1
|
Meller A, de Oliveira S, Davtyan A, Abramyan T, Bowman GR, 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. bioRxiv 2023:2023.03.22.533829. [PMID: 36993233 PMCID: PMC10055338 DOI: 10.1101/2023.03.22.533829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
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 (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.
Collapse
Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S Euclid Ave, St. Louis, MO, 63110
- Medical Scientist Training Program, Washington University in St. Louis, 660 S Euclid Ave., St. Louis, MO, 63110
| | - Saulo de Oliveira
- Atomwise, Inc., 717 Market Street, Suite 800, San Francisco, California 94103
| | - Aram Davtyan
- Atomwise, Inc., 717 Market Street, Suite 800, San Francisco, California 94103
| | - Tigran Abramyan
- Atomwise, Inc., 717 Market Street, Suite 800, San Francisco, California 94103
| | - Gregory R. Bowman
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA, 19104
| | - Henry van den Bedem
- Atomwise, Inc., 717 Market Street, Suite 800, San Francisco, California 94103
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94158
| |
Collapse
|
2
|
Meller A, De Oliveira S, Davtyan A, Abramyan T, Bowman GR, 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. Front Mol Biosci 2023; 10:1171143. [PMID: 37143823 PMCID: PMC10151774 DOI: 10.3389/fmolb.2023.1171143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 04/07/2023] [Indexed: 05/06/2023] Open
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.
Collapse
Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, MO, United States
- Medical Scientist Training Program, Washington University in St. Louis, St. Louis, MO, United States
| | | | - Aram Davtyan
- Atomwise, Inc., San Francisco, CA, United States
| | | | - Gregory R. Bowman
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA, United States
- *Correspondence: Gregory R. Bowman, ; Henry van den Bedem,
| | - Henry van den Bedem
- Atomwise, Inc., San Francisco, CA, United States
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, United States
- *Correspondence: Gregory R. Bowman, ; Henry van den Bedem,
| |
Collapse
|
3
|
Dilworth D, Hanley RP, Ferreira de Freitas R, Allali-Hassani A, Zhou M, Mehta N, Marunde MR, Ackloo S, Carvalho Machado RA, Khalili Yazdi A, Owens DDG, Vu V, Nie DY, Alqazzaz M, Marcon E, Li F, Chau I, Bolotokova A, Qin S, Lei M, Liu Y, Szewczyk MM, Dong A, Kazemzadeh S, Abramyan T, Popova IK, Hall NW, Meiners MJ, Cheek MA, Gibson E, Kireev D, Greenblatt JF, Keogh MC, Min J, Brown PJ, Vedadi M, Arrowsmith CH, Barsyte-Lovejoy D, James LI, Schapira M. A chemical probe targeting the PWWP domain alters NSD2 nucleolar localization. Nat Chem Biol 2022; 18:56-63. [PMID: 34782742 PMCID: PMC9189931 DOI: 10.1038/s41589-021-00898-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 09/09/2021] [Indexed: 01/03/2023]
Abstract
Nuclear receptor-binding SET domain-containing 2 (NSD2) is the primary enzyme responsible for the dimethylation of lysine 36 of histone 3 (H3K36), a mark associated with active gene transcription and intergenic DNA methylation. In addition to a methyltransferase domain, NSD2 harbors two proline-tryptophan-tryptophan-proline (PWWP) domains and five plant homeodomains (PHDs) believed to serve as chromatin reading modules. Here, we report a chemical probe targeting the N-terminal PWWP (PWWP1) domain of NSD2. UNC6934 occupies the canonical H3K36me2-binding pocket of PWWP1, antagonizes PWWP1 interaction with nucleosomal H3K36me2 and selectively engages endogenous NSD2 in cells. UNC6934 induces accumulation of endogenous NSD2 in the nucleolus, phenocopying the localization defects of NSD2 protein isoforms lacking PWWP1 that result from translocations prevalent in multiple myeloma (MM). Mutations of other NSD2 chromatin reader domains also increase NSD2 nucleolar localization and enhance the effect of UNC6934. This chemical probe and the accompanying negative control UNC7145 will be useful tools in defining NSD2 biology.
Collapse
Affiliation(s)
- David Dilworth
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada.
- BlueRock Therapeutics, Toronto, Ontario, Canada.
| | - Ronan P Hanley
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- C4 Therapeutics, Watertown, MA, USA
| | - Renato Ferreira de Freitas
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
- Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Rua Arcturus 3, São Bernardo do Campo, Brazil
| | - Abdellah Allali-Hassani
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
- Incyte, Wilmington, DE, USA
| | - Mengqi Zhou
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, China
| | - Naimee Mehta
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Nurix Therapeutics, San Francisco, CA, USA
| | | | - Suzanne Ackloo
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
| | | | | | - Dominic D G Owens
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
| | - Victoria Vu
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
| | - David Y Nie
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Mona Alqazzaz
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
| | - Edyta Marcon
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Fengling Li
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
| | - Irene Chau
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
| | - Albina Bolotokova
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
| | - Su Qin
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
- Life Science Research Center, Southern University of Science and Technology, Shenzhen, China
| | - Ming Lei
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, China
| | - Yanli Liu
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
- College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | | | - Aiping Dong
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
| | - Sina Kazemzadeh
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tigran Abramyan
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Atomwise, San Francisco, CA, USA
| | | | | | | | | | - Elisa Gibson
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
| | - Dmitri Kireev
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | | | - Jinrong Min
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, China
| | - Peter J Brown
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
| | - Masoud Vedadi
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
| | - Cheryl H Arrowsmith
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre and Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Dalia Barsyte-Lovejoy
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada.
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada.
| | - Lindsey I James
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Matthieu Schapira
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, Canada.
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada.
| |
Collapse
|