Dataset Information

Discovery of a Druggable, Cryptic Pocket in SARS-CoV-2 nsp16 Using Allosteric Inhibitors.

ABSTRACT: A collaborative, open-science team undertook discovery of novel small molecule inhibitors of the SARS-CoV-2 nsp16-nsp10 2'-O-methyltransferase using a high throughput screening approach with the potential to reveal new inhibition strategies. This screen yielded compound 5a, a ligand possessing an electron-deficient double bond, as an inhibitor of SARS-CoV-2 nsp16 activity. Surprisingly, X-ray crystal structures revealed that 5a covalently binds within a previously unrecognized cryptic pocket near the S-adenosylmethionine binding cleft in a manner that prevents occupation by S-adenosylmethionine. Using a multidisciplinary approach, we examined the mechanism of binding of compound 5a to the nsp16 cryptic pocket and developed 5a derivatives that inhibited nsp16 activity and murine hepatitis virus replication in rat lung epithelial cells but proved cytotoxic to cell lines canonically used to examine SARS-CoV-2 infection. Our study reveals the druggability of this newly discovered SARS-CoV-2 nsp16 cryptic pocket, provides novel tool compounds to explore the site, and suggests a new approach for discovery of nsp16 inhibition-based pan-coronavirus therapeutics through structure-guided drug design.

SUBMITTER: Inniss NL

PROVIDER: S-EPMC10961098 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

Discovery of a Druggable, Cryptic Pocket in SARS-CoV-2 nsp16 Using Allosteric Inhibitors.

Inniss Nicole L NL Kozic Ján J Li Fengling F Rosas-Lemus Monica M Minasov George G Rybáček Jiří J Zhu Yingjie Y Pohl Radek R Shuvalova Ludmilla L Rulíšek Lubomír L Brunzelle Joseph S JS Bednárová Lucie L Štefek Milan M Kormaník Ján Michael JM Andris Erik E Šebestík Jaroslav J Li Alice Shi Ming ASM Brown Peter J PJ Schmitz Uli U Saikatendu Kumar K Chang Edcon E Nencka Radim R Vedadi Masoud M Satchell Karla J F KJF

ACS infectious diseases 20230920 10

A collaborative, open-science team undertook discovery of novel small molecule inhibitors of the SARS-CoV-2 nsp16-nsp10 2'-O-methyltransferase using a high throughput screening approach with the potential to reveal new inhibition strategies. This screen yielded compound 5a, a ligand possessing an electron-deficient double bond, as an inhibitor of SARS-CoV-2 nsp16 activity. Surprisingly, X-ray crystal structures revealed that 5a covalently binds within a previously unrecogniz ...[more]

PMID: 37728236

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Project description:Coronaviruses have caused multiple epidemics in the past two decades, in addition to the current COVID-19 pandemic that is severely damaging global health and the economy. Coronaviruses employ between twenty and thirty proteins to carry out their viral replication cycle including infection, immune evasion, and replication. Among these, nonstructural protein 16 (Nsp16), a 2'-O-methyltransferase, plays an essential role in immune evasion. Nsp16 achieves this by mimicking its human homolog, CMTr1, which methylates mRNA to enhance translation efficiency and distinguish self from other. Unlike human CMTr1, Nsp16 requires a binding partner, Nsp10, to activate its enzymatic activity. The requirement of this binding partner presents two questions that we investigate in this manuscript. First, how does Nsp10 activate Nsp16? While experimentally-derived structures of the active Nsp16/Nsp10 complex exist, structures of inactive, monomeric Nsp16 have yet to be solved. Therefore, it is unclear how Nsp10 activates Nsp16. Using over one millisecond of molecular dynamics simulations of both Nsp16 and its complex with Nsp10, we investigate how the presence of Nsp10 shifts Nsp16's conformational ensemble in order to activate it. Second, guided by this activation mechanism and Markov state models (MSMs), we investigate if Nsp16 adopts inactive structures with cryptic pockets that, if targeted with a small molecule, could inhibit Nsp16 by stabilizing its inactive state. After identifying such a pocket in SARS-CoV-2 Nsp16, we show that this cryptic pocket also opens in SARS-CoV-1 and MERS, but not in human CMTr1. Therefore, it may be possible to develop pan-coronavirus antivirals that target this cryptic pocket. Coronaviruses are a major threat to human health. These viruses employ molecular machines, called proteins, to infect host cells and replicate. Characterizing the structure and dynamics of these proteins could provide a basis for designing small molecule antivirals. In this work, we use computer simulations to understand the moving parts of an essential SARS-CoV-2 protein, understand how a binding partner turns it on and off, and identify a novel pocket that antivirals could target to shut this protein off. The pocket is also present in other coronaviruses but not in the related human protein, so it could be a valuable target for pan-coronavirus antivirals.

Project description:Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τb = 0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τb = 0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.

Dataset Information

Discovery of a Druggable, Cryptic Pocket in SARS-CoV-2 nsp16 Using Allosteric Inhibitors.

Publications

Discovery of a Druggable, Cryptic Pocket in SARS-CoV-2 nsp16 Using Allosteric Inhibitors.

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