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Carpenter KA, Altman RB. Databases of ligand-binding pockets and protein-ligand interactions. Comput Struct Biotechnol J 2024; 23:1320-1338. [PMID: 38585646 PMCID: PMC10997877 DOI: 10.1016/j.csbj.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/16/2024] [Accepted: 03/17/2024] [Indexed: 04/09/2024] Open
Abstract
Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.
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Affiliation(s)
- Kristy A. Carpenter
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Russ B. Altman
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
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2
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Angeli A, De Luca V, Capasso C, Di Costanzo LF, Supuran CT. Comparative CO 2 and SiO 2 hydratase activity of an enzyme from the siliceous demosponge Suberitesdomuncula. Arch Biochem Biophys 2024; 758:110074. [PMID: 38936682 DOI: 10.1016/j.abb.2024.110074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 06/19/2024] [Accepted: 06/22/2024] [Indexed: 06/29/2024]
Abstract
Silicase, an enzyme that catalyzes the hydrolysis of silicon-oxygen bonds, is a crucial player in breaking down silicates into silicic acid, particularly in organisms like aquatic sponges with siliceous skeletons. Despite its significance, our understanding of silicase remains limited. This study comprehensively examines silicase from the demosponge Suberites domuncula, focusing on its kinetics toward CO2 as a substrate, as well as its silicase and esterase activity. It investigates inhibition and activation profiles with a range of inhibitors and activators belonging to various classes. By comparing its esterase activity to human carbonic anhydrase II, we gain insights into its enzymatic properties. Moreover, we investigate silicase's inhibition and activation profiles, providing valuable information for potential applications. We explore the evolutionary relationship of silicase with related enzymes, revealing potential functional roles in biological systems. Additionally, we propose a biochemical mechanism through three-dimensional modeling, shedding light on its catalytic mechanisms and structural features for both silicase activity and CO2 hydration. We highlight nature's utilization of enzymatic expertise in silica metabolism. This study enhances our understanding of silicase and contributes to broader insights into ecosystem functioning and Earth's geochemical cycles, emphasizing the intricate interplay between biology and the environment.
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Affiliation(s)
- Andrea Angeli
- NEUROFARBA Department, Sezione di Scienze Farmaceutiche, University of Florence, Via Ugo Schiff 6, 50019, Sesto Fiorentino, Florence, Italy
| | - Viviana De Luca
- Department of Biology, Agriculture and Food Sciences, Institute of Biosciences and Bioresources, CNR, Via Pietro Castellino 111, 80131, Napoli, Italy
| | - Clemente Capasso
- Department of Biology, Agriculture and Food Sciences, Institute of Biosciences and Bioresources, CNR, Via Pietro Castellino 111, 80131, Napoli, Italy.
| | - Luigi F Di Costanzo
- Department of Agriculture, University of Napoli Federico II, Via Università 100, 80055, Portici, NA, Italy.
| | - Claudiu T Supuran
- NEUROFARBA Department, Sezione di Scienze Farmaceutiche, University of Florence, Via Ugo Schiff 6, 50019, Sesto Fiorentino, Florence, Italy
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3
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Ge Y, Pande V, Seierstad MJ, Damm-Ganamet KL. Exploring the Application of SiteMap and Site Finder for Focused Cryptic Pocket Identification. J Phys Chem B 2024; 128:6233-6245. [PMID: 38904218 DOI: 10.1021/acs.jpcb.4c00664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
The characterization of cryptic pockets has been elusive, despite substantial efforts. Computational modeling approaches, such as molecular dynamics (MD) simulations, can provide atomic-level details of binding site motions and binding pathways. However, the time scale that MD can achieve at a reasonable cost often limits its application for cryptic pocket identification. Enhanced sampling techniques can improve the efficiency of MD simulations by focused sampling of important regions of the protein, but prior knowledge of the simulated system is required to define the appropriate coordinates. In the case of a novel, unknown cryptic pocket, such information is not available, limiting the application of enhanced sampling techniques for cryptic pocket identification. In this work, we explore the ability of SiteMap and Site Finder, widely used commercial packages for pocket identification, to detect focus points on the protein and further apply other advanced computational methods. The information gained from this analysis enables the use of computational modeling, including enhanced MD sampling techniques, to explore potential cryptic binding pockets suggested by SiteMap and Site Finder. Here, we examined SiteMap and Site Finder results on 136 known cryptic pockets from a combination of the PocketMiner dataset (a recently curated set of cryptic pockets), the Cryptosite Set (a classic set of cryptic pockets), and Natural killer group 2D (NKG2D, a protein target where a cryptic pocket is confirmed). Our findings demonstrate the application of existing, well-studied tools in efficiently mapping potential regions harboring cryptic pockets.
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Affiliation(s)
- Yunhui Ge
- Computer-Aided Drug Design, Therapeutics Discovery, Janssen Research & Development, 3210 Merryfield Row, San Diego, California 92121, United States
| | - Vineet Pande
- Computer-Aided Drug Design, Therapeutics Discovery, Janssen Research & Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Mark J Seierstad
- Computer-Aided Drug Design, Therapeutics Discovery, Janssen Research & Development, 3210 Merryfield Row, San Diego, California 92121, United States
| | - Kelly L Damm-Ganamet
- Computer-Aided Drug Design, Therapeutics Discovery, Janssen Research & Development, 3210 Merryfield Row, San Diego, California 92121, United States
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4
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Otazu K, Olivos-Ramirez GE, Fernández-Silva PD, Vilca-Quispe J, Vega-Chozo K, Jimenez-Avalos GM, Chenet-Zuta ME, Sosa-Amay FE, Cárdenas Cárdenas RG, Ropón-Palacios G, Dattani N, Camps I. The Malaria Box molecules: a source for targeting the RBD and NTD cryptic pocket of the spike glycoprotein in SARS-CoV-2. J Mol Model 2024; 30:217. [PMID: 38888748 DOI: 10.1007/s00894-024-06006-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 06/05/2024] [Indexed: 06/20/2024]
Abstract
CONTEXT SARS-CoV-2, responsible for COVID-19, has led to over 500 million infections and more than 6 million deaths globally. There have been limited effective treatments available. The study aims to find a drug that can prevent the virus from entering host cells by targeting specific sites on the virus's spike protein. METHOD We examined 13,397 compounds from the Malaria Box library against two specific sites on the spike protein: the receptor-binding domain (RBD) and a predicted cryptic pocket. Using virtual screening, molecular docking, molecular dynamics, and MMPBSA techniques, they evaluated the stability of two compounds. TCMDC-124223 showed high stability and binding energy in the RBD, while TCMDC-133766 had better binding energy in the cryptic pocket. The study also identified that the interacting residues are conserved, which is crucial for addressing various virus variants. The findings provide insights into the potential of small molecules as drugs against the spike protein.
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Affiliation(s)
- Kewin Otazu
- Laboratório de Modelagem Computacional - LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas-UNIFAL-MG, Alfenas, Minas Gerais, Brazil
| | - Gustavo E Olivos-Ramirez
- Laboratório de Modelagem Computacional - LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas-UNIFAL-MG, Alfenas, Minas Gerais, Brazil
- HPQC Labs, Waterloo, Canada
| | - Pablo D Fernández-Silva
- Laboratório de Modelagem Computacional - LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas-UNIFAL-MG, Alfenas, Minas Gerais, Brazil
| | - Julissa Vilca-Quispe
- Laboratório de Modelagem Computacional - LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas-UNIFAL-MG, Alfenas, Minas Gerais, Brazil
| | - Karolyn Vega-Chozo
- Laboratório de Modelagem Computacional - LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas-UNIFAL-MG, Alfenas, Minas Gerais, Brazil
| | | | | | - Frida E Sosa-Amay
- Laboratorio de Farmacología y Toxicología, Facultad de Farmacia y Bioquímica, Universidad Nacional de la Amazonía Peruana, Iquitos, Perú
| | | | - Georcki Ropón-Palacios
- Laboratório de Modelagem Computacional - LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas-UNIFAL-MG, Alfenas, Minas Gerais, Brazil.
- HPQC Labs, Waterloo, Canada.
| | - Nike Dattani
- HPQC College, Waterloo, Canada.
- HPQC Labs, Waterloo, Canada.
| | - Ihosvany Camps
- Laboratório de Modelagem Computacional - LaModel, Instituto de Ciências Exatas - ICEx, Universidade Federal de Alfenas-UNIFAL-MG, Alfenas, Minas Gerais, Brazil.
- HPQC Labs, Waterloo, Canada.
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5
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Sinha K, Basu I, Shah Z, Shah S, Chakrabarty S. Leveraging Bidirectional Nature of Allostery To Inhibit Protein-Protein Interactions (PPIs): A Case Study of PCSK9-LDLR Interaction. J Chem Inf Model 2024; 64:3923-3932. [PMID: 38615325 DOI: 10.1021/acs.jcim.4c00294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
The protein PCSK9 (proprotein convertase subtilisin/Kexin type 9) negatively regulates the recycling of LDLR (low-density lipoprotein receptor), leading to an elevated plasma level of LDL. Inhibition of PCSK9-LDLR interaction has emerged as a promising therapeutic strategy to manage hypercholesterolemia. However, the large interaction surface area between PCSK9 and LDLR makes it challenging to identify a small molecule competitive inhibitor. An alternative strategy would be to identify distal cryptic sites as targets for allosteric inhibitors that can remotely modulate PCSK9-LDLR interaction. Using several microseconds long molecular dynamics (MD) simulations, we demonstrate that on binding with LDLR, there is a significant conformational change (population shift) in a distal loop (residues 211-222) region of PCSK9. Consistent with the bidirectional nature of allostery, we establish a clear correlation between the loop conformation and the binding affinity with LDLR. Using a thermodynamic argument, we establish that the loop conformations predominantly present in the apo state of PCSK9 would have lower LDLR binding affinity, and they would be potential targets for designing allosteric inhibitors. We elucidate the molecular origin of the allosteric coupling between this loop and the LDLR binding interface in terms of the population shift in a set of salt bridges and hydrogen bonds. Overall, our work provides a general strategy toward identifying allosteric hotspots: compare the conformational ensemble of the receptor between the apo and bound states of the protein and identify distal conformational changes, if any. The inhibitors should be designed to bind and stabilize the apo-specific conformations.
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Affiliation(s)
- Krishnendu Sinha
- Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Kolkata 700 106, India
| | - Ipsita Basu
- Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Kolkata 700 106, India
| | - Zacharia Shah
- Hingez Therapeutics Inc., 8000 Towers Crescent Drive, STE 1331, Vienna, Virginia 22182, United States
| | - Salim Shah
- Hingez Therapeutics Inc., 8000 Towers Crescent Drive, STE 1331, Vienna, Virginia 22182, United States
| | - Suman Chakrabarty
- Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Kolkata 700 106, India
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6
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Feidakis CP, Krivak R, Hoksza D, Novotny M. AHoJ-DB: A PDB-wide assignment of apo & holo relationships based on individual protein-ligand interactions. J Mol Biol 2024:168545. [PMID: 38508305 DOI: 10.1016/j.jmb.2024.168545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/12/2024] [Accepted: 03/14/2024] [Indexed: 03/22/2024]
Abstract
A single protein structure is rarely sufficient to capture the conformational variability of a protein. Both bound and unbound (holo and apo) forms of a protein are essential for understanding its geometry and making meaningful comparisons. Nevertheless, docking or drug design studies often still consider only single protein structures in their holo form, which are for the most part rigid. With the recent explosion in the field of structural biology, large, curated datasets are urgently needed. Here, we use a previously developed application (AHoJ) to perform a comprehensive search for apo-holo pairs for 468,293 biologically relevant protein-ligand interactions across 27,983 proteins. In each search, the binding pocket is captured and mapped across existing structures within the same UniProt, and the mapped pockets are annotated as apo or holo, based on the presence or absence of ligands. We assemble the results into a database, AHoJ-DB (www.apoholo.cz/db), that captures the variability of proteins with identical sequences, thereby exposing the agents responsible for the observed differences in geometry. We report several metrics for each annotated pocket, and we also include binding pockets that form at the interface of multiple chains. Analysis of the database shows that about 24% of the binding sites occur at the interface of two or more chains and that less than 50% of the total binding sites processed have an apo form in the PDB. These results can be used to train and evaluate predictors, discover potentially druggable proteins, and reveal protein- and ligand-specific relationships that were previously obscured by intermittent or partial data. Availability: http://apoholo.cz/db.
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Affiliation(s)
- Christos P Feidakis
- Department of Cell Biology, Faculty of Science, Charles University, Prague 12843, Czech Republic
| | - Radoslav Krivak
- Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Prague 12116, Czech Republic; Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague 16000, Czech Republic
| | - David Hoksza
- Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Prague 12116, Czech Republic
| | - Marian Novotny
- Department of Cell Biology, Faculty of Science, Charles University, Prague 12843, Czech Republic.
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7
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Meller A, Kelly D, Smith LG, Bowman GR. Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants. Protein Sci 2024; 33:e4902. [PMID: 38358129 PMCID: PMC10868452 DOI: 10.1002/pro.4902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 12/01/2023] [Accepted: 01/04/2024] [Indexed: 02/16/2024]
Abstract
The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)-approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics-based precision medicine, a scalable framework that promises to improve our understanding of sequence-function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.
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Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular BiophysicsWashington University in St. LouisSt. LouisMissouriUSA
- Medical Scientist Training ProgramWashington University in St. LouisSt. LouisMissouriUSA
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Devin Kelly
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Louis G. Smith
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Gregory R. Bowman
- Departments of Biochemistry & Biophysics and BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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8
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Zhang J, Li F, Liu D, Liu Q, Song H. Engineering extracellular electron transfer pathways of electroactive microorganisms by synthetic biology for energy and chemicals production. Chem Soc Rev 2024; 53:1375-1446. [PMID: 38117181 DOI: 10.1039/d3cs00537b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
The excessive consumption of fossil fuels causes massive emission of CO2, leading to climate deterioration and environmental pollution. The development of substitutes and sustainable energy sources to replace fossil fuels has become a worldwide priority. Bio-electrochemical systems (BESs), employing redox reactions of electroactive microorganisms (EAMs) on electrodes to achieve a meritorious combination of biocatalysis and electrocatalysis, provide a green and sustainable alternative approach for bioremediation, CO2 fixation, and energy and chemicals production. EAMs, including exoelectrogens and electrotrophs, perform extracellular electron transfer (EET) (i.e., outward and inward EET), respectively, to exchange energy with the environment, whose rate determines the efficiency and performance of BESs. Therefore, we review the synthetic biology strategies developed in the last decade for engineering EAMs to enhance the EET rate in cell-electrode interfaces for facilitating the production of electricity energy and value-added chemicals, which include (1) progress in genetic manipulation and editing tools to achieve the efficient regulation of gene expression, knockout, and knockdown of EAMs; (2) synthetic biological engineering strategies to enhance the outward EET of exoelectrogens to anodes for electricity power production and anodic electro-fermentation (AEF) for chemicals production, including (i) broadening and strengthening substrate utilization, (ii) increasing the intracellular releasable reducing equivalents, (iii) optimizing c-type cytochrome (c-Cyts) expression and maturation, (iv) enhancing conductive nanowire biosynthesis and modification, (v) promoting electron shuttle biosynthesis, secretion, and immobilization, (vi) engineering global regulators to promote EET rate, (vii) facilitating biofilm formation, and (viii) constructing cell-material hybrids; (3) the mechanisms of inward EET, CO2 fixation pathway, and engineering strategies for improving the inward EET of electrotrophic cells for CO2 reduction and chemical production, including (i) programming metabolic pathways of electrotrophs, (ii) rewiring bioelectrical circuits for enhancing inward EET, and (iii) constructing microbial (photo)electrosynthesis by cell-material hybridization; (4) perspectives on future challenges and opportunities for engineering EET to develop highly efficient BESs for sustainable energy and chemical production. We expect that this review will provide a theoretical basis for the future development of BESs in energy harvesting, CO2 fixation, and chemical synthesis.
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Affiliation(s)
- Junqi Zhang
- Frontier Science Center for Synthetic Biology (Ministry of Education), Key Laboratory of Systems Bioengineering, and School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.
| | - Feng Li
- Frontier Science Center for Synthetic Biology (Ministry of Education), Key Laboratory of Systems Bioengineering, and School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.
| | - Dingyuan Liu
- Frontier Science Center for Synthetic Biology (Ministry of Education), Key Laboratory of Systems Bioengineering, and School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.
| | - Qijing Liu
- Frontier Science Center for Synthetic Biology (Ministry of Education), Key Laboratory of Systems Bioengineering, and School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.
| | - Hao Song
- Frontier Science Center for Synthetic Biology (Ministry of Education), Key Laboratory of Systems Bioengineering, and School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.
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9
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Astore MA, Pradhan AS, Thiede EH, Hanson SM. Protein dynamics underlying allosteric regulation. Curr Opin Struct Biol 2024; 84:102768. [PMID: 38215528 DOI: 10.1016/j.sbi.2023.102768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/16/2023] [Accepted: 12/18/2023] [Indexed: 01/14/2024]
Abstract
Allostery is the mechanism by which information and control are propagated in biomolecules. It regulates ligand binding, chemical reactions, and conformational changes. An increasing level of experimental resolution and control over allosteric mechanisms promises a deeper understanding of the molecular basis for life and powerful new therapeutics. In this review, we survey the literature for an up-to-date biological and theoretical understanding of protein allostery. By delineating five ways in which the energy landscape or the kinetics of a system may change to give rise to allostery, we aim to help the reader grasp its physical origins. To illustrate this framework, we examine three systems that display these forms of allostery: allosteric inhibitors of beta-lactamases, thermosensation of TRP channels, and the role of kinetic allostery in the function of kinases. Finally, we summarize the growing power of computational tools available to investigate the different forms of allostery presented in this review.
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Affiliation(s)
- Miro A Astore
- Center for Computational Biology, Flatiron Institute, New York, NY, USA; Center for Computational Mathematics, Flatiron Institute, New York, NY, USA. https://twitter.com/@miroastore
| | - Akshada S Pradhan
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
| | - Erik H Thiede
- Center for Computational Biology, Flatiron Institute, New York, NY, USA; Center for Computational Mathematics, Flatiron Institute, New York, NY, USA; Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA
| | - Sonya M Hanson
- Center for Computational Biology, Flatiron Institute, New York, NY, USA; Center for Computational Mathematics, Flatiron Institute, New York, NY, USA.
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10
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Amaya-Rodriguez CA, Carvajal-Zamorano K, Bustos D, Alegría-Arcos M, Castillo K. A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel. Front Pharmacol 2024; 14:1251061. [PMID: 38328578 PMCID: PMC10847257 DOI: 10.3389/fphar.2023.1251061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 12/14/2023] [Indexed: 02/09/2024] Open
Abstract
The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning.
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Affiliation(s)
- Cesar A. Amaya-Rodriguez
- Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Departamento de Fisiología y Comportamiento Animal, Facultad de Ciencias Naturales, Exactas y Tecnología, Universidad de Panamá, Ciudad de Panamá, Panamá
| | - Karina Carvajal-Zamorano
- Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Daniel Bustos
- Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado Universidad Católica del Maule, Talca, Chile
- Laboratorio de Bioinformática y Química Computacional, Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca, Chile
| | - Melissa Alegría-Arcos
- Núcleo de Investigación en Data Science, Facultad de Ingeniería y Negocios, Universidad de las Américas, Santiago, Chile
| | - Karen Castillo
- Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado Universidad Católica del Maule, Talca, Chile
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11
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Guo J. Improving structure-based protein-ligand affinity prediction by graph representation learning and ensemble learning. PLoS One 2024; 19:e0296676. [PMID: 38232063 DOI: 10.1371/journal.pone.0296676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/15/2023] [Indexed: 01/19/2024] Open
Abstract
Predicting protein-ligand binding affinity presents a viable solution for accelerating the discovery of new lead compounds. The recent widespread application of machine learning approaches, especially graph neural networks, has brought new advancements in this field. However, some existing structure-based methods treat protein macromolecules and ligand small molecules in the same way and ignore the data heterogeneity, potentially leading to incomplete exploration of the biochemical information of ligands. In this work, we propose LGN, a graph neural network-based fusion model with extra ligand feature extraction to effectively capture local features and global features within the protein-ligand complex, and make use of interaction fingerprints. By combining the ligand-based features and interaction fingerprints, LGN achieves Pearson correlation coefficients of up to 0.842 on the PDBbind 2016 core set, compared to 0.807 when using the features of complex graphs alone. Finally, we verify the rationalization and generalization of our model through comprehensive experiments. We also compare our model with state-of-the-art baseline methods, which validates the superiority of our model. To reduce the impact of data similarity, we increase the robustness of the model by incorporating ensemble learning.
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Affiliation(s)
- Jia Guo
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Beijing, P.R. China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
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12
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Kwon S, Safer J, Nguyen DT, Hoksza D, May P, Arbesfeld JA, Rubin AF, Campbell AJ, Burgin A, Iqbal S. Genomics 2 Proteins portal: A resource and discovery tool for linking genetic screening outputs to protein sequences and structures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.02.573913. [PMID: 38260256 PMCID: PMC10802383 DOI: 10.1101/2024.01.02.573913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics technologies have enabled the detection and generation of variants at an unprecedented scale. However, efficient tools and resources are needed to link these two disparate data types - to "map" variants onto protein structures, to better understand how the variation causes disease and thereby design therapeutics. Here we present the Genomics 2 Proteins Portal (G2P; g2p.broadinstitute.org/): a human proteome-wide resource that maps 19,996,443 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the G2P portal generalizes the capability of linking genomics to proteins beyond databases by allowing users to interactively upload protein residue-wise annotations (variants, scores, etc.) as well as the protein structure to establish the connection. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure-function relationship between natural or synthetic variations and their molecular phenotype.
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13
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Li M, Lan X, Lu X, Zhang J. A Structure-Based Allosteric Modulator Design Paradigm. HEALTH DATA SCIENCE 2023; 3:0094. [PMID: 38487194 PMCID: PMC10904074 DOI: 10.34133/hds.0094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 10/11/2023] [Indexed: 03/17/2024]
Abstract
Importance: Allosteric drugs bound to topologically distal allosteric sites hold a substantial promise in modulating therapeutic targets deemed undruggable at their orthosteric sites. Traditionally, allosteric modulator discovery has predominantly relied on serendipitous high-throughput screening. Nevertheless, the landscape has undergone a transformative shift due to recent advancements in our understanding of allosteric modulation mechanisms, coupled with a significant increase in the accessibility of allosteric structural data. These factors have extensively promoted the development of various computational methodologies, especially for machine-learning approaches, to guide the rational design of structure-based allosteric modulators. Highlights: We here presented a comprehensive structure-based allosteric modulator design paradigm encompassing 3 critical stages: drug target acquisition, allosteric binding site, and modulator discovery. The recent advances in computational methods in each stage are encapsulated. Furthermore, we delve into analyzing the successes and obstacles encountered in the rational design of allosteric modulators. Conclusion: The structure-based allosteric modulator design paradigm holds immense potential for the rational design of allosteric modulators. We hope that this review would heighten awareness of the use of structure-based computational methodologies in advancing the field of allosteric drug discovery.
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Affiliation(s)
- Mingyu Li
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiaobin Lan
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xun Lu
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jian Zhang
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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14
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Colombo G. Computing allostery: from the understanding of biomolecular regulation and the discovery of cryptic sites to molecular design. Curr Opin Struct Biol 2023; 83:102702. [PMID: 37716095 DOI: 10.1016/j.sbi.2023.102702] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/22/2023] [Accepted: 08/22/2023] [Indexed: 09/18/2023]
Abstract
The concept of allostery has become a central tenet in the study of biological systems. In parallel, the discovery of allosteric drugs is generating new opportunities to selectively modulate difficult targets involved in pathologic mechanisms. Molecular simulations can provide atomistically detailed insight into the processes involved in allosteric regulation and signaling, and at the same time, they have the potential to unveil regulatory hotspots or cryptic sites that are not immediately evident from the analysis of static structures. In this context, computational approaches should be able to connect the study of allosteric regulation at different scales to the possibility of leveraging this knowledge to expand the chemical space of new, active drugs. Here, we will discuss recent advances in the study of allosteric regulation via computational methods and connect the mechanistic knowledge generated to the possibility of designing new small molecules that can tweak the functions of their receptors.
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Affiliation(s)
- Giorgio Colombo
- Department of Chemistry, University of Pavia, Via Taramelli 12, 27100 Pavia, Italy.
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15
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Xia S, Chen E, Zhang Y. Integrated Molecular Modeling and Machine Learning for Drug Design. J Chem Theory Comput 2023; 19:7478-7495. [PMID: 37883810 PMCID: PMC10653122 DOI: 10.1021/acs.jctc.3c00814] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.
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Affiliation(s)
- Song Xia
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Eric Chen
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department
of Chemistry, New York University, New York, New York 10003, United States
- Simons
Center for Computational Physical Chemistry at New York University, New York, New York 10003, United States
- NYU-ECNU
Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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16
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Verkhivker G, Alshahrani M, Gupta G. Exploring Conformational Landscapes and Cryptic Binding Pockets in Distinct Functional States of the SARS-CoV-2 Omicron BA.1 and BA.2 Trimers: Mutation-Induced Modulation of Protein Dynamics and Network-Guided Prediction of Variant-Specific Allosteric Binding Sites. Viruses 2023; 15:2009. [PMID: 37896786 PMCID: PMC10610873 DOI: 10.3390/v15102009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/23/2023] [Accepted: 09/26/2023] [Indexed: 10/29/2023] Open
Abstract
A significant body of experimental structures of SARS-CoV-2 spike trimers for the BA.1 and BA.2 variants revealed a considerable plasticity of the spike protein and the emergence of druggable binding pockets. Understanding the interplay of conformational dynamics changes induced by the Omicron variants and the identification of cryptic dynamic binding pockets in the S protein is of paramount importance as exploring broad-spectrum antiviral agents to combat the emerging variants is imperative. In the current study, we explore conformational landscapes and characterize the universe of binding pockets in multiple open and closed functional spike states of the BA.1 and BA.2 Omicron variants. By using a combination of atomistic simulations, a dynamics network analysis, and an allostery-guided network screening of binding pockets in the conformational ensembles of the BA.1 and BA.2 spike conformations, we identified all experimentally known allosteric sites and discovered significant variant-specific differences in the distribution of binding sites in the BA.1 and BA.2 trimers. This study provided a structural characterization of the predicted cryptic pockets and captured the experimentally known allosteric sites, revealing the critical role of conformational plasticity in modulating the distribution and cross-talk between functional binding sites. We found that mutational and dynamic changes in the BA.1 variant can induce the remodeling and stabilization of a known druggable pocket in the N-terminal domain, while this pocket is drastically altered and may no longer be available for ligand binding in the BA.2 variant. Our results predicted the experimentally known allosteric site in the receptor-binding domain that remains stable and ranks as the most favorable site in the conformational ensembles of the BA.2 variant but could become fragmented and less probable in BA.1 conformations. We also uncovered several cryptic pockets formed at the inter-domain and inter-protomer interface, including functional regions of the S2 subunit and stem helix region, which are consistent with the known role of pocket residues in modulating conformational transitions and antibody recognition. The results of this study are particularly significant for understanding the dynamic and network features of the universe of available binding pockets in spike proteins, as well as the effects of the Omicron-variant-specific modulation of preferential druggable pockets. The exploration of predicted druggable sites can present a new and previously underappreciated opportunity for therapeutic interventions for Omicron variants through the conformation-selective and variant-specific targeting of functional sites involved in allosteric changes.
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Affiliation(s)
- Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
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17
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Wang Y, Wang Z, Liu Y, Yu Q, Liu Y, Luo C, Wang S, Liu H, Liu M, Zhang G, Fan Y, Li K, Huang L, Duan M, Zhou F. Reconstructing the cytokine view for the multi-view prediction of COVID-19 mortality. BMC Infect Dis 2023; 23:622. [PMID: 37735372 PMCID: PMC10514938 DOI: 10.1186/s12879-023-08291-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 04/28/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) is a rapidly developing and sometimes lethal pulmonary disease. Accurately predicting COVID-19 mortality will facilitate optimal patient treatment and medical resource deployment, but the clinical practice still needs to address it. Both complete blood counts and cytokine levels were observed to be modified by COVID-19 infection. This study aimed to use inexpensive and easily accessible complete blood counts to build an accurate COVID-19 mortality prediction model. The cytokine fluctuations reflect the inflammatory storm induced by COVID-19, but their levels are not as commonly accessible as complete blood counts. Therefore, this study explored the possibility of predicting cytokine levels based on complete blood counts. METHODS We used complete blood counts to predict cytokine levels. The predictive model includes an autoencoder, principal component analysis, and linear regression models. We used classifiers such as support vector machine and feature selection models such as adaptive boost to predict the mortality of COVID-19 patients. RESULTS Complete blood counts and original cytokine levels reached the COVID-19 mortality classification area under the curve (AUC) values of 0.9678 and 0.9111, respectively, and the cytokine levels predicted by the feature set alone reached the classification AUC value of 0.9844. The predicted cytokine levels were more significantly associated with COVID-19 mortality than the original values. CONCLUSIONS Integrating the predicted cytokine levels and complete blood counts improved a COVID-19 mortality prediction model using complete blood counts only. Both the cytokine level prediction models and the COVID-19 mortality prediction models are publicly available at http://www.healthinformaticslab.org/supp/resources.php .
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Affiliation(s)
- Yueying Wang
- College of Computer Science and Technology, Jilin University, 130012, Changchun, China
- School of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, 130012, Changchun, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, 130021, Changchun, Jilin Province, China
| | - Zhao Wang
- College of Software, Jilin University, 130012, Changchun, China
| | - Yaqing Liu
- College of Computer Science and Technology, Jilin University, 130012, Changchun, China
| | - Qiong Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, 130021, Changchun, Jilin Province, China
| | - Yujia Liu
- College of Software, Jilin University, 130012, Changchun, China
| | - Changfan Luo
- College of Software, Jilin University, 130012, Changchun, China
| | - Siyang Wang
- College of Software, Jilin University, 130012, Changchun, China
| | - Hongmei Liu
- School of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, 130012, Changchun, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, 550025, Guiyang, Guizhou, China
| | - Mingyou Liu
- School of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China
| | - Gongyou Zhang
- School of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China
| | - Yusi Fan
- College of Software, Jilin University, 130012, Changchun, China
| | - Kewei Li
- College of Computer Science and Technology, Jilin University, 130012, Changchun, China
- School of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China
| | - Lan Huang
- College of Computer Science and Technology, Jilin University, 130012, Changchun, China
- School of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China
| | - Meiyu Duan
- College of Computer Science and Technology, Jilin University, 130012, Changchun, China.
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, 130012, Changchun, China.
| | - Fengfeng Zhou
- College of Computer Science and Technology, Jilin University, 130012, Changchun, China.
- School of Biology and Engineering, Guizhou Medical University, 550025, Guiyang, Guizhou, China.
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, 130012, Changchun, China.
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18
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Mostofian B, Martin HJ, Razavi A, Patel S, Allen B, Sherman W, Izaguirre JA. Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods. J Chem Inf Model 2023; 63:5408-5432. [PMID: 37602861 PMCID: PMC10498452 DOI: 10.1021/acs.jcim.3c00603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Indexed: 08/22/2023]
Abstract
The therapeutic approach of targeted protein degradation (TPD) is gaining momentum due to its potentially superior effects compared with protein inhibition. Recent advancements in the biotech and pharmaceutical sectors have led to the development of compounds that are currently in human trials, with some showing promising clinical results. However, the use of computational tools in TPD is still limited, as it has distinct characteristics compared with traditional computational drug design methods. TPD involves creating a ternary structure (protein-degrader-ligase) responsible for the biological function, such as ubiquitination and subsequent proteasomal degradation, which depends on the spatial orientation of the protein of interest (POI) relative to E2-loaded ubiquitin. Modeling this structure necessitates a unique blend of tools initially developed for small molecules (e.g., docking) and biologics (e.g., protein-protein interaction modeling). Additionally, degrader molecules, particularly heterobifunctional degraders, are generally larger than conventional small molecule drugs, leading to challenges in determining drug-like properties like solubility and permeability. Furthermore, the catalytic nature of TPD makes occupancy-based modeling insufficient. TPD consists of multiple interconnected yet distinct steps, such as POI binding, E3 ligase binding, ternary structure interactions, ubiquitination, and degradation, along with traditional small molecule properties. A comprehensive set of tools is needed to address the dynamic nature of the induced proximity ternary complex and its implications for ubiquitination. In this Perspective, we discuss the current state of computational tools for TPD. We start by describing the series of steps involved in the degradation process and the experimental methods used to characterize them. Then, we delve into a detailed analysis of the computational tools employed in TPD. We also present an integrative approach that has proven successful for degrader design and its impact on project decisions. Finally, we examine the future prospects of computational methods in TPD and the areas with the greatest potential for impact.
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Affiliation(s)
- Barmak Mostofian
- OpenEye, Cadence Molecular Sciences, Boston, Massachusetts 02114 United States
| | - Holli-Joi Martin
- Laboratory
for Molecular Modeling, Division of Chemical Biology and Medicinal
Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599 United States
| | - Asghar Razavi
- ENKO
Chem, Inc, Mystic, Connecticut 06355 United States
| | - Shivam Patel
- Psivant
Therapeutics, Boston, Massachusetts 02210 United States
| | - Bryce Allen
- Differentiated
Therapeutics, San Diego, California 92056 United States
| | - Woody Sherman
- Psivant
Therapeutics, Boston, Massachusetts 02210 United States
| | - Jesus A Izaguirre
- Differentiated
Therapeutics, San Diego, California 92056 United States
- Atommap
Corporation, New York, New York 10013 United States
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19
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Ridgway H, Orbell JD, Matsoukas MT, Kelaidonis K, Moore GJ, Tsiodras S, Gorgoulis VG, Chasapis CT, Apostolopoulos V, Matsoukas JM. W254 in furin functions as a molecular gate promoting anti-viral drug binding: Elucidation of putative drug tunneling and docking by non-equilibrium molecular dynamics. Comput Struct Biotechnol J 2023; 21:4589-4612. [PMID: 37817778 PMCID: PMC10561063 DOI: 10.1016/j.csbj.2023.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023] Open
Abstract
Furins are serine endoproteases that process precursor proteins into their biologically active forms, and they play essential roles in normal metabolism and disease presentation, including promoting expression of bacterial virulence factors and viral pathogenesis. Thus, furins represent vital targets for development of antimicrobial and antiviral therapeutics. Recent experimental evidence indicated that dichlorophenyl (DCP)-pyridine "BOS" drugs (e.g., BOS-318) competitively inhibit human furin by an induced-fit mechanism in which tryptophan W254 in the furin catalytic cleft (FCC) functions as a molecular gate, rotating nearly 180o through a steep energy barrier about its chi-1 dihedral to an "open" orientation, exposing a buried (i.e., cryptic) hydrophobic pocket 1. Once exposed, the non-polar DCP group of BOS-318, and similar halo-phenyl groups of analogs, enter the cryptic pocket, stabilizing drug binding. Here, we demonstrate flexible-receptor docking of BOS-318 (and various analogs) was unable to emulate the induced-fit motif, even when tryptophan was replaced with less bulky phenylalanine or glycine. While either substitution allowed access to the hydrophobic pocket for most ligands tested, optimal binding was observed only for W254, inferring a stabilizing effect of the indole sidechain. Furthermore, non-equilibrium steered molecular dynamics (sMD) in which the bound drugs (or their fragments) were extracted from the FCC did not cause closure of the open W254 gate, consistent with the thermodynamic stability of the open or closed W254 orientations. Finally, interactive molecular dynamics (iMD) revealed two putative conduits of drug entry and binding into the FCC, each coupled with W254 dihedral rotation and opening of the cryptic pocket. The iMD simulations further revealed ligand entry and binding in the FCC is likely driven in part by energy fluxes stemming from disruption and re-formation of ligand and protein solvation shells during drug migration from the solution phase into the FCC.
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Affiliation(s)
- Harry Ridgway
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC 8001, Australia
- AquaMem Consultants, Rodeo, NM 88056, USA
| | - John D. Orbell
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC 8001, Australia
- College of Sport, Health & Engineering, Victoria University, Melbourne, VIC 8001, Australia
| | | | | | - Graham J. Moore
- Pepmetics Inc., 772 Murphy Place, Victoria, BC V8Y 3H4, Canada
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Sotiris Tsiodras
- Department of Internal Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Vasilis G. Gorgoulis
- Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
- Laboratory of Biology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
- Department of Histology and Embryology, Faculty of Medicine, National Kapodistrian University of Athens, GR-11527 Athens, Greece
- Faculty Institute for Cancer Sciences, Manchester Academic Health Sciences Centre, University of Manchester, M20 4GJ Manchester, UK
- Biomedical Research Foundation, Academy of Athens, GR-11527 Athens, Greece
- Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH Surrey, UK
| | - Christos T. Chasapis
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece
| | - Vasso Apostolopoulos
- Institute for Health and Sport, Immunology and Translational Research, Victoria University, Melbourne 3030, VIC, Australia
- Immunology Program, Australian Institute for Musculoskeletal Science (AIMSS), Melbourne 3021, VIC, Australia
| | - John M. Matsoukas
- NewDrug/NeoFar PC, Patras Science Park, Patras 26504, Greece
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
- Institute for Health and Sport, Immunology and Translational Research, Victoria University, Melbourne 3030, VIC, Australia
- Department of Chemistry, University of Patras, 26504 Patras, Greece
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20
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Verkhivker G, Alshahrani M, Gupta G, Xiao S, Tao P. From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches. Int J Mol Sci 2023; 24:7747. [PMID: 37175454 PMCID: PMC10178073 DOI: 10.3390/ijms24097747] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 04/22/2023] [Accepted: 04/23/2023] [Indexed: 05/15/2023] Open
Abstract
The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric regulation, the emerging structural biology technologies and AI approaches remains largely unexplored, calling for the development of AI-augmented integrative structural biology. In this review, we focus on the latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric regulatory mechanisms as well as on the new developments in AI methods for prediction and characterization of allosteric binding sites on the proteome level. We also discuss new AI-augmented structural biology approaches that expand our knowledge of the universe of protein dynamics and allostery. We conclude with an outlook and highlight the importance of developing an open science infrastructure for machine learning studies of allosteric regulation and validation of computational approaches using integrative studies of allosteric mechanisms. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field.
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Affiliation(s)
- Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
| | - Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75275, USA; (S.X.); (P.T.)
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75275, USA; (S.X.); (P.T.)
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21
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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 : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.22.533829. [PMID: 36993233 PMCID: PMC10055338 DOI: 10.1101/2023.03.22.533829] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [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.
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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
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22
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Meller A, Bhakat S, Solieva S, Bowman GR. Accelerating Cryptic Pocket Discovery Using AlphaFold. J Chem Theory Comput 2023. [PMID: 36948209 PMCID: PMC10373493 DOI: 10.1021/acs.jctc.2c01189] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
Cryptic pockets, or pockets absent in ligand-free, experimentally determined structures, hold great potential as drug targets. However, cryptic pocket openings are often beyond the reach of conventional biomolecular simulations because certain cryptic pocket openings involve slow motions. Here, we investigate whether AlphaFold can be used to accelerate cryptic pocket discovery either by generating structures with open pockets directly or generating structures with partially open pockets that can be used as starting points for simulations. We use AlphaFold to generate ensembles for 10 known cryptic pocket examples, including five that were deposited after AlphaFold's training data were extracted from the PDB. We find that in 6 out of 10 cases AlphaFold samples the open state. For plasmepsin II, an aspartic protease from the causative agent of malaria, AlphaFold only captures a partial pocket opening. As a result, we ran simulations from an ensemble of AlphaFold-generated structures and show that this strategy samples cryptic pocket opening, even though an equivalent amount of simulations launched from a ligand-free experimental structure fails to do so. Markov state models (MSMs) constructed from the AlphaFold-seeded simulations quickly yield a free energy landscape of cryptic pocket opening that is in good agreement with the same landscape generated with well-tempered metadynamics. Taken together, our results demonstrate that AlphaFold has a useful role to play in cryptic pocket discovery but that many cryptic pockets may remain difficult to sample using AlphaFold alone.
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Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
- Medical Scientist Training Program, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
| | - Soumendranath Bhakat
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Shahlo Solieva
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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23
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Meller A, Lotthammer JM, Smith LG, Novak B, Lee LA, Kuhn CC, Greenberg L, Leinwand LA, Greenberg MJ, Bowman GR. Drug specificity and affinity are encoded in the probability of cryptic pocket opening in myosin motor domains. eLife 2023; 12:83602. [PMID: 36705568 PMCID: PMC9995120 DOI: 10.7554/elife.83602] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/23/2023] [Indexed: 01/28/2023] Open
Abstract
The design of compounds that can discriminate between closely related target proteins remains a central challenge in drug discovery. Specific therapeutics targeting the highly conserved myosin motor family are urgently needed as mutations in at least six of its members cause numerous diseases. Allosteric modulators, like the myosin-II inhibitor blebbistatin, are a promising means to achieve specificity. However, it remains unclear why blebbistatin inhibits myosin-II motors with different potencies given that it binds at a highly conserved pocket that is always closed in blebbistatin-free experimental structures. We hypothesized that the probability of pocket opening is an important determinant of the potency of compounds like blebbistatin. To test this hypothesis, we used Markov state models (MSMs) built from over 2 ms of aggregate molecular dynamics simulations with explicit solvent. We find that blebbistatin's binding pocket readily opens in simulations of blebbistatin-sensitive myosin isoforms. Comparing these conformational ensembles reveals that the probability of pocket opening correctly identifies which isoforms are most sensitive to blebbistatin inhibition and that docking against MSMs quantitatively predicts blebbistatin binding affinities (R2=0.82). In a blind prediction for an isoform (Myh7b) whose blebbistatin sensitivity was unknown, we find good agreement between predicted and measured IC50s (0.67 μM vs. 0.36 μM). Therefore, we expect this framework to be useful for the development of novel specific drugs across numerous protein targets.
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Affiliation(s)
- Artur Meller
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
- Medical Scientist Training Program, Washington University in St. LouisPhiladelphiaUnited States
| | - Jeffrey M Lotthammer
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
| | - Louis G Smith
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
- Department of Biochemistry and Biophysics, University of PennsylvaniaPhiladelphiaUnited States
| | - Borna Novak
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
- Medical Scientist Training Program, Washington University in St. LouisPhiladelphiaUnited States
| | - Lindsey A Lee
- Molecular, Cellular, and Developmental Biology Department, University of Colorado BoulderBoulderUnited States
- BioFrontiers InstituteBoulderUnited States
| | - Catherine C Kuhn
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
| | - Lina Greenberg
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
| | - Leslie A Leinwand
- Molecular, Cellular, and Developmental Biology Department, University of Colorado BoulderBoulderUnited States
- BioFrontiers InstituteBoulderUnited States
| | - Michael J Greenberg
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
| | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University in St. LouisSt LouisUnited States
- Department of Biochemistry and Biophysics, University of PennsylvaniaPhiladelphiaUnited States
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24
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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: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [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.
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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,
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