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Discovery of a Novel DCAF1 Ligand Using a Drug–Target Interaction Prediction Model: Generalizing Machine Learning to New Drug Targets
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  • Serah W. Kimani
    Serah W. Kimani
    Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
    Princess Margaret Cancer Center, University Health Network, Toronto, Ontario M5G 2C4, Canada
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  • Julie Owen
    Julie Owen
    Recursion Pharmaceuticals Inc., Toronto, Ontario M5V 2A2, Canada
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  • Stuart R. Green
    Stuart R. Green
    Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
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    Orcidhttps://orcid.org/0000-0002-2960-9683
  • Fengling Li
    Fengling Li
    Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
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  • Yanjun Li
    Yanjun Li
    Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
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  • Aiping Dong
    Aiping Dong
    Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
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  • Peter J. Brown
    Peter J. Brown
    Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
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    Orcidhttps://orcid.org/0000-0002-8454-0367
  • Suzanne Ackloo
    Suzanne Ackloo
    Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
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    Orcidhttps://orcid.org/0000-0002-9696-1839
  • David Kuter
    David Kuter
    Recursion Pharmaceuticals Inc., Toronto, Ontario M5V 2A2, Canada
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    Orcidhttps://orcid.org/0000-0001-5393-6337
  • Cindy Yang
    Cindy Yang
    Recursion Pharmaceuticals Inc., Toronto, Ontario M5V 2A2, Canada
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  • Miranda MacAskill
    Miranda MacAskill
    Recursion Pharmaceuticals Inc., Toronto, Ontario M5V 2A2, Canada
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  • Stephen Scott MacKinnon
    Stephen Scott MacKinnon
    Recursion Pharmaceuticals Inc., Toronto, Ontario M5V 2A2, Canada
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  • Cheryl H. Arrowsmith
    Cheryl H. Arrowsmith
    Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
    Princess Margaret Cancer Center, University Health Network, Toronto, Ontario M5G 2C4, Canada
    Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5S 1A1, Canada
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    Orcidhttps://orcid.org/0000-0002-4971-3250
  • Matthieu Schapira*
    Matthieu Schapira
    Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
    Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario M5S 1A1, Canada
    *Email: [email protected]. Tel.: +1 416-978-3092.
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    Orcidhttps://orcid.org/0000-0002-1047-3309
  • Vijay Shahani*
    Vijay Shahani
    Recursion Pharmaceuticals Inc., Toronto, Ontario M5V 2A2, Canada
    *Email: [email protected]. Tel.: +1 647-444-6226.
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  • Levon Halabelian*
    Levon Halabelian
    Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
    Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario M5S 1A1, Canada
    *Email: [email protected]. Tel.: +1 416-946-3876.
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    Orcidhttps://orcid.org/0000-0003-4361-3619
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Journal of Chemical Information and Modeling

Cite this: J. Chem. Inf. Model. 2023, 63, 13, 4070–4078
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https://doi.org/10.1021/acs.jcim.3c00082
Published June 23, 2023

Copyright © 2023 The Authors. Published by American Chemical Society. This publication is licensed under

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Abstract

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DCAF1 functions as a substrate recruitment subunit for the RING-type CRL4DCAF1 and the HECT family EDVPDCAF1 E3 ubiquitin ligases. The WDR domain of DCAF1 serves as a binding platform for substrate proteins and is also targeted by HIV and SIV lentiviral adaptors to induce the ubiquitination and proteasomal degradation of antiviral host factors. It is therefore attractive both as a potential therapeutic target for the development of chemical inhibitors and as an E3 ligase that could be recruited by novel PROTACs for targeted protein degradation. In this study, we used a proteome-scale drug–target interaction prediction model, MatchMaker, combined with cheminformatics filtering and docking to identify ligands for the DCAF1 WDR domain. Biophysical screening and X-ray crystallographic studies of the predicted binders confirmed a selective ligand occupying the central cavity of the WDR domain. This study shows that artificial intelligence-enabled virtual screening methods can successfully be applied in the absence of previously known ligands.

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Introduction

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The modern science of making drugs has diverged into many different subdisciplines since its humble beginnings in the 19th century. At its inception, the main focus of drug discovery was isolating and understanding the effects of natural substances. Since Langley and Ehrlich proposed receptor theory, (1,2) drug development has more frequently relied on rational drug design methods. In this approach, a single protein pocket is selected as the receptor, and drug candidates are designed to fit in a complementary manner to that receptor as predicted by the lock-and-key model of protein binding. In the past two decades, the application of artificial intelligence (AI) in drug design has gained popularity, largely due to an increase in available datasets and improved computational processing power. However, the lock-and-key model still prevails as the guiding principle for drug designers.
The limitations of structure-based drug design (SBDD) and ligand-based drug design (LBDD) have been reviewed previously, prompting the search for new predictive solutions and consequently leading to the recent emergence of drug–target interaction (DTI) prediction models. (3) Relative to SBDD techniques, DTI models inherit several of the key functional advantages of their LBDD counterparts, including the opportunity to continuously learn from observed outcomes and computational efficiency, such that models can enable much larger chemical library screens or counter screening strategies. However, DTI models further extend beyond standard LBDD modeling approaches by integrating various protein representations as a means to generalize training and inference across protein targets (Figure 1). In this study, we demonstrate the application of a DTI-based machine learning model MatchMaker, in the discovery of a small-molecule compound binding the WD40-repeat (WDR) domain of human DDB1-Cul4 associated factor 1 (DCAF1), a previously ligand-less target.

Figure 1

Figure 1. Conceptual diagram highlighting differences between ligand-based drug discovery (LBDD) models and drug–target interaction (DTI) models. LBDD models treat each protein as its own machine learning model, thereby limiting inference (prediction) to targets that already have sufficient data to train models. DTI models train a global model to predict binding drug–target pairs, such that protein targets learn from the bioactivities of similar proteins.

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DCAF1 is a 1507-amino acid protein composed of several domains and motifs (Figure S1), most of which are involved in protein–protein interaction events. The WDR domain that is the subject of this study is located on the C-terminal region of the DCAF1 protein (residues 1081–1388) and contains seven WD40 repeats, each spanning 40–60 amino acid residues and folding into four antiparallel beta strands “blades” that assemble into a seven-bladed β-propeller (doughnut-shaped) structure (4) (Figure S1).
DCAF1 is mainly involved in the ubiquitin proteasome-mediated protein degradation pathway and therefore plays an important role in cellular homeostasis. DCAF1 is the substrate recognition subunit for two E3 ubiquitin ligases, the RING-type Cullin RING ligase 4 (CRL4) complex (with which it has been extensively characterized) (5,6) and the HECT-type EDVP (UBR5/DYRK2) E3 ligase complex first identified by Maddika and Chen. (7) In both cases, DCAF1 interacts with the adaptor protein DNA damage-binding protein 1 (DDB1) via its WDR and helix–loop–helix (HLH) H-box modules (8,9) (see domains in Figure S1). In this configuration, the WDR domain provides a planar solvent-exposed structure that can bind substrate proteins on the top surfaces, the sides, and inside the central channel of the doughnut ring. (4) None of the identified substrates are shared between the CRL4DCAF1 and EDVPDCAF1 E3 ligase complexes, highlighting DCAF1 as a unique protein that can service two distinct E3 ubiquitin ligases, (10) which makes it an attractive target for the development of targeted protein degraders including proteolysis targeting chimeras (PROTACS). (11)
A common strategy used by pathogenic viruses to override host protective cellular processes is through hijacking the function of E3 ubiquitin ligases. (6) Indeed, DCAF1 is targeted by the primate lentiviral (HIV and SIV) Vpr and Vpx accessory proteins, (12) which bind to its WDR domain and recruit hosts’ protective proteins for ubiquitination by the CRL4DCAF1 complex and subsequent proteasomal degradation. The paralogous lentiviral accessory proteins have been shown to play distinct roles, with Vpr manipulating targets to cause a G2 phase cell cycle arrest, thus allowing viral propagation, and the Vpx acting to overcome host restriction factors, thus enabling viral infectivity. (10) Vpr has also been shown to hijack the EDVPDCAF1 complex to disrupt centrosome homeostasis, thus contributing to HIV pathogenesis. (13) DCAF1-associated E3 ligases are therefore attractive targets for development of protein–protein interaction inhibitors that can disrupt DCAF1-Vpr/x interactions.
DCAF1 is involved in regulating a variety of normal physiological processes including cell proliferation and survival, cell cycle progression, DNA replication, DNA damage responses, and microRNA biogenesis, among others (reviewed by Nakagawa et al. and Schabla et al.). (10,14) DCAF1 impacts these processes as a standalone protein or in the E3 ligase complexes, regulating respective target proteins on the transcriptional and/or the protein levels. Importantly, owing to its role in cell proliferation and survival, deregulation of DCAF1 has been shown to promote tumorigenesis, with reduced levels of tumor suppressor genes like p53 and its target genes being a common mechanism directly linked to DCAF1’s roles in protein ubiquitination and gene silencing. (15,16) Depletion of DCAF1 in some cancer cell lines has been shown to increase expression of tumor suppressor proteins. (15,16)
To enable experimental investigation of DCAF1 as a therapeutic or PROTAC target, we used AI to identify a DCAF1 ligand and confirmed its binding pose crystallographically. The reversible binding mode of this compound is complementary to the covalent engagement of DCAF1 ligands and PROTAC recently reported. (17)

Experimental Methods

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Computational Screening

Compound Library and Procurement

The molecules described in this study were selected from the Enamine Screening Collection compound library, which was accessed from Enamine’s website on June 15, 2020 (https://enamine.net/compound-collections/screening-collection). The library contained 2,878,172 off-the-shelf molecules. All compounds selected for testing in the hit identification stage of the study were procured directly from Enamine (Kyiv, Ukraine).

Human Proteome and DCAF1 Pockets

Each MatchMaker model is released with a reference human proteome that enumerates plausible ligand binding sites on experimental and predicted protein structures. The 2020Q2 reference proteome used in this study contains 29,293 pockets from 8525 PDB and SwissModel (18) cocomplex structures and has been previously described in other studies. (19,20) At the time of this study, DCAF1 had no known ligand, so pockets were added to the proteome by structure-based predictions. Multiple plausible DCAF1 pockets were detected using P2Rank v2.1, (21) operating on the DCAF1-SAMHD1-Vpx cocomplex 3D structure (PDB code 4CC9). (22) Three pockets were chosen based on the P2Rank predictions as well as the DCAF1/Vpx interface from the cocomplex structure (PDB 4CC9) (22) and named accordingly. P2Rank provided single Cartesian coordinates representing the center of each pocket in the reference frame of the 3D structure. These three pockets were used as the primary design objective for the computational hit discovery workflow (Figure 2).

Figure 2

Figure 2. Pocket selection. (A) Visualization of P2Rank predicted pockets. (B,C) Cartoon representation of Vpx from the ternary complex of DCAF1-SAMHD1-Vpx (PDB code 4CC9) showing overlay of pocket prediction with protein contacts. (D) Inside pocket. (E) Top pocket. (F) Side pocket.

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Modeling Drug–Target Interactions

Drug–target interactions were modeled using Cyclica’s 2020Q2 release MatchMaker model. (19) MatchMaker is a neural network trained to discriminate bioactive drug–target pairs from randomized pairs. (3) The network’s input layer concatenates ligand and protein representations, where ligands are represented as a combination of molecular descriptors and fingerprints and proteins are represented as functional annotations retrieved from Uniprot, (23) as well as structural descriptors of the target’s ligand binding site. Positive training examples were obtained by mapping bioactive drug–target interaction data onto available 3D protein structures sourced from the Protein Data Bank (24) or SwissModel. (18) Specifically, DTI binding sites were inferred based on chemical similarity to known cocrystal ligands or the superimposed cocrystal ligands from homologous proteins. Each positive drug–target pair was shuffled 19 times to generate negative training examples.

DTI Evaluations

Novel DCAF1 binders were discovered using a DTI screening workflow driven by Cyclica’s 2020Q2 release MatchMaker model. MatchMaker evaluated all interactions between 2,878,172 molecules from the Enamine Screening Collection and 29,293 total protein pockets (84.3 billion total inferences) comprising the human proteome including the three predicted DCAF1 pockets. A proteome binding profile was generated for each Enamine molecule by sorting all evaluated pockets according to their respective MatchMaker scores and selecting the top-ranked pocket to represent each protein’s score. DCAF1’s rank within the proteome binding profile was used as a MatchMaker signal specificity metric (see Candidate Selection). Inference was performed using 13 instances of 16 CPU, 60 GB virtual machines over 24 h using first-generation Intel Skylake or Intel Xeon E5-series processors (approximately 4700 evaluations per CPU-second).

Candidate Selection

The first selection criterion was primary target engagement based on MatchMaker binding probabilities. The 10,000 top scoring compounds for each of the three DCAF1 pockets were pooled, yielding a set of 23,261 unique molecules. Pooled molecules were subsequently filtered on the basis of MatchMaker proteome binding profiles to avoid compounds with nonspecific DCAF1 predictive binding signals. Compounds whose DCAF1 proteome binding ranks were larger than 101 for the inside pocket (Figure 2D) were excluded (4321 molecules remained). Subsequently, compounds were manually assessed and removed with consideration given to poor physicochemical properties, specifically number of rotatable bonds ≤ 12, number of H-bond acceptors ≤ 10, and molecular weight ≥ 550 Da. Since MatchMaker performs pose-independent DTI predictions, the remaining 2225 compounds were subjected to a final molecular docking ranking step for the inside and top pockets (see Figure 2) using default parameters with ICM-Pro (v. 3.8-2c, MolSoft, CA, USA). Finally, 101 compounds were selected for in vitro testing, using a combination of favorable docking scores and inspection of docked poses.

Biophysical Characterization of Computational Hits

Protein Expression and Purification

The selected compounds were tested against the WDR domains of human DCAF1 and human WDR5 (another WDR protein) as a negative control. For DCAF1 expression, a DNA fragment encoding human DCAF1 residues 1038–1400 was amplified by PCR and subcloned into an in-house insect cell expression vector pFBD-BirA (a derivative of the pFastBac Dual vector from Invitrogen) carrying an N-terminal AviTag, a C-terminal His6-tag, and coexpression of BirA. The resulting plasmid was transformed into DH10BacTM competent Escherichia coli (E. coli) cells (Invitrogen), and the recombinant viral DNA bacmid was purified and used in the recombinant expression of biotinylated DCAF1 protein in a baculovirus-Sf9 expression system as described by Hutchinson and Seitova (25) using biotin-supplemented media. For WDR5 expression, DNA encoding residues 2–334 of the human WDR5 were cloned into an in-house E. coli expression vector pNIC-Bio2 having an N-terminal His10-tag followed by a TEV cleavage site and a C-terminal AviTag. Biotinylated WDR5 protein was expressed in E. coli using biotin-supplemented media. Both proteins were purified using a similar purification protocol. Briefly, cells were harvested and lysed, and the proteins were purified through a pre-equilibrated HisPur Ni-NTA resin (Thermo Scientific) column followed by gel filtration on an ÄKTA PURE system (GE Healthcare), using a Superdex200 26/600 column (GE Healthcare) pre-equilibrated with 20 mM Tris–HCl pH 8.0, 150 mM NaCl, 5% glycerol, and 2 mM 2-mercaptoethanol for DCAF1 and 50 mM Tris–HCl pH 8.0, 150 mM NaCl, and 0.5 mM TCEP for WDR5. The yield of the purified biotinylated proteins was 7 mg/L for DCAF1 and 9.8 mg/L for WDR5.

Surface Plasmon Resonance (SPR) Binding Studies

SPR studies were performed using a Biacore T200 (GE Health Sciences, Inc.) at 20 °C. Biotinylated DCAF1 (1038–1400 aa) and WDR5 control (2–334 aa) proteins were each captured onto one flow cell of a streptavidin-conjugated SA chip (according to the manufacturer’s protocol) achieving 7000 response units (RU), while another flow cell was left empty for reference subtraction. Compounds were dissolved in 100% DMSO (20 mM stock) and diluted to working concentrations in 100% DMSO before 3-fold serial dilutions prepared in buffer with DMSO yielding five concentrations. For SPR analysis, compounds were diluted in HBS-EP+ (10 mM HEPES pH 7.4, 150 mM NaCl, 3 mM EDTA, and 0.05% Tween-20) giving a final concentration of 1% DMSO. Kinetic determination experiments were performed using single-cycle kinetics with an association time of 60 s, a dissociation time of 120 s, and a flow rate of 75 μL/min. Kinetic curve fittings and KD calculations were performed using a steady-state affinity model and Biacore T200 Evaluation software (GE Health Sciences, Inc.).

Protein Crystallography

DCAF1 WDR Domain Gene Cloning, Protein Expression, and Purification

The gene of the human DCAF1 WDR domain (UniProtKB Q9Y4B6; residues 1077–1390) having residues 1077 (Phe) and 1079 (Arg) mutated to alanine was subcloned into an in-house insect cell expression vector pFBOH-MHL, yielding an expression construct with an N-terminal His6-tag followed by a TEV cleavage site. DCAF1 protein expression was carried out in a baculovirus-Sf9 expression system as described by Hutchinson and Seitova. (25) The collected cells were resuspended in lysis buffer (50 mM Tris pH 7.5, 0.4 M NaCl, 5% glycerol, protease inhibitor cocktail, 0.1% NP40, and benzonase endonuclease) and lysed by sonication. The supernatant (cell-free extract) was collected by centrifugation.
Protein purification was performed by immobilized metal ion affinity chromatography (IMAC). Briefly, the supernatant was incubated with TALON cobalt affinity resin equilibrated with a binding buffer containing 50 mM Tris–HCl pH 7.5, 0.4 M NaCl, and 5% glycerol. After binding, the resin was washed with the binding buffer followed by two consecutive washes with the binding buffer supplemented with 5 and 10 mM imidazole. The protein was then eluted using a buffer containing 50 mM Tris–HCL pH 7.5, 0.4 M NaCl, 5% glycerol, and 250 mM imidazole. The eluted DCAF1 protein was then dialyzed overnight into a buffer containing 50 mM Tris–HCl pH 7.5, 0.4 M NaCl, 5% glycerol, and 10 mM β-mercaptoethanol in the presence of TEV protease to remove the polyhistidine purification tag. The protein sample was then applied to TALON resin, and the unbound (cleaved) protein was collected.
The collected protein was concentrated and loaded onto a HiLoad 26/600 Superdex 200 gel filtration column (on an ÄKTA Pure chromatography system (GE Healthcare)) running in the final protein buffer containing 20 mM Tris–HCl pH 7.5, 150 mM NaCl, and 1 mM TCEP. Protein fractions containing pure DCAF1 protein, as confirmed by SDS-PAGE, were pooled and concentrated using 10 kDa cutoff spin columns (Millipore). The final protein concentration was determined using a NanoDrop UV–vis spectrophotometer (Thermo Scientific), with the DCAF1 protein extinction coefficient of 35,410 M–1 cm–1 as computed from the amino acid sequence using Expasy ProtParam (https://web.expasy.org/protparam/). The purified protein was concentrated to 25 mg/mL, with the total protein yield of 1.6 mg/L.

Protein Crystallization

Purified DCAF1 protein was cocrystallized with CYCA-117-70 using a precipitant solution containing 25% PEG3350, 0.1 M ammonium sulfate, and 0.1 M Bis-Tris pH 5.5. Briefly, the protein in gel filtration buffer (20 mM Tris–HCl pH 7.5, 150 mM NaCl, and 1 mM TCEP) at a 10 mg/mL (0.2812 mM) concentration was mixed with 4.218 mM (15 times molar excess) CYCA-117-70 and incubated at room temperature for 30 min prior to the crystallization setup. Equal volumes of protein–compound complexes and precipitant solution were set up in 1 μL drops over a 90 μL reservoir solution using the sitting-drop vapor-diffusion method. Crystals were observed within 72 h at 18 °C.

Diffraction Data Collection, Structure Determination, and Refinement

Crystals were briefly soaked into a cryo-protectant solution containing the crystallization mother liquor supplemented with 10% ethylene glycol and 1 mM compound before cryo-cooling into liquid nitrogen. Diffraction data were collected on the beamline 24-ID-C at the Advanced Photon Source in Argonne National Laboratory. The data were integrated and indexed using HKL3000. (26) The structure was solved by molecular replacement with Phaser (27) using the DCAF1 crystal structure (PDB ID 4PXW) as a starting model. Refinement was performed by alternating rounds of manual rebuilding in Coot (28) followed by refinement with Refmac (29) within the CCP4 crystallography suite. (30) The MolProbity server (31) was used for model validation before deposition. The structure was analyzed using UCSF Chimera (32) and molecular graphic images rendered using PyMOL. (33) The compound, CYCA-117-70, was designated B1I, and the model coordinates were deposited in the RCSB PDB, PDB ID 7SSE.

Results

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Candidate Selection and Experimental Confirmation of the Hit Compound CYCA-117-70

Candidate compounds were selected for in vitro screening based on the MatchMaker binding probabilities, relative rank of MatchMaker probabilities with respect to the MatchMaker proteome (proteome binding rank), physicochemical properties, and molecular docking. From the virtual screening campaign, 101 commercially available compounds were procured for experimental testing to measure the binding affinities to the DCAF1 WDR domain using surface plasmon resonance (SPR). Among them, we identified an initial hit, CYCA-117-70, that binds to DCAF1 with an estimated KD of 70 μM (Figure 3) and a calculated ligand efficiency (LE) (34) of 0.21 (tested at 293 K). CYCA-117-70 showed no significant binding to WDR5 protein, a WDR family protein, indicating that it was selective for the DCAF1 WDR domain. This finding is in agreement with results from the MatchMaker proteome screening for this compound, which had a relative rank of 15 for DCAF1 compared to 927 for WDR5. We found a discrepancy in binding affinities between the original batch and the reordered batch of CYCA-117-70, as the latter was less soluble and appeared less potent than the original batch that was crystallized with DCAF1.

Figure 3

Figure 3. Binding of CYCA-117-70 to DCAF1 and WDR5 by SPR. (A) Binding of CYCA-117-70 to DCAF1 WDR. (B) Binding to WDR5. SPR binding data (representative plot of N = 2) represented in the steady-state response (black circles) with the steady state 1:1 binding model fitting (red dashed line) and the sensorgram (solid green) with the kinetic fit (black dots). CYCA-117-70 showed binding to DCAF1 with an estimated KD of 70 μM (since the binding curve does not fully reach saturation) and no significant binding to WDR5 (KD not determined).

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Crystal Structure of the Human DCAF1 WDR Domain Bound to CYCA-117-70

To characterize the CYCA-117-70 interaction with DCAF1, we determined the 1.62 Å resolution structure of CYCA-117-70 bound to the human DCAF1 WDR domain (residue range of 1077 to 1390), where residues 1077 and 1079 were mutated to alanine to promote crystallization, referred to here as DCAF1-CYCA-117-70 (PDB ID 7SSE). The two mutated residues were identified following several rounds of construct design and crystal optimization. Table 1 summarizes the crystallographic data collection, refinement, and validation statistics.
Table 1. Data Collection and Refinement Statisticsa
 DCAF1-CYCA-117-70
PDB ID7SSE
Wavelength (nm)0.9791
Resolution range (Å)50–1.62 (1.65–1.62)
Space groupP 1 21 1
Unit cell (Å)48.956, 87.919, 73.878
Total reflections269,845
Unique reflections76,489
Multiplicity3.5 (2.1)
Completeness (%)97.4 (78.4)
Mean I/sigma (I)30.92 (1.45)
R-merge0.041 (0.516)
R-meas0.048 (0.651)
R-pim0.024 (0.390)
CC1/20.998 (0.666)
CC*0.999 (0.894)
Reflections used in refinement72,666
Reflections used for R-free3750
R-work0.206
R-free0.234
CC (work)0.960
CC (free)0.947
Number of nonhydrogen atoms 
Macromolecules4608
Ligands26
Solvent224
Protein residues 
RMS (bonds)0.005
RMS (angles)1.307
Ramachandran favored (%)96.88
Ramachandran allowed (%)96.81
Ramachandran outliers (%)0.0
Poor rotamer (%)0.8
Clash score2.32
Average B-factor 
Macromolecules29.153
Ligands35.953
Solvent33.077
a

Statistics for the highest-resolution shell are shown in parentheses.

The DCAF1-CYCA-117-70 structure contains two copies of the DCAF1 WDR domain in the crystal asymmetric unit. Well-resolved electron density for the entire CYCA-117-70 molecule was observed only in one of the two DCAF1 WDR chains (Figure S2). CYCA-117-70 binds close to the surface of the central channel of the DCAF1 WDR domain ring (Figure 4A), where it is positioned via hydrophobic interactions and a water-mediated hydrogen bond with surrounding residues (Figure 4B). Specifically, the 3-fluorophenyl group is nested in a pocket formed by the side chains of L1313, R1298, R1225, and C1227. The amino piperidine moiety packs against the side chains of H1140, T1181, and P329, while the pyrimidine group is sandwiched between T1139 and P1329 and engages in a water-mediated hydrogen bond with the backbone of R1298 and F1355. The morpholine group occupies a space close to the side chain of T1097 and T1135 (Figure 4B).

Figure 4

Figure 4. Cocrystal structure of the DCAF1 WDR domain in complex with CYCA-117-70. (A) Top and side views of the DCAF1 WDR domain shown as a cartoon representation in slate blue, bound to CYCA-117-70 shown as yellow sticks. The compound binds close to the surface of the WDR ring central channel. (B) Zoomed-in view of the CYCA-117-70 binding site in chain A of the DCAF1-CYCA-117-70 cocrystal structure. CYCA-117-70 is shown as yellow sticks, water molecules are shown as red spheres, and the putative hydrogen bond is shown as black dashes. (C) Overlay of the DCAF1 monomer (slate blue surface) bound to CYCA-117-70 (yellow sticks) on to lentiviral Vpx (green) (PDB ID 4CC9, data from ref (22), revealing a steric overlap between the two ligands.

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Comparison of the DCAF1-CYCA-117-70 cocrystal structure with that of the human DCAF1 in complex with the lentiviral accessory protein Vpx and the Mandrill SAMHD1 (PDB ID 4CC9) (22) reveals that the compound binds close to the Vpx binding site and appears to overlap with part of the Vpx helix that binds to the surface of the DCAF1 WDR ring, with the Vpx Lys84 and Phe80 side chains clashing with the compound (Figure 4C).

Discussion

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MatchMaker uses the predictive power of deep learning to generalize DTI datasets to low-data or data-less targets. Combining this approach with cheminformatics filtering and assessing docking poses led to the first cocrystal structure of DCAF1 in complex with a small-molecule ligand (PDB 7SSE). The DTI-based hit identification workflow selected 101 ligand candidates from a source library of over 2.8 M molecules, from which one compound was later experimentally validated. This result illustrates the potential of DTI models, which offer a receptor-based machine learning strategy without the explicit need of target-specific training data. While acknowledging that 1 in 101 validated hit rates may be low relative to alternative virtual screening methodologies under ideal target conditions, it is important to note that (1) small-molecule ligands have been reported for only a handful of WDR domains, (2) the central pocket of WDR domains is poorly conserved, and (3) no DCAF1 chemical ligand was known when the virtual screening was conducted. WDR domains in general, and DCAF1 in particular, are therefore absent from training sets for AI applications. As an AI model, however, there is an opportunity to learn from the hits and misses to improve future hit rates. The DTI model used in this study was trained on simulated random negative training examples, but future models may consider training on explicit, experimentally observed negative training examples, potentially improving performance on low-data targets. While a KD of 70 μM is weak, the cocrystal structure provides a specific binding pose at the critical interface between Vpr/x and DCAF1, which could serve as a valuable starting point for developing inhibitors of the protein binding interactions of DCAF1 and for the generation of proximity-induced degraders. Further, CYCA-117-70 is a modular and developable molecule with an LE of 0.21 with no concerning functional groups, which can be optimized with minimal medicinal chemistry efforts. Our results support the idea that the structural chemistry of receptor–drug interactions learned from the PDB can be applied to orphan proteins.
An additional noteworthy feature of CYCA-117-70 is the observed experimental specificity for DCAF1 over WDR5. Since MatchMaker is able to explicitly compute the rank of a molecule for a given target relative to thousands of proteins in the proteome, it inherently considers polypharmacology as a bias during compound selection. Indeed, we find that the experimental selectivity observed by CYCA-117-70 is in line with its predicted rank of 15 for DCAF1 and 927 for WDR5, despite not explicitly counter screening against WDR5. Considering that polypharmacology during the hit-finding stage certainly has its advantages and disadvantages, on the one hand, an approach that successfully identifies a selective molecule upfront has the potential to accelerate drug discovery over the obligate multistep approach that is typical in probe-development/medicinal chemistry. On the other hand, there is the potential to miss viable hits for the target, which can be later augmented for selectivity. Molecular docking can complement the inherent pose-independent limitations of DTI models, offering a means to exclude library molecules on the basis of geometric incompatibility or situationally discriminating between closely related proteins when SBDD circumstances are more favorable. There is a large variety of computational drug discovery tools used in hit identification, lead optimization, and drug design to select from, with no single “magic bullet” application. DTI-based hit identification can be used independently or as a complementary method alongside SBDD, particularly for situations where SBDD may prove more difficult, such as using apo structures and/or predicted (AlphaFold) structures.
Importantly, our cocrystal structure shows that, while CYCA-117-70 occupies the central channel of the DCAF1 WDR domain (Figure 4A), it remains solvent-accessible, and its pyrimidine group could potentially serve as an anchor point for future DCAF1-recruiting PROTACs. However, a first step would be to explore the SAR around this chemical template to improve its binding affinity. Critically, the ligand-induced fit occurs at the protein–protein interaction (PPI) site, making this a great starting point to disrupt PPI with viral accessory proteins.
DCAF1 was recently targeted by electrophylic PROTACs covalently engaging its WDR domain, leading to ubiquitination and degradation of FKBP12 protein. (17) We believe that more potent analogs of CYCA-117-70 would be ideal chemical handles for future PROTACs recruiting DCAF1 noncovalently, as a reversible binding mode could translate into different selectivity and toxicity profiles.
Since the deposition of the DCAF1-CYCA-117-70 structure (PDB 7SSE), the first publicly released cocrystal structure of DCAF1 bound to a small molecule, there have been notable advances in the discovery of small molecules for DCAF1, (17,35,36) further validating that DCAF1 is not only a novel target but also a highly tractable and promising E3 ligase. Indeed, while this manuscript was under revision, two series of DCAF1 ligands were reported. (35,36) One series is derived from a ligand discovered via DNA-encoded chemical library screening (37) that binds deeper in the WDR central cavity and therefore does not overlap with viral accessory proteins Vpr/x binding to the DCAF1 WDR domain (35) (Figure S3A). A compound from the second series subsequently turned into a DCAF1-recruiting PROTAC (36) is less deeply bound in the central WDR cavity, in a binding pose that is very similar to CYCA-117-70 (Figure S3B). This similarity supports the potential for further development of more potent analogs of CYCA-117-70 as antagonists of viral accessory proteins, as well as chemical handles for targeted protein degradation, but also highlights the potential of the DTI approach as a promising tool for ligand discovery in nonligand and low-data protein targets.

Conclusions

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Here, we demonstrate that AI-based virtual screening can enable the rapid discovery of chemical ligands for orphan proteins. Combining the speed of MatchMaker with structure-based molecular docking allowed us to rapidly explore millions of compounds and identify the first published cocrystal structure of DCAF1 bound to CYCA-117-70. Our open science public–private collaborative framework allowed disclosure of our hit in the PDB within weeks of its discovery. We believe that adoption of this or similar working models would benefit precompetitive research and accelerate drug discovery.

Data Availability

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Atomic coordinates and structure factors for the reported crystal structure have been deposited in the Protein Data Bank under the accession code 7SSE. MatchMaker is a commercial software developed by Cyclica, Inc. and now owned by Recursion Pharmaceuticals Inc.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.3c00082.

  • Figure showing the domain architecture of human DCAF1 and the WDR fold, figure of the electron density of CYCA-117-70 and its binding site, figure depicting the comparison of CYCA-117-70 with other recently reported DCAF1 ligands (PDF)

  • SMILES string of the 101 compounds computationally selected for experimental testing (XLSX)

  • SPR raw data for CYCA-117-70 (XLSX)

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 Authors
    • Matthieu Schapira - Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada;  Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario M5S 1A1, Canada;  Orcidhttps://orcid.org/0000-0002-1047-3309;  Email: [email protected]
    • Vijay Shahani - Recursion Pharmaceuticals Inc., Toronto, Ontario M5V 2A2, Canada;  Email: [email protected]
    • Levon Halabelian - Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada;  Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario M5S 1A1, Canada;  Orcidhttps://orcid.org/0000-0003-4361-3619;  Email: [email protected]
  • Authors
    • Serah W. Kimani - Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada;  Princess Margaret Cancer Center, University Health Network, Toronto, Ontario M5G 2C4, Canada
    • Julie Owen - Recursion Pharmaceuticals Inc., Toronto, Ontario M5V 2A2, Canada
    • Stuart R. Green - Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada;  Orcidhttps://orcid.org/0000-0002-2960-9683
    • Fengling Li - Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
    • Yanjun Li - Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
    • Aiping Dong - Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada
    • Peter J. Brown - Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada;  Orcidhttps://orcid.org/0000-0002-8454-0367
    • Suzanne Ackloo - Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada;  Orcidhttps://orcid.org/0000-0002-9696-1839
    • David Kuter - Recursion Pharmaceuticals Inc., Toronto, Ontario M5V 2A2, Canada;  Orcidhttps://orcid.org/0000-0001-5393-6337
    • Cindy Yang - Recursion Pharmaceuticals Inc., Toronto, Ontario M5V 2A2, Canada
    • Miranda MacAskill - Recursion Pharmaceuticals Inc., Toronto, Ontario M5V 2A2, Canada
    • Stephen Scott MacKinnon - Recursion Pharmaceuticals Inc., Toronto, Ontario M5V 2A2, Canada
    • Cheryl H. Arrowsmith - Structural Genomics Consortium, University of Toronto, Toronto, Ontario M5G 1L7, Canada;  Princess Margaret Cancer Center, University Health Network, Toronto, Ontario M5G 2C4, Canada;  Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5S 1A1, Canada;  Orcidhttps://orcid.org/0000-0002-4971-3250
  • Author Contributions

    S.W.K. and J.O. contributed equally.

  • Notes
    The authors declare the following competing financial interest(s): JO, DK, CY, SS, MK, and VS are employees of Recursion Pharmaceuticals Inc. and may own stock in Recursion Pharmaceutical Inc. Recursion Pharmaceuticals owns and maintains MatchMaker. All authors declare no other conflicts of interest.

Acknowledgments

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We would like to thank Peter Loppnau, Almagul Seitova, Ashley Hutchinson, Pegah Ghiabi, and Taraneh Hajian for protein expression and purification. This work is based upon research conducted at the Northeastern Collaborative Access Team beamlines, which are funded by the National Institute of General Medical Sciences from the National Institutes of Health (P30 GM124165). This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. Structural Genomics Consortium is a registered charity (no. 1097737) that receives funds from Bayer AG, Boehringer Ingelheim, Bristol Myers Squibb, Genentech, Genome Canada through Ontario Genomics Institute [OGI-196], EU/EFPIA/OICR/McGill/KTH/Diamond Innovative Medicines Initiative 2 Joint Undertaking [EUbOPEN grant 875510], Janssen, Merck KGaA (aka EMD in Canada and US), Pfizer, and Takeda. M.S. gratefully acknowledges financial support from NSERC [Grant RGPIN-2019-04416].

Abbreviations

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AI

artificial intelligence

CLR4

cullin-4 RING ubiquitin ligase

DCAF1

DDB1 and CUL4 associated factor 1

DDB1

DNA damage-binding protein 1

DTI

drug–target interaction

EDVP

EDD, DDB1, and VPRBP E3 ligase complex

HECT

homologous to the E6-AP carboxyl terminus

HIV

human immunodeficiency virus

LBDD

ligand-based drug design

RING

really interesting new gene

PROTACS

proteolysis targeting chimeras

SBDD

structure-based drug design

SIV

simian immunodeficiency virus

Vpr/Vpx

accessory proteins r or x

WDR

WD40 repeat

References

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    Vpr is an HIV-1 auxiliary regulatory protein packaged in the virion. It has been shown to enhance the nuclear transport of the HIV-1 pre-integration complex, activate transcription of cellular and viral promoters, and arrest the cell cycle at the G2/M check-point. We previously identified a cellular protein of 180 kDa (RIP) that interacted with HIV-1 Vpr specifically. We now rename this cellular protein as Vpr-binding protein, or VprBP. In this report, we describe the cloning of the VprBP cDNA that encodes 1507 aa residues and is identical to the previously cloned cDNA KIAA0800. We demonstrate that Vpr specifically interacts with recombinantly expressed VprBP in vitro as well as in vivo. Furthermore, Vpr interacts with the cellular endogenous VprBP in the context of the HIV-1 life cycle. Mutational anal. of VprBP suggests that the Vpr binding domain is located within the C-terminal half of VprBP, which has a Pro-rich domain and several Phe-x-x-Phe repeats. Subcellular fractionation studies show that both the endogenous VprBP and the adenovirus-expressed VprBP are distributed predominantly in the cytoplasmic fraction. Consistent with previous reports, the adenovirus-expressed Vpr is distributed in both the cytoplasmic and the nuclear fractions. However, when VprBP and Vpr are expressed together, Vpr is found almost exclusively in the cytoplasm. Expression of VprBP does not affect the nuclear transport of the adenoviral nuclear protein, pTP. VprBP expressed in insect cells also blocks the nuclear transport of a Vpr-GFP fusion protein, and VprBP mutants incapable of interacting with Vpr fail to block Vpr-GFP nuclear transport. We hypothesize that Vpr interaction with VprBP may cause changes in the host cell cytoplasm that affect HIV-1 pathogenesis as well as HIV-1 replication.
  13. 13
    Hossain, D.; Ferreira Barbosa, J. A.; Cohen, É. A.; Tsang, W. Y. HIV-1 Vpr Hijacks EDD-DYRK2-DDB1DCAF1 to Disrupt Centrosome Homeostasis. J. Biol. Chem. 2018, 293, 9448– 9460,  DOI: 10.1074/jbc.RA117.001444
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    13
    HIV-1 Vpr hijacks EDD-DYRK2-DDB1DCAF1 to disrupt centrosome homeostasis
    Hossain, Delowar; Ferreira Barbosa, Jeremy A.; Cohen, Eric A.; Tsang, William Y.
    Journal of Biological Chemistry (2018), 293 (24), 9448-9460CODEN: JBCHA3; ISSN:0021-9258. (American Society for Biochemistry and Molecular Biology)
    Viruses exploit the host cell machinery for their own profit. To evade innate immune sensing and promote viral replication, HIV type 1 (HIV-1) subverts DNA repair regulatory proteins and induces G2/M arrest. The preintegration complex of HIV-1 is known to traffic along microtubules and accumulate near the microtubule-organizing center. The centrosome is the major microtubule-organizing center in most eukaryotic cells, but precisely how HIV-1 impinges on centrosome biol. remains poorly understood. We report here that the HIV-1 accessory protein viral protein R (Vpr) localized to the centrosome through binding to DCAF1, forming a complex with the ubiquitin ligase EDD-DYRK2-DDB1DCAF1 and Cep78, a resident centrosomal protein previously shown to inhibit EDD-DYRK2-DDB1DCAF1. Vpr did not affect ubiquitination of Cep78. Rather, it enhanced ubiquitination of an EDD-DYRK2-DDB1DCAF1 substrate, CP110, leading to its degrdn., an effect that could be overcome by Cep78 expression. The down-regulation of CP110 and elongation of centrioles provoked by Vpr were independent of G2/M arrest. Infection of T lymphocytes with HIV-1, but not with HIV-1 lacking Vpr, promoted CP110 degrdn. and centriole elongation. Elongated centrioles recruited more γ-tubulin to the centrosome, resulting in increased microtubule nucleation. Our results suggest that Vpr is targeted to the centrosome where it hijacks a ubiquitin ligase, disrupting organelle homeostasis, which may contribute to HIV-1 pathogenesis.
  14. 14
    Schabla, N. M.; Mondal, K.; Swanson, P. C. DCAF1 (VprBP): Emerging Physiological Roles for a Unique Dual-Service E3 Ubiquitin Ligase Substrate Receptor. J. Mol. Cell Biol. 2019, 11, 725– 735,  DOI: 10.1093/jmcb/mjy085
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    14
    DCAF1 (VprBP): emerging physiological roles for a unique dual-service E3 ubiquitin ligase substrate receptor
    Schabla, N. Max; Mondal, Koushik; Swanson, Patrick C.
    Journal of Molecular Cell Biology (Oxford, United Kingdom) (2019), 11 (9), 725-735CODEN: JMCBCU; ISSN:1759-4685. (Oxford University Press)
    A review. Cullin-RING ligases (CRLs) comprise a large group of modular eukaryotic E3 ubiquitin ligases. Within this family, the CRL4 ligase (consisting of the Cullin4 [CUL4] scaffold protein, the Rbx1 RING finger domain protein, the DNA damage-binding protein 1 [DDB1], and one of many DDB1-assocd. substrate receptor proteins) has been intensively studied in recent years due to its involvement in regulating various cellular processes, its role in cancer development and progression, and its subversion by viral accessory proteins. Initially discovered as a target for hijacking by the human immunodeficiency virus accessory protein r, the normal targets and function of the CRL4 substrate receptor protein DDB1-Cul4-assocd. factor 1 (DCAF1; also known as VprBP) had remained elusive, but newer studies have begun to shed light on these questions. Here, we review recent progress in understanding the diverse physiol. roles of this DCAF1 in supporting various general and cell type-specific cellular processes in its context with the CRL4 E3 ligase, as well as another HECT-type E3 ligase with which DCAF1 also assocs., called EDD/UBR5. We also discuss emerging questions and areas of future study to uncover the dynamic roles of DCAF1 in normal physiol.
  15. 15
    Hrecka, K.; Gierszewska, M.; Srivastava, S.; Kozaczkiewicz, L.; Swanson, S. K.; Florens, L.; Washburn, M. P.; Skowronski, J. Lentiviral Vpr Usurps Cul4-DDB1[VprBP] E3 Ubiquitin Ligase to Modulate Cell Cycle. Proc. Natl. Acad. Sci. U. S. A. 2007, 104, 11778– 11783,  DOI: 10.1073/pnas.0702102104
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    15
    Lentiviral Vpr usurps Cul4-DDB1[VprBP] E3 ubiquitin ligase to modulate cell cycle
    Hrecka, Kasia; Gierszewska, Magdalena; Srivastava, Smita; Kozackiewicz, Lukasz; Swanson, Selene K.; Florens, Laurence; Washburn, Michael P.; Skowronski, Jacek
    Proceedings of the National Academy of Sciences of the United States of America (2007), 104 (28), 11778-11783, S11778/1-S11778/6CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
    The replication of viruses depends on the cell cycle status of the infected cells. Viruses have evolved functions that alleviate restrictions imposed on their replication by the host. Vpr, an accessory factor of primate lentiviruses, arrests cells at the DNA damage checkpoint in G2 phase of the cell cycle, but the mechanism underlying this effect has remained elusive. Here we report that Vpr proteins of both the human (HIV-1) and the distantly related simian (SIVmac) immunodeficiency viruses specifically assoc. with a protein complex comprising subunits of E3 ubiquitin ligase assembled on Cullin-4 scaffold (Cul4-DDB1[VprBP]). We show that Vpr binding to Cul4-DDB1[VprBP] leads to increased neddylation and elevated intrinsic ubiquitin ligase activity of this E3. This effect is mediated through the VprBP subunit of the complex, which recently has been suggested to function as a substrate receptor for Cu14. We also demonstrate that VprBP regulates G, phase and is essential for the completion of DNA replication in S phase. Furthermore, the ability of Vpr to arrest cells in G2 phase correlates with its ability to interact with Cu14-DDB1[VprBP] E3 complex. Our studies identify the Cu14-DDB1[VprBP] E3 ubiquitin ligase complex as the downstream effector of lentiviral Vpr for the induction of cell cycle arrest in G2 phase and suggest that Vpr may use this complex to perturb other aspects of the cell cycle and DNA metab. in infected cells.
  16. 16
    Guo, Z.; Kong, Q.; Liu, C.; Zhang, S.; Zou, L.; Yan, F.; Whitmire, J. K.; Xiong, Y.; Chen, X.; Wan, Y. Y. DCAF1 Controls T-Cell Function via P53-Dependent and -Independent Mechanisms. Nat. Commun. 2016, 7, 10307,  DOI: 10.1038/ncomms10307
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    16
    DCAF1 controls T-cell function via p53-dependent and -independent mechanisms
    Guo, Zengli; Kong, Qing; Liu, Cui; Zhang, Song; Zou, Liyun; Yan, Feng; Whitmire, Jason K.; Xiong, Yue; Chen, Xian; Wan, Yisong Y.
    Nature Communications (2016), 7 (), 10307CODEN: NCAOBW; ISSN:2041-1723. (Nature Publishing Group)
    On activation, naive T cells grow in size and enter cell cycle to mount immune response. How the fundamental processes of T-cell growth and cell cycle entry are regulated is poorly understood. Here we report that DCAF1 (Ddb1-cullin4-assocd.-factor 1) is essential for these processes. The deletion of DCAF1 in T cells impairs their peripheral homeostasis. DCAF1 is upregulated on T-cell receptor activation and crit. for activation-induced T-cell growth, cell cycle entry and proliferation. In addn., DCAF1 is required for T-cell expansion and function during anti-viral and autoimmune responses in vivo. DCAF1 deletion leads to a drastic stabilization of p53 protein, which can be attributed to a requirement of DCAF1 for MDM2-mediated p53 poly-ubiquitination. Importantly, p53 deletion rescues the cell cycle entry defect but not the growth defect of DCAF1-deficient cells. Therefore, DCAF1 is vital for T-cell function through p53-dependent and -independent mechanisms.
  17. 17
    Tao, Y.; Remillard, D.; Vinogradova, E. V.; Yokoyama, M.; Banchenko, S.; Schwefel, D.; Melillo, B.; Schreiber, S. L.; Zhang, X.; Cravatt, B. F. Targeted Protein Degradation by Electrophilic PROTACs That Stereoselectively and Site-Specifically Engage DCAF1. J. Am. Chem. Soc. 2022, 144, 18688– 18699,  DOI: 10.1021/jacs.2c08964
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    17
    Targeted Protein Degradation by Electrophilic PROTACs that Stereoselectively and Site-Specifically Engage DCAF1
    Tao, Yongfeng; Remillard, David; Vinogradova, Ekaterina V.; Yokoyama, Minoru; Banchenko, Sofia; Schwefel, David; Melillo, Bruno; Schreiber, Stuart L.; Zhang, Xiaoyu; Cravatt, Benjamin F.
    Journal of the American Chemical Society (2022), 144 (40), 18688-18699CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
    Targeted protein degrdn. induced by heterobifunctional compds. and mol. glues presents an exciting avenue for chem. probe and drug discovery. To date, small-mol. ligands have been discovered for only a limited no. of E3 ligases, which is an important limiting factor for realizing the full potential of targeted protein degrdn. We report herein the discovery by chem. proteomics of azetidine acrylamides that stereoselectively and site-specifically react with a cysteine (C1113) in the E3 ligase substrate receptor DCAF1. We demonstrate that the azetidine acrylamide ligands for DCAF1 can be developed into electrophilic proteolysis-targeting chimeras (PROTACs) that mediated targeted protein degrdn. in human cells. We show that this process is stereoselective and does not occur in cells expressing a C1113A mutant of DCAF1. Mechanistic studies indicate that only low fractional engagement of DCAF1 is required to support protein degrdn. by electrophilic PROTACs. These findings, taken together, demonstrate how the chem. proteomic anal. of stereochem. defined electrophilic compd. sets can uncover ligandable sites on E3 ligases that support targeted protein degrdn.
  18. 18
    Waterhouse, A.; Bertoni, M.; Bienert, S.; Studer, G.; Tauriello, G.; Gumienny, R.; Heer, F. T.; de Beer, T. A. P.; Rempfer, C.; Bordoli, L.; Lepore, R.; Schwede, T. 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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    18
    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.
  19. 19
    Sugiyama, M. G.; Cui, H.; Redka, D. S.; Karimzadeh, M.; Rujas, E.; Maan, H.; Hayat, S.; Cheung, K.; Misra, R.; McPhee, J. B.; Viirre, R. D.; Haller, A.; Botelho, R. J.; Karshafian, R.; Sabatinos, S. A.; Fairn, G. D.; Madani Tonekaboni, S. A.; Windemuth, A.; Julien, J.-P.; Shahani, V.; MacKinnon, S. S.; Wang, B.; Antonescu, C. N. Multiscale Interactome Analysis Coupled with Off-Target Drug Predictions Reveals Drug Repurposing Candidates for Human Coronavirus Disease. Sci. Rep. 2021, 11, 23315,  DOI: 10.1038/s41598-021-02432-7
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    19
    Multiscale interactome analysis coupled with off-target drug predictions reveals drug repurposing candidates for human coronavirus disease
    Sugiyama, Michael G.; Cui, Haotian; Redka, Dar'ya S.; Karimzadeh, Mehran; Rujas, Edurne; Maan, Hassaan; Hayat, Sikander; Cheung, Kyle; Misra, Rahul; McPhee, Joseph B.; Viirre, Russell D.; Haller, Andrew; Botelho, Roberto J.; Karshafian, Raffi; Sabatinos, Sarah A.; Fairn, Gregory D.; Madani Tonekaboni, Seyed Ali; Windemuth, Andreas; Julien, Jean-Philippe; Shahani, Vijay; MacKinnon, Stephen S.; Wang, Bo; Antonescu, Costin N.
    Scientific Reports (2021), 11 (1), 23315CODEN: SRCEC3; ISSN:2045-2322. (Nature Research)
    Abstr.: The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clin.-relevant drug compds. for the treatment of a range of human coronavirus diseases. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome anal. coupled to off-target drug predictions. Cell-based exptl. assessment reveals several clin.-relevant drug repurposing candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.
  20. 20
    He, Y.; Yang, C.; Wang, Y.; Sacher, J. R.; Sims, M. M.; Pfeffer, L. M.; Miller, D. D. Novel Structural-Related Analogs of PFI-3 (SRAPs) That Target the BRG1 Catalytic Subunit of the SWI/SNF Complex Increase the Activity of Temozolomide in Glioblastoma Cells. Bioorg. Med. Chem. 2022, 53, 116533  DOI: 10.1016/j.bmc.2021.116533
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    20
    Novel structural-related analogs of PFI-3 (SRAPs) that target the BRG1 catalytic subunit of the SWI/SNF complex increase the activity of temozolomide in glioblastoma cells
    He, Yali; Yang, Chuanhe; Wang, Yinan; Sacher, Joshua R.; Sims, Michelle M.; Pfeffer, Lawrence M.; Miller, Duane D.
    Bioorganic & Medicinal Chemistry (2022), 53 (), 116533CODEN: BMECEP; ISSN:0968-0896. (Elsevier B.V.)
    Glioblastoma (GBM) is the most aggressive and treatment-refractory malignant adult brain cancer. After std. of care therapy, the overall median survival for GBM is only ∼6 mo with a 5-yr survival <10%. Although some patients initially respond to the DNA alkylating agent temozolomide (TMZ), unfortunately most patients become resistant to therapy and brain tumors eventually recur. We previously found that knockout of BRG1 or treatment with PFI-3, a small mol. inhibitor of the BRG1 bromodomain, enhances sensitivity of GBM cells to temozolomide in vitro and in vivo GBM animal models. Those results demonstrated that the BRG1 catalytic subunit of the SWI/SNF chromatin remodeling complex appears to play a crit. role in regulating TMZ-sensitivity. In the present study we designed and synthesized Structurally Related Analogs of PFI-3 (SRAPs) and tested their bioactivity in vitro. Among of the SRAPs, 9f and 11d show better efficacy than PFI-3 in sensitizing GBM cells to the antiproliferative and cell death inducing effects of temozolomide in vitro, as well as enhancing the inhibitor effect of temozolomide on the growth of s.c. GBM tumors.
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    Krivák, R.; Hoksza, D. P2Rank: Machine Learning Based Tool for Rapid and Accurate Prediction of Ligand Binding Sites from Protein Structure. Aust. J. Chem. 2018, 10, 39,  DOI: 10.1186/s13321-018-0285-8
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    Schwefel, D.; Groom, H. C. T.; Boucherit, V. C.; Christodoulou, E.; Walker, P. A.; Stoye, J. P.; Bishop, K. N.; Taylor, I. A. Structural Basis of Lentiviral Subversion of a Cellular Protein Degradation Pathway. Nature 2014, 505, 234– 238,  DOI: 10.1038/nature12815
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    22
    Structural basis of lentiviral subversion of a cellular protein degradation pathway
    Schwefel, David; Groom, Harriet C. T.; Boucherit, Virginie C.; Christodoulou, Evangelos; Walker, Philip A.; Stoye, Jonathan P.; Bishop, Kate N.; Taylor, Ian A.
    Nature (London, United Kingdom) (2014), 505 (7482), 234-238CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
    Lentiviruses contain accessory genes that have evolved to counteract the effects of host cellular defense proteins that inhibit productive infection. One such restriction factor, SAMHD1, inhibits human immunodeficiency virus (HIV)-1 infection of myeloid-lineage cells as well as resting CD4+ T cells by reducing the cellular deoxynucleoside 5'-triphosphate (dNTP) concn. to a level at which the viral reverse transcriptase cannot function. In other lentiviruses, including HIV-2 and related simian immunodeficiency viruses (SIVs), SAMHD1 restriction is overcome by the action of viral accessory protein x (Vpx) or the related viral protein r (Vpr) that target and recruit SAMHD1 for proteasomal degrdn. The mol. mechanism by which these viral proteins are able to usurp the host cell's ubiquitination machinery to destroy the cell's protection against these viruses has not been defined. Here we present the crystal structure of a ternary complex of Vpx with the human E3 ligase substrate adaptor DCAF1 and the carboxy-terminal region of human SAMHD1. Vpx is made up of a three-helical bundle stabilized by a zinc finger motif, and wraps tightly around the disk-shaped DCAF1 mol. to present a new mol. surface. This adapted surface is then able to recruit SAMHD1 via its C terminus, making it a competent substrate for the E3 ligase to mark for proteasomal degrdn. The structure reported here provides a mol. description of how a lentiviral accessory protein is able to subvert the cell's normal protein degrdn. pathway to inactivate the cellular viral defense system.
  23. 23
    The UniProt Consortium UniProt: The Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 2023, 51, D523– D531,  DOI: 10.1093/nar/gkac1052
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    Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235– 242,  DOI: 10.1093/nar/28.1.235
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    The Protein Data Bank
    Berman, Helen M.; Westbrook, John; Feng, Zukang; Gilliland, Gary; Bhat, T. N.; Weissig, Helge; Shindyalov, Ilya N.; Bourne, Philip E.
    Nucleic Acids Research (2000), 28 (1), 235-242CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
    The Protein Data Bank (PDB; http://www.rcsb.org/pdb/)is the single worldwide archive of structural data of biol. macromols. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
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    Hutchinson, A.; Seitova, A. Production of Recombinant PRMT Proteins Using the Baculovirus Expression Vector System. J. Vis. Exp. JoVE 2021, No. 173.  DOI: 10.3791/62510 .
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    Minor, W.; Cymborowski, M.; Otwinowski, Z.; Chruszcz, M. HKL-3000: The Integration of Data Reduction and Structure Solution--from Diffraction Images to an Initial Model in Minutes. Acta Crystallogr. D Biol. Crystallogr. 2006, 62, 859– 866,  DOI: 10.1107/S0907444906019949
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    26
    HKL-3000: the integration of data reduction and structure solution - from diffraction images to an initial model in minutes
    Minor, Wladek; Cymborowski, Marcin; Otwinowski, Zbyszek; Chruszcz, Maksymilian
    Acta Crystallographica, Section D: Biological Crystallography (2006), D62 (8), 859-866CODEN: ABCRE6; ISSN:0907-4449. (Blackwell Publishing Ltd.)
    A new approach that integrates data collection, data redn., phasing and model building significantly accelerates the process of structure detn. and on av. minimizes the no. of data sets and synchrotron time required for structure soln. Initial testing of the HKL-3000 system (the beta version was named HKL-2000_ph) with more than 140 novel structure detns. has proven its high value for MAD/SAD expts. The heuristics for choosing the best computational strategy at different data resoln. limits of phasing signal and crystal diffraction are being optimized. The typical end result is an interpretable electron-d. map with a partially built structure and, in some cases, an almost complete refined model. The current development is oriented towards very fast structure soln. in order to provide feedback during the diffraction expt. Work is also proceeding towards improving the quality of phasing calcn. and model building.
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    McCoy, A. J.; Grosse-Kunstleve, R. W.; Adams, P. D.; Winn, M. D.; Storoni, L. C.; Read, R. J. Phaser Crystallographic Software. J. Appl. Crystallogr. 2007, 40, 658– 674,  DOI: 10.1107/S0021889807021206
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    Phaser crystallographic software
    McCoy, Airlie J.; Grosse-Kunstleve, Ralf W.; Adams, Paul D.; Winn, Martyn D.; Storoni, Laurent C.; Read, Randy J.
    Journal of Applied Crystallography (2007), 40 (4), 658-674CODEN: JACGAR; ISSN:0021-8898. (International Union of Crystallography)
    Phaser is a program for phasing macromol. crystal structures by both mol. replacement and exptl. phasing methods. The novel phasing algorithms implemented in Phaser have been developed using max. likelihood and multivariate statistics. For mol. replacement, the new algorithms have proved to be significantly better than traditional methods in discriminating correct solns. from noise, and for single-wavelength anomalous dispersion exptl. phasing, the new algorithms, which account for correlations between F+ and F-, give better phases (lower mean phase error with respect to the phases given by the refined structure) than those that use mean F and anomalous differences ΔF. One of the design concepts of Phaser was that it be capable of a high degree of automation. To this end, Phaser (written in C++) can be called directly from Python, although it can also be called using traditional CCP4 keyword-style input. Phaser is a platform for future development of improved phasing methods and their release, including source code, to the crystallog. community.
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    Emsley, P.; Cowtan, K. Coot: Model-Building Tools for Molecular Graphics. Acta Crystallogr. D Biol. Crystallogr. 2004, 60, 2126– 2132,  DOI: 10.1107/S0907444904019158
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    Coot: model-building tools for molecular graphics
    Emsley, Paul; Cowtan, Kevin
    Acta Crystallographica, Section D: Biological Crystallography (2004), D60 (12, Pt. 1), 2126-2132CODEN: ABCRE6; ISSN:0907-4449. (Blackwell Publishing Ltd.)
    CCP4mg is a project that aims to provide a general-purpose tool for structural biologists, providing tools for x-ray structure soln., structure comparison and anal., and publication-quality graphics. The map-fitting tools are available as a stand-alone package, distributed as 'Coot'.
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    Murshudov, G. N.; Vagin, A. A.; Dodson, E. J. Refinement of Macromolecular Structures by the Maximum-Likelihood Method. Acta Crystallogr. D Biol. Crystallogr. 1997, 53, 240– 255,  DOI: 10.1107/S0907444996012255
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    Refinement of macromolecular structures by the maximum-likelihood method
    Murshudov, Garib N.; Vagin, Alexei A.; Dodson, Eleanor J.
    Acta Crystallographica, Section D: Biological Crystallography (1997), D53 (3), 240-255CODEN: ABCRE6; ISSN:0907-4449. (Munksgaard)
    A review with many refs. on the math. basis of max. likelihood. The likelihood function for macromol. structures is extended to include prior phase information and exptl. std. uncertainties. The assumption that different parts of a structure might have different errors is considered. A method for estg. σA using "free" reflections is described and its effects analyzed. The derived equations have been implemented in the program REFMAC. This has been tested on several proteins at different stages of refinement (bacterial α-amylase, cytochrome c', cross-linked insulin and oligopeptide binding protein). The results derived using the max.-likelihood residual are consistently better than those obtained from least-squares refinement.
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    Winn, M. D.; Ballard, C. C.; Cowtan, K. D.; Dodson, E. J.; Emsley, P.; Evans, P. R.; Keegan, R. M.; Krissinel, E. B.; Leslie, A. G. W.; McCoy, A.; McNicholas, S. J.; Murshudov, G. N.; Pannu, N. S.; Potterton, E. A.; Powell, H. R.; Read, R. J.; Vagin, A.; Wilson, K. S. Overview of the CCP4 Suite and Current Developments. Acta Crystallogr. D Biol. Crystallogr. 2011, 67, 235– 242,  DOI: 10.1107/S0907444910045749
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    Overview of the CCP4 suite and current developments
    Winn, Martyn D.; Ballard, Charles C.; Cowtan, Kevin D.; Dodson, Eleanor J.; Emsley, Paul; Evans, Phil R.; Keegan, Ronan M.; Krissinel, Eugene B.; Leslie, Andrew G. W.; McCoy, Airlie; McNicholas, Stuart J.; Murshudov, Garib N.; Pannu, Navraj S.; Potterton, Elizabeth A.; Powell, Harold R.; Read, Randy J.; Vagin, Alexei; Wilson, Keith S.
    Acta Crystallographica, Section D: Biological Crystallography (2011), 67 (4), 235-242CODEN: ABCRE6; ISSN:0907-4449. (International Union of Crystallography)
    A review. The CCP4 (Collaborative Computational Project, No. 4) software suite is a collection of programs and assocd. data and software libraries which can be used for macromol. structure detn. by X-ray crystallog. The suite is designed to be flexible, allowing users a no. of methods of achieving their aims. The programs are from a wide variety of sources but are connected by a common infrastructure provided by std. file formats, data objects and graphical interfaces. Structure soln. by macromol. crystallog. is becoming increasingly automated and the CCP4 suite includes several automation pipelines. After giving a brief description of the evolution of CCP4 over the last 30 years, an overview of the current suite is given. While detailed descriptions are given in the accompanying articles, here it is shown how the individual programs contribute to a complete software package.
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    Chen, V. B.; Arendall, W. B.; Headd, J. J.; Keedy, D. A.; Immormino, R. M.; Kapral, G. J.; Murray, L. W.; Richardson, J. S.; Richardson, D. C. MolProbity: All-Atom Structure Validation for Macromolecular Crystallography. Acta Crystallogr. D Biol. Crystallogr. 2010, 66, 12– 21,  DOI: 10.1107/S0907444909042073
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  • Abstract

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    Figure 1

    Figure 1. Conceptual diagram highlighting differences between ligand-based drug discovery (LBDD) models and drug–target interaction (DTI) models. LBDD models treat each protein as its own machine learning model, thereby limiting inference (prediction) to targets that already have sufficient data to train models. DTI models train a global model to predict binding drug–target pairs, such that protein targets learn from the bioactivities of similar proteins.

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    Figure 2

    Figure 2. Pocket selection. (A) Visualization of P2Rank predicted pockets. (B,C) Cartoon representation of Vpx from the ternary complex of DCAF1-SAMHD1-Vpx (PDB code 4CC9) showing overlay of pocket prediction with protein contacts. (D) Inside pocket. (E) Top pocket. (F) Side pocket.

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    Figure 3

    Figure 3. Binding of CYCA-117-70 to DCAF1 and WDR5 by SPR. (A) Binding of CYCA-117-70 to DCAF1 WDR. (B) Binding to WDR5. SPR binding data (representative plot of N = 2) represented in the steady-state response (black circles) with the steady state 1:1 binding model fitting (red dashed line) and the sensorgram (solid green) with the kinetic fit (black dots). CYCA-117-70 showed binding to DCAF1 with an estimated KD of 70 μM (since the binding curve does not fully reach saturation) and no significant binding to WDR5 (KD not determined).

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    Figure 4

    Figure 4. Cocrystal structure of the DCAF1 WDR domain in complex with CYCA-117-70. (A) Top and side views of the DCAF1 WDR domain shown as a cartoon representation in slate blue, bound to CYCA-117-70 shown as yellow sticks. The compound binds close to the surface of the WDR ring central channel. (B) Zoomed-in view of the CYCA-117-70 binding site in chain A of the DCAF1-CYCA-117-70 cocrystal structure. CYCA-117-70 is shown as yellow sticks, water molecules are shown as red spheres, and the putative hydrogen bond is shown as black dashes. (C) Overlay of the DCAF1 monomer (slate blue surface) bound to CYCA-117-70 (yellow sticks) on to lentiviral Vpx (green) (PDB ID 4CC9, data from ref (22), revealing a steric overlap between the two ligands.

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      Angers, Stephane; Li, Ti; Yi, Xianhua; MacCoss, Michael J.; Moon, Randall T.; Zheng, Ning
      Nature (London, United Kingdom) (2006), 443 (7111), 590-593CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
      Protein ubiquitination is a common form of post-translational modification that regulates a broad spectrum of protein substrates in diverse cellular pathways. Through a three-enzyme (E1-E2-E3) cascade, the attachment of ubiquitin to proteins is catalyzed by the E3 ubiquitin ligase, which is best represented by the superfamily of cullin-RING complexes. Conserved from yeast to human, the DDB1-CUL4-ROC1 complex is a recently identified cullin-RING ubiquitin ligase, which regulates DNA repair, DNA replication and transcription, and can also be subverted by pathogenic viruses to benefit viral infection. Lacking a canonical SKP1-like cullin adaptor and a defined substrate recruitment module, how the DDB1-CUL4-ROC1 E3 app. is assembled for ubiquitinating various substrates remains unclear. Here we present crystallog. analyses of the virally hijacked form of the human DDB1-CUL4A-ROC1 machinery, which show that DDB1 uses one β-propeller domain for cullin scaffold binding and a variably attached sep. double-β-propeller fold for substrate presentation. Through tandem-affinity purifn. of human DDB1 and CUL4A complexes followed by mass spectrometry anal., we also identify a novel family of WD40-repeat proteins, which directly bind to the double-propeller fold of DDB1 and serve as the substrate-recruiting module of the E3. Together, our structural and proteomic results reveal the structural mechanisms and mol. logic underlying the assembly and versatility of a new family of cullin-RING E3 complexes.
    9. 9
      Fischer, E. S.; Scrima, A.; Böhm, K.; Matsumoto, S.; Lingaraju, G. M.; Faty, M.; Yasuda, T.; Cavadini, S.; Wakasugi, M.; Hanaoka, F.; Iwai, S.; Gut, H.; Sugasawa, K.; Thomä, N. H. The Molecular Basis of CRL4DDB2/CSA Ubiquitin Ligase Architecture, Targeting, and Activation. Cell 2011, 147, 1024– 1039,  DOI: 10.1016/j.cell.2011.10.035
      9
      The Molecular Basis of CRL4DDB2/CSA Ubiquitin Ligase Architecture, Targeting, and Activation
      Fischer, Eric S.; Scrima, Andrea; Boehm, Kerstin; Matsumoto, Syota; Lingaraju, Gondichatnahalli M.; Faty, Mahamadou; Yasuda, Takeshi; Cavadini, Simone; Wakasugi, Mitsuo; Hanaoka, Fumio; Iwai, Shigenori; Gut, Heinz; Sugasawa, Kaoru; Thomae, Nicolas H.
      Cell (Cambridge, MA, United States) (2011), 147 (5), 1024-1039CODEN: CELLB5; ISSN:0092-8674. (Cell Press)
      The DDB1-CUL4-RBX1 (CRL4) ubiquitin ligase family regulates a diverse set of cellular pathways through dedicated substrate receptors (DCAFs). The DCAF DDB2 detects UV-induced pyrimidine dimers in the genome and facilitates nucleotide excision repair. We provide the mol. basis for DDB2 receptor-mediated cyclobutane pyrimidine dimer recognition in chromatin. The structures of the fully assembled DDB1-DDB2-CUL4A/B-RBX1 (CRL4DDB2) ligases reveal that the mobility of the ligase arm creates a defined ubiquitination zone around the damage, which precludes direct ligase activation by DNA lesions. Instead, the COP9 signalosome (CSN) mediates the CRL4DDB2 inhibition in a CSN5 independent, nonenzymic, fashion. In turn, CSN inhibition is relieved upon DNA damage binding to the DDB2 module within CSN-CRL4DDB2. The Cockayne syndrome A DCAF complex crystal structure shows that CRL4DCAF(WD40) ligases share common architectural features. Our data support a general mechanism of ligase activation, which is induced by CSN displacement from CRL4DCAF on substrate binding to the DCAF. Binding pyrimidine dimers on chromatin displaces an inhibitory factor from the CRL4-DDB2 ubiquitin ligase complex and activates it. Other CLR4 complexes share similar architectural features, suggesting a general mechanism of ligase activation.
    10. 10
      Nakagawa, T.; Mondal, K.; Swanson, P. C. VprBP (DCAF1): A Promiscuous Substrate Recognition Subunit That Incorporates into Both RING-Family CRL4 and HECT-Family EDD/UBR5 E3 Ubiquitin Ligases. BMC Mol. Biol. 2013, 14, 22,  DOI: 10.1186/1471-2199-14-22
      10
      VprBP (DCAF1): a promiscuous substrate recognition subunit that incorporates into both RING-family CRL4 and HECT-family EDD/UBR5 E3 ubiquitin ligases
      Nakagawa, Tadashi; Mondal, Koushik; Swanson, Patrick C.
      BMC Molecular Biology (2013), 14 (), 22CODEN: BMBMC4; ISSN:1471-2199. (BioMed Central Ltd.)
      A review. The terminal step in the ubiquitin modification system relies on an E3 ubiquitin ligase to facilitate transfer of ubiquitin to a protein substrate. The substrate recognition and ubiquitin transfer activities of the E3 ligase may be mediated by a single polypeptide or may rely on sep. subunits. The latter organization is particularly prevalent among members of largest class of E3 ligases, the RING family, although examples of this type of arrangement have also been reported among members of the smaller HECT family of E3 ligases. This review describes recent discoveries that reveal the surprising and distinctive ability of VprBP (DCAF1) to serve as a substrate recognition subunit for a member of both major classes of E3 ligase, the RING-type CRL4 ligase and the HECT-type EDD/UBR5 ligase. The cellular processes normally regulated by VprBP-assocd. E3 ligases, and their targeting and subversion by viral accessory proteins are also discussed. Taken together, these studies provide important insights and raise interesting new questions regarding the mechanisms that regulate or subvert VprBP function in the context of both the CRL4 and EDD/UBR5 E3 ligases.
    11. 11
      Sakamoto, K. M.; Kim, K. B.; Kumagai, A.; Mercurio, F.; Crews, C. M.; Deshaies, R. J. Protacs: Chimeric Molecules That Target Proteins to the Skp1-Cullin-F Box Complex for Ubiquitination and Degradation. Proc. Natl. Acad. Sci. U. S. A. 2001, 98, 8554– 8559,  DOI: 10.1073/pnas.141230798
      11
      Protacs: chimeric molecules that target proteins to the Skp1-Cullin-F box complex for ubiquitination and degradation
      Sakamoto, Kathleen M.; Kim, Kyung B.; Kumagai, Akiko; Mercurio, Frank; Crews, Craig M.; Deshaies, Raymond J.
      Proceedings of the National Academy of Sciences of the United States of America (2001), 98 (15), 8554-8559CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      The intracellular levels of many proteins are regulated by ubiquitin-dependent proteolysis. One of the best-characterized enzymes that catalyzes the attachment of ubiquitin to proteins is a ubiquitin ligase complex, Skp1-Cullin-F box complex contg. Hrt1 (SCF). We sought to artificially target a protein to the SCF complex for ubiquitination and degrdn. To this end, we tested methionine aminopeptidase-2 (MetAP-2), which covalently binds the angiogenesis inhibitor ovalicin. A chimeric compd., protein-targeting chimeric mol. 1 (Protac-1), was synthesized to recruit MetAP-2 to SCF. One domain of Protac-1 contains the IκBα phosphopeptide that is recognized by the F-box protein β-TRCP, whereas the other domain is composed of ovalicin. We show that MetAP-2 can be tethered to SCFβ-TRCP, ubiquitinated, and degraded in a Protac-1-dependent manner. In the future, this approach may be useful for conditional inactivation of proteins, and for targeting disease-causing proteins for destruction.
    12. 12
      Zhang, S.; Feng, Y.; Narayan, O.; Zhao, L. J. Cytoplasmic Retention of HIV-1 Regulatory Protein Vpr by Protein-Protein Interaction with a Novel Human Cytoplasmic Protein VprBP. Gene 2001, 263, 131– 140,  DOI: 10.1016/s0378-1119(00)00583-7
      12
      Cytoplasmic retention of HIV-1 regulatory protein Vpr by protein-protein interaction with a novel human cytoplasmic protein VprBP
      Zhang, S.; Feng, Y.; Narayan, O.; Zhao, L.-J.
      Gene (2001), 263 (1-2), 131-140CODEN: GENED6; ISSN:0378-1119. (Elsevier Science B.V.)
      Vpr is an HIV-1 auxiliary regulatory protein packaged in the virion. It has been shown to enhance the nuclear transport of the HIV-1 pre-integration complex, activate transcription of cellular and viral promoters, and arrest the cell cycle at the G2/M check-point. We previously identified a cellular protein of 180 kDa (RIP) that interacted with HIV-1 Vpr specifically. We now rename this cellular protein as Vpr-binding protein, or VprBP. In this report, we describe the cloning of the VprBP cDNA that encodes 1507 aa residues and is identical to the previously cloned cDNA KIAA0800. We demonstrate that Vpr specifically interacts with recombinantly expressed VprBP in vitro as well as in vivo. Furthermore, Vpr interacts with the cellular endogenous VprBP in the context of the HIV-1 life cycle. Mutational anal. of VprBP suggests that the Vpr binding domain is located within the C-terminal half of VprBP, which has a Pro-rich domain and several Phe-x-x-Phe repeats. Subcellular fractionation studies show that both the endogenous VprBP and the adenovirus-expressed VprBP are distributed predominantly in the cytoplasmic fraction. Consistent with previous reports, the adenovirus-expressed Vpr is distributed in both the cytoplasmic and the nuclear fractions. However, when VprBP and Vpr are expressed together, Vpr is found almost exclusively in the cytoplasm. Expression of VprBP does not affect the nuclear transport of the adenoviral nuclear protein, pTP. VprBP expressed in insect cells also blocks the nuclear transport of a Vpr-GFP fusion protein, and VprBP mutants incapable of interacting with Vpr fail to block Vpr-GFP nuclear transport. We hypothesize that Vpr interaction with VprBP may cause changes in the host cell cytoplasm that affect HIV-1 pathogenesis as well as HIV-1 replication.
    13. 13
      Hossain, D.; Ferreira Barbosa, J. A.; Cohen, É. A.; Tsang, W. Y. HIV-1 Vpr Hijacks EDD-DYRK2-DDB1DCAF1 to Disrupt Centrosome Homeostasis. J. Biol. Chem. 2018, 293, 9448– 9460,  DOI: 10.1074/jbc.RA117.001444
      13
      HIV-1 Vpr hijacks EDD-DYRK2-DDB1DCAF1 to disrupt centrosome homeostasis
      Hossain, Delowar; Ferreira Barbosa, Jeremy A.; Cohen, Eric A.; Tsang, William Y.
      Journal of Biological Chemistry (2018), 293 (24), 9448-9460CODEN: JBCHA3; ISSN:0021-9258. (American Society for Biochemistry and Molecular Biology)
      Viruses exploit the host cell machinery for their own profit. To evade innate immune sensing and promote viral replication, HIV type 1 (HIV-1) subverts DNA repair regulatory proteins and induces G2/M arrest. The preintegration complex of HIV-1 is known to traffic along microtubules and accumulate near the microtubule-organizing center. The centrosome is the major microtubule-organizing center in most eukaryotic cells, but precisely how HIV-1 impinges on centrosome biol. remains poorly understood. We report here that the HIV-1 accessory protein viral protein R (Vpr) localized to the centrosome through binding to DCAF1, forming a complex with the ubiquitin ligase EDD-DYRK2-DDB1DCAF1 and Cep78, a resident centrosomal protein previously shown to inhibit EDD-DYRK2-DDB1DCAF1. Vpr did not affect ubiquitination of Cep78. Rather, it enhanced ubiquitination of an EDD-DYRK2-DDB1DCAF1 substrate, CP110, leading to its degrdn., an effect that could be overcome by Cep78 expression. The down-regulation of CP110 and elongation of centrioles provoked by Vpr were independent of G2/M arrest. Infection of T lymphocytes with HIV-1, but not with HIV-1 lacking Vpr, promoted CP110 degrdn. and centriole elongation. Elongated centrioles recruited more γ-tubulin to the centrosome, resulting in increased microtubule nucleation. Our results suggest that Vpr is targeted to the centrosome where it hijacks a ubiquitin ligase, disrupting organelle homeostasis, which may contribute to HIV-1 pathogenesis.
    14. 14
      Schabla, N. M.; Mondal, K.; Swanson, P. C. DCAF1 (VprBP): Emerging Physiological Roles for a Unique Dual-Service E3 Ubiquitin Ligase Substrate Receptor. J. Mol. Cell Biol. 2019, 11, 725– 735,  DOI: 10.1093/jmcb/mjy085
      14
      DCAF1 (VprBP): emerging physiological roles for a unique dual-service E3 ubiquitin ligase substrate receptor
      Schabla, N. Max; Mondal, Koushik; Swanson, Patrick C.
      Journal of Molecular Cell Biology (Oxford, United Kingdom) (2019), 11 (9), 725-735CODEN: JMCBCU; ISSN:1759-4685. (Oxford University Press)
      A review. Cullin-RING ligases (CRLs) comprise a large group of modular eukaryotic E3 ubiquitin ligases. Within this family, the CRL4 ligase (consisting of the Cullin4 [CUL4] scaffold protein, the Rbx1 RING finger domain protein, the DNA damage-binding protein 1 [DDB1], and one of many DDB1-assocd. substrate receptor proteins) has been intensively studied in recent years due to its involvement in regulating various cellular processes, its role in cancer development and progression, and its subversion by viral accessory proteins. Initially discovered as a target for hijacking by the human immunodeficiency virus accessory protein r, the normal targets and function of the CRL4 substrate receptor protein DDB1-Cul4-assocd. factor 1 (DCAF1; also known as VprBP) had remained elusive, but newer studies have begun to shed light on these questions. Here, we review recent progress in understanding the diverse physiol. roles of this DCAF1 in supporting various general and cell type-specific cellular processes in its context with the CRL4 E3 ligase, as well as another HECT-type E3 ligase with which DCAF1 also assocs., called EDD/UBR5. We also discuss emerging questions and areas of future study to uncover the dynamic roles of DCAF1 in normal physiol.
    15. 15
      Hrecka, K.; Gierszewska, M.; Srivastava, S.; Kozaczkiewicz, L.; Swanson, S. K.; Florens, L.; Washburn, M. P.; Skowronski, J. Lentiviral Vpr Usurps Cul4-DDB1[VprBP] E3 Ubiquitin Ligase to Modulate Cell Cycle. Proc. Natl. Acad. Sci. U. S. A. 2007, 104, 11778– 11783,  DOI: 10.1073/pnas.0702102104
      15
      Lentiviral Vpr usurps Cul4-DDB1[VprBP] E3 ubiquitin ligase to modulate cell cycle
      Hrecka, Kasia; Gierszewska, Magdalena; Srivastava, Smita; Kozackiewicz, Lukasz; Swanson, Selene K.; Florens, Laurence; Washburn, Michael P.; Skowronski, Jacek
      Proceedings of the National Academy of Sciences of the United States of America (2007), 104 (28), 11778-11783, S11778/1-S11778/6CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      The replication of viruses depends on the cell cycle status of the infected cells. Viruses have evolved functions that alleviate restrictions imposed on their replication by the host. Vpr, an accessory factor of primate lentiviruses, arrests cells at the DNA damage checkpoint in G2 phase of the cell cycle, but the mechanism underlying this effect has remained elusive. Here we report that Vpr proteins of both the human (HIV-1) and the distantly related simian (SIVmac) immunodeficiency viruses specifically assoc. with a protein complex comprising subunits of E3 ubiquitin ligase assembled on Cullin-4 scaffold (Cul4-DDB1[VprBP]). We show that Vpr binding to Cul4-DDB1[VprBP] leads to increased neddylation and elevated intrinsic ubiquitin ligase activity of this E3. This effect is mediated through the VprBP subunit of the complex, which recently has been suggested to function as a substrate receptor for Cu14. We also demonstrate that VprBP regulates G, phase and is essential for the completion of DNA replication in S phase. Furthermore, the ability of Vpr to arrest cells in G2 phase correlates with its ability to interact with Cu14-DDB1[VprBP] E3 complex. Our studies identify the Cu14-DDB1[VprBP] E3 ubiquitin ligase complex as the downstream effector of lentiviral Vpr for the induction of cell cycle arrest in G2 phase and suggest that Vpr may use this complex to perturb other aspects of the cell cycle and DNA metab. in infected cells.
    16. 16
      Guo, Z.; Kong, Q.; Liu, C.; Zhang, S.; Zou, L.; Yan, F.; Whitmire, J. K.; Xiong, Y.; Chen, X.; Wan, Y. Y. DCAF1 Controls T-Cell Function via P53-Dependent and -Independent Mechanisms. Nat. Commun. 2016, 7, 10307,  DOI: 10.1038/ncomms10307
      16
      DCAF1 controls T-cell function via p53-dependent and -independent mechanisms
      Guo, Zengli; Kong, Qing; Liu, Cui; Zhang, Song; Zou, Liyun; Yan, Feng; Whitmire, Jason K.; Xiong, Yue; Chen, Xian; Wan, Yisong Y.
      Nature Communications (2016), 7 (), 10307CODEN: NCAOBW; ISSN:2041-1723. (Nature Publishing Group)
      On activation, naive T cells grow in size and enter cell cycle to mount immune response. How the fundamental processes of T-cell growth and cell cycle entry are regulated is poorly understood. Here we report that DCAF1 (Ddb1-cullin4-assocd.-factor 1) is essential for these processes. The deletion of DCAF1 in T cells impairs their peripheral homeostasis. DCAF1 is upregulated on T-cell receptor activation and crit. for activation-induced T-cell growth, cell cycle entry and proliferation. In addn., DCAF1 is required for T-cell expansion and function during anti-viral and autoimmune responses in vivo. DCAF1 deletion leads to a drastic stabilization of p53 protein, which can be attributed to a requirement of DCAF1 for MDM2-mediated p53 poly-ubiquitination. Importantly, p53 deletion rescues the cell cycle entry defect but not the growth defect of DCAF1-deficient cells. Therefore, DCAF1 is vital for T-cell function through p53-dependent and -independent mechanisms.
    17. 17
      Tao, Y.; Remillard, D.; Vinogradova, E. V.; Yokoyama, M.; Banchenko, S.; Schwefel, D.; Melillo, B.; Schreiber, S. L.; Zhang, X.; Cravatt, B. F. Targeted Protein Degradation by Electrophilic PROTACs That Stereoselectively and Site-Specifically Engage DCAF1. J. Am. Chem. Soc. 2022, 144, 18688– 18699,  DOI: 10.1021/jacs.2c08964
      17
      Targeted Protein Degradation by Electrophilic PROTACs that Stereoselectively and Site-Specifically Engage DCAF1
      Tao, Yongfeng; Remillard, David; Vinogradova, Ekaterina V.; Yokoyama, Minoru; Banchenko, Sofia; Schwefel, David; Melillo, Bruno; Schreiber, Stuart L.; Zhang, Xiaoyu; Cravatt, Benjamin F.
      Journal of the American Chemical Society (2022), 144 (40), 18688-18699CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
      Targeted protein degrdn. induced by heterobifunctional compds. and mol. glues presents an exciting avenue for chem. probe and drug discovery. To date, small-mol. ligands have been discovered for only a limited no. of E3 ligases, which is an important limiting factor for realizing the full potential of targeted protein degrdn. We report herein the discovery by chem. proteomics of azetidine acrylamides that stereoselectively and site-specifically react with a cysteine (C1113) in the E3 ligase substrate receptor DCAF1. We demonstrate that the azetidine acrylamide ligands for DCAF1 can be developed into electrophilic proteolysis-targeting chimeras (PROTACs) that mediated targeted protein degrdn. in human cells. We show that this process is stereoselective and does not occur in cells expressing a C1113A mutant of DCAF1. Mechanistic studies indicate that only low fractional engagement of DCAF1 is required to support protein degrdn. by electrophilic PROTACs. These findings, taken together, demonstrate how the chem. proteomic anal. of stereochem. defined electrophilic compd. sets can uncover ligandable sites on E3 ligases that support targeted protein degrdn.
    18. 18
      Waterhouse, A.; Bertoni, M.; Bienert, S.; Studer, G.; Tauriello, G.; Gumienny, R.; Heer, F. T.; de Beer, T. A. P.; Rempfer, C.; Bordoli, L.; Lepore, R.; Schwede, T. SWISS-MODEL: Homology Modelling of Protein Structures and Complexes. Nucleic Acids Res. 2018, 46, W296– W303,  DOI: 10.1093/nar/gky427
      18
      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.
    19. 19
      Sugiyama, M. G.; Cui, H.; Redka, D. S.; Karimzadeh, M.; Rujas, E.; Maan, H.; Hayat, S.; Cheung, K.; Misra, R.; McPhee, J. B.; Viirre, R. D.; Haller, A.; Botelho, R. J.; Karshafian, R.; Sabatinos, S. A.; Fairn, G. D.; Madani Tonekaboni, S. A.; Windemuth, A.; Julien, J.-P.; Shahani, V.; MacKinnon, S. S.; Wang, B.; Antonescu, C. N. Multiscale Interactome Analysis Coupled with Off-Target Drug Predictions Reveals Drug Repurposing Candidates for Human Coronavirus Disease. Sci. Rep. 2021, 11, 23315,  DOI: 10.1038/s41598-021-02432-7
      19
      Multiscale interactome analysis coupled with off-target drug predictions reveals drug repurposing candidates for human coronavirus disease
      Sugiyama, Michael G.; Cui, Haotian; Redka, Dar'ya S.; Karimzadeh, Mehran; Rujas, Edurne; Maan, Hassaan; Hayat, Sikander; Cheung, Kyle; Misra, Rahul; McPhee, Joseph B.; Viirre, Russell D.; Haller, Andrew; Botelho, Roberto J.; Karshafian, Raffi; Sabatinos, Sarah A.; Fairn, Gregory D.; Madani Tonekaboni, Seyed Ali; Windemuth, Andreas; Julien, Jean-Philippe; Shahani, Vijay; MacKinnon, Stephen S.; Wang, Bo; Antonescu, Costin N.
      Scientific Reports (2021), 11 (1), 23315CODEN: SRCEC3; ISSN:2045-2322. (Nature Research)
      Abstr.: The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clin.-relevant drug compds. for the treatment of a range of human coronavirus diseases. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome anal. coupled to off-target drug predictions. Cell-based exptl. assessment reveals several clin.-relevant drug repurposing candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.
    20. 20
      He, Y.; Yang, C.; Wang, Y.; Sacher, J. R.; Sims, M. M.; Pfeffer, L. M.; Miller, D. D. Novel Structural-Related Analogs of PFI-3 (SRAPs) That Target the BRG1 Catalytic Subunit of the SWI/SNF Complex Increase the Activity of Temozolomide in Glioblastoma Cells. Bioorg. Med. Chem. 2022, 53, 116533  DOI: 10.1016/j.bmc.2021.116533
      20
      Novel structural-related analogs of PFI-3 (SRAPs) that target the BRG1 catalytic subunit of the SWI/SNF complex increase the activity of temozolomide in glioblastoma cells
      He, Yali; Yang, Chuanhe; Wang, Yinan; Sacher, Joshua R.; Sims, Michelle M.; Pfeffer, Lawrence M.; Miller, Duane D.
      Bioorganic & Medicinal Chemistry (2022), 53 (), 116533CODEN: BMECEP; ISSN:0968-0896. (Elsevier B.V.)
      Glioblastoma (GBM) is the most aggressive and treatment-refractory malignant adult brain cancer. After std. of care therapy, the overall median survival for GBM is only ∼6 mo with a 5-yr survival <10%. Although some patients initially respond to the DNA alkylating agent temozolomide (TMZ), unfortunately most patients become resistant to therapy and brain tumors eventually recur. We previously found that knockout of BRG1 or treatment with PFI-3, a small mol. inhibitor of the BRG1 bromodomain, enhances sensitivity of GBM cells to temozolomide in vitro and in vivo GBM animal models. Those results demonstrated that the BRG1 catalytic subunit of the SWI/SNF chromatin remodeling complex appears to play a crit. role in regulating TMZ-sensitivity. In the present study we designed and synthesized Structurally Related Analogs of PFI-3 (SRAPs) and tested their bioactivity in vitro. Among of the SRAPs, 9f and 11d show better efficacy than PFI-3 in sensitizing GBM cells to the antiproliferative and cell death inducing effects of temozolomide in vitro, as well as enhancing the inhibitor effect of temozolomide on the growth of s.c. GBM tumors.
    21. 21
      Krivák, R.; Hoksza, D. P2Rank: Machine Learning Based Tool for Rapid and Accurate Prediction of Ligand Binding Sites from Protein Structure. Aust. J. Chem. 2018, 10, 39,  DOI: 10.1186/s13321-018-0285-8
      There is no corresponding record for this reference.
    22. 22
      Schwefel, D.; Groom, H. C. T.; Boucherit, V. C.; Christodoulou, E.; Walker, P. A.; Stoye, J. P.; Bishop, K. N.; Taylor, I. A. Structural Basis of Lentiviral Subversion of a Cellular Protein Degradation Pathway. Nature 2014, 505, 234– 238,  DOI: 10.1038/nature12815
      22
      Structural basis of lentiviral subversion of a cellular protein degradation pathway
      Schwefel, David; Groom, Harriet C. T.; Boucherit, Virginie C.; Christodoulou, Evangelos; Walker, Philip A.; Stoye, Jonathan P.; Bishop, Kate N.; Taylor, Ian A.
      Nature (London, United Kingdom) (2014), 505 (7482), 234-238CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)
      Lentiviruses contain accessory genes that have evolved to counteract the effects of host cellular defense proteins that inhibit productive infection. One such restriction factor, SAMHD1, inhibits human immunodeficiency virus (HIV)-1 infection of myeloid-lineage cells as well as resting CD4+ T cells by reducing the cellular deoxynucleoside 5'-triphosphate (dNTP) concn. to a level at which the viral reverse transcriptase cannot function. In other lentiviruses, including HIV-2 and related simian immunodeficiency viruses (SIVs), SAMHD1 restriction is overcome by the action of viral accessory protein x (Vpx) or the related viral protein r (Vpr) that target and recruit SAMHD1 for proteasomal degrdn. The mol. mechanism by which these viral proteins are able to usurp the host cell's ubiquitination machinery to destroy the cell's protection against these viruses has not been defined. Here we present the crystal structure of a ternary complex of Vpx with the human E3 ligase substrate adaptor DCAF1 and the carboxy-terminal region of human SAMHD1. Vpx is made up of a three-helical bundle stabilized by a zinc finger motif, and wraps tightly around the disk-shaped DCAF1 mol. to present a new mol. surface. This adapted surface is then able to recruit SAMHD1 via its C terminus, making it a competent substrate for the E3 ligase to mark for proteasomal degrdn. The structure reported here provides a mol. description of how a lentiviral accessory protein is able to subvert the cell's normal protein degrdn. pathway to inactivate the cellular viral defense system.
    23. 23
      The UniProt Consortium UniProt: The Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 2023, 51, D523– D531,  DOI: 10.1093/nar/gkac1052
      There is no corresponding record for this reference.
    24. 24
      Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235– 242,  DOI: 10.1093/nar/28.1.235
      24
      The Protein Data Bank
      Berman, Helen M.; Westbrook, John; Feng, Zukang; Gilliland, Gary; Bhat, T. N.; Weissig, Helge; Shindyalov, Ilya N.; Bourne, Philip E.
      Nucleic Acids Research (2000), 28 (1), 235-242CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
      The Protein Data Bank (PDB; http://www.rcsb.org/pdb/)is the single worldwide archive of structural data of biol. macromols. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
    25. 25
      Hutchinson, A.; Seitova, A. Production of Recombinant PRMT Proteins Using the Baculovirus Expression Vector System. J. Vis. Exp. JoVE 2021, No. 173.  DOI: 10.3791/62510 .
      There is no corresponding record for this reference.
    26. 26
      Minor, W.; Cymborowski, M.; Otwinowski, Z.; Chruszcz, M. HKL-3000: The Integration of Data Reduction and Structure Solution--from Diffraction Images to an Initial Model in Minutes. Acta Crystallogr. D Biol. Crystallogr. 2006, 62, 859– 866,  DOI: 10.1107/S0907444906019949
      26
      HKL-3000: the integration of data reduction and structure solution - from diffraction images to an initial model in minutes
      Minor, Wladek; Cymborowski, Marcin; Otwinowski, Zbyszek; Chruszcz, Maksymilian
      Acta Crystallographica, Section D: Biological Crystallography (2006), D62 (8), 859-866CODEN: ABCRE6; ISSN:0907-4449. (Blackwell Publishing Ltd.)
      A new approach that integrates data collection, data redn., phasing and model building significantly accelerates the process of structure detn. and on av. minimizes the no. of data sets and synchrotron time required for structure soln. Initial testing of the HKL-3000 system (the beta version was named HKL-2000_ph) with more than 140 novel structure detns. has proven its high value for MAD/SAD expts. The heuristics for choosing the best computational strategy at different data resoln. limits of phasing signal and crystal diffraction are being optimized. The typical end result is an interpretable electron-d. map with a partially built structure and, in some cases, an almost complete refined model. The current development is oriented towards very fast structure soln. in order to provide feedback during the diffraction expt. Work is also proceeding towards improving the quality of phasing calcn. and model building.
    27. 27
      McCoy, A. J.; Grosse-Kunstleve, R. W.; Adams, P. D.; Winn, M. D.; Storoni, L. C.; Read, R. J. Phaser Crystallographic Software. J. Appl. Crystallogr. 2007, 40, 658– 674,  DOI: 10.1107/S0021889807021206
      27
      Phaser crystallographic software
      McCoy, Airlie J.; Grosse-Kunstleve, Ralf W.; Adams, Paul D.; Winn, Martyn D.; Storoni, Laurent C.; Read, Randy J.
      Journal of Applied Crystallography (2007), 40 (4), 658-674CODEN: JACGAR; ISSN:0021-8898. (International Union of Crystallography)
      Phaser is a program for phasing macromol. crystal structures by both mol. replacement and exptl. phasing methods. The novel phasing algorithms implemented in Phaser have been developed using max. likelihood and multivariate statistics. For mol. replacement, the new algorithms have proved to be significantly better than traditional methods in discriminating correct solns. from noise, and for single-wavelength anomalous dispersion exptl. phasing, the new algorithms, which account for correlations between F+ and F-, give better phases (lower mean phase error with respect to the phases given by the refined structure) than those that use mean F and anomalous differences ΔF. One of the design concepts of Phaser was that it be capable of a high degree of automation. To this end, Phaser (written in C++) can be called directly from Python, although it can also be called using traditional CCP4 keyword-style input. Phaser is a platform for future development of improved phasing methods and their release, including source code, to the crystallog. community.
    28. 28
      Emsley, P.; Cowtan, K. Coot: Model-Building Tools for Molecular Graphics. Acta Crystallogr. D Biol. Crystallogr. 2004, 60, 2126– 2132,  DOI: 10.1107/S0907444904019158
      28
      Coot: model-building tools for molecular graphics
      Emsley, Paul; Cowtan, Kevin
      Acta Crystallographica, Section D: Biological Crystallography (2004), D60 (12, Pt. 1), 2126-2132CODEN: ABCRE6; ISSN:0907-4449. (Blackwell Publishing Ltd.)
      CCP4mg is a project that aims to provide a general-purpose tool for structural biologists, providing tools for x-ray structure soln., structure comparison and anal., and publication-quality graphics. The map-fitting tools are available as a stand-alone package, distributed as 'Coot'.
    29. 29
      Murshudov, G. N.; Vagin, A. A.; Dodson, E. J. Refinement of Macromolecular Structures by the Maximum-Likelihood Method. Acta Crystallogr. D Biol. Crystallogr. 1997, 53, 240– 255,  DOI: 10.1107/S0907444996012255
      29
      Refinement of macromolecular structures by the maximum-likelihood method
      Murshudov, Garib N.; Vagin, Alexei A.; Dodson, Eleanor J.
      Acta Crystallographica, Section D: Biological Crystallography (1997), D53 (3), 240-255CODEN: ABCRE6; ISSN:0907-4449. (Munksgaard)
      A review with many refs. on the math. basis of max. likelihood. The likelihood function for macromol. structures is extended to include prior phase information and exptl. std. uncertainties. The assumption that different parts of a structure might have different errors is considered. A method for estg. σA using "free" reflections is described and its effects analyzed. The derived equations have been implemented in the program REFMAC. This has been tested on several proteins at different stages of refinement (bacterial α-amylase, cytochrome c', cross-linked insulin and oligopeptide binding protein). The results derived using the max.-likelihood residual are consistently better than those obtained from least-squares refinement.
    30. 30
      Winn, M. D.; Ballard, C. C.; Cowtan, K. D.; Dodson, E. J.; Emsley, P.; Evans, P. R.; Keegan, R. M.; Krissinel, E. B.; Leslie, A. G. W.; McCoy, A.; McNicholas, S. J.; Murshudov, G. N.; Pannu, N. S.; Potterton, E. A.; Powell, H. R.; Read, R. J.; Vagin, A.; Wilson, K. S. Overview of the CCP4 Suite and Current Developments. Acta Crystallogr. D Biol. Crystallogr. 2011, 67, 235– 242,  DOI: 10.1107/S0907444910045749
      30
      Overview of the CCP4 suite and current developments
      Winn, Martyn D.; Ballard, Charles C.; Cowtan, Kevin D.; Dodson, Eleanor J.; Emsley, Paul; Evans, Phil R.; Keegan, Ronan M.; Krissinel, Eugene B.; Leslie, Andrew G. W.; McCoy, Airlie; McNicholas, Stuart J.; Murshudov, Garib N.; Pannu, Navraj S.; Potterton, Elizabeth A.; Powell, Harold R.; Read, Randy J.; Vagin, Alexei; Wilson, Keith S.
      Acta Crystallographica, Section D: Biological Crystallography (2011), 67 (4), 235-242CODEN: ABCRE6; ISSN:0907-4449. (International Union of Crystallography)
      A review. The CCP4 (Collaborative Computational Project, No. 4) software suite is a collection of programs and assocd. data and software libraries which can be used for macromol. structure detn. by X-ray crystallog. The suite is designed to be flexible, allowing users a no. of methods of achieving their aims. The programs are from a wide variety of sources but are connected by a common infrastructure provided by std. file formats, data objects and graphical interfaces. Structure soln. by macromol. crystallog. is becoming increasingly automated and the CCP4 suite includes several automation pipelines. After giving a brief description of the evolution of CCP4 over the last 30 years, an overview of the current suite is given. While detailed descriptions are given in the accompanying articles, here it is shown how the individual programs contribute to a complete software package.
    31. 31
      Chen, V. B.; Arendall, W. B.; Headd, J. J.; Keedy, D. A.; Immormino, R. M.; Kapral, G. J.; Murray, L. W.; Richardson, J. S.; Richardson, D. C. MolProbity: All-Atom Structure Validation for Macromolecular Crystallography. Acta Crystallogr. D Biol. Crystallogr. 2010, 66, 12– 21,  DOI: 10.1107/S0907444909042073
      31
      MolProbity: all-atom structure validation for macromolecular crystallography
      Chen, Vincent B.; Arendall, W. Bryan, III; Headd, Jeffrey J.; Keedy, Daniel A.; Immormino, Robert M.; Kapral, Gary J.; Murray, Laura W.; Richardson, Jane S.; Richardson, David C.
      Acta Crystallographica, Section D: Biological Crystallography (2010), 66 (1), 12-21CODEN: ABCRE6; ISSN:0907-4449. (International Union of Crystallography)
      MolProbity is a structure-validation web service that provides broad-spectrum solidly based evaluation of model quality at both the global and local levels for both proteins and nucleic acids. It relies heavily on the power and sensitivity provided by optimized hydrogen placement and all-atom contact anal., complemented by updated versions of covalent-geometry and torsion-angle criteria. Some of the local corrections can be performed automatically in MolProbity and all of the diagnostics are presented in chart and graphical forms that help guide manual rebuilding. X-ray crystallog. provides a wealth of biol. important mol. data in the form of at. three-dimensional structures of proteins, nucleic acids and increasingly large complexes in multiple forms and states. Advances in automation, in everything from crystn. to data collection to phasing to model building to refinement, have made solving a structure using crystallog. easier than ever. However, despite these improvements, local errors that can affect biol. interpretation are widespread at low resoln. and even high-resoln. structures nearly all contain at least a few local errors such as Ramachandran outliers, flipped branched protein side chains and incorrect sugar puckers. It is crit. both for the crystallographer and for the end user that there are easy and reliable methods to diagnose and correct these sorts of errors in structures. MolProbity is the authors' contribution to helping solve this problem and this article reviews its general capabilities, reports on recent enhancements and usage, and presents evidence that the resulting improvements are now beneficially affecting the global database.
    32. 32
      Pettersen, E. F.; Goddard, T. D.; Huang, C. C.; Couch, G. S.; Greenblatt, D. M.; Meng, E. C.; Ferrin, T. E. UCSF Chimera--a Visualization System for Exploratory Research and Analysis. J. Comput. Chem. 2004, 25, 1605– 1612,  DOI: 10.1002/jcc.20084
      32
      UCSF Chimera-A visualization system for exploratory research and analysis
      Pettersen, Eric F.; Goddard, Thomas D.; Huang, Conrad C.; Couch, Gregory S.; Greenblatt, Daniel M.; Meng, Elaine C.; Ferrin, Thomas E.
      Journal of Computational Chemistry (2004), 25 (13), 1605-1612CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)
      The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale mol. assemblies such as viral coats, and Collab., which allows researchers to share a Chimera session interactively despite being at sep. locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and assocd. structures; ViewDock, for screening docked ligand orientations; Movie, for replaying mol. dynamics trajectories; and Vol. Viewer, for display and anal. of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/.
    33. 33
      DeLano, W.; Schrödinger, L. PyMOL. Retrieved Httpwwwpymolorgpymol.
      There is no corresponding record for this reference.
    34. 34
      Hopkins, A. L.; Groom, C. R.; Alex, A. Ligand Efficiency: A Useful Metric for Lead Selection. Drug Discovery Today 2004, 9, 430– 431,  DOI: 10.1016/S1359-6446(04)03069-7
      34
      Ligand efficiency: a useful metric for lead selection
      Hopkins Andrew L; Groom Colin R; Alex Alexander
      Drug discovery today (2004), 9 (10), 430-1 ISSN:1359-6446.
      There is no expanded citation for this reference.
    35. 35
      Li, A. S. M.; Kimani, S.; Wilson, B.; Noureldin, M.; González-Álvarez, H.; Mamai, A.; Hoffer, L.; Guilinger, J. P.; Zhang, Y.; von Rechenberg, M.; Disch, J. S.; Mulhern, C. J.; Slakman, B. L.; Cuozzo, J. W.; Dong, A.; Poda, G.; Mohammed, M.; Saraon, P.; Mittal, M.; Modh, P.; Rathod, V.; Patel, B.; Ackloo, S.; Santhakumar, V.; Szewczyk, M. M.; Barsyte-Lovejoy, D.; Arrowsmith, C. H.; Marcellus, R.; Guié, M.-A.; Keefe, A. D.; Brown, P. J.; Halabelian, L.; Al-Awar, R.; Vedadi, M. Discovery of Nanomolar DCAF1 Small Molecule Ligands. J. Med. Chem. 2023, 66, 5041– 5060,  DOI: 10.1021/acs.jmedchem.2c02132
      35
      Discovery of Nanomolar DCAF1 Small Molecule Ligands
      Li, Alice Shi Ming; Kimani, Serah; Wilson, Brian; Noureldin, Mahmoud; Gonzalez-Alvarez, Hector; Mamai, Ahmed; Hoffer, Laurent; Guilinger, John P.; Zhang, Ying; von Rechenberg, Moritz; Disch, Jeremy S.; Mulhern, Christopher J.; Slakman, Belinda L.; Cuozzo, John W.; Dong, Aiping; Poda, Gennady; Mohammed, Mohammed; Saraon, Punit; Mittal, Manish; Modh, Pratik; Rathod, Vaibhavi; Patel, Bhashant; Ackloo, Suzanne; Santhakumar, Vijayaratnam; Szewczyk, Magdalena M.; Barsyte-Lovejoy, Dalia; Arrowsmith, Cheryl H.; Marcellus, Richard; Guie, Marie-Aude; Keefe, Anthony D.; Brown, Peter J.; Halabelian, Levon; Al-awar, Rima; Vedadi, Masoud
      Journal of Medicinal Chemistry (2023), 66 (7), 5041-5060CODEN: JMCMAR; ISSN:0022-2623. (American Chemical Society)
      DCAF1 is a substrate receptor of two distinct E3 ligases (CRL4DCAF1 and EDVP), plays a crit. physiol. role in protein degrdn., and is considered a drug target for various cancers. Antagonists of DCAF1 could be used toward the development of therapeutics for cancers and viral treatments. We used the WDR domain of DCAF1 to screen a 114-billion-compd. DNA encoded library (DEL) and identified candidate compds. using similarity search and machine learning. This led to the discovery of a compd. (Z1391232269) with an SPR KD of 11μM. Structure-guided hit optimization led to the discovery of OICR-8268 (26e) with an SPR KD of 38 nM and cellular target engagement with EC50 of 10μM as measured by cellular thermal shift assay (CETSA). OICR-8268 is an excellent tool compd. to enable the development of next-generation DCAF1 ligands toward cancer therapeutics, further investigation of DCAF1 functions in cells, and the development of DCAF1-based PROTACs.
    36. 36
      Schröder, M.; Renatus, M.; Liang, X.; Meili, F.; Zoller, T.; Ferrand, S.; Gauter, F.; Li, X.; Sigoillot, F.; Gleim, S.; Stachyra, M.-T.; Thomas, J.; Begue, D.; Lefeuvre, P.; Andraos-Rey, R.; Chung, B. Y.; Ma, R.; Carbonneau, S.; Pinch, B.; Hofmann, A.; Schirle, M.; Schmiedberg, N.; Imbach, P.; Gorses, D.; Calkins, K.; Bauer-Probst, B.; Maschlej, M.; Niederst, M.; Maher, R.; Henault, M.; Alford, J.; Ahrne, E.; Hollingworth, G.; Thomä, N. H.; Vulpetti, A.; Radimerski, T.; Holzer, P.; Thoma, C. R. Reinstating Targeted Protein Degradation with DCAF1 PROTACs in CRBN PROTAC Resistant Settings. bioRxiv 2023,  DOI: 10.1101/2023.04.09.536153
      There is no corresponding record for this reference.
    37. 37
      Gironda-Martínez, A.; Donckele, E. J.; Samain, F.; Neri, D. DNA-Encoded Chemical Libraries: A Comprehensive Review with Succesful Stories and Future Challenges. ACS Pharmacol. Transl. Sci. 2021, 4, 1265– 1279,  DOI: 10.1021/acsptsci.1c00118
      37
      DNA-encoded chemical libraries: A comprehensive review with successful stories and future challenges
      Gironda-Martinez, Adrian; Donckele, Etienne J.; Samain, Florent; Neri, Dario
      ACS Pharmacology & Translational Science (2021), 4 (4), 1265-1279CODEN: APTSFN; ISSN:2575-9108. (American Chemical Society)
      A review. DNA-encoded chem. libraries (DELs) represent a versatile and powerful technol. platform for the discovery of small-mol. ligands to protein targets of biol. and pharmaceutical interest. DELs are collections of mols., individually coupled to distinctive DNA tags serving as amplifiable identification barcodes. Thanks to advances in DNA-compatible reactions, selection methodologies, next-generation sequencing, and data anal., DEL technol. allows the construction and screening of libraries of unprecedented size, which has led to the discovery of highly potent ligands, some of which have progressed to clin. trials. In this Review, we present an overview of diverse approaches for the generation and screening of DEL mol. repertoires. Recent success stories are described, detailing how novel ligands were isolated from DEL screening campaigns and were further optimized by medicinal chem. The goal of the Review is to capture some of the most recent developments in the field, while also elaborating on future challenges to further improve DEL technol. as a therapeutic discovery platform.

  • Supporting Information

    Supporting Information

    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.3c00082.

    • Figure showing the domain architecture of human DCAF1 and the WDR fold, figure of the electron density of CYCA-117-70 and its binding site, figure depicting the comparison of CYCA-117-70 with other recently reported DCAF1 ligands (PDF)

    • SMILES string of the 101 compounds computationally selected for experimental testing (XLSX)

    • SPR raw data for CYCA-117-70 (XLSX)


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