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Discovery of All-d-Peptide Inhibitors of SARS-CoV-2 3C-like Protease

  • Raphael J. Eberle
    Raphael J. Eberle
    Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, Germany
    Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225Düsseldorf, Germany
  • Marc Sevenich
    Marc Sevenich
    Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, Germany
    Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225Düsseldorf, Germany
    Priavoid GmbH, Merowingerplatz 1, 40225Düsseldorf, Germany
  • Ian Gering
    Ian Gering
    Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, Germany
    More by Ian Gering
  • Lara Scharbert
    Lara Scharbert
    Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, Germany
    Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225Düsseldorf, Germany
  • Birgit Strodel
    Birgit Strodel
    Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, Germany
    Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225Düsseldorf, Germany
  • Nils A. Lakomek
    Nils A. Lakomek
    Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, Germany
    Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225Düsseldorf, Germany
  • Karoline Santur
    Karoline Santur
    Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, Germany
    Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225Düsseldorf, Germany
  • Jeannine Mohrlüder
    Jeannine Mohrlüder
    Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, Germany
  • Mônika A. Coronado*
    Mônika A. Coronado
    Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, Germany
    *Email: [email protected]
  • , and 
  • Dieter Willbold*
    Dieter Willbold
    Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, Germany
    Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225Düsseldorf, Germany
    *Email: [email protected]
Cite this: ACS Chem. Biol. 2023, 18, 2, 315–330
Publication Date (Web):January 16, 2023
https://doi.org/10.1021/acschembio.2c00735

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

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Abstract

During the replication process of SARS-CoV-2, the main protease of the virus [3-chymotrypsin-like protease (3CLpro)] plays a pivotal role and is essential for the life cycle of the pathogen. Numerous studies have been conducted so far, which have confirmed 3CLpro as an attractive drug target to combat COVID-19. We describe a novel and efficient next-generation sequencing (NGS) supported phage display selection strategy for the identification of a set of SARS-CoV-2 3CLpro targeting peptide ligands that inhibit the 3CL protease, in a competitive or noncompetitive mode, in the low μM range. From the most efficient l-peptides obtained from the phage display, we designed all-d-peptides based on the retro-inverso (ri) principle. They had IC50 values also in the low μM range and in combination, even in the sub-micromolar range. Additionally, the combination with Rutinprivir decreases 10-fold the IC50 value of the competitive inhibitor. The inhibition modes of these d-ri peptides were the same as their respective l-peptide versions. Our results demonstrate that retro-inverso obtained all-d-peptides interact with high affinity and inhibit the SARS-CoV-2 3CL protease, thus reinforcing their potential for further development toward therapeutic agents. The here described d-ri peptides address limitations associated with current l-peptide inhibitors and are promising lead compounds. Further optimization regarding pharmacokinetic properties will allow the development of even more potent d-peptides to be used for the prevention and treatment of COVID-19.

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1. Introduction

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The spread of Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with increasing levels of infectivity and transmissibility, has strained human health and public safety worldwide. (1) By May 2022, the COVID-19 pandemic has resulted in more than 524 million confirmed cases and more than 6.2 million confirmed deaths, according to the World Health Organization (WHO). (2) To date, vaccination is considered the key strategy for ending the pandemic (3) and the worldwide vaccination campaign using clinical safe and efficient vaccines against SARS-CoV-2 have controlled the number of death. However, not yet the spread of the diseases. (4−7) So far, more than 11.8 billion vaccine doses have been administered. (2) Remdesivir, Dexamethasone, Favipiravir, Lopinavir/Ritonavir, Nirmatrelvir/Ritonavir (Main protease inhibitor), and Darunavir have been approved for emergency use to inhibit SARS-CoV-2 infection and replication. (8−12) Given a considerable limitation of direct-acting antivirals for COVID-19 and an increasing presence of SARS-CoV-2 variants (B.1.1.529, B.1.617.2, B.1.1.7, B.1.351, A.23.1, B.1.525, B.1.526 and P.1), (13) it remains a strategic priority to develop new drug candidates with minimal side effects and also targeting the new variants.
For the expression and replication of the CoV gene, proteases play essential roles involving the proteolytic processing of replicase polyproteins, which makes them attractive targets for drug development. (14,15) The main protease, also called 3CL protease (3CLpro), is the 3-chymotrypsin-like cysteine protease that features a His41-Cys145 catalytic dyad. A glutamine (Gln) residue is commonly located at the P1 site on the protease substrate. No known human cysteine protease cleaves after Gln, thus offering potential selectivity for this viral target over human proteases. (16,17) Viral proteases have been attractive targets for oral small-molecule therapies in treating HIV and HCV. (18,19) The use of SARS-CoV-2 3CLpro as a drug target has several advantages over other virus proteins: (i) its essential role in the viral replication process, (ii) its potential for mechanistic safety, and (iii) the expected lack of variant resistance challenges. (8) 3CLpro inhibition represents an attractive approach for a safe and orally available antiviral therapy to treat COVID-19.
Drugs are conventionally classified into two molecule weight classes: “small-molecule” drugs (<500 Da) and protein/peptide-based drugs (>1000 Da). (20) Besides the obvious advantages of small molecules like favorable oral bioavailability and a rational design, (21) they often have low target selectivity, ultimately resulting in side effects. In comparison, peptides were often neglected as potential molecules for drug development, despite binding to their target proteins with high affinity and specificity. They are smaller than proteins and can be obtained synthetically by well-established and cost-efficient methods. (22) The disadvantages of peptide drugs are their potential immunogenicity and low bioavailability due to degradation and short half-lives. Several approaches have been developed to approach these problems. d-Enantiomeric amino acid residues effectively enhance the resistance to degradation since most proteolytic enzymes have substrate specificity for l-peptide bonds, allowing oral administration of d-enantiomeric peptides. (23,24) Furthermore, all-d-peptides are less immunogenic. (25)
Several studies described the development of the first all-d-peptide designed to treat Alzheimer’s disease, and it proved to be efficient in animal models and safe in humans. (26−28) In the case of SARS-CoV-2, several studies describe the identification of d-peptide inhibitors against SARS-CoV-2 Spike protein and 3CLpro. (29−32) Of particular interest are all-d-peptides composed of d-amino acid residues. However, many peptide-based drug development strategies, like phage display selection, end up with l-peptides. So-called d-retro-inverso (d-ri) peptides are composed solely of d-amino acid residues in the reverse sequence of their parental l-peptide (Figure 1). Such a peptide assembled in reversed sequence from d-amino acid residues will have almost the same structure, stability, and bioactivity as its parent peptide made of l-amino acid residues, but with the advantage of being more resistant to proteolytic degradation. This combination makes d-ri peptides attractive drug candidates. (33) The overall orientation of the side chains of the d-ri peptides is very similar to the original l-peptides, which, however, does not lead to perfect d-ri peptides, which is undoubtedly one reason why the retro-inverso approach was not successful in every case. (34−36)

Figure 1

Figure 1. Schematic overview of the investigated d-ri peptides and their mother l-peptides.

However, in this paper, we demonstrate the inhibition potential of l-peptides selected using phage display targeting SARS-CoV-2 3CLpro. Subsequently, its retro-inverso versions were explored, also in combination.

2. Results

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2.1. Characterization of SARS-CoV-2 3CLpro Inhibition by l- and d-Retro-Inverso Peptides

All experiments were performed with recombinant produced SARS-CoV-2 3CLpro protein (Supporting Information, Figure S1). To select binding l-peptides to the 3CLpro, M13 phage display selection was performed (Figure 2A). Detailed information about the phage display process is described in Supporting Information, Text S1, Figures S2 and S3, and Tables S1–S3. To verify whether the selected peptides showed inhibition toward the target protein, they were primary tested using a final concentration of 10 μM of each peptide, separately, in an enzyme-based fluorescence assay (Figure 2B). With more than 60% inhibition, the peptides 3CVL-2 (SPHGWPSQSIEVQPQW), 3CVL-4 (AHEGWTWDWTPQYSWK), and 3CVL-7 (TVAPLHAHYWDVEERH) were selected for further analysis (Figure 2C–E). Concerning their potential to inhibit 100% of the protease activity, determination of the IC50 was performed using 0–140 μM (3CVL-2), 0–120 μM (3CVL-4), and 0–100 (3CVL-7). The 3CVL-2 at a final concentration of 140 μM showed 100% inhibition of the protease (Supporting Information, Figure S4A) with a calculated IC50 value of 4.90 ± 0.8 μM (Table 1 and Figure 2C). In a similar concentration range, the tested 3CVL-4 inhibited 100% of the protease activity at a final concentration of 120 μM (Supporting Information, Figure S4C), presenting an IC50 value of 2.44 ± 0.6 μM (Table 1 and Figure 2D). The selected peptide 3CVL-7 performs even better and inhibits 100% of the recombinant SARS-CoV-2 protease activity at a final concentration of 80 μM (Supporting Information, Figure S4E) with an IC50 value of 0.98 ± 0.2 μM (Table 1 and Figure 2E). Further inhibition mode assays identified 3CVL-2 and 3CVL-7 as competitive inhibitors. These peptides compete with the substrate for the active and/or binding sites of the protease (Supporting Information, Figure S4B,F). Remarkably, the inhibition mode has shown that 3CLpro can be allosterically inhibited by 3CVL-4, which discloses a noncompetitive inhibition mode (Supporting Information, Figure S4D).

Figure 2

Figure 2. Phage display scheme and screening of phage display l-peptides against SARS-CoV-2 3CLpro activity. (A) Schematic diagram of the phage display process. (B) Primary screen of the 11 selected peptides (10 μM) against SARS-CoV-2 3CLpro activity. 3CVL-2, 3CVL-4, and 3CVL-7 inhibit the virus protease activity by more than 60%. (C–E) Selected peptide inhibiting SARS-CoV-2 3Lpro. Dose–response curves for IC50 values of 3CVL-2 (C), 3CVL-4 (D), and 3CVL-7 (E) were determined by nonlinear regression. Data shown are the mean ± standard deviation (SD) from three independent measurements (n = 3). Asterisks mean that the data differ from the control (0 μM inhibitor) significantly at p < 0.05 (*), p < 0.01 (**), and p < 0.001 (***) levels according to analyses of variance (ANOVA) and Tukey’s test.

Table 1. Summary of the SARS-CoV-2 3CLpro Inhibition Experiments by 3CVL-2, -4, and -7
peptideIC50 (μM)inhibition mode
3CVL-24.90 ± 0.8competitive
3CVL-42.44 ± 0.6noncompetitive
3CVL-70.98 ± 0.2competitive
To improve the poor proteolytic stability of l-enantiomeric peptides without losing affinity and binding specificity to their target protein, the peptides 3CVL-2, 3CVL-4, and 3CVL-7 were synthesized in retro-inverse d-enantiomeric (d-ri) form and designated as 3CVLri-2, 3CVLri-4, and 3CVLri-7, respectively. Glycine does not have a chiral center that was the reason that the d-ri peptides have identical amino acid sequences, except for glycine, which was replaced by alanine. Additionally, an arginine was added to the C-termini of each d-ri peptide to increase the permeability of the peptides. The representative scheme is shown in Figure 3A,B.

Figure 3

Figure 3. Principle for obtaining d-retro-inverso peptides and inhibitory effect against SARS-CoV-2 3CLpro activity. (A) Schematic diagram of d-retro-inverso (d-ri) peptides. (B) Sequences of 3CVLri-2, -4, -7 and their mother l-peptides. In the d-ri peptides, glycine (G) was replaced by alanine (a). (C–E) All-d-ri peptides inhibit SARS-CoV-2 3CLpro activity. Dose–response curves for IC50 values of 3CVLri-2 (C), 3CVLri-4 (D), and 3CVLri-7 (E) were determined by nonlinear regression. Data shown are the mean ± SD from three independent measurements (n = 3).

Like the l-peptides, the d-ri-peptides were tested using a concentration range of up to 140 μM (3CVLri-2 and 3CVLri-4) and up to 100 μM (3CVLri-7), which showed 100% inhibition of the protease by the corresponding used final concentration (Supporting Information, Figure S5A,C,E). Based on the enzymatic assay, the IC50 values were determined for each d-ri peptide, displaying for 3CVLri-2 (9.09 ± 0.5 μM), 3CVLri-4 (5.36 ± 1.7 μM) and 3CVLri-7 (1.57 ± 0.2 μM) an increase of the IC50 values of about 2-fold compared to the l-peptides (Figure 3C–E and Table 2). The mode of inhibition of the d-ri peptides was maintained, displaying a competitive mode of 3CLVri-2 and -7 and an allosteric mode of inhibition for 3CVLri-7 (Supporting Information, Figure S5B,D,F). A sequence analysis between the preferred 3CLpro cleavage sequence pattern at the substrate-binding site and the identified inhibitors demonstrated that the competitive inhibitors have a higher similarity (ranging from 62.5 to 12.5%) compared to the noncompetitive inhibitors (no similarity) (Supporting Information, Figure S6).
Table 2. Summary of the SARS-CoV-2 3CLpro Inhibition Experiments by 3CVLri-2, -4 and -7
peptideIC50 (μM)inhibition mode
3CVLri-29.09 ± 0.5competitive
3CVLri-45.36 ± 1.7noncompetitive
3CVLri-71.57 ± 0.2competitive

2.2. Competitive and Noncompetitive d-Retro-Inverso Peptides Act Cooperatively to Inhibit SARS-CoV-2 3CLpro

To presume the effect of the competitive (3CVLri-2 and 3CVLri-7) and noncompetitive (3CVLri-4) d-retro-inverso peptides, a combined inhibitory assay was performed (3VLri-2 + 3CVLri-4 and 3CVL-7 + 3CVL-4). Both peptides were mixed in a 1:1 volume ratio and tested against the protease activity (Figure 4).

Figure 4

Figure 4. SARS-CoV-2 3CLpro inhibition by a combination of competitive and noncompetitive d-retro-inverso peptides simultaneously. Dose–response curves for IC50 values of the combination were determined by nonlinear regression. Data shown are the mean ± SD from three independent measurements (n = 3). The corresponding normalized activity and inhibition plots of SARS-CoV-2 3CLpro under the influence of the combined peptides are shown in the Supporting Information, Figure S7. (A) Dose–response curve of the 3CVLri-2 + 3CVLri-4 combination. (B) Dose–response curve of the 3CVLri-7 + 3CVLri-4 combination.

The calculated IC50 values for the 3CVLri-2 + 3CVLri-4 combination and the 3CVLri-7 + 3CVLri-4 combination were 0.87 ± 0.3 and 0.39 ± 0.1 μM (Figure 4A,B).

2.3. Cooperative Inhibition of SARS-CoV-2 3CLpro by Rupintrivir and 3CVLri-4

Rupintrivir is a known inhibitor of the 3C cysteine protease family but showed a weak inhibitory potential against the SARS-CoV-2 3CLpro. (37−39) The determined IC50 value for Rupintrivir was 109 ± 14.40 μM (Figure 5A) and is in agreement with published data. (34,35) The IC50 value for the Rupintrivir/3CVLri-4 combination was 15.10 ± 1.60 μM (Figure 5B).

Figure 5

Figure 5. SARS-CoV-2 3CLpro inhibition by Rupintrivir and Rupintrivir/3CVLri-4 combination. Dose–response curves of the IC50 values were determined by nonlinear regression. Data shown are the mean ± SD from three independent measurements (n = 3). The corresponding normalized activity and inhibition plots of SARS-CoV-2 3CLpro under the influence of the combined peptides are shown in Supporting Information, Figure S8. (A) Dose–response curve of Rutinprivir. (B) Dose–response curve of the Rutinprivir/3CVLri-4 combination.

2.4. Binding Affinities of Selected 3CLprol- and d-Peptide Inhibitors

The interaction kinetics of 3CVL-2, -4, and -7 with 3CLpro and its d-ri form was determined using surface plasmon resonance (SPR) experiments. 3CLpro was immobilized via covalent primary amino group coupling, and 3CVL and 3CVLri peptides were injected as analytes. Supporting Information Figures S9 and S10 show the SPR sensorgrams for 3CVL peptides at assay concentrations of 50, 25, 12.5, 6.25, 3.12, 1.56, 0.78, and 0.39 μM on 3CLpro immobilized on the CM5 sensor chip. The fit for the competitive inhibitors was made using a heterogeneous ligand model in which the two binding sites on the dimer are assumed to behave differently, as described for the 3CLpro dimer active sites. (40−42) The fit for the noncompetitive inhibitor was made using a 1:1 stoichiometric kinetic fitting.
Heterogeneous ligand fitting revealed dissociation constants from the low μM to low nM range for the higher affine binding site. Global 1:1 fitting showed dissociation constants in the low μM range (Supporting Information, Figures S9 and S10 and Table 3).
Table 3. KD Values Determined by SPR Experiments
heterogeneous ligand fitting
peptideKD1KD2
3CVL-20.5 nM6.5 μM
3CVL-71.8 μM7.8 μM
3CVLri-246.8 μM1.7 μM
3CVLri-72.4 nM35 μM
1:1 ligand fitting
peptideKD
3CVL-45.1 μM
3CVLri-425.2 μM
The binding kinetic parameters of the association rate ka, dissociation rate kd, and dissociation constant KD (kd/ka) for the interaction of 3CLpro with peptides 3CVL-2, -4, -7, and 3CVLri-2, -4, and -7 are shown in Supporting Information, Figures S9 and S10.

2.5. 24 h Stability and Promiscuous Assays of 3CVLri Peptides

Peptide-based inhibitors targeting active proteolytic proteases can lose their inhibitory effect over time. Based on our focus, the retro-inverso peptides were tested regarding their stability over 24 h. The results demonstrated a constant inhibition of SARS-CoV-2 3CLpro over time, showing that the peptides are not prone to the 3CL protease digestion (Supporting Information, Figure S11). Furthermore, a detergent-based control was carried out to exclude peptide inhibitors that possibly act as an aggregator of 3CLpro, so-called “promiscuous” inhibitors. The experiment was performed by adding 0.001, 0.01, and 0.1% of Triton X-100 detergent to the reaction. Suppose that a molecule exhibits significant inhibition of 3CLpro, but this is diminished by detergent; it may act as an aggregation-based inhibitor. This was not observed for the tested 3CVLri peptides (Supporting Information, Figure S12).

2.6. Metabolic Stability of 3CVLri Peptides

To investigate the 3CVLri-2, 3CVLri-4, and 3CVLri-7 peptides’ resistance against enzymatic degradation, the peptides were incubated in media simulating the gastrointestinal tract, blood, and liver, and the unmetabolized peptides were quantified by reversed-phase high-performance liquid chromatography (RP-HPLC). In simulated gastric fluid (SGF), the resistance of 3CVLri-2, 3CVLri-4, and 3CVLri-7 was monitored over 8 h. The results showed that the 3CVLri-4 and -7 peptides are relatively stable (≥80 ± 3%) within 8 h. In contrast, 3CVLri-2 was remarkably metabolized (∼75 ± 2%) (Figure 6A). The resistance of the d-ri peptides was also investigated using a simulated intestinal fluid (SIF). The peptides 3CVLri-2 and 3CVLri-4 remained rather stable over the selected time (≥90 ± 1.5%), as well as 3CVLri-7 (≥85 ± 5%) (Figure 6B). In human plasma, the stability of 3CVLri-2 and 3CVLri-7 was monitored for 48 h, with approximately 5% being metabolized (≥95 ± 4.5%). In contrast, the 3CVLri-4 showed about 21 ± 5.6% of degradation within 48 h (Figure 6C). Furthermore, human liver microsomes were monitored for 24 h, and 3CVLri-2 and 3CVLri-7 were about 5 ± 1.1% metabolized. However, 3CVLri-7 was almost 50% metabolized (≥45 ± 3.7%) in the same period (Figure 6D). The corresponding HPLC profiles of the stability tests are shown in Supporting Information, Figures S13–S16.

Figure 6

Figure 6. Stability of 3CVLri-2, 3CVLri-4, and 3CVLri-7 peptides incubated in SGF, SIF, human plasma, and human liver microsomes. (A) SGF, (B) SIF, (C) human plasma, and (D) human liver microsomes. Unmetabolized peptides were quantified by P-HPLC. The peak areas of the unmetabolized peptides after different incubation times were normalized to the peptides’ peak areas after direct extraction from the media. Data are presented as mean ± SD (n = 3).

2.7. In Silico Structural Properties of 3CVL and 3CVLri Peptides and Their Similar Interaction Mode with SARS-CoV-2 3CLpro

Protease-peptide docking and subsequent molecular dynamics (MD) simulations were performed to investigate the possible binding interface between 3CLpro-L and d-ri-peptide complexes. The secondary structure of the three-dimensional (3D) models of the l-peptides (3CVL-2, -4, and -7) and their d-ri counterparts corroborates with circular dichroism (CD) spectroscopy results.
The models are supported by the results of the inhibition assay. The inhibition mode assay identified two peptides as competitive inhibitors, which requires interaction in the active site and/or in the substrate-binding site. Another peptide was identified as a noncompetitive inhibitor, which requires interaction with an allosteric site. Indeed, the two competitive inhibitors docked in the active site formed a very stable interaction during the MD simulations. The noncompetitive inhibitor was docked in an allosteric site, which was described previously and showed a stable interaction through MD simulation.
During the 1 μs MD simulations of the unbound peptides, they mainly contained a random coiled secondary structure. However, the l- and d-ri-3CVL-4 peptides also frequently formed an α-helix (Supporting Information, Figure S17A–C). The atomic coordinates of SARS-CoV-2 3CLpro dimer (PDB entry: 6M2N) and ten representative structures per peptide from the peptide simulations were submitted for docking using AutoDock Vina. (43)
The competitive peptides 3CVL-2/3CVLri-2 and 3CVL-7/3CVLri-7 were docked into the active site and substrate-binding site of the protease (Supporting Information, Table S4). The noncompetitive peptides 3CVL-4 and 3CVLri-4 were docked in a potential allosteric binding site (Supporting Information, Table S4). Previous studies have indicated that only one active site of the dimer is functional. (40,42) Based on that information, 200 ns control MD simulation of the dimer without ligands was performed to identify a potentially less stable protomer or collapsing binding sites. Based on the volume of the active sites, we did not identify any differences between both protomers (Supporting Information, Figure S18). Therefore, we docked the competitive peptides into the active site of chain A. After docking, 100 ns MD simulations with 3 different initial structures of the 3CLpro/peptide complexes obtained from docking were conducted to further assess their stabilities. The initial structures were selected based on the calculated binding free energy and the distance to the critical catalytic dyad residues (Cys145 and His41). The flexibility of the peptides in complex with the protease was monitored by calculating the root-mean-square deviation (RMSD), root-mean-square fluctuations (RMSF), and ΔH as the enthalpy of binding (Supporting Information, Figures S19–S21). In all three simulations of each 3CLpro/peptide complex, the peptide maintained its overall conformation in the binding pocket and no unbinding event was detected. Representative structures for all three simulations of each l- and d-ri-peptide are shown in Supporting Information, Figures S22 and S23. To investigate the 3CLpro peptide interactions in more detail, we selected one of the three MD simulations per peptide based on several criteria, such as the RMSD, RMSF, and the binding free energy, elongated the simulations to 200 ns and conducted them in triplicate. Each replica of the elongated simulations showed a stable interaction between the l- and d-ri-3CVL peptides and their respective binding site of the 3CLpro dimer throughout the simulation.
To identify the most popular binding modes sampled during the elongated simulations, we applied conformational clustering but considered only the nonflexible/interacting residues (RMSF ≤ 5 Å) of the peptides (Supporting Information, Figure S24).
Interactions between the residues of the peptides and the protease were analyzed by calculating the minimum distance between them for the first three clusters per complex (Table 4).
Table 4. Summary of MD Simulation Triplicates Considering Nonflexible/Interacting Residues of Peptides
peptide#clusterrepresentative frames first 3 clusters (%)
3CVL-2897
3CVL-41288
3CVL-72580
3CVLri-21283
3CVLri-41099
3CVLri-71493
Analysis of the 3CLpro-peptide complexes provides valuable information on the interaction interface similarities between 3CVL and 3CVLri peptides. Structural representation (Figure 7) demonstrated that 3CVL-2 extends along with S1′, S1, and S3 subpockets of the protease substrate-binding site (Figure 7A).

Figure 7

Figure 7. Comparison of the competitive 3CVL and 3CVLri peptide inhibitors binding position in the 3CVLpro active and substrate-binding site. 3CLpro is shown in ribbon view: chain A (gray) and chain B (turquoise). The 3CVL (green) and 3CVLri (orange) peptides are shown as sticks. The surface view demonstrates a preferred position of the peptide in the substrate-binding region, and the single subsites are marked by different colors (S1′: yellow; S1: blue; S2: pink; S3: red). The distance between the 3CLpro substrate-binding site residues and the peptides shows that active site residues His41 and Cys145 are blocked by the inhibitors: (A) 3CVL-2, (B) 3CVLri-2, (C) 3CVL-7, (D) 3CVLri-7.

In contrast, the 3CVLri-2 interacts with the substrate-binding site differently. However, it extends along with S1′, S1, S2, and S3 (Figure 7B), which shows that one of the d-Gln residues of the 3CVLri-2 is very well accommodated. The 3CVL-7 and 3CVLri-7 interaction areas comprise residues of the S1′, S1, S2 and S3 subsites (Figure 7C,D). The graphs in Figure 7 that show the minimum distance between peptide and 3CLpro residues reveal that all analyzed peptides formed several interactions with 3CLpro active/binding site residues. The localization in the substrate-binding region of the 3CVL peptides and their d-ri counterparts, even when it does not interact with the catalytic dyad (His41 and Cys145), showed the potential to block the entrance of the substrate.
To investigate the impact of the competitive inhibitors 3CVL-2, 3CVLri-2, 3CVL-7, and 3CVLri-7, two-dimensional NMR 1H-15N correlation spectra of 100 μM 15N 3CLpro were recorded in the presence of an equimolar amount of the respective peptides (1:1 ratio). Supporting Information, Figure S25A shows the reference spectrum of 15N 3CLpro in the absence of the peptides. Spectral resonances are well resolved and display the properties of a globular protein containing both β-sheets and α-helices. Several other 15N 3CLpro resonances appear in the presence of 3CVL-2 (orange spectrum) compared to the reference spectrum (black) (Supporting Information, Figure S25B). That could point either to a second 3CLpro conformation becoming visible or previously invisible parts of 3CLpro becoming more dynamic and visible in the presence of the peptide. The effect seems less intense for 3CVLri-2 (Supporting Information, Figure S25C, light blue spectrum).
The NMR spectra of 15N 3CLpro in the presence of 3CVL-7 (Supporting Information, Figure S26B) look very similar to those for 3CVL-2; several new resonances of 15N 3CLpro appear in the presence of 3CVL-7 and 3CVLri-7 (Supporting Information, Figure S26B,C), indicating higher conformational or overall dynamics.
Contrary, the noncompetitive inhibitors 3CVL-4 and 3CVLri-4 were docked in a suggested allosteric site at the interface of the protomers. Structural representation of the interfaces of 3CLpro in complex with the 3CVL-4 and 3CVLri-4 peptides are shown in Figure 8A,B.

Figure 8

Figure 8. Comparison of the noncompetitive 3CVL-4 and 3CVLri-4 peptide inhibitors in a supposed 3CLpro allosteric site. 3CLpro is shown in ribbon view, and each protomer is colored differently: chain A (gray) and chain B (turquoise). 3CVL (green) and 3CVLri (orange) peptides are shown as sticks. The surface view demonstrates a preferred position of the peptide in the proposed allosteric site in a cleft between both protease protomers: (A) 3CVL-4 and (B) 3CVLri-4.

Analysis of the distance between residues of the allosteric site of each protomer demonstrated that several residues of both protomer chains interact with the protease, in particular Lys12, 97, and 100 (Figure 8).
MD simulations of 3CVL-4 and 3CVLri-4 in a proposed allosteric site showed that this interaction induces conformational changes in the protease’s active site.
Determination of B-chain active site volume demonstrates a reduction compared to the control simulation due to the binding of 3CVL-4 and 3CVLri-4, respectively (Figure 9A–C and Supporting Information, Figure S27).

Figure 9

Figure 9. Comparison of the active site volumes of control and after binding of the noncompetitive 3CVL-4 and 3CVLri-4 peptide inhibitors in a supposed 3CLpro allosteric site. 3CLpro is shown in surface view, and each protomer is colored differently: chain A (gray) and chain B (turquoise). The substrate-binding subsites are colored differently. The active sites and substrate-binding sites of each protomer are shown enlarged with the corresponding volume in Å3. Amino acids forming the active site and substrate-binding site are shown in sticks, red arrows demonstrate the change of the position of the residues because of the conformational change induced by the noncompetitive inhibitors: (A) 3CLpro, (B) 3CLpro and 3CVL-4, (C) 3CLpro and 3CVLri-4.

To determine the contribution of specific residues of the 3CVL and 3CVLri peptides to the interaction with 3CLpro we performed a mutational analysis using alanine scanning. Alanine mutations that result in positive ΔΔH values relative to the parent structure suggest loss of binding, which indicates that the mutated amino acid was involved in an overall favorable network of electrostatic interactions. Mutations that result in negative ΔΔH suggest gain of binding, which indicates that the mutated amino acid was involved in an overall unfavorable network of electrostatic interactions.
In the case of 3CVLri-2 and its parental l-peptide Gln9 and Gln8, respectively, are important for the interaction with 3CLpro. The Ser8 residue (Ser7 in the l-peptide) situated at the P2 position seems to have a weak effect on the interaction with 3CLpro (Supporting Information, Figure S28A). The glutamine residues are in the P1 position of the peptides, as in the substrate already described for the protease, the P2 site has a preference for hydrophobic residues (Supporting Information, Figure S6). Important residues in the interaction between 3CVLri-7 (3CVL-7) and 3CLpro are His9 (His6) and His11 (His8), unfavorable residues are Asp6 (Asp11) and Ala10 (Ala7) (Supporting Information, Figure S28A). In the case of the noncompetitive inhibitors, 3CVLri-4 (3CVL-4), Trp8 (Trp9), and Tyr4 (Tyr13) are mainly involved in the interaction with the virus protease. Interestingly, Asp8 in the mother l-peptide (Supporting Information, Figure S28B) is unfavorable for the interaction, but the corresponding residue in the d-ri peptide is involved in the interaction with 3CLpro.

3. Discussion and Conclusions

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Even with the development of several vaccines against SARS-CoV-2, the development of suitable therapeutics against COVID-19 is important since no causative therapy is known. Instead of inhibiting the interaction of the spike protein with the cell receptors, (44,45) we intend to target one of the most important proteases in virus replication, the 3CL protease, by d-enantiomeric peptide ligands preventing the cleavage function of 3CLpro. To this end, a phage display selection was performed. The identified l-peptides showed a strong inhibitory effect in the enzyme activity assay, with IC50 values varying between 0.9 and 9 μM. As we demonstrated experimentally, 3CVL-4 is a noncompetitive inhibitor with an IC50 value of 2.4 μM, which was maintained in its d-enantiomer retro-inverso form; however, with an increased IC50 value of about 2-fold (5.4 μM). A 1:1 molar combination of the competitive and noncompetitive d-ri-peptides reduced the IC50 values remarkably. 3CVLri-2 + 3CVLri-4 combination reduces the IC50 value to 0.9 μM and the combination of 3CVLri-7 + 3CVLri-4 to 0.4 μM. It was clearly demonstrated that a combination of competitive and noncompetitive inhibitors improved the inhibitory potential of all d-peptides. Similarly, in combination with Rupintrivir, a weak SARS-CoV-2 3CLpro competitive inhibitor (IC50 > 100 μM), the inhibitory effect improved after the combination with the noncompetitive 3CVLri-4 (IC50 of 15.10 ± 1.60 μM). The cooperative inhibition of SARS-CoV-2 3CLpro by competitive and noncompetitive inhibitors was also observed for Quinacrine/Suramin. (42) The structural analysis of 3CLpro in complex with the studied peptides by docking followed by molecular dynamics simulation predicted a stable conformation for each complex, indicating significant intermolecular interaction. Of note, when not directly interacting with the catalytic dyad, amino acid residues of the peptide are located close to the pocket entry, blocking the substrate entrance. The predicted structures of 3CLpro in complex with 3CVLri-2 indicate the occurrence of significant intermolecular interaction between the d-glutamine of the all-d-ri peptide and amino acid residues of the S1 subpocket, which explains the strong preference for d-glutamine at P1. (46)
A combination of competitive and noncompetitive d-ri-peptides (3CVLri-2/3CVLri-4 and 3CVLri-7/3CVLri-4) increased the inhibitory effect against 3CLpro.
The all-d-peptides 3CVLri-2 and -7 showed a remarkable resistance against metabolization over 8 h.
Two-dimensional 1H-15N TROSY-NMR correlation spectra of 15N 3CLpro with 3CVL-2/3CVLri-2 and 3CVL-7/3CVLri-7 demonstrated the appearance of new and similar resonances, which may indicate the same binding region for the peptides.
Our study yielded all-d-peptides with a promising potential for further development toward therapeutic use against SARS-CoV-2 3CLpro.

4. Materials and Methods

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4.1. Peptides

All peptides were purchased from Caslo (Lyngby, Denmark) as a lyophilized powder with >98% purity. Peptides 3CVL 1–10 consist of 16 amino acid residues in l-configuration with amidated C-termini. The d-retro-inverso (d-ri) peptides 3CVLri-2, 3CVLri-4, and 3CVLri-7 have identical amino acid sequences, except for glycine, which was replaced by alanine in the case of 3CVLri-2 and 3CVLri-4. All amino acid residues in d-configuration and with a reversal peptide bond. Additionally, an arginine was added to the C-termini of each ri-peptide resulting in 17 amino acid residues. The C-termini of the d-ri-peptides are amidated, and the N-termini are acetylated. An analysis certificate from Caslo (Lyngby, Denmark) demonstrated the purity of each peptide (Supporting Information, Figures S29–S32 and Table S5). High-performance liquid chromatography (HPLC) analysis was performed with a C18 column. For the mass spectrometry certificate, the matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) method was used.

4.2. Protein Expression and Purification

SARS-CoV-2 3CLpro (Uniprot entry: P0DTD1, virus strain: hCoV-19/Wuhan/WIV04/2019) was cloned, expressed, and purified as described previously. (41)

4.3. Phage Display

Two independent phage display selections (selection A and B), each with three rounds, were performed to obtain peptides that bind 3CLpro. Therefore, in both selections, 100 μg/mL purified 3CLpro diluted in 100 μL of 20 mM potassium phosphate buffer pH 7.5 with 150 mM sodium chloride were immobilized on an amino plate (Nunc Amino Immobilizer 96-well plates, polystyrene; Thermo Scientific, Waltham). After 30 min at room temperature (RT), the 3CLpro solution was removed, and the surface was quenched with 1 M ethanolamine pH 8.5 (Cytiva; Chicago) for 1 h at RT. Additionally, the surface was blocked with 200 μL of 10 mg/mL BSA in 20 mM potassium phosphate buffer pH 7.5 with 150 mM sodium chloride in the first round and with 200 μL of 10 mg/mL milk powder in 20 mM potassium phosphate buffer pH 7.5 with 150 mM sodium chloride in the second round to reduce unspecific binding events. In the third round, the surface was not additionally blocked to vary the selection conditions. The surface was washed six times with 200 μL of washing buffer (20 mM potassium phosphate buffer pH 7.5 with 150 mM sodium chloride and 0.05% tween-20). During the first and second rounds, 2 mg/mL BSA or milk powder was added to the washing buffer, respectively. Then, 7.2 × 1010 phages (TriCo-16 phage display peptide library; Creative Biolabs, New York) in 100 μL of the corresponding washing buffer were added and incubated for 20 min at RT with the exception of the third round of selection B. Here, the incubation time was extended to 30 min. After removing of nonbinding phages, the well was washed with 200 μL of the respective washing buffer of the corresponding round. The number of washing steps was varied in each selection round (selection A: 2-5-5; selection B: 5-8-10). Elution of phages was performed by incubation with 100 μL of 0.2 M Glycine-HCl pH 2.2 for 10 min at RT. The phage-containing solution was then removed from the plate and neutralized by transferring it to a tube containing 25 μL of 1 M Tris-HCl pH 9.1. To determine the output titer, 5 μL of eluted phages were used. Therefore, a dilution series from 10-2 to 10-8 was prepared with the eluted phages in a total volume of 100 μL of LB medium. Each dilution was mixed with 100 μL of Escherichia coli K12 ER2738 (OD600 of 0.6) and plated with 800 μL of top agar on plates (35 × 10 mm; Sarstedt, Nümbrecht, Germany) containing LB-Agar-IPTG-XGal. After overnight incubation at 37 °C, the plaques were counted to determine the output titer.
The remaining eluted phages (120 μL) were amplified in 20 mL of E. coli K12 ER2738, starting with an OD600 of 0.1 for 3.5 h at 37 °C and 120 rpm. The culture was then centrifuged for 20 min at 2700g and 4 °C. The pellet was discarded, and the supernatant was incubated with 7 mL of PEG-8000/2.5 M sodium chloride overnight at 4 °C for phage precipitation. Subsequently, the solution was centrifuged for 1 h at 4 °C, and 2700g. The phage-containing pellet was dissolved in 1 mL of 1 × phosphate-buffered saline (PBS) before another centrifugation step at 4 °C and 11,000g for 5 min to get rid of residual bacterial components. Afterward, the supernatant was added to 200 μL of PEG-8000/2.5 M sodium chloride and incubated for 1 h on ice, followed by final centrifugation for 45 min at 2700g and 4 °C. The phage-containing pellet was properly resuspended in 100 μL of 1 × PBS. The input titer was determined by spectrophotometry (40) in 1 × PBS using a 1:10 dilution. The resulting phages were then used in the next selection round with the same phage amount as before and further used for an enrichment enzyme-linked immunosorbent assay (ELISA) and prepared for next-generation sequencing (NGS) analysis.
Besides the primary selection with 3CLpro as the target (Target Selection, TS), two control selections were performed as described before (46) to enable an efficient evaluation of the selection success and to facilitate further the identification of target-binding peptides over those peptides that have accumulated, e.g., because of an affinity to the surface. The first control selection, called Empty Selection (ES), was performed in the same way as TS but without 3CLpro. During immobilization, 20 mM potassium phosphate buffer pH 7.5 with 150 mM sodium chloride was used instead of 3CLpro. One further control, Direct Control (DC), was performed starting from the second round without 3CLpro, but in contrast to ES, the phages resulting from each round of TS were used for the following selection rounds.

4.4. Enrichment ELISA

An enrichment ELISA was performed to validate the success of the phage display selection. Therefore, 100 μg/mL 3CLpro diluted in 100 μL of 20 mM potassium phosphate buffer pH 7.5 with 150 mM sodium chloride was immobilized on an amino plate (Nunc Immobilizer Amino 96-well plate, polystyrene; Thermo Scientific, Waltham) for 30 min at RT. Noncoated wells (20 mM potassium phosphate buffer pH 7.5 with 150 mM sodium chloride without 3CLpro) were used as a control for each selection round. The solution was removed, and the surface was quenched with 200 μL of 1 M ethanolamine pH 8.5 (Cytiva; Chicago) for 1 h at RT, followed by a blocking step with 200 μL of blocking solution (10 mg/mL BSA in 20 mM potassium phosphate buffer pH 7.5 with 150 mM sodium chloride) for 15 min at RT. After three washing steps with 200 μL of washing buffer (150 mM sodium chloride, 0.05% Tween-20, 2 mg/mL BSA in 20 mM potassium phosphate buffer pH 7.5), the amplified and purified phages of each target selection round were diluted in washing buffer to a total amount of 5 × 1010 phages in 100 μL. The wells immobilized with 3CLpro and noncoated wells were incubated with the corresponding phage solution for 1 h at RT. Unbound phages were removed by five washing steps with 150 μL of washing buffer. The anti-M13 antibody (HRP conjugated mouse monoclonal antibacteriophage M13 antibody; Sino Biological, Peking, China) was diluted in washing buffer to a final concentration of 0.35 ng/μL, and 100 μL was added to each well for 1 h at RT. After 6 washing steps with 150 μL of washing buffer, the supernatant was removed entirely, and 100 μL of the 3,3′,5,5′-tetramethylbenzidine (TMB) solution (TMB was previously dissolved in 1 mL of dimethyl sulfoxide (DMSO) and diluted with 9 mL of 0.05 M phosphate citrate buffer pH 5) were added into each well. After 2.5 min incubation at RT, the reaction was stopped with 100 μL of 2 M H2SO4. The absorption was quantified at 450 nm using a microplate reader (BMG Labtech, Ortenberg, Germany).
In addition to the samples from the target selection (TS1, TS2, TS3), the library and the wild-type phage without peptides were analyzed. The measurement was performed in single determination to save protein samples. Noncoated wells that were incubated with washing buffer without phages were used as a control for the background signal.

4.5. Sequence Analysis

Extraction and the amplification of the single-stranded phage DNA from each sample from the phage display selection were purified and prepared for NGS analysis as described previously. (46) NGS analysis was performed by the Biologisch-Medizinisches Forschungszentrum at the Heinrich Heine University Düsseldorf, Germany. The resulting sequences were further processed and evaluated with the targeted sequencing analysis tool (TSAT) and the open-source software Hammock for subsequent sequence alignment. (47,48)

4.6. SARS-CoV-2 3CLpro Activity Assay

SARS-CoV-2 3CLpro activity assay was performed as described previously. (42,49−51) A fluorogenic substrate DABCYL-KTSAVLQ↓SGFRKME-EDANS (Bachem, Bubendorf, Switzerland) was used, and the assay buffer contained 20 mM Tris pH 7.2, 200 mM NaCl, 1 mM ethylenediaminetetraacetic acid (EDTA), and 1 mM tris(2-carboxyethyl)phosphine (TCEP). The reaction mixture was pipetted in a Corning 96-Well plate (Merck, Darmstadt, Germany) consisting of 0.5 μM 3CLpro. The assay was initiated with the addition of the substrate at a final concentration of 50 μM. The fluorescence intensities were measured at 60 s intervals over 30 min using an Infinite 200 PRO plate reader (Tecan, Männedorf, Switzerland), and the temperature was set to 37 °C. The excitation and emission wavelengths were 360 and 460 nm, respectively.

4.7. SARS-CoV-2 3CLpro Inhibition Assay

Inhibition of SARS-CoV-2 3CLpro activity by 3CVL-1 to 10 and 3CVLri-2, -4, and -7 was investigated using the described activity assay. The l-Peptides (10 μM) were used for a preliminary screening test. For the final inhibition assays, 0.5 μM protein was incubated with 0–140 μM 3CVL-2, 0–120 μM 3CVL-4, and 0–100 μM 3CVL-7. The corresponding 3CVLri peptides were incubated with the same inhibitor concentrations, except for 3CVLri-4 (0–140 μM). The mixtures were incubated for 30 min at RT. When the substrate with a final concentration of 50 μM was added to the mixture, the fluorescence intensities were measured at 60 s intervals over 30 min using an Infinite 200 PRO plate reader (Tecan, Männedorf, Switzerland). The temperature was set to 37 °C, and the excitation and emission wavelengths were 360 and 460 nm, respectively. Inhibition assays were performed as triplicates.
For the 3CVLri-2 + 3CVLri-4 and 3CVLri-4 + 3CVLri-7 combination tests, a 1:1 stock solution of the molecules was prepared. Protein (0.5 μM) was incubated with 0–75 μM (3CVLri-2 + 3CVLri-4) and 0–50 μM (3CVLri-4 + 3CVLri-7) of the combined molecules. The IC50 values were calculated by plotting the initial velocity against various concentrations of the peptides using a dose–response curve in GraphPad Prism software version 8 (San Diego, CA). Data are presented as mean ± SD.

4.8. Determination of Inhibition Mode

The inhibition mode was determined using different final concentrations of the inhibitors and substrate. Briefly, 0.5 μM SARS-CoV-2 3CLpro was incubated with the inhibitor, in various concentrations, for 30 min at RT. Subsequently, the reaction was initiated by adding the corresponding concentration series of the substrate. The data were analyzed using a Lineweaver–Burk plot; therefore, the reciprocal of velocity (1/V) vs the reciprocal of the substrate concentration (1/[S]) was compared. (52,53) All measurements were performed in triplicate, and data are presented as mean ± SD.

4.9. Inhibitor Stability over 24 h

Stable inhibition of SARS-CoV-2 3CLpro by 3CVLri-2, -4, and -7 was followed via a 24 h inhibition experiment. Briefly, 0.5 μM SARS-CoV-2 3CLpro was incubated with 5 μM (d-ri peptides 2 and 4) and 20 μM (d-ri peptide 7) and incubated for 1/2, 1, 2, 3, 4, 5, and 24 h at RT. The control was performed with 3CLpro without the peptides and measured together after each time point. Subsequently, the reaction was initiated by the addition of the substrate. All measurements were performed in triplicate, and data are presented as mean ± SD.

4.10. Assay to Exclude d-ri Peptides as Promiscuous Inhibitors

A detergent-based control assay was performed to exclude inhibitors that possibly act as promiscuous aggregators of the 3CLpro by adding 0.001, 0.01, and 0.1% of Triton X-100 to the reaction. (54) Four concentrations of 3CVLri-2 (1, 5, 10, and 20 μM), 3CVLri-4 (1, 5, 10, and 20 μM), and 3CVLri-7 (0.25, 0.5, 1, and 5 μM) were tested. All measurements were performed in triplicate, and data are presented as mean ± SD.

4.11. Circular Dichroism Spectroscopy

Circular dichroism (CD) measurements were carried out with a Jasco J-1100 spectropolarimeter (Jasco, Germany). Far-UV spectra were measured between 190 to 260 nm using 5 μM 3CLpro in 20 mM K2HPO4/KH2PO4 pH 7.4 and a single peptide concentration of 30 μM in H2O. The secondary structure of 3CVL-2, 3CVL-4, 3CVL-7, and the related d-ri peptides was checked. A 1 mm path length cell was used for the measurements; 15 repeat scans were obtained for each sample, and five scans were conducted to establish the respective baselines. The averaged baseline spectrum was subtracted from the averaged sample spectrum. The results are presented as molar ellipticity [θ], according to eq 1
[θ]λ=θ/(c*0.001*l*n)
(1)
where θ is the ellipticity measured at the wavelength λ (deg), c is the peptide concentration (mol/L), 0.001 is the cell path length (cm), and n is the number of amino acids.

4.12. Surface Plasmon Resonance Measurements

Surface plasmon resonance (SPR) measurements with 3CVL peptides and SARS-CoV-2 3CLpro were performed using a Biacore 8K device (GE Healthcare, Uppsala, Sweden). Recombinant 3CLpro was immobilized on a CM5 sensor surface (GE Healthcare, Uppsala, Sweden) via covalent coupling of primary amino groups. The protease was diluted to a final concentration of 15 μg/mL in 10 mM NaAc pH 5.5 and injected for 600 s at 10 μL/min to reach a final immobilization level of 2700 to 3000 RU. After quenching with Ehanolamine at pH 8.5, kinetic experiments were performed as parallel injections on all eight flow channels. 3CVL and 3CVLri peptides were diluted in 20 mM Tris pH 7.4, 100 mM NaCl, 1 mM EDTA and 0.05% Tween-20 in the concentration range of 0.03–50 μM with a 1:1 dilution series and injected at 30 μL/min. Surface regeneration was performed after dissociation ended for 2 × 45 s at 30 μL/min using an acidic regeneration solution consisting of 150 mM oxalic acid, 150 mM phosphoric acid, 150 mM formic acid, and 150 mM malonic acid at pH 5.0. Data evaluation was performed using a heterogeneous ligand fit model for the competitive inhibitors with Biacore Insight Evaluation Software v3.0.12 (GE Healthcare, Uppsala, Sweden). The heterogeneous ligand model assumes analyte binding at two independent ligand sites. Each ligand site binds the analyte independently and with a different rate constant. A global 1:1 kinetic fit model was used for the noncompetitive inhibitor.

4.13. Nuclear Magnetic Resonance

Two-dimensional 1H-15N TROSY-NMR correlation spectra (55) of 100 μM 15N 3CLpro dissolved in 20 mM MES pH 6.5 buffer containing 150 mM NaCl were recorded in the presence and absence of 3CVL-2 and 3CVLri-2 as well as 3CVL-7 and 3CVLri-7 peptides. Experiments for 3CVL-4 and 3CVLri-4 could not be performed because of the peptide’s instability at pH 6.5. Spectra were recorded on a Bruker 1.2 GHz AVANCE neo spectrometer equipped with a triple-resonance 1H,15N,13C cryogenic probe. The experimental temperature was 25 °C. Spectral dimensions were 16.02 ppm (1H) × 32.89 ppm (15N), with 1024 complex points recorded in the 1H dimension and 64 complex points recorded in the 15N dimension, and the Echo/Anti-echo scheme used for quadrature detection, resulting in an acquisition time of 53.2 ms in the 1H dimension and 16 ms in the 15N dimension. The carrier was positioned at 4.7 ppm in the 1H dimension and 117 ppm in the 15N dimension; 512 scans were recorded for each increment, with a recovery delay of 1 s between scans, resulting in an overall experimental time of 19.7 h per spectrum.
To study the interaction between 3CLpro and the various peptides, only the protease was 15N-labeled and, therefore, NMR-visible, while the peptides were unlabeled and invisible in the 1H-15N correlation spectra. For the interaction study, 100 μM 15N-labeled 3CLpro was mixed with an equimolar amount of the respective peptide. Spectra were overlayed and compared to the reference spectrum of 3CLpro (equal final concentration, in the absence of the peptide). Spectra analysis was performed using the software Bruker Topspin 4.1.3. and CCPN Analysis 3.0. (56)

4.14. Metabolic Stability

Metabolic stability experiments of the 3CVLri peptides were performed as described before. (57,58) Solutions simulating gastric and intestinal fluid (SGF and SIF) were prepared according to the European Pharmacopoeia 7.0. Human plasma samples were purchased from Innovative Research (Novi, Michigan), and human liver microsomes were purchased from Sekisui XenoTech (Kansas City; H1000). The liver microsomes were diluted in an NADPH regenerating system (NRS). For the stability tests, 150 μM 3CVLri peptides were incubated in SGF, SIF, human plasma, and human liver microsomes in triplicate at 37 °C with slight shaking for different periods. The peptides were extracted by precipitating the proteins with a solution containing acetonitrile and 0.5% formic acid. Afterward, the mixture was mixed and heated for 5 min at 95 °C. The sample was subsequently centrifuged at 14.000g for 10 min at 4 °C. The extracted peptides are in the supernatant and were analyzed by reversed-phase high-performance liquid chromatography (RP-HPLC). The RP-HPLC system (Agilent Technologies, Santa Clara; 1260 series) consisted of an autosampler, quaternary pump, a thermostatted column compartment, and a variable-wavelength detector. Chromatography was performed with a C18 column (Agilent Technologies, Santa Clara; ZORBAX 300SB-C18 5 μm, 4.6 × 250 mm) at 25 °C and 214 nm with a flow rate of 1 mL/min. The sample injection volume was 20 μL. Chromatograms were recorded and analyzed with the Agilent software OpenLab version 2–5. Mobile phases were acetonitrile + 0.1% trifluoroacetic acid (TFA) (A) and water + 0.1% TFA (B) for sample analysis from stability tests. The samples were measured with an initial isocratic step at 15% solvent A for 3 min followed by a gradient elution to 45% A in 15 min. All measurements were performed in triplicate, and data are presented as mean ± SD.

4.15. Statistical Analysis

All data are expressed as the mean ± standard deviations (SDs). The statistical significance of the mean values’ differences was assessed with one-way analyses of variance (ANOVA), followed by Tukeys’ multiple comparison test. Significant differences were considered at p < 0.05 (*), p < 0.01 (**), and p < 0.001 (***). All statistical analyses were performed with GraphPad Prism software version 8 (San Diego, CA).

4.16. Docking and Molecular Dynamics

4.16.1. Peptide and Protein Structure Preparation

The peptide models were constructed as linear chains using the python module PeptideBuilder. (59) To obtain d-retro-inverso (d-ri) peptides, the peptides were first generated as l-retro-inverso molecules, followed by the inversion of their stereochemical configuration by flipping the structure files with cartesian coordinates along the x-axis. The sequences of the peptides are shown in Table 5.
Table 5. Sequences of 3CVL and 3CVLri Peptide Models
peptidesequence
3CVL-2SPHGWPSQSIEVQPQW
3CVL-4AHEGWTWDWTPQYSWK
3CVL-7TVAPLHAHYWDVEERH
3CVLri-2wqpqveisqspwahpsr
3CVLri-4kwsyqptwdwtwaehar
3CVLri-7hreevdwyhahlpavtr
The initial structural data of the 3CLpro dimer were obtained from a crystal structure (PDB: 6M2N).

4.16.2. Docking of the Peptides against the 3CLpro Dimer

3CVL-2, 3CVL-7, 3CVLri-2, and 3CVLri-7 were docked against the active site of chain A of the 3CLpro dimer (PDB: 6M2N), which can be defined by the residues His41, Met49, Tyr54, Phe140, Leu141, Asn142, Gly143, Ser144, Cys145, His163, His164, Met165, Glu166, Leu167, Asp187, Arg188, Gln189, Thr190, and Gln192. (60)
3CVL-4 and 3CVLri-4 bind 3CLpro allosterically, and both peptides were docked against the potential binding site residues Lys12, Lys97, Lys100, Tyr101, Lys102, Phe103, Val104, and Arg105. (36) Autodock Tools (61,62) were used to generate a docking grid around the positions of the binding site residues of the 3CLpro dimer. The grid was centered at x,y,z-position of −33.572, −62.854, 40.9 (Active site) and −49.16, −47.323, 38.911 (Allosteric binding site). After adding hydrogen atoms to the 3CLpro dimer and the peptides, Gasteiger partial charges were computed and added to the dimer and the peptides using AutoDock Tool. The protein and peptide models were saved in the PDBQT format. The docking was then performed using AutoDock Vina, (43) which treats the ligand as flexible while keeping the receptor rigid.

4.16.3. Simulation Setup and Production Run

All MD simulations and analyses were performed with GROMACS 2021.4. (63) AMBER14SB (64) was used as a force field, and the TIP3P water model (65) for explicit water simulation. The peptides and the protein–peptide complexes were centered in a dodecahedron box with 240.8–370.5 and 872.5 nm3, solvated with water, and neutralized with Na+ and Cl ions resulting in a system size of ∼23,500–36,000 and ∼86,000 atoms, respectively.
The systems’ energy was minimized using the steepest descent algorithm. (66) The systems were then equilibrated in two steps. First, a 0.1 ns simulation was performed in the NVT ensemble, and second, a 1 ns simulation in the NpT ensemble at 310 K─velocity-rescaling thermostat (67) and 1.0 bar─Berendsen barostat. (68) First, 1 μs peptide MD simulations were performed. After the peptides were docked against the 3CLpro dimer, we performed 100 ns of protein–peptide complexes with the temperature set to 310 K (Nosé–Hoover thermostat) (69) and the pressure set to 1 bar (Parinello–Rahman barostat). (70) One selected simulation of each protein–peptide complex was then extended to 200 ns and ran in triplicates.
Electrostatic interactions were processed with the particle-mesh Ewald method, (71,72) combined with periodic boundary conditions and a real-space cutoff of 12 Å. The same cutoff was applied to Lennard-Jones (LJ) interactions. The leapfrog stochastic dynamics integrator was used with a time step of 2 fs to integrate the equations of motion. During the MD simulations, all bond lengths were constrained using the LINCS algorithm. (73)

4.16.4. Molecular Dynamics Analysis

The peptide’s root means square deviation (RMSD) indicates how flexible the ligand is within the binding site. The protein structures sampled during the MD simulations were aligned to the MD starting structure, and then the RMSD was calculated for the ligand using gmx rms.
To investigate the effect of the allosteric peptides and the occupation of the peptides that bind to the active site, we used the VMD plugin Epock 1.0.5 to evaluate the pocket volume of both active sites of the dimer for 1000 snapshots of the whole MD simulation after fitting the protein to the starting structure. (74) The following simulations were conducted for the second half of the MD simulations: The root-mean-square fluctuation (RMSF) was determined for each peptide with gmx rmsf to identify the flexible and rigid areas of the peptide within the protein–peptide complex, and the average minimum distance between specific residues of the protein and peptide was determined using gmx mindist.
We used the MM/PBSA method as implemented in gmx_mmpbsa to calculate the binding free energy ΔGbind between the protein and the peptide and to perform alanine scanning. (75) The linear PB equation was solved using the level set function to build the dielectric interface between the solvent and solute. (76) The ionic strength was set to 150 mM. The total nonpolar solvation free energy was modeled as a single term linearly proportional to the solvent-accessible surface area. (77) Here, the binding free energy is defined as
ΔGbind=GcomplexGreceptorGligand
with ⟨⟩ indicating the average of over 500 snapshots. The free energy for each entity is given as
Gx=Ebonded+Enonbonded+Gpolar+GnonpolarTS
where Ebonded describes the bonded interactions, Enonbonded indicates the nonbonded, i.e., the Coulomb and Lennard-Jones interactions, and Gpolar and Gnonpolar are the polar and nonpolar contributions to the solvation free energy, respectively. The TS term includes the absolute temperature, T, and the entropy, S.
ΔGbind can also be stated as
ΔGbind=ΔHTΔS
with ΔH the enthalpy of binding and −TΔS the conformational entropy after ligand binding. To determine the entropic component, gmx_mmpbsa provides different methods, such as quasi-harmonic (QH) approximation, interaction entropy (IE), or C2 entropy approach. However, these methods were inappropriate for our system, as they either required a large number of snapshots (≥3N, N = number of complex atoms) or a standard deviation of the interaction energy (sIE) of <3.6 kcal/mol to obtain realistic entropies. Thus, we decided to omit the entropic term, as ΔH is usually sufficient to compare a set of similar ligands by their relative binding free energies. (75) To elucidate the contribution of each peptide residue to the binding free energy, we performed alanine scanning using gmx_mmpbsa. Each residue of the 3CVL and 3CVL-ri peptides (Except Gly and Pro) was substituted by Ala and the enthalpy of binding difference ΔΔHX->A upon alanine mutation was calculated in the following way
ΔΔHX>A=ΔHAΔHX
To identify the peptide structures with the most populated configuration or binding pose in the protein–peptide complex, the algorithm of Daura et al. was used as implemented in GROMACS (gmx cluster). (78)
The cutoff for clustering the peptide configurations was set to 3.5 Å, and the average structures of the 10 most populated clusters were used for docking against the dimer of the main protease. The binding poses of the peptides in the elongated protein–peptide complex simulations were clustered using a cutoff of 3.0 Å. To avoid biasing of nonbinding flexible areas of the peptide, only those peptide residues with an RMSF of <6 Å were considered for clustering. The three most prominent clusters were then analyzed for interacting residues by generating a contact map.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acschembio.2c00735.

  • Additional experiments and supplementary figures (phage display outcome, inhibitor tests, SPR analysis, HPLC profiles, NMR spectra, and MD analysis) and tables (analysis of peptide sequences selected by phage display and list of amino acids forming the 3CLpro substrate-binding site and allosteric site) (PDF)

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Author Information

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  • Corresponding Authors
    • Mônika A. Coronado - Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, Germany Email: [email protected]
    • Dieter Willbold - Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, GermanyInstitut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225Düsseldorf, GermanyOrcidhttps://orcid.org/0000-0002-0065-7366 Email: [email protected]
  • Authors
    • Raphael J. Eberle - Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, GermanyInstitut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225Düsseldorf, GermanyOrcidhttps://orcid.org/0000-0002-8763-3884
    • Marc Sevenich - Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, GermanyInstitut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225Düsseldorf, GermanyPriavoid GmbH, Merowingerplatz 1, 40225Düsseldorf, Germany
    • Ian Gering - Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, Germany
    • Lara Scharbert - Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, GermanyInstitut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225Düsseldorf, Germany
    • Birgit Strodel - Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, GermanyInstitut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225Düsseldorf, GermanyOrcidhttps://orcid.org/0000-0002-8734-7765
    • Nils A. Lakomek - Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, GermanyInstitut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225Düsseldorf, Germany
    • Karoline Santur - Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, GermanyInstitut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225Düsseldorf, Germany
    • Jeannine Mohrlüder - Institute of Biological Information Processing, Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52425Jülich, GermanyOrcidhttps://orcid.org/0000-0002-9922-2986
  • Author Contributions

    Conceptualization: R.J.E., J.M., and M.A.C.; methodology: R.J.E., M.S., I.G., L.S., B.S., N.A.L., K.B.S., J.M., and M.A.C.; validation: R.J.E., M.S., I.G., L.S., B.S., N.A.L., K.B.S., J.M., and M.A.C.; formal analysis: R.J.E., M.S., I.G., L.S., B.S., N.A.L., K.B.S., J.M., and M.A.C.; investigation: R.J.E. and M.A.C.; resources: B.S. and D.W.; writing─original draft preparation: R.J.E. and M.A.C.; writing─review and editing: R.J.E., M.S., I.G., L.S., B.S., N.A.L., K.B.S., J.M., M.A.C., and D.W. All authors have read and agreed to the published version of the manuscript.

  • Funding

    D.W. received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project-ID 267205415, SFB 1208. B.S. and L.S. received funding from the VolkswagenStiftung for that project (project number 99761). N.A.L. thanks the German Science Foundation (DFG, grant project number 433700474) for funding as well as support by the project “Virological and immunological determinants of COVID-19 pathogenesis─lessons to get prepared for future pandemics (KA1-Co-02 “COVIPA”)”, a grant from the Helmholtz Association’s Initiative and Networking Fund.

  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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Dedicated to Karl-Erich Jaeger for his pioneering contributions in the field of molecular enzyme technology. The authors thank the support of the Institute of Biological Information Processing (IBI-7) Forschungszentrum Jülich, Germany. L.S. and B.S. gratefully acknowledge the computing time granted by the JARA Vergabegremium and provided under project “3cldtp” on the JARA Partition part of the supercomputer JURECA at Forschungszentrum Jülich.

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  • Abstract

    Figure 1

    Figure 1. Schematic overview of the investigated d-ri peptides and their mother l-peptides.

    Figure 2

    Figure 2. Phage display scheme and screening of phage display l-peptides against SARS-CoV-2 3CLpro activity. (A) Schematic diagram of the phage display process. (B) Primary screen of the 11 selected peptides (10 μM) against SARS-CoV-2 3CLpro activity. 3CVL-2, 3CVL-4, and 3CVL-7 inhibit the virus protease activity by more than 60%. (C–E) Selected peptide inhibiting SARS-CoV-2 3Lpro. Dose–response curves for IC50 values of 3CVL-2 (C), 3CVL-4 (D), and 3CVL-7 (E) were determined by nonlinear regression. Data shown are the mean ± standard deviation (SD) from three independent measurements (n = 3). Asterisks mean that the data differ from the control (0 μM inhibitor) significantly at p < 0.05 (*), p < 0.01 (**), and p < 0.001 (***) levels according to analyses of variance (ANOVA) and Tukey’s test.

    Figure 3

    Figure 3. Principle for obtaining d-retro-inverso peptides and inhibitory effect against SARS-CoV-2 3CLpro activity. (A) Schematic diagram of d-retro-inverso (d-ri) peptides. (B) Sequences of 3CVLri-2, -4, -7 and their mother l-peptides. In the d-ri peptides, glycine (G) was replaced by alanine (a). (C–E) All-d-ri peptides inhibit SARS-CoV-2 3CLpro activity. Dose–response curves for IC50 values of 3CVLri-2 (C), 3CVLri-4 (D), and 3CVLri-7 (E) were determined by nonlinear regression. Data shown are the mean ± SD from three independent measurements (n = 3).

    Figure 4

    Figure 4. SARS-CoV-2 3CLpro inhibition by a combination of competitive and noncompetitive d-retro-inverso peptides simultaneously. Dose–response curves for IC50 values of the combination were determined by nonlinear regression. Data shown are the mean ± SD from three independent measurements (n = 3). The corresponding normalized activity and inhibition plots of SARS-CoV-2 3CLpro under the influence of the combined peptides are shown in the Supporting Information, Figure S7. (A) Dose–response curve of the 3CVLri-2 + 3CVLri-4 combination. (B) Dose–response curve of the 3CVLri-7 + 3CVLri-4 combination.

    Figure 5

    Figure 5. SARS-CoV-2 3CLpro inhibition by Rupintrivir and Rupintrivir/3CVLri-4 combination. Dose–response curves of the IC50 values were determined by nonlinear regression. Data shown are the mean ± SD from three independent measurements (n = 3). The corresponding normalized activity and inhibition plots of SARS-CoV-2 3CLpro under the influence of the combined peptides are shown in Supporting Information, Figure S8. (A) Dose–response curve of Rutinprivir. (B) Dose–response curve of the Rutinprivir/3CVLri-4 combination.

    Figure 6

    Figure 6. Stability of 3CVLri-2, 3CVLri-4, and 3CVLri-7 peptides incubated in SGF, SIF, human plasma, and human liver microsomes. (A) SGF, (B) SIF, (C) human plasma, and (D) human liver microsomes. Unmetabolized peptides were quantified by P-HPLC. The peak areas of the unmetabolized peptides after different incubation times were normalized to the peptides’ peak areas after direct extraction from the media. Data are presented as mean ± SD (n = 3).

    Figure 7

    Figure 7. Comparison of the competitive 3CVL and 3CVLri peptide inhibitors binding position in the 3CVLpro active and substrate-binding site. 3CLpro is shown in ribbon view: chain A (gray) and chain B (turquoise). The 3CVL (green) and 3CVLri (orange) peptides are shown as sticks. The surface view demonstrates a preferred position of the peptide in the substrate-binding region, and the single subsites are marked by different colors (S1′: yellow; S1: blue; S2: pink; S3: red). The distance between the 3CLpro substrate-binding site residues and the peptides shows that active site residues His41 and Cys145 are blocked by the inhibitors: (A) 3CVL-2, (B) 3CVLri-2, (C) 3CVL-7, (D) 3CVLri-7.

    Figure 8

    Figure 8. Comparison of the noncompetitive 3CVL-4 and 3CVLri-4 peptide inhibitors in a supposed 3CLpro allosteric site. 3CLpro is shown in ribbon view, and each protomer is colored differently: chain A (gray) and chain B (turquoise). 3CVL (green) and 3CVLri (orange) peptides are shown as sticks. The surface view demonstrates a preferred position of the peptide in the proposed allosteric site in a cleft between both protease protomers: (A) 3CVL-4 and (B) 3CVLri-4.

    Figure 9

    Figure 9. Comparison of the active site volumes of control and after binding of the noncompetitive 3CVL-4 and 3CVLri-4 peptide inhibitors in a supposed 3CLpro allosteric site. 3CLpro is shown in surface view, and each protomer is colored differently: chain A (gray) and chain B (turquoise). The substrate-binding subsites are colored differently. The active sites and substrate-binding sites of each protomer are shown enlarged with the corresponding volume in Å3. Amino acids forming the active site and substrate-binding site are shown in sticks, red arrows demonstrate the change of the position of the residues because of the conformational change induced by the noncompetitive inhibitors: (A) 3CLpro, (B) 3CLpro and 3CVL-4, (C) 3CLpro and 3CVLri-4.

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    • Additional experiments and supplementary figures (phage display outcome, inhibitor tests, SPR analysis, HPLC profiles, NMR spectra, and MD analysis) and tables (analysis of peptide sequences selected by phage display and list of amino acids forming the 3CLpro substrate-binding site and allosteric site) (PDF)


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