  DOI: 10.1134/S0006297920050065
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    Freydank, A. C.; Brandt, W.; Dräger, B. Protein structure modeling indicates hexahistidine-tag interference with enzyme activity. Proteins Struct. Funct. Genet. 2008, 72, 173– 183,  DOI: 10.1002/prot.21905
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    Yeon, Y. J.; Park, H. J.; Park, H. Y.; Yoo, Y. J. Effect of His-tag location on the catalytic activity of 3-hydroxybutyrate dehydrogenase. Biotechnol. Bioprocess Eng. 2014, 19, 798– 802,  DOI: 10.1007/s12257-014-0089-2
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    Yeon, Y. J.; Park, H. Y.; Yoo, Y. J. Enzymatic reduction of levulinic acid by engineering the substrate specificity of 3-hydroxybutyrate dehydrogenase. Bioresour. Technol. 2013, 134, 377– 380,  DOI: 10.1016/j.biortech.2013.01.078
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    Enzymatic reduction of levulinic acid by engineering the substrate specificity of 3-hydroxybutyrate dehydrogenase
    Yeon, Young Joo; Park, Hyung-Yeon; Yoo, Young Je
    Bioresource Technology (2013), 134 (), 377-380CODEN: BIRTEB; ISSN:0960-8524. (Elsevier Ltd.)
    Enzymic redn. of levulinic acid (LA) was performed for the synthesis of 4-hydroxyvaleric acid (4HV) - a monomer of bio-polyester and a precursor of bio-fuels - using 3-hydroxybutyrate dehydrogenase (3HBDH) from Alcaligenes faecalis. Due to the catalytic inactivity of the wild-type enzyme toward LA, engineering of the substrate specificity of the enzyme was performed. A rational design approach with mol. docking simulation was applied, and a double mutant, His144Leu/Trp187Phe, which has catalytic activity (kcat/Km = 578.0 min-1 M-1) toward LA was generated. Approx. 57% conversion of LA to 4HV was achieved with this double mutant in 24 h, while no conversion was achieved with the wild-type enzyme.
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    Zhang, J.; Cui, T.; Li, X. Screening and identification of an Enterobacter ludwigii strain expressing an active β-xylosidase. Ann. Microbiol. 2018, 68, 261– 271,  DOI: 10.1007/s13213-018-1334-2
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    Li, Y. Recombinant glutamine synthetase (GS) from C. glutamicum existed as both hexamers & dedocamers and C-terminal His-tag enhanced inclusion bodies formation in E. coli. Appl. Biochem. Biotechnol. 2009, 159, 614– 622,  DOI: 10.1007/s12010-008-8493-8
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    Flores, S. S. His-tag β-galactosidase supramolecular performance. Biophys. Chem. 2022, 281, 106739  DOI: 10.1016/j.bpc.2021.106739
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    His-tag β-galactosidase supramolecular performance
    Flores, Sandra S.; Clop, Pedro D.; Barra, Jose L.; Argarana, Carlos E.; Perillo, Maria A.; Nolan, Veronica; Sanchez, Julieta M.
    Biophysical Chemistry (2022), 281 (), 106739CODEN: BICIAZ; ISSN:0301-4622. (Elsevier B.V.)
    β-Galactosidase is an important biotechnol. enzyme used in the dairy industry, pharmacol. and in mol. biol. In our lab. we have overexpressed a recombinant β-galactosidase in Escherichia coli (E. coli). This enzyme differs from its native version (β-GalWT) in that 6 histidine residues have been added to the carboxyl terminus in the primary sequence (β-GalHis), which allows its purifn. by immobilized metal affinity chromatog. (IMAC). In this work we compared the functionality and structure of both proteins and evaluated their catalytic behavior on the kinetics of lactose hydrolysis. We obsd. a significant redn. in the enzymic activity of β-GalHis with respect to β-GalWT. Although, both enzymes showed a similar catalytic profile as a function of temp., β-GalHis presented a higher resistance to the thermal inactivation compared to β-GalWT. At room temp., β-GalHis showed a fluorescence spectrum compatible with a partially unstructured protein, however, it exhibited a lower tendency to the thermal-induced unfolding with respect to β-GalWT. The distinctively supramol. arranges of the proteins would explain the effect of the presence of His-tag on the enzymic activity and thermal stability.
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    Kutyshenko, V. P. Effect of C-terminal His-tag and purification routine on the activity and structure of the metalloenzyme, L-alanyl-D-glutamate peptidase of the bacteriophage T5. Int. J. Biol. Macromol. 2019, 124, 810– 818,  DOI: 10.1016/j.ijbiomac.2018.11.219
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    Effect of C-terminal His-tag and purification routine on the activity and structure of the metalloenzyme, L-alanyl-D-glutamate peptidase of the bacteriophage T5
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    International Journal of Biological Macromolecules (2019), 124 (), 810-818CODEN: IJBMDR; ISSN:0141-8130. (Elsevier B.V.)
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  • Abstract

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    Figure 1

    Figure 1. Simplified representation of Terminator’s structure–analysis algorithm on a hypothetical two-dimensional protein. Light and dark gray regions represent separate protein chains. Beige to purple circles represent simplified protein residues colored by their ConSurf-derived color value and numbered according to their ConSurf-derived conservation value. The N-terminus is represented by the cyan “N” circle, with its cavity–assignment radius as a large light-blue region. An ActiveSite and ConservedSite are represented as navy and purple regions, respectively. A cavity is represented as the orange region with its assigned residues outlined in orange. To the right, a flowchart illustrating how each object recognized by Terminator (ActiveSite, ConservedSite, cavity, and terminus) defines its own properties and aggregates the properties of subordinate objects, leading to an evaluation of the status of a terminus.

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    Figure 2

    Figure 2. Comparison of false positive and false negative rates at a variety of risk cutoffs for 14 proteins, which were experimentally tested with His-tags shorter than 15 residues in published works. Risk cutoff referring to the highest value of risk for a terminus with which it is still assigned ST status. False positive refers to predicting AT for an experimentally demonstrated ST, while false negative refers to predicting ST for an experimentally demonstrated AT. At the selected cutoff value of 8, true positives: [p4N, p5C, p13C, p14C, p15C, p16N, p16C, p17N, p17C]; no false positives; true negatives: [p3C, p5N, p2C, p6N]; false negatives: [p4C].

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    Figure 3

    Figure 3. (A) Comparison of Terminator’s selection of the safer terminus versus experimentally determined preference, organized by the length of the longer affinity tag (between the N- and C-terminus) employed in experiments. (B) Comparison of Terminator’s predicted AT/ST status for individual termini versus experimentally determined status. Data points refer to (listed as length: protein) for agreeing with experimental data in A [6: p4, p10, p12; 10: p8, p9; 14: p5; 20: p7, p11; 22: p30]. For disagreeing with experimental data in A [14: p17; 20: p29; 21: p24; 32: p25; 50: p26]. Termini agreeing with experimental data in B [6: p3C, p4N, p14C; 8: p2C, p13C, p16C; 9: p16N; 10: p15C, p17C; 11: p5C, p6N; 14: p5N, p17N; 20: p29N, p30N; 21: p18N; 22: p19N, p30C; 24: p20N, p21N]. Disagreeing with experimental data in B [6: p4C; 16: p29C; 19: p27N, p28N; 20: p31N; 21: p22N, p23N].

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    Figure 4

    Figure 4. Crystal structure of trII from Datura stramonium (p9, UniProt B1VK30) (A) focused on C-terminus and identified cavity and (B) tilted down 90° and rotated left 90° to focus on the N-terminus. The structure is colored by chain (shades of gray), N-terminus (blue), and C-terminus (red). Pseudoatoms generated by Terminator to detect cavities (orange spheres), representing a single, structure spanning cleft, with the protein’s active site located in the dense collection immediately below the C-terminus.

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    Figure 5

    Figure 5. Crystal structure of 3-hydroxybutyrate dehydrogenase from Alcaligenes faecalis (p10, UniProt D0VWQ0) (A) focused on the analyzed C-terminus (red) and other adjacent C-terminus (magenta) and (B) tilted down 90° to focus on analyzed N-terminus (blue) and other adjacent N-terminus (cyan). Cavity pseudoatoms (small spheres) for the C-terminus assigned cavities C1 (dark green), C2 (lime green), and C3 (pale green). N-terminus assigned cavity N1 (yellow spheres). Protein chains are shown in shades of gray. Although only a small opening is visualized here, cavity C2 is rather large at the interior of the quaternary structure (as suggested by a large volume in Table 2). With so many avenues for structural interference at the C-terminus, it may be difficult to imagine what orientation of C-terminal 6xHis tags produces the nonzero experimental activity reported for this protein.

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    Figure 6

    Figure 6. GabT from E. coli (p4, UniProt P22256) colored by the B-factor. N- and C-termini outlined in blue and red, respectively, and pointed to with arrows. Cavity pseudoatoms generated by Terminator indicating the location of the active site (purple spheres). Notice the generally lower B-factor (blue to cyan surfaces) surrounding the N-terminus, compared to the higher B-factors (green to orange) surrounding the C-terminus.

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    Figure 7

    Figure 7. Structure of uvsY from bacteriophage T4 (p17, UniProt P04537, PDB entry 4ZWQ) (A) focused on C-termini and (B) tilted down 90° (C-termini now on bottom of structure) to focus on N-termini. N- and C-termini selected for analysis by Terminator in blue and red, respectively. Other N- and C-termini in cyan and magenta, respectively. Alternating protein chains in shades of gray. Notice the significantly different local environment of the “other” termini relative to those analyzed, being further away from the lengthwise opening of the quaternary structure. The hypothesized catalytic mechanism involves gp32-bound DNA wrapping around the top end of the barrel structure in panel (B), opposite of the bottom region dense with C-termini.

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      Escherichia coli maltose-binding protein is uncommonly effective at promoting the solubility of polypeptides to which it is fused
      Kapust, Rachel B.; Waugh, David S.
      Protein Science (1999), 8 (8), 1668-1674CODEN: PRCIEI; ISSN:0961-8368. (Cambridge University Press)
      Although it is usually possible to achieve a favorable yield of a recombinant protein in Escherichia coli, obtaining the protein in a sol., biol. active form continues to be a major challenge. Sometimes this problem can be overcome by fusing an aggregation-prone polypeptide to a highly sol. partner. To study this phenomenon in greater detail, we compared the ability of three sol. fusion partners-maltose-binding protein (MBP), glutathione S-transferase (GST), and thioredoxin (TRX)-to inhibit the aggregation of six diverse proteins that normally accumulate in an insol. form. Remarkably, we found that MBP is a far more effective solubilizing agent than the other two fusion partners. Moreover, we demonstrated that in some cases fusion to MBP can promote the proper folding of the attached protein into its biol. active conformation. Thus, MBP seems to be capable of functioning as a general mol. chaperone in the context of a fusion protein. A model is proposed to explain how MBP promotes the soly. and influences the folding of its fusion partners.
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    10. 10
      Zhang, H. Soluble expression and purification of recombinant bovine ferritin H-chain. Protein Expr. Purif. 2023, 211, 106340  DOI: 10.1016/j.pep.2023.106340
      There is no corresponding record for this reference.
    11. 11
      Mordaka, P. M.; Hall, S. J.; Minton, N.; Stephens, G. Recombinant expression and characterisation of the oxygensensitive 2-enoate reductase from Clostridium sporogenes. Microbiol. (United Kingdom) 2018, 164, 122– 132,  DOI: 10.1099/mic.0.000568
      There is no corresponding record for this reference.
    12. 12
      Alpdağtaş, S.; Çelik, A.; Ertan, F.; Binay, B. DMSO tolerant NAD(P)H recycler enzyme from a pathogenic bacterium, Burkholderia dolosa PC543: effect of N-/C-terminal His Tag extension on protein solubility and activity. Eng. Life Sci. 2018, 18, 893– 903,  DOI: 10.1002/elsc.201800036
      There is no corresponding record for this reference.
    13. 13
      Halliwell, C. M.; Morgan, G.; Ou, C. P.; Cass, A. E. G. Introduction of a (poly)histidine tag in L-lactate dehydrogenase produces a mixture of active and inactive molecules. Anal. Biochem. 2001, 295, 257– 261,  DOI: 10.1006/abio.2001.5182
      There is no corresponding record for this reference.
    14. 14
      Majorek, K. A.; Kuhn, M. L.; Chruszcz, M.; Anderson, W. F.; Minor, W. Double trouble - Buffer selection and his-tag presence may be responsible for nonreproducibility of biomedical experiments. Protein Sci. 2014, 23, 1359– 1368,  DOI: 10.1002/pro.2520
      14
      Double trouble-Buffer selection and His-tag presence may be responsible for nonreproducibility of biomedical experiments
      Majorek, Karolina A.; Kuhn, Misty L.; Chruszcz, Maksymilian; Anderson, Wayne F.; Minor, Wladek
      Protein Science (2014), 23 (10), 1359-1368CODEN: PRCIEI; ISSN:1469-896X. (Wiley-Blackwell)
      The availability of purified and active protein is the starting point for the majority of in vitro biomedical, biochem., and drug discovery expts. The use of polyhistidine affinity tags has resulted in great increases of the efficiency of the protein purifn. process, but can neg. affect structure and/or activity measurements. Similarly, buffer mols. may perturb the conformational stability of a protein or its activity. During the detn. of the structure of a Gcn5-related N-acetyltransferase (GNAT) from Pseudomonas aeruginosa (PA4794), we found that both HEPES and the polyhistidine affinity tag bind (sep.) in the substrate-binding site. In the case of HEPES, the mol. induces conformational changes in the active site, but does not significantly affect enzyme activity. In contrast, the uncleaved His-tag does not induce major conformational changes but acts as a weak competitive inhibitor of peptide substrate. In two other GNAT enzymes, we obsd. that the presence of the His-tag had a strong influence on the activity of these proteins. The influence of protein prepn. on functional studies may affect the reproducibility of expts. in other labs., even when changes between protocols seem at first glance to be insignificant. Moreover, the results presented here show how crit. it is to adjust the exptl. conditions for each protein or family of proteins, and investigate the influence of these factors on protein activity and structure, as they may significantly alter the effectiveness of functional characterization and screening methods. Thus, we show that a polyhistidine tag and the buffer mol. HEPES bind in the substrate-binding site and influence the conformation of the active site and the activity of GNAT acetyltransferases. We believe that such discrepancies can influence the reproducibility of some expts. and therefore could have a significant "ripple effect" on subsequent studies.
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    15. 15
      Tanner, A.; Bornemann, S. Bacillus subtilis yvrK is an acid-induced oxalate decarboxylase. J. Bacteriol. 2000, 182, 5271– 5273,  DOI: 10.1128/JB.182.18.5271-5273.2000
      15
      Bacillus subtilis YvrK is an acid-induced oxalate decarboxylase
      Tanner, Adam; Bornemann, Stephen
      Journal of Bacteriology (2000), 182 (18), 5271-5273CODEN: JOBAAY; ISSN:0021-9193. (American Society for Microbiology)
      B. subtilis strain 168 was shown to express a cytosolic oxalate decarboxylase (EC 4.1.1.2). The enzyme was induced in acidic growth media, particularly at pH 5.0, but not by oxalate. The enzyme was purified, and N-terminal sequencing identified the protein to be encoded by gene yvrK. The role of the 1st oxalate decarboxylase to be identified in a prokaryote is discussed.
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    16. 16
      Just, V. J. A Closed Conformation of Bacillus subtilis Oxalate Decarboxylase OxdC Provides Evidence for the True Identity of the Active Site. J. Biol. Chem. 2004, 279, 19867– 19874,  DOI: 10.1074/jbc.M313820200
      16
      A Closed Conformation of Bacillus subtilis Oxalate Decarboxylase OxdC Provides Evidence for the True Identity of the Active Site
      Just, Victoria J.; Stevenson, Clare E. M.; Bowater, Laura; Tanner, Adam; Lawson, David M.; Bornemann, Stephen
      Journal of Biological Chemistry (2004), 279 (19), 19867-19874CODEN: JBCHA3; ISSN:0021-9258. (American Society for Biochemistry and Molecular Biology)
      Oxalate decarboxylase (EC 4.1.1.2) catalyzes the conversion of oxalate to formate and carbon dioxide and utilizes dioxygen as a cofactor. By contrast, the evolutionarily related oxalate oxidase (EC 1.2.3.4) converts oxalate and dioxygen to carbon dioxide and hydrogen peroxide. Divergent free radical catalytic mechanisms have been proposed for these enzymes that involve the requirement of an active site proton donor in the decarboxylase but not the oxidase reaction. The oxidase possesses only one domain and manganese binding site per subunit, while the decarboxylase has two domains and two manganese sites per subunit. A structure of the decarboxylase together with a limited mutagenesis study has recently been interpreted as evidence that the C-terminal domain manganese binding site (site 2) is the catalytic site and that Glu-333 is the crucial proton donor (Anand, R., Dorrestein, P. C., Kinsland, C., Begley, T. P., and Ealick, S. E. (2002) Biochem. 41, 7659-7669). The N-terminal binding site (site 1) of this structure is solvent-exposed (open) and lacks a suitable proton donor for the decarboxylase reaction. We report a new structure of the decarboxylase that shows a loop contg. a 310 helix near site 1 in an alternative conformation. This loop adopts a "closed" conformation forming a lid covering the entrance to site 1. This conformational change brings Glu-162 close to the manganese ion, making it a new candidate for the crucial proton donor. Site-directed mutagenesis of equiv. residues in each domain provides evidence that Glu-162 performs this vital role and that the N-terminal domain is either the sole or the dominant catalytically active domain.
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    17. 17
      Hollingsworth, S. A.; Dror, R. O. Molecular Dynamics Simulation for All. Neuron 2018, 99, 1129– 1143,  DOI: 10.1016/j.neuron.2018.08.011
      17
      Molecular Dynamics Simulation for All
      Hollingsworth, Scott A.; Dror, Ron O.
      Neuron (2018), 99 (6), 1129-1143CODEN: NERNET; ISSN:0896-6273. (Cell Press)
      A review. The impact of mol. dynamics (MD) simulations in mol. biol. and drug discovery has expanded dramatically in recent years. These simulations capture the behavior of proteins and other biomols. in full at. detail and at very fine temporal resoln. Major improvements in simulation speed, accuracy, and accessibility, together with the proliferation of exptl. structural data, have increased the appeal of biomol. simulation to experimentalists-a trend particularly noticeable in, although certainly not limited to, neuroscience. Simulations have proven valuable in deciphering functional mechanisms of proteins and other biomols., in uncovering the structural basis for disease, and in the design and optimization of small mols., peptides, and proteins. Here we describe, in practical terms, the types of information MD simulations can provide and the ways in which they typically motivate further exptl. work.
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    18. 18
      Altschul, S. F.; Gish, W.; Miller, W.; Myers, E. W.; Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 1990, 215, 403– 410,  DOI: 10.1016/S0022-2836(05)80360-2
      18
      Basic local alignment search tool
      Altschul, Stephen F.; Gish, Warren; Miller, Webb; Myers, Eugene W.; Lipman, David J.
      Journal of Molecular Biology (1990), 215 (3), 403-10CODEN: JMOBAK; ISSN:0022-2836.
      A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent math. results on the stochastic properties of MSP scores allow an anal. of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a no. of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the anal. of multiple regions of similarity in long DNA sequences. In addn. to its flexibility and tractability to math. anal., BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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    19. 19
      Waterhouse, A. SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Res. 2018, 46, W296– W303,  DOI: 10.1093/nar/gky427
      19
      SWISS-MODEL: homology modelling of protein structures and complexes
      Waterhouse, Andrew; Bertoni, Martino; Bienert, Stefan; Studer, Gabriel; Tauriello, Gerardo; Gumienny, Rafal; Heer, Florian T.; de Beer, Tjaart A. P.; Rempfer, Christine; Bordoli, Lorenza; Lepore, Rosalba; Schwede, Torsten
      Nucleic Acids Research (2018), 46 (W1), W296-W303CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)
      Homol. modeling has matured into an important technique in structural biol., significantly contributing to narrowing the gap between known protein sequences and exptl. detd. structures. Fully automated workflows and servers simplify and streamline the homol. modeling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modeling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modeling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modeling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homol. modeling. Other major improvements include the implementation of a new modeling engine, ProMod3 and the introduction a new local model quality estn. method, QMEANDisCo.
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    20. 20
      Sayers, E. W. Database resources of the national center for biotechnology information. Nucleic Acids Res. 2022, 50, D20– D26,  DOI: 10.1093/nar/gkab1112
      20
      Database resources of the national center for biotechnology information
      Sayers, Eric W.; Bolton, Evan E.; Brister, J. Rodney; Canese, Kathi; Chan, Jessica; Comeau, Donald C.; Connor, Ryan; Funk, Kathryn; Kelly, Chris; Kim, Sunghwan; Madej, Tom; Marchler-Bauer, Aron; Lanczycki, Christopher; Lathrop, Stacy; Lu, Zhiyong; Thibaud-Nissen, Francoise; Murphy, Terence; Phan, Lon; Skripchenko, Yuri; Tse, Tony; Wang, Jiyao; Williams, Rebecca; Trawick, Barton W.; Pruitt, Kim D.; Sherry, Stephen T.
      Nucleic Acids Research (2022), 50 (D1), D20-D26CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)
      A review. The National Center for Biotechnol. Information (NCBI) produces a variety of online information resources for biol., including the GenBank nucleic acid sequence database and the PubMed database of citations and abstrs. published in life science journals. NCBI provides search and retrieval operations for most of these data from 35 distinct databases. The E-utilities serve as the programming interface for the most of these databases. Resources receiving significant updates in the past year include PubMed, PMC, Bookshelf, RefSeq, SRA, Virus, dbSNP, dbVar, ClinicalTrials.gov, MMDB, iCn3D and PubChem.
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    21. 21
      Celniker, G. ConSurf: Using evolutionary data to raise testable hypotheses about protein function. Isr. J. Chem. 2013, 53, 199– 206,  DOI: 10.1002/ijch.201200096
      21
      ConSurf: Using Evolutionary Data to Raise Testable Hypotheses about Protein Function
      Celniker, Gershon; Nimrod, Guy; Ashkenazy, Haim; Glaser, Fabian; Martz, Eric; Mayrose, Itay; Pupko, Tal; Ben-Tal, Nir
      Israel Journal of Chemistry (2013), 53 (3-4), 199-206CODEN: ISJCAT; ISSN:0021-2148. (Wiley-VCH Verlag GmbH & Co. KGaA)
      A review. Many mutations disappear from the population because they impair protein function and/or stability. Thus, amino acid positions that are essential for proper function evolve more slowly than others, or in other words, the slow evolutionary rate of a position reflects its importance. ConSurf (http://consurfτac.il), reviewed in this manuscript, exploits this to reveal key amino acid positions that are important for maintaining the native conformation(s) of the protein and its function, be it binding, catalysis, transport, etc. Given the sequence or 3D structure of the query protein as input, a search for similar sequences is conducted and the sequences are aligned. The multiple sequence alignment is subsequently used to calc. the evolutionary rates of each amino acid site, using Bayesian or max.-likelihood algorithms. Both algorithms take into account the evolutionary relationships between the sequences, reflected in phylogenetic trees, to alleviate problems due to uneven (biased) sampling in sequence space. This is particularly important when the no. of sequences is low. The ConSurf-DB, a new release of which is presented here, provides precalcd. ConSurf conservation anal. of nearly all available structures in the Protein DataBank (PDB). The usefulness of ConSurf for the study of individual proteins and mutations, as well as a range of large-scale, genome-wide applications, is reviewed.
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    22. 22
      Pastore, A. J. Oxalate decarboxylase uses electron hole hopping for catalysis. J. Biol. Chem. 2021, 297, 100857  DOI: 10.1016/j.jbc.2021.100857
      There is no corresponding record for this reference.
    23. 23
      Armon, A.; Graur, D.; Ben-Tal, N. ConSurf: An algorithmic tool for the identification of functional regions in proteins by surface mapping of phylogenetic information. J. Mol. Biol. 2001, 307, 447– 463,  DOI: 10.1006/jmbi.2000.4474
      23
      ConSurf: An algorithmic tool for the identification of functional regions in proteins by surface mapping of phylogenetic information
      Armon, Aharon; Graur, Dan; Ben-Tal, Nir
      Journal of Molecular Biology (2001), 307 (1), 447-463CODEN: JMOBAK; ISSN:0022-2836. (Academic Press)
      Exptl. approaches for the identification of functionally important regions on the surface of a protein involve mutagenesis, in which exposed residues are replaced one after another while the change in binding to other proteins or changes in activity are recorded. However, practical considerations limit the use of these methods to small-scale studies, precluding a full mapping of all the functionally important residues on the surface of a protein. We present here an alternative approach involving the use of evolutionary data in the form of multiple-sequence alignment for a protein family to identify hot spots and surface patches that are likely to be in contact with other proteins, domains, peptides, DNA, RNA or ligands. The underlying assumption in this approach is that key residues that are important for binding should be conserved throughout evolution, just like residues that are crucial for maintaining the protein fold, i.e. buried residues. A main limitation in the implementation of this approach is that the sequence space of a protein family may be unevenly sampled, e.g. mammals may be overly represented. Thus, a seemingly conserved position in the alignment may reflect a taxonomically uneven sampling, rather than being indicative of structural or functional importance. To avoid this problem, we present here a novel methodol. based on evolutionary relations among proteins as revealed by inferred phylogenetic trees, and demonstrate its capabilities for mapping binding sites in SH2 and PTB signaling domains. A computer program that implements these ideas is available freely at: http://ashtoretτac.il/∼rony. (c) 2001 Academic Press.
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    24. 24
      Chang, A. BRENDA, the ELIXIR core data resource in 2021: New developments and updates. Nucleic Acids Res. 2021, 49, D498– D508,  DOI: 10.1093/nar/gkaa1025
      24
      BRENDA, the ELIXIR core data resource in 2021: new developments and updates
      Chang, Antje; Jeske, Lisa; Ulbrich, Sandra; Hofmann, Julia; Koblitz, Julia; Schomburg, Ida; Neumann-Schaal, Meina; Jahn, Dieter; Schomburg, Dietmar
      Nucleic Acids Research (2021), 49 (D1), D498-D508CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)
      A review. The BRENDA enzyme database, established in 1987, has evolved into the main collection of functional enzyme and metab. data. In 2018, BRENDA was selected as an ELIXIR Core Data Resource. BRENDA provides reliable data, continuous curation and updates of classified enzymes, and the integration of newly discovered enzymes. The main part contains >5 million data for ~ 90 000 enzymes from ~ 13 000 organisms, manually extd. from ~ 157 000 primary literature refs., combined with information of text and data mining, data integration, and prediction algorithms. Supplements comprise disease-related data, protein sequences, 3D structures, genome annotations, ligand information, taxonomic, bibliog., and kinetic data. BRENDA offers an easy access to enzyme information from quick to advanced searches, text- and structured-based queries for enzyme-ligand interactions, word maps, and visualization of enzyme data. The BRENDA Pathway Maps are completely revised and updated for an enhanced interactive and intuitive usability. The new design of the Enzyme Summary Page provides an improved access to each individual enzyme. A new protein structure 3D viewer was integrated. The prediction of the intracellular localization of eukaryotic enzymes has been implemented. The new EnzymeDetector combines BRENDA enzyme annotations with protein and genome databases for the detection of eukaryotic and prokaryotic enzymes.
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    25. 25
      Fan, C.; Bobik, T. A. Functional characterization and mutation analysis of human ATP:Cob(I)alamin adenosyltransferase. Biochemistry 2008, 47, 2806– 2813,  DOI: 10.1021/bi800084a
      25
      Functional characterization and mutation analysis of human ATP:cob(I)alamin adenosyltransferase
      Fan, Chenguang; Bobik, Thomas A.
      Biochemistry (2008), 47 (9), 2806-2813CODEN: BICHAW; ISSN:0006-2960. (American Chemical Society)
      ATP:cob(I)alamin adenosyltransferase (ATR) catalyzes the final step in the conversion of vitamin B12 into the active coenzyme, adenosylcobalamin. Inherited defects in the gene for the human adenosyltransferase (hATR) result in methylmalonyl aciduria (MMA), a rare but life-threatening illness. In this study, we conducted a random mutagenesis of the hATR coding sequence. An ATR-deficient strain of Salmonella was used as a surrogate host to screen for mutations that impaired hATR activity in vivo. Fifty-seven missense mutations were isolated. These mapped to 30 positions of the hATR, 25 of which had not previously been shown to impair enzyme activity. Kinetic anal. and in vivo tests for enzyme activity were performed on the hATR variants, and mutations were mapped onto a hATR structural model. These studies functionally defined the hATR active site and tentatively implicated three amino acid residues in facilitating the redn. of cob(II)alamin to cob(I)alamin which is a prerequisite to adenosylation.
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    26. 26
      Smits, S. H. J.; Mueller, A.; Grieshaber, M. K.; Schmitt, L. Coenzyme- and His-tag-induced crystallization of octopine dehydrogenase. Acta Crystallogr. Sect. F Struct. Biol. Cryst. Commun. 2008, 64, 836– 839,  DOI: 10.1107/S1744309108025487
      There is no corresponding record for this reference.
    27. 27
      Marx, C. K.; Hertel, T. C.; Pietzsch, M. Purification and activation of a recombinant histidine-tagged pro-transglutaminase after soluble expression in Escherichia coli and partial characterization of the active enzyme. Enzyme Microb. Technol. 2008, 42, 568– 575,  DOI: 10.1016/j.enzmictec.2008.03.003
      27
      Purification and activation of a recombinant histidine-tagged pro-transglutaminase after soluble expression in Escherichia coli and partial characterization of the active enzyme
      Marx, Christian K.; Hertel, Thomas C.; Pietzsch, Markus
      Enzyme and Microbial Technology (2008), 42 (7), 568-575CODEN: EMTED2; ISSN:0141-0229. (Elsevier Inc.)
      Pro-transglutaminase from Streptomyces mobaraensis was expressed in Escherichia coli as a fusion protein carrying a C-terminal histidine tag (pro-MTG-His6). The recombinant organism was cultivated in 15 L bioreactor scale and pro-MTG-His6 was purified by immobilized metal affinity chromatog. Activation of the inactive pro-enzyme using trypsin resulted in an unexpected degrdn. of the transglutaminase and a concomitant loss of activity. Therefore, a set of com. available proteases was investigated for their activation potential without destroying the target enzyme. Besides trypsin, chymotrypsin and proteinase K were found to activate but hydrolyze the (pro-MTG-His6). Cathepsin B, dispase I, and thrombin were shown to specifically hydrolyze pro-MTG-His6 without deactivation. TAMEP, the endogenous protease from S. mobaraensis was purified for comparison and also found to activate the recombinant histidine-tagged transglutaminase without degrdn. The TAMEP activated MTG-His6 was purified and characterized. The specific activity (23 U/mg) of the recombinant histidine-tagged transglutaminase, the temp. optimum (50 °C), and the temp. stability (t 1/2 at 60 °C = 1.7 min) were comparable to the wild-type enzyme. A C-terminal peptide tag did neither affect the activity nor the stability but facilitated the purifn. The purifn. of the histidine-tagged protein is possible before or after activation.
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    28. 28
      Meng, L. Effects of His-tag on Catalytic Activity and Enantioselectivity of Recombinant Transaminases. Appl. Biochem. Biotechnol. 2020, 190, 880– 895,  DOI: 10.1007/s12010-019-03117-8
      There is no corresponding record for this reference.
    29. 29
      Mason, A. B. Expression, purification, and characterization of recombinant nonglycosylated human serum transferrin containing a C-terminal hexahistidine tag. Protein Expr. Purif. 2001, 23, 142– 150,  DOI: 10.1006/prep.2001.1480
      29
      Expression, Purification, and Characterization of Recombinant Nonglycosylated Human Serum Transferrin Containing a C-Terminal Hexahistidine Tag
      Mason, Anne B.; He, Qing-Yu; Adams, Ty E.; Gumerov, Dmitry R.; Kaltashov, Igor A.; Nguyen, Vinh; MacGillivray, Ross T. A.
      Protein Expression and Purification (2001), 23 (1), 142-150CODEN: PEXPEJ; ISSN:1046-5928. (Academic Press)
      Attachment of a hexa-His tag is a common strategy in recombinant protein prodn. The use of such a tag greatly simplifies the purifn. of the protein from the complex mixt. of other proteins in the media or cell ext. We describe the prodn. of two recombinant nonglycosylated human serum transferrins (hTF-NG), contg. a factor Xa cleavage site and a hexa-His tag at their carboxyl-terminal ends. One of the constructs comprises the entire coding region for hTF (residues 1-679), while the other lacks the final three carboxyl-terminal amino acids. After insertion of the His-tagged hTFs into the pNUT vector, transfection into baby hamster kidney (BHK) cells, and selection with methotrexate, the secreted recombinant proteins were isolated from the tissue culture medium. Av. max. expression levels of the His-tagged hTFs were about 40 mg/L compared to an av. max. of 50 mg/L for hTF-NG. The first step of purifn. involved an anion exchange column. The second step utilized a Poros metal chelate column preloaded with copper from which the His-tagged sample was eluted with a linear imidazole gradient. The His-tagged hTFs were characterized and compared to both recombinant hTF-NG and glycosylated hTF from human serum. The identity of each of the His-tagged hTFs constructs was verified by electrospray mass spectroscopy. In summary, the His-tagged hTF constructs simplify the purifn. of these metal-binding proteins with minimal effects on many of their phys. properties. The His-tagged hTFs share many features common to hTF, including reversible iron binding, reactivity with a monoclonal antibody, and presence as a monomer in soln. (c) 2001 Academic Press.
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    30. 30
      Mason, A. B. Differential effect of a His tag at the N- and C-termini: Functional studies with recombinant human serum transferrin. Biochemistry 2002, 41, 9448– 9454,  DOI: 10.1021/bi025927l
      30
      Differential Effect of a His Tag at the N- and C-Termini: Functional Studies with Recombinant Human Serum Transferrin
      Mason, Anne B.; He, Qing-Yu; Halbrooks, Peter J.; Everse, Stephen J.; Gumerov, Dmitry R.; Kaltashov, Igor A.; Smith, Valerie C.; Hewitt, Jeff; MacGillivray, Ross T. A.
      Biochemistry (2002), 41 (30), 9448-9454CODEN: BICHAW; ISSN:0006-2960. (American Chemical Society)
      Attachment of a cleavable hexa His tag is a common strategy for the prodn. of recombinant proteins. Prodn. of two recombinant nonglycosylated human serum transferrins (hTF-NG), contg. a factor Xa cleavage site and a hexa His tag at the carboxyl terminus, has been described [Mason et al. (2001) Prot. Exp. Purif 23, 142-150]. More recently, hTF-NG with an amino-terminal His tag and a factor Xa cleavage site has been expressed (>30 mg/L) in baby hamster kidney cells and purified from the tissue culture medium. Although it is frequently assumed that addn. of a His tag has little or no effect on function, this is not always confirmed exptl. In the present study, in vitro quant. data clearly shows that the presence of the C-terminal His tag has an effect on the release of iron from recombinant hTF at pH 7.4 and 5.6. Measurement of the rate of release from both the N- and C-lobes is reduced 2-4-fold. These findings provide further compelling evidence that the two lobes communicate with each other and highlight the importance of the C-terminal portion of the C-terminal lobe in this interaction. In contrast to these results, we demonstrate that the presence of a His tag at the N-terminus of hTF has no effect on the rate of iron release from either lobe. In a competition expt., both unlabeled N- and C-terminal His-tagged constructs were equally effective at inhibiting the binding of radio-iodinated diferric glycosylated hTF from a com. source to receptors on HeLa cells as the unlabeled recombinant diferric hTF-NG control. Thus, the presence of a His tag at either the N- or C-terminus of hTF-NG has no apparent effect on the ability of these hTF species to bind to transferrin receptors.
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    31. 31
      Lin, Y. W.; Ying, T. L.; Liao, L. F. Molecular modeling and dynamics simulation of a histidine-tagged cytochrome b 5. J. Mol. Model. 2011, 17, 971– 978,  DOI: 10.1007/s00894-010-0795-4
      31
      Molecular modeling and dynamics simulation of a histidine-tagged cytochrome b 5
      Lin, Ying-Wu; Ying, Tian-Lei; Liao, Li-Fu
      Journal of Molecular Modeling (2011), 17 (5), 971-978CODEN: JMMOFK; ISSN:0948-5023. (Springer)
      Although an affinity tag such as six consecutive histidines, (His)6-tag, has been widely used to obtain high quantity of recombinant proteins, little is known about its influences on heme proteins for lack of structural information. When (His)6-tag was introduced to the N-terminus of a small heme protein, cytochrome b 5, exptl. results showed the resultant protein, (His)6-cyt b 5, has similar property and function to that of isolated cyt b 5. To provide structural information for this observation, we herein performed a structural prediction of (His)6-cyt b 5 by mol. modeling in combination with mol. dynamics simulation. The predicted structure, as assessed by a series of criteria with good quality, reveals that the (His)6-tag adopts a helical conformation and packs against the hydrophobic core 2 of cyt b 5 through salt bridges, hydrogen bonding and hydrophobic interactions. The heme group, with the axial His ligands slightly rotated, was found to have similar conformation as in isolated cyt b 5, which indicates that the N-terminal (His)6-tag does not alter the heme active site, resulting in similar dynamics properties for core 1. This study provides valuable information of interactions between (His)6-tag and the rest of the protein, aiding in rational design and application of functional His-tagged proteins.
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    32. 32
      Parshin, P. D. Effect of His6-tag Position on the Expression and Properties of Phenylacetone Monooxygenase from Thermobifida fusca. Biochem. 2020, 85, 575– 582,  DOI: 10.1134/S0006297920050065
      There is no corresponding record for this reference.
    33. 33
      Freydank, A. C.; Brandt, W.; Dräger, B. Protein structure modeling indicates hexahistidine-tag interference with enzyme activity. Proteins Struct. Funct. Genet. 2008, 72, 173– 183,  DOI: 10.1002/prot.21905
      There is no corresponding record for this reference.
    34. 34
      Yeon, Y. J.; Park, H. J.; Park, H. Y.; Yoo, Y. J. Effect of His-tag location on the catalytic activity of 3-hydroxybutyrate dehydrogenase. Biotechnol. Bioprocess Eng. 2014, 19, 798– 802,  DOI: 10.1007/s12257-014-0089-2
      There is no corresponding record for this reference.
    35. 35
      Yeon, Y. J.; Park, H. Y.; Yoo, Y. J. Enzymatic reduction of levulinic acid by engineering the substrate specificity of 3-hydroxybutyrate dehydrogenase. Bioresour. Technol. 2013, 134, 377– 380,  DOI: 10.1016/j.biortech.2013.01.078
      35
      Enzymatic reduction of levulinic acid by engineering the substrate specificity of 3-hydroxybutyrate dehydrogenase
      Yeon, Young Joo; Park, Hyung-Yeon; Yoo, Young Je
      Bioresource Technology (2013), 134 (), 377-380CODEN: BIRTEB; ISSN:0960-8524. (Elsevier Ltd.)
      Enzymic redn. of levulinic acid (LA) was performed for the synthesis of 4-hydroxyvaleric acid (4HV) - a monomer of bio-polyester and a precursor of bio-fuels - using 3-hydroxybutyrate dehydrogenase (3HBDH) from Alcaligenes faecalis. Due to the catalytic inactivity of the wild-type enzyme toward LA, engineering of the substrate specificity of the enzyme was performed. A rational design approach with mol. docking simulation was applied, and a double mutant, His144Leu/Trp187Phe, which has catalytic activity (kcat/Km = 578.0 min-1 M-1) toward LA was generated. Approx. 57% conversion of LA to 4HV was achieved with this double mutant in 24 h, while no conversion was achieved with the wild-type enzyme.
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    36. 36
      Zhang, J.; Cui, T.; Li, X. Screening and identification of an Enterobacter ludwigii strain expressing an active β-xylosidase. Ann. Microbiol. 2018, 68, 261– 271,  DOI: 10.1007/s13213-018-1334-2
      There is no corresponding record for this reference.
    37. 37
      Li, Y. Recombinant glutamine synthetase (GS) from C. glutamicum existed as both hexamers & dedocamers and C-terminal His-tag enhanced inclusion bodies formation in E. coli. Appl. Biochem. Biotechnol. 2009, 159, 614– 622,  DOI: 10.1007/s12010-008-8493-8
      There is no corresponding record for this reference.
    38. 38
      Flores, S. S. His-tag β-galactosidase supramolecular performance. Biophys. Chem. 2022, 281, 106739  DOI: 10.1016/j.bpc.2021.106739
      38
      His-tag β-galactosidase supramolecular performance
      Flores, Sandra S.; Clop, Pedro D.; Barra, Jose L.; Argarana, Carlos E.; Perillo, Maria A.; Nolan, Veronica; Sanchez, Julieta M.
      Biophysical Chemistry (2022), 281 (), 106739CODEN: BICIAZ; ISSN:0301-4622. (Elsevier B.V.)
      β-Galactosidase is an important biotechnol. enzyme used in the dairy industry, pharmacol. and in mol. biol. In our lab. we have overexpressed a recombinant β-galactosidase in Escherichia coli (E. coli). This enzyme differs from its native version (β-GalWT) in that 6 histidine residues have been added to the carboxyl terminus in the primary sequence (β-GalHis), which allows its purifn. by immobilized metal affinity chromatog. (IMAC). In this work we compared the functionality and structure of both proteins and evaluated their catalytic behavior on the kinetics of lactose hydrolysis. We obsd. a significant redn. in the enzymic activity of β-GalHis with respect to β-GalWT. Although, both enzymes showed a similar catalytic profile as a function of temp., β-GalHis presented a higher resistance to the thermal inactivation compared to β-GalWT. At room temp., β-GalHis showed a fluorescence spectrum compatible with a partially unstructured protein, however, it exhibited a lower tendency to the thermal-induced unfolding with respect to β-GalWT. The distinctively supramol. arranges of the proteins would explain the effect of the presence of His-tag on the enzymic activity and thermal stability.
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    39. 39
      Kutyshenko, V. P. Effect of C-terminal His-tag and purification routine on the activity and structure of the metalloenzyme, L-alanyl-D-glutamate peptidase of the bacteriophage T5. Int. J. Biol. Macromol. 2019, 124, 810– 818,  DOI: 10.1016/j.ijbiomac.2018.11.219
      39
      Effect of C-terminal His-tag and purification routine on the activity and structure of the metalloenzyme, L-alanyl-D-glutamate peptidase of the bacteriophage T5
      Kutyshenko, Victor P.; Mikoulinskaia, Galina V.; Chernyshov, Sergei V.; Yegorov, Alexander Y.; Prokhorov, Dmitry A.; Uversky, Vladimir N.
      International Journal of Biological Macromolecules (2019), 124 (), 810-818CODEN: IJBMDR; ISSN:0141-8130. (Elsevier B.V.)
      In this work, we studied the effect of the C-terminally attached poly-histidine tag (His-tag), as well as the peculiarities of the protein purifn. procedure by the immobilized metal affinity chromatog. (IMAC) on the activity and structure of the metalloenzyme, L-alanyl-D-glutamate peptidase of bacteriophage T5 (EndoT5), whose zinc binding site and catalytic aspartate are located near the C-terminus. By itself, His-tag did not have a significant effect on either activity or folding of the polypeptide chain, nor on the binding of zinc and calcium ions to the protein. However, the His-tagged EndoT5 samples had low shelf-life, with storage of these samples resulting in an increased propensity for protein self-assocn. and decreased enzymic activity of EndoT5. Furthermore, disastrous effects on the activity of the enzyme were exerted by the presence of imidazole and nickel ions accompanying metal chelate chromatog. The activity of the protein can be restored by thorough washing off of these low mol. impurities via the prolonged dialysis of the His-tagged EndoT5 samples at the specifically elaborated conditions.
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    40. 40
      Nichols, E. R.; Craig, D. B. Single molecule assays reveal differences between in vitro and in vivo synthesized β-galactosidase. Protein J. 2008, 27, 376– 383,  DOI: 10.1007/s10930-008-9147-y
      There is no corresponding record for this reference.
    41. 41
      Olchowy, J.; Kur, K.; Sachadyn, P.; Milewski, S. Construction, purification, and functional characterization of His-tagged Candida albicans glucosamine-6-phosphate synthase expressed in Escherichia coli. Protein Expr. Purif. 2006, 46, 309– 315,  DOI: 10.1016/j.pep.2005.07.030
      There is no corresponding record for this reference.
    42. 42
      Juma, K. M. Modified uvsY by N-terminal hexahistidine tag addition enhances efficiency of recombinase polymerase amplification to detect SARS-CoV-2 DNA. Mol. Biol. Rep. 2022, 49, 2847– 2856,  DOI: 10.1007/s11033-021-07098-y
      There is no corresponding record for this reference.
    43. 43
      Wolucka, B. A.; Van Montagu, M. GDP-Mannose 3′,5′-Epimerase Forms GDP-L-gulose, a Putative Intermediate for the de Novo Biosynthesis of Vitamin C in Plants. J. Biol. Chem. 2003, 278, 47483– 47490,  DOI: 10.1074/jbc.M309135200
      43
      GDP-Mannose 3',5'-Epimerase Forms GDP-L-gulose, a Putative Intermediate for the de Novo Biosynthesis of Vitamin C in Plants
      Wolucka, Beata A.; Van Montagu, Marc
      Journal of Biological Chemistry (2003), 278 (48), 47483-47490CODEN: JBCHA3; ISSN:0021-9258. (American Society for Biochemistry and Molecular Biology)
      Despite its importance for agriculture, bioindustry, and nutrition, the fundamental process of L-ascorbic acid (vitamin C) biosynthesis in plants is not completely elucidated, and little is known about its regulation. The recently identified GDP-Man 3',5'-epimerase catalyzes a reversible epimerization of GDP-D-mannose that precedes the committed step in the biosynthesis of vitamin C, resulting in the hydrolysis of the highly energetic glycosyl-pyrophosphoryl linkage. Here, we characterize the native and recombinant GDP-Man 3',5'-epimerase of Arabidopsis thaliana. GDP and GDP-D-glucose are potent competitive inhibitors of the enzyme, whereas GDP-L-fucose gives a complex type of inhibition. The epimerase contains a modified version of the NAD binding motif and is inhibited by NAD(P)H and stimulated by NAD(P)+. A feedback inhibition of vitamin C biosynthesis is obsd. apparently at the level of GDP-Man 3',5'-epimerase. The epimerase catalyzes at least two distinct epimerization reactions and releases not only the well-known GDP-L-galactose, but also the novel intermediate GDP-L-gulose. The yield of the epimerization varies and seems to depend on the mol. form of the enzyme. Both recombinant and native enzymes co-purified with a Hsp70 heat-shock protein (Escherichia coli DnaK and A. thaliana Hsc70.3, resp.). We speculate therefore that the Hsp70 mol. chaperones might be involved in folding and/or regulation of the epimerase. In summary, the plant epimerase undergoes a complex regulation and could control the carbon flux into the vitamin C pathway in response to the redox state of the cell, stress conditions, and GDP-sugar demand for the cell wall/glycoprotein biosynthesis. Exogenous L-gulose and L-gulono-1,4-lactone serve as direct precursors of L-ascorbic acid in plant cells. We propose an L-gulose pathway for the de novo biosynthesis of vitamin C in plants.
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    44. 44
      Wolucka, B. A. Partial purification and identification of GDP-mannose 3″,5″-epimerase of Arabidopsis thaliana, a key enzyme of the plant vitamin C pathway. Proc. Natl. Acad. Sci. U. S. A. 2001, 98, 14843– 14848,  DOI: 10.1073/pnas.011578198
      There is no corresponding record for this reference.
    45. 45
      De Almeida, J. M. Tailoring recombinant lipases: Keeping the His-Tag favors esterification reactions, removing it favors hydrolysis reactions. Sci. Rep. 2018, 8 (1), 10000,  DOI: 10.1038/s41598-018-27579-8
      There is no corresponding record for this reference.
    46. 46
      Özdemir, F. İ.; Tülek, A.; Erdoğan, D. Identification and Heterologous Production of a Lipase from Geobacillus kaustophilus DSM 7263T and Tailoring Its N-Terminal by a His-Tag Epitope. Protein J. 2021, 40, 436– 447,  DOI: 10.1007/s10930-021-09987-4
      There is no corresponding record for this reference.
    47. 47
      Esen, H.; Alpdağtaş, S.; Mervan Çakar, M.; Binay, B. Tailoring of recombinant FDH: effect of histidine tag location on solubility and catalytic properties of Chaetomium thermophilum formate dehydrogenase (CtFDH). Prep. Biochem. Biotechnol. 2019, 49, 529– 534,  DOI: 10.1080/10826068.2019.1599394
      There is no corresponding record for this reference.
    48. 48
      Zhu, Z. C. Interactions between EB1 and microtubules: Dramatic effect of affinity tags and evidence for cooperative behavior. J. Biol. Chem. 2009, 284, 32651– 32661,  DOI: 10.1074/jbc.M109.013466
      There is no corresponding record for this reference.
    49. 49
      Chen, Z.; Li, Y.; Yuan, Q. Study the effect of His-tag on chondroitinase ABC I based on characterization of enzyme. Int. J. Biol. Macromol. 2015, 78, 96– 101,  DOI: 10.1016/j.ijbiomac.2015.03.068
      There is no corresponding record for this reference.
    50. 50
      Aslantas, Y.; Surmeli, N. B. Effects of N-terminal and C-terminal polyhistidine tag on the stability and function of the thermophilic P450 CYP119. Bioinorg. Chem. Appl. 2019, 2019, 8080697  DOI: 10.1155/2019/8080697
      There is no corresponding record for this reference.
    51. 51
      Hyun, J.; Abigail, M.; Choo, J. W.; Ryu, J.; Kim, H. K. Effects of N-/C-terminal extra tags on the optimal reaction conditions, activity, and quaternary structure of Bacillus thuringiensis glucose 1-dehydrogenase. J. Microbiol. Biotechnol. 2016, 26, 1708– 1716,  DOI: 10.4014/jmb.1603.03021
      There is no corresponding record for this reference.
    52. 52
      Mosbah, H.; Sayari, A.; Bezzine, S.; Gargouri, Y. Expression, purification, and characterization of His-tagged Staphylococcus xylosus lipase wild-type and its mutant Asp 290 Ala. Protein Expr. Purif. 2006, 47, 516– 523,  DOI: 10.1016/j.pep.2005.11.013
      There is no corresponding record for this reference.
    53. 53
      Sayari, A.; Mosbah, H.; Gargouri, Y. Importance of the residue Asp 290 on chain length selectivity and catalytic efficiency of recombinant Staphylococcus simulans lipase expressed in E. coli. Mol. Biotechnol. 2007, 36, 14– 22,  DOI: 10.1007/s12033-007-0008-2
      There is no corresponding record for this reference.
    54. 54
      Horchani, H.; Ouertani, S.; Gargouri, Y.; Sayari, A. The N-terminal His-tag and the recombination process affect the biochemical properties of Staphylococcus aureus lipase produced in Escherichia coli. J. Mol. Catal. B Enzym. 2009, 61, 194– 201,  DOI: 10.1016/j.molcatb.2009.07.002
      There is no corresponding record for this reference.
    55. 55
      Horchani, H. Heterologous expression and N-terminal His-tagging processes affect the catalytic properties of staphylococcal lipases: A monolayer study. J. Colloid Interface Sci. 2010, 350, 586– 594,  DOI: 10.1016/j.jcis.2010.07.021
      There is no corresponding record for this reference.
    56. 56
      The PyMOL Molecular Graphics System, Version 3.0; Schrödinger, LLC., 2015.
      There is no corresponding record for this reference.
    57. 57
      Liu, W. Crystal structures of unbound and aminooxyacetate-bound Escherichia coli γ-aminobutyrate aminotransferase. Biochemistry 2004, 43, 10896– 10905,  DOI: 10.1021/bi049218e
      57
      Crystal Structures of Unbound and Aminooxyacetate-Bound Escherichia coli γ-Aminobutyrate Aminotransferase
      Liu, Wenshe; Peterson, Peter E.; Carter, Richard J.; Zhou, Xianzhi; Langston, James A.; Fisher, Andrew J.; Toney, Michael D.
      Biochemistry (2004), 43 (34), 10896-10905CODEN: BICHAW; ISSN:0006-2960. (American Chemical Society)
      The x-ray crystal structures of Escherichia coli γ-aminobutyrate aminotransferase unbound and bound to the inhibitor aminooxyacetate are reported. The enzyme crystallizes from ammonium sulfate solns. in the P3221 space group with a tetramer in the asym. unit. Diffraction data were collected to 2.4 Å resoln. for the unliganded enzyme and 1.9 Å resoln. for the aminooxyacetate complex. The overall structure of the enzyme is similar to those of other aminotransferase subgroup II enzymes. The ability of γ-aminobutyrate aminotransferase to act on primary amine substrates (γ-aminobutyrate) in the first half-reaction and α-amino acids in the second is proposed to be enabled by the presence of Glu-211, whose side chain carboxylate alternates between interactions with Arg-398 in the primary amine half-reaction and an alternative binding site in the α-amino acid half-reaction, in which Arg-398 binds the substrate α-carboxylate. The specificity for a carboxylate group on the substrate side chain is due primarily to the presence of Arg-141, but also requires substantial local main chain rearrangements relative to the structurally homologous enzyme dialkylglycine decarboxylase, which is specific for small alkyl side chains. No iron-sulfur cluster is found in the bacterial enzyme as was found in the pig enzyme. The binding of aminooxyacetate causes remarkably small changes in the active site structure, and no large domain movements are obsd. Active site structure comparisons with pig γ-aminobutyrate aminotransferase and dialkylglycine decarboxylase are discussed.
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    58. 58
      Sun, Z.; Liu, Q.; Qu, G.; Feng, Y.; Reetz, M. T. Utility of B-Factors in Protein Science: Interpreting Rigidity, Flexibility, and Internal Motion and Engineering Thermostability. Chem. Rev. 2019, 119, 1626– 1665,  DOI: 10.1021/acs.chemrev.8b00290
      58
      Utility of B-Factors in Protein Science: Interpreting Rigidity, Flexibility, and Internal Motion and Engineering Thermostability
      Sun, Zhoutong; Liu, Qian; Qu, Ge; Feng, Yan; Reetz, Manfred T.
      Chemical Reviews (Washington, DC, United States) (2019), 119 (3), 1626-1665CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)
      A review. The term B-factor, sometimes called the Debye-Waller factor, temp. factor, or at. displacement parameter, is used in protein crystallog. to describe the attenuation of X-ray or neutron scattering caused by thermal motion. This review begins with analyses of early protein studies which suggested that B-factors, available from the Protein Data Bank, can be used to identify the flexibility of atoms, side chains, or even whole regions. This requires a technique for obtaining normalized B-factors. Since then the exploitation of B-factors has been extensively elaborated and applied in a variety of studies with quite different goals, all having in common the identification and interpretation of rigidity, flexibility, and/or internal motion which are crucial in enzymes and in proteins in general. Importantly, this review includes a discussion of limitations and possible pitfalls when using B-factors. A second research area, which likewise exploits B-factors, is also reviewed, namely, the development of the so-called B-FIT-directed evolution method for increasing the thermostability of enzymes as catalysts in org. chem. and biotechnol. In both research areas, a max. of structural and mechanistic insights is gained when B-factor analyses are combined with other exptl. and computational techniques.
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    59. 59
      Gajewski, S. Structure and mechanism of the phage T4 recombination mediator protein UvsY. Proc. Natl. Acad. Sci. U. S. A. 2016, 113, 3275– 3280,  DOI: 10.1073/pnas.1519154113
      There is no corresponding record for this reference.
    60. 60
      Xu, H.; Beernink, H. T. H.; Morrical, S. W. DNA-binding properties of T4 UvsY recombination mediator protein: Polynucleotide wrapping promotes high-affinity binding to single-stranded DNA. Nucleic Acids Res. 2010, 38, 4821– 4833,  DOI: 10.1093/nar/gkq219
      There is no corresponding record for this reference.

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