Open Access
Frontiers in Molecular Biosciences, volume 10
Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors
Artur Meller
1, 2
,
Saulo De Oliveira
3
,
Aram Davtyan
3
,
Tigran Abramyan
3
,
Gregory R. Bowman
4
,
Henry van den Bedem
3, 5
1
Department of Biochemistry and Molecular Biophysics, United States
|
2
Medical Scientist Training Program, United States
|
3
Atomwise, Inc., United States
|
4
Department of Biochemistry and Biophysics, United States
|
5
Department of Bioengineering and Therapeutic Sciences, United States
|
Publication type: Journal Article
Publication date: 2023-04-18
Journal:
Frontiers in Molecular Biosciences
Quartile SCImago
Q1
Quartile WOS
Q2
Impact factor: 5
ISSN: 2296889X
Biochemistry
Molecular Biology
Biochemistry, Genetics and Molecular Biology (miscellaneous)
Abstract
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.
Top-30
Citations by journals
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1
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Viruses
1 publication, 16.67%
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Protein Science
1 publication, 16.67%
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Journal of Chemical Theory and Computation
1 publication, 16.67%
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Russian Chemical Reviews
1 publication, 16.67%
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Annual Review of Biomedical Data Science
1 publication, 16.67%
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1
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Citations by publishers
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1
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Cold Spring Harbor Laboratory
1 publication, 16.67%
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Multidisciplinary Digital Publishing Institute (MDPI)
1 publication, 16.67%
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Wiley
1 publication, 16.67%
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American Chemical Society (ACS)
1 publication, 16.67%
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Autonomous Non-profit Organization Editorial Board of the journal Uspekhi Khimii
1 publication, 16.67%
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Annual Reviews
1 publication, 16.67%
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1
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- We do not take into account publications without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
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Meller A. et al. Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors // Frontiers in Molecular Biosciences. 2023. Vol. 10.
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Meller A., De Oliveira S., Davtyan A., Abramyan T., Bowman G. R., van den Bedem H. Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors // Frontiers in Molecular Biosciences. 2023. Vol. 10.
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TY - JOUR
DO - 10.3389/fmolb.2023.1171143
UR - https://doi.org/10.3389/fmolb.2023.1171143
TI - Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors
T2 - Frontiers in Molecular Biosciences
AU - Meller, Artur
AU - De Oliveira, Saulo
AU - Davtyan, Aram
AU - Abramyan, Tigran
AU - Bowman, Gregory R.
AU - van den Bedem, Henry
PY - 2023
DA - 2023/04/18 00:00:00
PB - Frontiers Media S.A.
VL - 10
SN - 2296-889X
ER -
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@article{2023_Meller,
author = {Artur Meller and Saulo De Oliveira and Aram Davtyan and Tigran Abramyan and Gregory R. Bowman and Henry van den Bedem},
title = {Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors},
journal = {Frontiers in Molecular Biosciences},
year = {2023},
volume = {10},
publisher = {Frontiers Media S.A.},
month = {apr},
url = {https://doi.org/10.3389/fmolb.2023.1171143},
doi = {10.3389/fmolb.2023.1171143}
}