Cargando…

Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors

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 pr...

Descripción completa

Detalles Bibliográficos
Autores principales: Meller, Artur, de Oliveira, Saulo, Davtyan, Aram, Abramyan, Tigran, Bowman, Gregory R., van den Bedem, Henry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055338/
https://www.ncbi.nlm.nih.gov/pubmed/36993233
http://dx.doi.org/10.1101/2023.03.22.533829
_version_ 1785015858494439424
author Meller, Artur
de Oliveira, Saulo
Davtyan, Aram
Abramyan, Tigran
Bowman, Gregory R.
van den Bedem, Henry
author_facet Meller, Artur
de Oliveira, Saulo
Davtyan, Aram
Abramyan, Tigran
Bowman, Gregory R.
van den Bedem, Henry
author_sort Meller, Artur
collection PubMed
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 (τ(b)=0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τ(b)=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.
format Online
Article
Text
id pubmed-10055338
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Cold Spring Harbor Laboratory
record_format MEDLINE/PubMed
spelling pubmed-100553382023-03-30 Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors Meller, Artur de Oliveira, Saulo Davtyan, Aram Abramyan, Tigran Bowman, Gregory R. van den Bedem, Henry bioRxiv Article 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 (τ(b)=0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τ(b)=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. Cold Spring Harbor Laboratory 2023-03-24 /pmc/articles/PMC10055338/ /pubmed/36993233 http://dx.doi.org/10.1101/2023.03.22.533829 Text en https://creativecommons.org/licenses/by-nd/4.0/This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Meller, Artur
de Oliveira, Saulo
Davtyan, Aram
Abramyan, Tigran
Bowman, Gregory R.
van den Bedem, Henry
Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors
title Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors
title_full Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors
title_fullStr Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors
title_full_unstemmed Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors
title_short Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors
title_sort discovery of a cryptic pocket in the ai-predicted structure of ppm1d phosphatase explains the binding site and potency of its allosteric inhibitors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055338/
https://www.ncbi.nlm.nih.gov/pubmed/36993233
http://dx.doi.org/10.1101/2023.03.22.533829
work_keys_str_mv AT mellerartur discoveryofacrypticpocketintheaipredictedstructureofppm1dphosphataseexplainsthebindingsiteandpotencyofitsallostericinhibitors
AT deoliveirasaulo discoveryofacrypticpocketintheaipredictedstructureofppm1dphosphataseexplainsthebindingsiteandpotencyofitsallostericinhibitors
AT davtyanaram discoveryofacrypticpocketintheaipredictedstructureofppm1dphosphataseexplainsthebindingsiteandpotencyofitsallostericinhibitors
AT abramyantigran discoveryofacrypticpocketintheaipredictedstructureofppm1dphosphataseexplainsthebindingsiteandpotencyofitsallostericinhibitors
AT bowmangregoryr discoveryofacrypticpocketintheaipredictedstructureofppm1dphosphataseexplainsthebindingsiteandpotencyofitsallostericinhibitors
AT vandenbedemhenry discoveryofacrypticpocketintheaipredictedstructureofppm1dphosphataseexplainsthebindingsiteandpotencyofitsallostericinhibitors