1
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Díaz-Holguín A, Saarinen M, Vo DD, Sturchio A, Branzell N, Cabeza de Vaca I, Hu H, Mitjavila-Domènech N, Lindqvist A, Baranczewski P, Millan MJ, Yang Y, Carlsson J, Svenningsson P. AlphaFold accelerated discovery of psychotropic agonists targeting the trace amine-associated receptor 1. SCIENCE ADVANCES 2024; 10:eadn1524. [PMID: 39110804 PMCID: PMC11305387 DOI: 10.1126/sciadv.adn1524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 06/28/2024] [Indexed: 08/10/2024]
Abstract
Artificial intelligence is revolutionizing protein structure prediction, providing unprecedented opportunities for drug design. To assess the potential impact on ligand discovery, we compared virtual screens using protein structures generated by the AlphaFold machine learning method and traditional homology modeling. More than 16 million compounds were docked to models of the trace amine-associated receptor 1 (TAAR1), a G protein-coupled receptor of unknown structure and target for treating neuropsychiatric disorders. Sets of 30 and 32 highly ranked compounds from the AlphaFold and homology model screens, respectively, were experimentally evaluated. Of these, 25 were TAAR1 agonists with potencies ranging from 12 to 0.03 μM. The AlphaFold screen yielded a more than twofold higher hit rate (60%) than the homology model and discovered the most potent agonists. A TAAR1 agonist with a promising selectivity profile and drug-like properties showed physiological and antipsychotic-like effects in wild-type but not in TAAR1 knockout mice. These results demonstrate that AlphaFold structures can accelerate drug discovery.
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Affiliation(s)
- Alejandro Díaz-Holguín
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Marcus Saarinen
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
| | - Duc Duy Vo
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Andrea Sturchio
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
- Department of Neurology, James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, USA
| | - Niclas Branzell
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
| | - Israel Cabeza de Vaca
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Huabin Hu
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Núria Mitjavila-Domènech
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Annika Lindqvist
- Department of Pharmacy, SciLifeLab Drug Discovery and Development Platform, Uppsala University, Box 580, SE-751 23 Uppsala, Sweden
| | - Pawel Baranczewski
- Department of Pharmacy, SciLifeLab Drug Discovery and Development Platform, Uppsala University, Box 580, SE-751 23 Uppsala, Sweden
| | - Mark J. Millan
- Neuroinflammation Therapeutic Area, Institut de Recherches Servier, Centre de Recherches de Croissy, Paris, France and Institute of Neuroscience and Psychology, College of Medicine, Vet and Life Sciences, Glasgow University, Scotland, Glasgow, UK
| | - Yunting Yang
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Per Svenningsson
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
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2
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Zhang H, Lan J, Wang H, Lu R, Zhang N, He X, Yang J, Chen L. AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease. Front Mol Biosci 2024; 11:1414916. [PMID: 39139810 PMCID: PMC11319189 DOI: 10.3389/fmolb.2024.1414916] [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: 04/09/2024] [Accepted: 07/15/2024] [Indexed: 08/15/2024] Open
Abstract
Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind's AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology.
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Affiliation(s)
- Hong Zhang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Jiajing Lan
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Huijie Wang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Ruijie Lu
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Nanqi Zhang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Xiaobai He
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
- Key Laboratory of Biomarkers and In Vitro Diagnosis Translation of Zhejiang Province, Hangzhou, China
| | - Jun Yang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Linjie Chen
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
- Zhejiang Engineering Research Centre for Key Technology of Diagnostic Testing, Hangzhou, China
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3
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Aguilar-Pineda J, González-Melchor M. Influence of the Water Model on the Structure and Interactions of the GPR40 Protein with the Lipid Membrane and the Solvent: Rigid versus Flexible Water Models. J Chem Theory Comput 2024; 20:6369-6387. [PMID: 38991114 PMCID: PMC11270832 DOI: 10.1021/acs.jctc.4c00571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 06/07/2024] [Accepted: 06/21/2024] [Indexed: 07/13/2024]
Abstract
G protein-coupled receptors (GPCR) are responsible for modulating various physiological functions and are thus related to the pathophysiology of different diseases. Being potential therapeutic targets, multiple computational methodologies have been developed to analyze their behavior and interactions with other species. The solvent, on the other hand, has received much less attention. In this work, we analyzed the effect of four explicit water models on the structure and interactions of the GPR40 receptor in its apo form. We employed the rigid SPC/E and TIP4P models, and their flexible versions, the FBA/ϵ and TIP4P/ϵflex. We explored the structural changes and their correlation with some bulk dynamic properties of water. Our results showed an adverse effect on the conservation of the secondary structure of the receptor with all the models due to the breaking of the intramolecular hydrogen bond network, being more evident for the TIP4P models. Notably, all four models brought the receptor to states similar to the active one, modifying the intracellular part of the TM5 and TM6 domains in a "hinge" type movement, allowing the opening of the structure. Regarding the dynamic properties, the rigid models showed results comparable to those obtained in other studies on membrane systems. However, flexible models exhibit disparities in the molecular representation of systems. Surprisingly, the FBA/ϵ model improves the molecular picture of several properties, even though their agreement with bulk diffusion is poorer. These findings reinforce our idea that exploring other water models or improving the current ones, to better represent the membrane interface, can lead to a positive impact on the description of the signal transduction mechanisms and the search of new drugs by targeting these receptors.
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Affiliation(s)
- Jorge
Alberto Aguilar-Pineda
- Instituto de Física
“Luis Rivera Terrazas”, Benemérita Universidad
Autónoma de Puebla, Av San Claudio, Cd Universitaria, Apdo. Postal
J-48, Puebla 72570, México
| | - Minerva González-Melchor
- Instituto de Física
“Luis Rivera Terrazas”, Benemérita Universidad
Autónoma de Puebla, Av San Claudio, Cd Universitaria, Apdo. Postal
J-48, Puebla 72570, México
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4
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Baselious F, Hilscher S, Hagemann S, Tripathee S, Robaa D, Barinka C, Hüttelmaier S, Schutkowski M, Sippl W. Utilization of an optimized AlphaFold protein model for structure-based design of a selective HDAC11 inhibitor with anti-neuroblastoma activity. Arch Pharm (Weinheim) 2024:e2400486. [PMID: 38996352 DOI: 10.1002/ardp.202400486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 06/26/2024] [Accepted: 06/27/2024] [Indexed: 07/14/2024]
Abstract
AlphaFold is an artificial intelligence approach for predicting the three-dimensional (3D) structures of proteins with atomic accuracy. One challenge that limits the use of AlphaFold models for drug discovery is the correct prediction of folding in the absence of ligands and cofactors, which compromises their direct use. We have previously described the optimization and use of the histone deacetylase 11 (HDAC11) AlphaFold model for the docking of selective inhibitors such as FT895 and SIS17. Based on the predicted binding mode of FT895 in the optimized HDAC11 AlphaFold model, a new scaffold for HDAC11 inhibitors was designed, and the resulting compounds were tested in vitro against various HDAC isoforms. Compound 5a proved to be the most active compound with an IC50 of 365 nM and was able to selectively inhibit HDAC11. Furthermore, docking of 5a showed a binding mode comparable to FT895 but could not adopt any reasonable poses in other HDAC isoforms. We further supported the docking results with molecular dynamics simulations that confirmed the predicted binding mode. 5a also showed promising activity with an EC50 of 3.6 µM on neuroblastoma cells.
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Affiliation(s)
- Fady Baselious
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, Halle (Saale), Germany
| | - Sebastian Hilscher
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, Halle (Saale), Germany
| | - Sven Hagemann
- Institute of Molecular Medicine, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Sunita Tripathee
- Institute of Molecular Medicine, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Dina Robaa
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, Halle (Saale), Germany
| | - Cyril Barinka
- Institute of Biotechnology of the Czech Academy of Sciences, BIOCEV, Vestec, Czech Republic
| | - Stefan Hüttelmaier
- Institute of Molecular Medicine, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Mike Schutkowski
- Charles Tanford Protein Center, Department of Enzymology, Institute of Biochemistry and Biotechnology, Martin-Luther-University of Halle-Wittenberg, Halle (Saale), Germany
| | - Wolfgang Sippl
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, Halle (Saale), Germany
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5
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Xu G, Luo Z, Yan Y, Wang Q, Ma J. OPUS-Rota5: A highly accurate protein side-chain modeling method with 3D-Unet and RotaFormer. Structure 2024; 32:1001-1010.e2. [PMID: 38657613 DOI: 10.1016/j.str.2024.03.015] [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: 11/24/2023] [Revised: 02/06/2024] [Accepted: 03/28/2024] [Indexed: 04/26/2024]
Abstract
Accurate protein side-chain modeling is crucial for protein folding and design. This is particularly true for molecular docking as ligands primarily interact with side chains. In this study, we introduce a two-stage side-chain modeling approach called OPUS-Rota5. It leverages a modified 3D-Unet to capture the local environmental features, including ligand information of each residue, and then employs the RotaFormer module to aggregate various types of features. Evaluation on three test sets, including recently released targets from CAMEO and CASP15, shows that OPUS-Rota5 significantly outperforms some other leading side-chain modeling methods. We also employ OPUS-Rota5 to refine the side chains of 25 G protein-coupled receptor targets predicted by AlphaFold2 and achieve a significantly improved success rate in a subsequent "back" docking of their natural ligands. Therefore, OPUS-Rota5 is a useful and effective tool for molecular docking, particularly for targets with relatively accurate predicted backbones but not side chains such as high-homology targets.
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Affiliation(s)
- Gang Xu
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China; Shanghai AI Laboratory, Shanghai 200030, China
| | - Zhenwei Luo
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China; Shanghai AI Laboratory, Shanghai 200030, China
| | - Yaming Yan
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China
| | - Qinghua Wang
- Center for Biomolecular Innovation, Harcam Biomedicines, Shanghai 200131, China
| | - Jianpeng Ma
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China; Shanghai AI Laboratory, Shanghai 200030, China.
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6
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Nguyen ATN, Nguyen DTN, Koh HY, Toskov J, MacLean W, Xu A, Zhang D, Webb GI, May LT, Halls ML. The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery. Br J Pharmacol 2024; 181:2371-2384. [PMID: 37161878 DOI: 10.1111/bph.16140] [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: 09/26/2022] [Revised: 04/14/2023] [Accepted: 04/27/2023] [Indexed: 05/11/2023] Open
Abstract
The application of artificial intelligence (AI) approaches to drug discovery for G protein-coupled receptors (GPCRs) is a rapidly expanding area. Artificial intelligence can be used at multiple stages during the drug discovery process, from aiding our understanding of the fundamental actions of GPCRs to the discovery of new ligand-GPCR interactions or the prediction of clinical responses. Here, we provide an overview of the concepts behind artificial intelligence, including the subfields of machine learning and deep learning. We summarise the published applications of artificial intelligence to different stages of the GPCR drug discovery process. Finally, we reflect on the benefits and limitations of artificial intelligence and share our vision for the exciting potential for further development of applications to aid GPCR drug discovery. In addition to making the drug discovery process "faster, smarter and cheaper," we anticipate that the application of artificial intelligence will create exciting new opportunities for GPCR drug discovery. LINKED ARTICLES: This article is part of a themed issue Therapeutic Targeting of G Protein-Coupled Receptors: hot topics from the Australasian Society of Clinical and Experimental Pharmacologists and Toxicologists 2021 Virtual Annual Scientific Meeting. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.14/issuetoc.
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Affiliation(s)
- Anh T N Nguyen
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Diep T N Nguyen
- Department of Information Technology, Faculty of Engineering and Technology, Vietnam National University, Cau Giay, Hanoi, Vietnam
| | - Huan Yee Koh
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
- Monash Data Futures Institute and Department of Data Science and Artificial Intelligence, Monash University, Clayton, Victoria, Australia
| | - Jason Toskov
- Monash DeepNeuron, Monash University, Clayton, Victoria, Australia
| | - William MacLean
- Monash DeepNeuron, Monash University, Clayton, Victoria, Australia
| | - Andrew Xu
- Monash DeepNeuron, Monash University, Clayton, Victoria, Australia
| | - Daokun Zhang
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
- Monash Data Futures Institute and Department of Data Science and Artificial Intelligence, Monash University, Clayton, Victoria, Australia
| | - Geoffrey I Webb
- Monash Data Futures Institute and Department of Data Science and Artificial Intelligence, Monash University, Clayton, Victoria, Australia
| | - Lauren T May
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Michelle L Halls
- Drug Discovery Biology Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
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7
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Lyu J, Kapolka N, Gumpper R, Alon A, Wang L, Jain MK, Barros-Álvarez X, Sakamoto K, Kim Y, DiBerto J, Kim K, Glenn IS, Tummino TA, Huang S, Irwin JJ, Tarkhanova OO, Moroz Y, Skiniotis G, Kruse AC, Shoichet BK, Roth BL. AlphaFold2 structures guide prospective ligand discovery. Science 2024; 384:eadn6354. [PMID: 38753765 PMCID: PMC11253030 DOI: 10.1126/science.adn6354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 04/24/2024] [Indexed: 05/18/2024]
Abstract
AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ2 and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules and comparing results with those obtained from docking against the experimental structures. Hit rates were high and similar for the experimental and AF2 structures, as were affinities. Success in docking against the AF2 models was achieved despite differences between orthosteric residue conformations in the AF2 models and the experimental structures. Determination of the cryo-electron microscopy structure for one of the more potent 5-HT2A ligands from the AF2 docking revealed residue accommodations that resembled the AF2 prediction. AF2 models may sample conformations that differ from experimental structures but remain low energy and relevant for ligand discovery, extending the domain of structure-based drug design.
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Affiliation(s)
- Jiankun Lyu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
- The Evnin Family Laboratory of Computational Molecular Discovery, The Rockefeller University, New York, NY 10065, USA
| | - Nicholas Kapolka
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Ryan Gumpper
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Assaf Alon
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Liang Wang
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94035, USA
| | - Manish K. Jain
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Ximena Barros-Álvarez
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94035, USA
| | - Kensuke Sakamoto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Yoojoong Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Jeffrey DiBerto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Kuglae Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Isabella S. Glenn
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Tia A. Tummino
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Sijie Huang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - John J. Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | | | - Yurii Moroz
- Chemspace LLC, Kyiv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Kyiv 01601, Ukraine
- Enamine Ltd., Kyiv 02094, Ukraine
| | - Georgios Skiniotis
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94035, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Andrew C. Kruse
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Brian K. Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Bryan L. Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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8
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He X, Zhao L, Tian Y, Li R, Chu Q, Gu Z, Zheng M, Wang Y, Li S, Jiang H, Jiang Y, Wen L, Wang D, Cheng X. Highly accurate carbohydrate-binding site prediction with DeepGlycanSite. Nat Commun 2024; 15:5163. [PMID: 38886381 PMCID: PMC11183243 DOI: 10.1038/s41467-024-49516-2] [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: 11/27/2023] [Accepted: 06/10/2024] [Indexed: 06/20/2024] Open
Abstract
As the most abundant organic substances in nature, carbohydrates are essential for life. Understanding how carbohydrates regulate proteins in the physiological and pathological processes presents opportunities to address crucial biological problems and develop new therapeutics. However, the diversity and complexity of carbohydrates pose a challenge in experimentally identifying the sites where carbohydrates bind to and act on proteins. Here, we introduce a deep learning model, DeepGlycanSite, capable of accurately predicting carbohydrate-binding sites on a given protein structure. Incorporating geometric and evolutionary features of proteins into a deep equivariant graph neural network with the transformer architecture, DeepGlycanSite remarkably outperforms previous state-of-the-art methods and effectively predicts binding sites for diverse carbohydrates. Integrating with a mutagenesis study, DeepGlycanSite reveals the guanosine-5'-diphosphate-sugar-recognition site of an important G-protein coupled receptor. These findings demonstrate DeepGlycanSite is invaluable for carbohydrate-binding site prediction and could provide insights into molecular mechanisms underlying carbohydrate-regulation of therapeutically important proteins.
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Affiliation(s)
- Xinheng He
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Lifen Zhao
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Yinping Tian
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Rui Li
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Qinyu Chu
- School of Pharmaceutical Science and Technology, Hangzhou Institute of Advanced Study, Hangzhou, China
| | - Zhiyong Gu
- School of Pharmaceutical Science and Technology, Hangzhou Institute of Advanced Study, Hangzhou, China
| | - Mingyue Zheng
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute of Advanced Study, Hangzhou, China
| | - Yusong Wang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China
| | - Shaoning Li
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Hualiang Jiang
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute of Advanced Study, Hangzhou, China
- Lingang Laboratory, Shanghai, China
| | - Yi Jiang
- Lingang Laboratory, Shanghai, China
| | - Liuqing Wen
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | | | - Xi Cheng
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
- School of Pharmaceutical Science and Technology, Hangzhou Institute of Advanced Study, Hangzhou, China.
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9
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Wang L, Wen Z, Liu SW, Zhang L, Finley C, Lee HJ, Fan HJS. Overview of AlphaFold2 and breakthroughs in overcoming its limitations. Comput Biol Med 2024; 176:108620. [PMID: 38761500 DOI: 10.1016/j.compbiomed.2024.108620] [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: 10/29/2023] [Revised: 05/01/2024] [Accepted: 05/14/2024] [Indexed: 05/20/2024]
Abstract
Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein's three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.
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Affiliation(s)
- Lei Wang
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China
| | - Zehua Wen
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China
| | - Shi-Wei Liu
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China
| | - Lihong Zhang
- Digestive Department, Binhai New Area Hospital of TCM Tianjin, Tianjin, 300451, China
| | - Cierra Finley
- Department of Natural Sciences, Southwest Tennessee Community College, Memphis, TN, 38015, USA
| | - Ho-Jin Lee
- Department of Natural Sciences, Southwest Tennessee Community College, Memphis, TN, 38015, USA; Division of Natural & Mathematical Sciences, LeMoyne-Own College, Memphis, TN, 38126, USA.
| | - Hua-Jun Shawn Fan
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China.
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10
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Zhang H, Fan H, Wang J, Hou T, Saravanan KM, Xia W, Kan HW, Li J, Zhang JZH, Liang X, Chen Y. Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery. Brief Bioinform 2024; 25:bbae281. [PMID: 38864340 PMCID: PMC11167311 DOI: 10.1093/bib/bbae281] [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: 02/28/2024] [Revised: 05/05/2024] [Accepted: 05/29/2024] [Indexed: 06/13/2024] Open
Abstract
G-protein coupled receptors (GPCRs), crucial in various diseases, are targeted of over 40% of approved drugs. However, the reliable acquisition of experimental GPCRs structures is hindered by their lipid-embedded conformations. Traditional protein-ligand interaction models falter in GPCR-drug interactions, caused by limited and low-quality structures. Generalized models, trained on soluble protein-ligand pairs, are also inadequate. To address these issues, we developed two models, DeepGPCR_BC for binary classification and DeepGPCR_RG for affinity prediction. These models use non-structural GPCR-ligand interaction data, leveraging graph convolutional networks and mol2vec techniques to represent binding pockets and ligands as graphs. This approach significantly speeds up predictions while preserving critical physical-chemical and spatial information. In independent tests, DeepGPCR_BC surpassed Autodock Vina and Schrödinger Dock with an area under the curve of 0.72, accuracy of 0.68 and true positive rate of 0.73, whereas DeepGPCR_RG demonstrated a Pearson correlation of 0.39 and root mean squared error of 1.34. We applied these models to screen drug candidates for GPR35 (Q9HC97), yielding promising results with three (F545-1970, K297-0698, S948-0241) out of eight candidates. Furthermore, we also successfully obtained six active inhibitors for GLP-1R. Our GPCR-specific models pave the way for efficient and accurate large-scale virtual screening, potentially revolutionizing drug discovery in the GPCR field.
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Affiliation(s)
- Haiping Zhang
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China
| | - Hongjie Fan
- Ganjiang Chinese Medicine Innovation Center, Xinqizhou East Road 888, Ganjiang New Area, Nanchang 330000, China
| | - Jixia Wang
- Ganjiang Chinese Medicine Innovation Center, Xinqizhou East Road 888, Ganjiang New Area, Nanchang 330000, China
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, No. 457 Zhongshan Road, Dalian 116023, China
| | - Tao Hou
- Ganjiang Chinese Medicine Innovation Center, Xinqizhou East Road 888, Ganjiang New Area, Nanchang 330000, China
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, No. 457 Zhongshan Road, Dalian 116023, China
| | - Konda Mani Saravanan
- Department of Biotechnology, Bharath Institute of Higher Education and Research, Agharam Road 173, Selaiyur, Chennai, Tamil Nadu 600073, India
| | - Wei Xia
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China
| | - Hei Wun Kan
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China
| | - Junxin Li
- Shenzhen Laboratory of Human Antibody Engineering, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China
| | - John Z H Zhang
- Faculty of Synthetic Biology and Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, No. 1068 Xueyuan Boulevard, Nanshan District, Shenzhen 518055, Guangdong Province, China
| | - Xinmiao Liang
- Ganjiang Chinese Medicine Innovation Center, Xinqizhou East Road 888, Ganjiang New Area, Nanchang 330000, China
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, No. 457 Zhongshan Road, Dalian 116023, China
| | - Yang Chen
- Ganjiang Chinese Medicine Innovation Center, Xinqizhou East Road 888, Ganjiang New Area, Nanchang 330000, China
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, No. 457 Zhongshan Road, Dalian 116023, China
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11
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Zhang Y, Liu Y, Wu X, Lu X, Wang M, Ye D, Iqbal C, Sun W, Zhang X, Zhang L, Yang X. A Novel Peptidomimetic Insecticide: Dippu-AstR-Based Rational Design and Biological Activity of Allatostatin Analogs. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:11341-11350. [PMID: 38713071 DOI: 10.1021/acs.jafc.3c09231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Insect neuropeptides play an essential role in regulating growth, development, reproduction, nerve conduction, metabolism, and behavior in insects; therefore, G protein-coupled receptors of neuropeptides are considered important targets for designing green insecticides. Cockroach-type allatostatins (ASTs) (FGLamides allatostatins) are important insect neuropeptides in Diploptera punctata that inhibit juvenile hormone (JH) synthesis in the corpora allata and affect growth, development, and reproduction of insects. Therefore, the pursuit of novel insecticides targeting the allatostatin receptor (AstR) holds significant importance. Previously, we identified an AST analogue, H17, as a promising candidate for pest control. Herein, we first modeled the 3D structure of AstR in D. punctata (Dippu-AstR) and predicted the binding mode of H17 with Dippu-AstR to study the critical interactions and residues favorable to its bioactivity. Based on this binding mode, we designed and synthesized a series of H17 derivatives and assessed their insecticidal activity against D. punctata. Among them, compound Q6 showed higher insecticidal activity than H17 against D. punctata by inhibiting JH biosynthesis, indicating that Q6 is a potential candidate for a novel insect growth regulator (IGR)-based insecticide. Moreover, Q6 exhibited insecticidal activity against Plutella xylostella, indicating that these AST analogs may have a wider insecticidal spectrum. The underlying mechanisms and molecular conformations mediating the interactions of Q6 with Dippu-AstR were explored to understand its effects on the bioactivity. The present work clarifies how a target-based strategy facilitates the discovery of new peptide mimics with better bioactivity, enabling improved IGR-based insecticide potency in sustainable agriculture.
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Affiliation(s)
- Yimeng Zhang
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Yan Liu
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Xiaoqing Wu
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Xingxing Lu
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Meizi Wang
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Dexing Ye
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Chandni Iqbal
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Wenyu Sun
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Xinyuan Zhang
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
| | - Li Zhang
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
- Key Laboratory of National Forestry and Grassland Administration on Pest Chemical Control, China Agricultural University, Beijing 100193, China
| | - Xinling Yang
- Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
- Key Laboratory of National Forestry and Grassland Administration on Pest Chemical Control, China Agricultural University, Beijing 100193, China
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12
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Dunn I, Koes DR. Accelerating Inference in Molecular Diffusion Models with Latent Representations of Protein Structure. ARXIV 2024:arXiv:2311.13466v2. [PMID: 38764591 PMCID: PMC11100911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 05/21/2024]
Abstract
Diffusion generative models have emerged as a powerful framework for addressing problems in structural biology and structure-based drug design. These models operate directly on 3D molecular structures. Due to the unfavorable scaling of graph neural networks (GNNs) with graph size as well as the relatively slow inference speeds inherent to diffusion models, many existing molecular diffusion models rely on coarse-grained representations of protein structure to make training and inference feasible. However, such coarse-grained representations discard essential information for modeling molecular interactions and impair the quality of generated structures. In this work, we present a novel GNN-based architecture for learning latent representations of molecular structure. When trained end-to-end with a diffusion model for de novo ligand design, our model achieves comparable performance to one with an all-atom protein representation while exhibiting a 3-fold reduction in inference time.
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Affiliation(s)
- Ian Dunn
- Dept. of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260
| | - David Ryan Koes
- Dept. of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260
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13
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Zhang M, Chen T, Lu X, Lan X, Chen Z, Lu S. G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery. Signal Transduct Target Ther 2024; 9:88. [PMID: 38594257 PMCID: PMC11004190 DOI: 10.1038/s41392-024-01803-6] [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: 08/15/2023] [Revised: 02/19/2024] [Accepted: 03/13/2024] [Indexed: 04/11/2024] Open
Abstract
G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.
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Affiliation(s)
- Mingyang Zhang
- Key Laboratory of Protection, Development and Utilization of Medicinal Resources in Liupanshan Area, Ministry of Education, Peptide & Protein Drug Research Center, School of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Ting Chen
- Department of Cardiology, Changzheng Hospital, Affiliated to Naval Medical University, Shanghai, 200003, China
| | - Xun Lu
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xiaobing Lan
- Key Laboratory of Protection, Development and Utilization of Medicinal Resources in Liupanshan Area, Ministry of Education, Peptide & Protein Drug Research Center, School of Pharmacy, Ningxia Medical University, Yinchuan, 750004, China
| | - Ziqiang Chen
- Department of Orthopedics, Changhai Hospital, Affiliated to Naval Medical University, Shanghai, 200433, China.
| | - Shaoyong Lu
- Key Laboratory of Protection, Development and Utilization of Medicinal Resources in Liupanshan Area, Ministry of Education, Peptide & Protein Drug Research Center, School of Pharmacy, Ningxia Medical University, Yinchuan, 750004, 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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Liu Y, Xu Y, Xu Y, Zhao Z, Cheng GJ, Ren R, Chiang YC. Identifying Residues for Substrate Recognition in Human GPAT4 by Molecular Dynamics Simulations. Int J Mol Sci 2024; 25:3729. [PMID: 38612541 PMCID: PMC11011501 DOI: 10.3390/ijms25073729] [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: 01/25/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
Glycerol-3-phosphate acyltransferase (GPAT) catalyzes the first step in triacylglycerol synthesis. Understanding its substrate recognition mechanism may help to design drugs to regulate the production of glycerol lipids in cells. In this work, we investigate how the native substrate, glycerol-3-phosphate (G3P), and palmitoyl-coenzyme A (CoA) bind to the human GPAT isoform GPAT4 via molecular dynamics simulations (MD). As no experimentally resolved GPAT4 structure is available, the AlphaFold model is employed to construct the GPAT4-substrate complex model. Using another isoform, GPAT1, we demonstrate that once the ligand binding is properly addressed, the AlphaFold complex model can deliver similar results to the experimentally resolved structure in MD simulations. Following the validated protocol of complex construction, we perform MD simulations using the GPAT4-substrate complex. Our simulations reveal that R427 is an important residue in recognizing G3P via a stable salt bridge, but its motion can bring the ligand to different binding hotspots on GPAT4. Such high flexibility can be attributed to the flexible region that exists only on GPAT4 and not on GPAT1. Our study reveals the substrate recognition mechanism of GPAT4 and hence paves the way towards designing GPAT4 inhibitors.
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Affiliation(s)
- Yulan Liu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yunong Xu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yinuo Xu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Zhihao Zhao
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Gui-Juan Cheng
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Ruobing Ren
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai 200438, China
- Shanghai Qi Zhi Institute, Shanghai 200232, China
| | - Ying-Chih Chiang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
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15
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Floch A, Galochkina T, Pirenne F, Tournamille C, de Brevern AG. Molecular dynamics of the human RhD and RhAG blood group proteins. Front Chem 2024; 12:1360392. [PMID: 38566898 PMCID: PMC10985258 DOI: 10.3389/fchem.2024.1360392] [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: 12/22/2023] [Accepted: 03/07/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction: Blood group antigens of the RH system (formerly known as "Rhesus") play an important role in transfusion medicine because of the severe haemolytic consequences of antibodies to these antigens. No crystal structure is available for RhD proteins with its partner RhAG, and the precise stoichiometry of the trimer complex remains unknown. Methods: To analyse their structural properties, the trimers formed by RhD and/or RhAG subunits were generated by protein modelling and molecular dynamics simulations were performed. Results: No major differences in structural behaviour were found between trimers of different compositions. The conformation of the subunits is relatively constant during molecular dynamics simulations, except for three large disordered loops. Discussion: This work makes it possible to propose a reasonable stoichiometry and demonstrates the potential of studying the structural behaviour of these proteins to investigate the hundreds of genetic variants relevant to transfusion medicine.
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Affiliation(s)
- Aline Floch
- University Paris Est Créteil, INSERM U955 Equipe Transfusion et Maladies du Globule Rouge, IMRB, Créteil, France
- Laboratoire de Biologie Médicale de Référence en Immuno-Hématologie Moléculaire, Etablissement Français du Sang Ile-de-France, Créteil, France
| | - Tatiana Galochkina
- Université Paris Cité and Université des Antilles and Université de la Réunion, Biologie Intégrée du Globule Rouge, UMR_S1134, BIGR, INSERM, DSIMB Bioinformatics team, Paris, France
| | - France Pirenne
- University Paris Est Créteil, INSERM U955 Equipe Transfusion et Maladies du Globule Rouge, IMRB, Créteil, France
- Laboratoire de Biologie Médicale de Référence en Immuno-Hématologie Moléculaire, Etablissement Français du Sang Ile-de-France, Créteil, France
| | - Christophe Tournamille
- University Paris Est Créteil, INSERM U955 Equipe Transfusion et Maladies du Globule Rouge, IMRB, Créteil, France
- Laboratoire de Biologie Médicale de Référence en Immuno-Hématologie Moléculaire, Etablissement Français du Sang Ile-de-France, Créteil, France
| | - Alexandre G. de Brevern
- Université Paris Cité and Université des Antilles and Université de la Réunion, Biologie Intégrée du Globule Rouge, UMR_S1134, BIGR, INSERM, DSIMB Bioinformatics team, Paris, France
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16
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Lyu J, Kapolka N, Gumpper R, Alon A, Wang L, Jain MK, Barros-Álvarez X, Sakamoto K, Kim Y, DiBerto J, Kim K, Tummino TA, Huang S, Irwin JJ, Tarkhanova OO, Moroz Y, Skiniotis G, Kruse AC, Shoichet BK, Roth BL. AlphaFold2 structures template ligand discovery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.20.572662. [PMID: 38187536 PMCID: PMC10769324 DOI: 10.1101/2023.12.20.572662] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
AlphaFold2 (AF2) and RosettaFold have greatly expanded the number of structures available for structure-based ligand discovery, even though retrospective studies have cast doubt on their direct usefulness for that goal. Here, we tested unrefined AF2 models prospectively, comparing experimental hit-rates and affinities from large library docking against AF2 models vs the same screens targeting experimental structures of the same receptors. In retrospective docking screens against the σ2 and the 5-HT2A receptors, the AF2 structures struggled to recapitulate ligands that we had previously found docking against the receptors' experimental structures, consistent with published results. Prospective large library docking against the AF2 models, however, yielded similar hit rates for both receptors versus docking against experimentally-derived structures; hundreds of molecules were prioritized and tested against each model and each structure of each receptor. The success of the AF2 models was achieved despite differences in orthosteric pocket residue conformations for both targets versus the experimental structures. Intriguingly, against the 5-HT2A receptor the most potent, subtype-selective agonists were discovered via docking against the AF2 model, not the experimental structure. To understand this from a molecular perspective, a cryoEM structure was determined for one of the more potent and selective ligands to emerge from docking against the AF2 model of the 5-HT2A receptor. Our findings suggest that AF2 models may sample conformations that are relevant for ligand discovery, much extending the domain of applicability of structure-based ligand discovery.
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Affiliation(s)
- Jiankun Lyu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
- The Evnin Family Laboratory of Computational Molecular Discovery, The Rockefeller University, New York, NY 10065, USA (present address)
| | - Nicholas Kapolka
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Ryan Gumpper
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Assaf Alon
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Pharmacology Department, Yale School of Medicine, New Haven, CT 06510, USA (present address)
| | - Liang Wang
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Manish K Jain
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Ximena Barros-Álvarez
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kensuke Sakamoto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Yoojoong Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Jeffrey DiBerto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Kuglae Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
- Department of Pharmacy, College of Pharmacy, Yonsei University, Incheon 21983, Korea (present address)
| | - Tia A Tummino
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Sijie Huang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | | | - Yurii Moroz
- Chemspace LLC, Kyiv, 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Kyiv, 01601, Ukraine
- Enamine Ltd., Kyiv, 02094, Ukraine
| | - Georgios Skiniotis
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, US
| | - Andrew C Kruse
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7360, USA
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17
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Smith MD, Darryl Quarles L, Demerdash O, Smith JC. Drugging the entire human proteome: Are we there yet? Drug Discov Today 2024; 29:103891. [PMID: 38246414 DOI: 10.1016/j.drudis.2024.103891] [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: 10/18/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024]
Abstract
Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.
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Affiliation(s)
- Micholas Dean Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA
| | - L Darryl Quarles
- Departments of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA; ORRxD LLC, 3404 Olney Drive, Durham, NC 27705, USA
| | - Omar Demerdash
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Jeremy C Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA.
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18
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Cui W, Yuan S. Will the hype of automated drug discovery finally be realized? Expert Opin Drug Discov 2024; 19:259-262. [PMID: 38078415 DOI: 10.1080/17460441.2023.2293157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 12/06/2023] [Indexed: 02/27/2024]
Affiliation(s)
- Wenqiang Cui
- Shenzhen Institute of Advanced Technology Chinese Academy of Sciences, Shenzhen, China
| | - Shuguang Yuan
- Shenzhen Institute of Advanced Technology Chinese Academy of Sciences, Shenzhen, China
- Computational Drug Discovery, AlphaMol Science Ltd, Shenzhen, China
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19
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Yao H, Wang X, Chi J, Chen H, Liu Y, Yang J, Yu J, Ruan Y, Xiang X, Pi J, Xu JF. Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures. Molecules 2024; 29:964. [PMID: 38474476 DOI: 10.3390/molecules29050964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/29/2024] [Accepted: 02/09/2024] [Indexed: 03/14/2024] Open
Abstract
Major Depressive Disorder (MDD) is a complex mental disorder that involves alterations in signal transmission across multiple scales and structural abnormalities. The development of effective antidepressants (ADs) has been hindered by the dominance of monoamine hypothesis, resulting in slow progress. Traditional ADs have undesirable traits like delayed onset of action, limited efficacy, and severe side effects. Recently, two categories of fast-acting antidepressant compounds have surfaced, dissociative anesthetics S-ketamine and its metabolites, as well as psychedelics such as lysergic acid diethylamide (LSD). This has led to structural research and drug development of the receptors that they target. This review provides breakthroughs and achievements in the structure of depression-related receptors and novel ADs based on these. Cryo-electron microscopy (cryo-EM) has enabled researchers to identify the structures of membrane receptors, including the N-methyl-D-aspartate receptor (NMDAR) and the 5-hydroxytryptamine 2A (5-HT2A) receptor. These high-resolution structures can be used for the development of novel ADs using virtual drug screening (VDS). Moreover, the unique antidepressant effects of 5-HT1A receptors in various brain regions, and the pivotal roles of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and tyrosine kinase receptor 2 (TrkB) in regulating synaptic plasticity, emphasize their potential as therapeutic targets. Using structural information, a series of highly selective ADs were designed based on the different role of receptors in MDD. These molecules have the favorable characteristics of rapid onset and low adverse drug reactions. This review offers researchers guidance and a methodological framework for the structure-based design of ADs.
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Affiliation(s)
- Hanbo Yao
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Xiaodong Wang
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiaxin Chi
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Haorong Chen
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Yilin Liu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiayi Yang
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiaqi Yu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Yongdui Ruan
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
| | - Xufu Xiang
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics and Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jiang Pi
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jun-Fa Xu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
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20
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Cao D, Zhang P, Wang S. Advances in structure-based drug design: The potential for precision therapeutics in psychiatric disorders. Neuron 2024; 112:526-538. [PMID: 38290517 DOI: 10.1016/j.neuron.2024.01.004] [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: 10/21/2023] [Revised: 12/15/2023] [Accepted: 01/04/2024] [Indexed: 02/01/2024]
Abstract
Over the years, the field of GPCR drug design has undergone a remarkable evolution, fueled by advancements in science and technology. This evolution has given rise to a diverse range of ideas and approaches in structure-based drug design, bolstering the versatility and strength of the GPCR drug design toolbox. This review encapsulates the iterative development process, navigating challenges and opportunities in structure-based drug design within GPCRs. With a focused emphasis on its impact on psychiatric disorders, the review accentuates recent advancements and delves into the potentials unlocked by emerging technologies. The review explores the intricate interplay between scientific progress and iterative refinement, offering profound insights into the potential pathways that lie ahead for GPCR drug design.
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Affiliation(s)
- Dongmei Cao
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Pei Zhang
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Sheng Wang
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China; Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
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21
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Li H, Sun X, Cui W, Xu M, Dong J, Ekundayo BE, Ni D, Rao Z, Guo L, Stahlberg H, Yuan S, Vogel H. Computational drug development for membrane protein targets. Nat Biotechnol 2024; 42:229-242. [PMID: 38361054 DOI: 10.1038/s41587-023-01987-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 09/13/2023] [Indexed: 02/17/2024]
Abstract
The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.
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Affiliation(s)
- Haijian Li
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Xiaolin Sun
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Wenqiang Cui
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Marc Xu
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Junlin Dong
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Babatunde Edukpe Ekundayo
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Dongchun Ni
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Zhili Rao
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Liwei Guo
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Henning Stahlberg
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
| | - Shuguang Yuan
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
| | - Horst Vogel
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
- Institut des Sciences et Ingénierie Chimiques (ISIC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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22
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Baselious F, Hilscher S, Robaa D, Barinka C, Schutkowski M, Sippl W. Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor. Int J Mol Sci 2024; 25:1358. [PMID: 38279359 PMCID: PMC10816272 DOI: 10.3390/ijms25021358] [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: 12/11/2023] [Revised: 01/14/2024] [Accepted: 01/20/2024] [Indexed: 01/28/2024] Open
Abstract
HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold is a machine learning approach that can predict the 3D structure of proteins with high accuracy even in absence of similar structures. However, the fact that AlphaFold models are predicted in the absence of small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an HDAC11 AlphaFold model by adding the catalytic zinc ion and minimization in the presence of reported HDAC11 inhibitors. In the current study, we implement a comparative structure-based virtual screening approach utilizing the previously optimized HDAC11 AlphaFold model to identify novel and selective HDAC11 inhibitors. The stepwise virtual screening approach was successful in identifying a hit that was subsequently tested using an in vitro enzymatic assay. The hit compound showed an IC50 value of 3.5 µM for HDAC11 and could selectively inhibit HDAC11 over other HDAC subtypes at 10 µM concentration. In addition, we carried out molecular dynamics simulations to further confirm the binding hypothesis obtained by the docking study. These results reinforce the previously presented AlphaFold optimization approach and confirm the applicability of AlphaFold models in the search for novel inhibitors for drug discovery.
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Affiliation(s)
- Fady Baselious
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (F.B.); (S.H.); (D.R.)
| | - Sebastian Hilscher
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (F.B.); (S.H.); (D.R.)
| | - Dina Robaa
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (F.B.); (S.H.); (D.R.)
| | - Cyril Barinka
- Institute of Biotechnology of the Czech Academy of Sciences, BIOCEV, 252 50 Vestec, Czech Republic;
| | - Mike Schutkowski
- Charles Tanford Protein Center, Department of Enzymology, Institute of Biochemistry and Biotechnology, Martin-Luther-University of Halle-Wittenberg, 06120 Halle (Saale), Germany;
| | - Wolfgang Sippl
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (F.B.); (S.H.); (D.R.)
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23
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Kozma MT, Pérez-Moreno JL, Gandhi NS, Hernandez Jeppesen L, Durica DS, Ventura T, Mykles DL. In silico analysis of crustacean hyperglycemic hormone family G protein-coupled receptor candidates. Front Endocrinol (Lausanne) 2024; 14:1322800. [PMID: 38298185 PMCID: PMC10828670 DOI: 10.3389/fendo.2023.1322800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 12/01/2023] [Indexed: 02/02/2024] Open
Abstract
Ecdysteroid molting hormone synthesis is directed by a pair of molting glands or Y-organs (YOs), and this synthesis is inhibited by molt-inhibiting hormone (MIH). MIH is a member of the crustacean hyperglycemic hormone (CHH) neuropeptide superfamily, which includes CHH and insect ion transport peptide (ITP). It is hypothesized that the MIH receptor is a Class A (Rhodopsin-like) G protein-coupled receptor (GPCR). The YO of the blackback land crab, Gecarcinus lateralis, expresses 49 Class A GPCRs, three of which (Gl-CHHR-A9, -A10, and -A12) were provisionally assigned as CHH-like receptors. CrusTome, a transcriptome database assembled from 189 crustaceans and 12 ecdysozoan outgroups, was used to deorphanize candidate MIH/CHH GPCRs, relying on sequence homology to three functionally characterized ITP receptors (BNGR-A2, BNGR-A24, and BNGR-A34) in the silk moth, Bombyx mori. Phylogenetic analysis and multiple sequence alignments across major taxonomic groups revealed extensive expansion and diversification of crustacean A2, A24, and A34 receptors, designated CHH Family Receptor Candidates (CFRCs). The A2 clade was divided into three subclades; A24 clade was divided into five subclades; and A34 was divided into six subclades. The subclades were distinguished by conserved motifs in extracellular loop (ECL) 2 and ECL3 in the ligand-binding region. Eleven of the 14 subclades occurred in decapod crustaceans. In G. lateralis, seven CFRC sequences, designated Gl-CFRC-A2α1, -A24α, -A24β1, -A24β2, -A34α2, -A34β1, and -A34β2, were identified; the three A34 sequences corresponded to Gl-GPCR-A12, -A9, and A10, respectively. ECL2 in all the CFRC sequences had a two-stranded β-sheet structure similar to human Class A GPCRs, whereas the ECL2 of decapod CFRC-A34β1/β2 had an additional two-stranded β-sheet. We hypothesize that this second β-sheet on ECL2 plays a role in MIH/CHH binding and activation, which will be investigated further with functional assays.
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Affiliation(s)
- Mihika T. Kozma
- Department of Biology, Colorado State University, Fort Collins, CO, United States
| | | | - Neha S. Gandhi
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
- School of Chemistry and Physics, Queensland University of Technology, Brisbane, QLD, Australia
| | | | - David S. Durica
- Department of Biology, University of Oklahoma, Norman, OK, United States
| | - Tomer Ventura
- Centre for BioInnovation and School of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD, Australia
| | - Donald L. Mykles
- Department of Biology, Colorado State University, Fort Collins, CO, United States
- Coastal and Marine Sciences Institute, University of California-Davis Bodega Marine Laboratory, Bodega Bay, CA, United States
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24
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Addis P, Bali U, Baron F, Campbell A, Harborne S, Jagger L, Milne G, Pearce M, Rosethorne EM, Satchell R, Swift D, Young B, Unitt JF. Key aspects of modern GPCR drug discovery. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2024; 29:1-22. [PMID: 37625784 DOI: 10.1016/j.slasd.2023.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/07/2023] [Accepted: 08/22/2023] [Indexed: 08/27/2023]
Abstract
G-protein-coupled receptors (GPCRs) are the largest and most versatile cell surface receptor family with a broad repertoire of ligands and functions. We've learned an enormous amount about discovering drugs of this receptor class since the first GPCR was cloned and expressed in 1986, such that it's now well-recognized that GPCRs are the most successful target class for approved drugs. Here we take the reader through a GPCR drug discovery journey from target to the clinic, highlighting the key learnings, best practices, challenges, trends and insights on discovering drugs that ultimately modulate GPCR function therapeutically in patients. The future of GPCR drug discovery is inspiring, with more desirable drug mechanisms and new technologies enabling the delivery of better and more successful drugs.
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Affiliation(s)
- Phil Addis
- Bioscience, Medicinal Chemistry, Pharmacology and Protein Science Departments, Sygnature Discovery Ltd, BioCity, Pennyfoot Street, Nottingham NG1 1GR, UK
| | - Utsav Bali
- Bioscience, Medicinal Chemistry, Pharmacology and Protein Science Departments, Sygnature Discovery Ltd, BioCity, Pennyfoot Street, Nottingham NG1 1GR, UK
| | - Frank Baron
- Bioscience, Medicinal Chemistry, Pharmacology and Protein Science Departments, Sygnature Discovery Ltd, BioCity, Pennyfoot Street, Nottingham NG1 1GR, UK
| | - Adrian Campbell
- Bioscience, Medicinal Chemistry, Pharmacology and Protein Science Departments, Sygnature Discovery Ltd, BioCity, Pennyfoot Street, Nottingham NG1 1GR, UK
| | - Steven Harborne
- Bioscience, Medicinal Chemistry, Pharmacology and Protein Science Departments, Sygnature Discovery Ltd, BioCity, Pennyfoot Street, Nottingham NG1 1GR, UK
| | - Liz Jagger
- Bioscience, Medicinal Chemistry, Pharmacology and Protein Science Departments, Sygnature Discovery Ltd, BioCity, Pennyfoot Street, Nottingham NG1 1GR, UK
| | - Gavin Milne
- Bioscience, Medicinal Chemistry, Pharmacology and Protein Science Departments, Sygnature Discovery Ltd, BioCity, Pennyfoot Street, Nottingham NG1 1GR, UK
| | - Martin Pearce
- Bioscience, Medicinal Chemistry, Pharmacology and Protein Science Departments, Sygnature Discovery Ltd, BioCity, Pennyfoot Street, Nottingham NG1 1GR, UK
| | - Elizabeth M Rosethorne
- Bioscience, Medicinal Chemistry, Pharmacology and Protein Science Departments, Sygnature Discovery Ltd, BioCity, Pennyfoot Street, Nottingham NG1 1GR, UK
| | - Rupert Satchell
- Bioscience, Medicinal Chemistry, Pharmacology and Protein Science Departments, Sygnature Discovery Ltd, BioCity, Pennyfoot Street, Nottingham NG1 1GR, UK
| | - Denise Swift
- Bioscience, Medicinal Chemistry, Pharmacology and Protein Science Departments, Sygnature Discovery Ltd, BioCity, Pennyfoot Street, Nottingham NG1 1GR, UK
| | - Barbara Young
- Bioscience, Medicinal Chemistry, Pharmacology and Protein Science Departments, Sygnature Discovery Ltd, BioCity, Pennyfoot Street, Nottingham NG1 1GR, UK
| | - John F Unitt
- Bioscience, Medicinal Chemistry, Pharmacology and Protein Science Departments, Sygnature Discovery Ltd, BioCity, Pennyfoot Street, Nottingham NG1 1GR, UK.
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25
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Abstract
Computational prediction of protein structure has been pursued intensely for decades, motivated largely by the goal of using structural models for drug discovery. Recently developed machine-learning methods such as AlphaFold 2 (AF2) have dramatically improved protein structure prediction, with reported accuracy approaching that of experimentally determined structures. To what extent do these advances translate to an ability to predict more accurately how drugs and drug candidates bind to their target proteins? Here, we carefully examine the utility of AF2 protein structure models for predicting binding poses of drug-like molecules at the largest class of drug targets, the G-protein-coupled receptors. We find that AF2 models capture binding pocket structures much more accurately than traditional homology models, with errors nearly as small as differences between structures of the same protein determined experimentally with different ligands bound. Strikingly, however, the accuracy of ligand-binding poses predicted by computational docking to AF2 models is not significantly higher than when docking to traditional homology models and is much lower than when docking to structures determined experimentally without these ligands bound. These results have important implications for all those who might use predicted protein structures for drug discovery.
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Affiliation(s)
- Masha Karelina
- Biophysics Program, Stanford UniversityStanfordUnited States
- Department of Computer Science, Stanford UniversityStanfordUnited States
- Department of Molecular and Cellular Physiology, Stanford University School of MedicineStanfordUnited States
- Department of Structural Biology, Stanford University School of MedicineStanfordUnited States
- Institute for Computational and Mathematical Engineering, Stanford UniversityStanfordUnited States
| | - Joseph J Noh
- Department of Computer Science, Stanford UniversityStanfordUnited States
- Department of Molecular and Cellular Physiology, Stanford University School of MedicineStanfordUnited States
- Department of Structural Biology, Stanford University School of MedicineStanfordUnited States
- Institute for Computational and Mathematical Engineering, Stanford UniversityStanfordUnited States
| | - Ron O Dror
- Biophysics Program, Stanford UniversityStanfordUnited States
- Department of Computer Science, Stanford UniversityStanfordUnited States
- Department of Molecular and Cellular Physiology, Stanford University School of MedicineStanfordUnited States
- Department of Structural Biology, Stanford University School of MedicineStanfordUnited States
- Institute for Computational and Mathematical Engineering, Stanford UniversityStanfordUnited States
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26
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Cen LP, Ng TK, Ji J, Lin JW, Yao Y, Yang R, Dong G, Cao Y, Chen C, Yao SQ, Wang WY, Huang Z, Qiu K, Pang CP, Liu Q, Zhang M. Artificial Intelligence-based database for prediction of protein structure and their alterations in ocular diseases. Database (Oxford) 2023; 2023:baad083. [PMID: 38109881 PMCID: PMC10727695 DOI: 10.1093/database/baad083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 07/17/2023] [Accepted: 12/15/2023] [Indexed: 12/20/2023]
Abstract
The aim of the study is to establish an online database for predicting protein structures altered in ocular diseases by Alphafold2 and RoseTTAFold algorithms. Totally, 726 genes of multiple ocular diseases were collected for protein structure prediction. Both Alphafold2 and RoseTTAFold algorithms were built locally using the open-source codebases. A dataset with 48 protein structures from Protein Data Bank (PDB) was adopted for algorithm set-up validation. A website was built to match ocular genes with the corresponding predicted tertiary protein structures for each amino acid sequence. The predicted local distance difference test-Cα (pLDDT) and template modeling (TM) scores of the validation protein structure and the selected ocular genes were evaluated. Molecular dynamics and molecular docking simulations were performed to demonstrate the applications of the predicted structures. For the validation dataset, 70.8% of the predicted protein structures showed pLDDT greater than 90. Compared to the PDB structures, 100% of the AlphaFold2-predicted structures and 97.9% of the RoseTTAFold-predicted structure showed TM score greater than 0.5. Totally, 1329 amino acid sequences of 430 ocular disease-related genes have been predicted, of which 75.9% showed pLDDT greater than 70 for the wildtype sequences and 76.1% for the variant sequences. Small molecule docking and molecular dynamics simulations revealed that the predicted protein structures with higher confidence scores showed similar molecular characteristics with the structures from PDB. We have developed an ocular protein structure database (EyeProdb) for ocular disease, which is released for the public and will facilitate the biological investigations and structure-based drug development for ocular diseases. Database URL: http://eyeprodb.jsiec.org.
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Affiliation(s)
| | - Tsz Kin Ng
- Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, North Dongxia Road (Guangxia New Town), Shantou, Guangdong 515041, China
- Shantou University Medical College, 22 Xinling Road, Shantou, Guangdong 515041, China
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, 147K Argyle Street, KLN, Hong Kong
| | - Jie Ji
- Network & Information Centre, Shantou University, 243 Daxue Road, Shantou, Guangdong 515063, China
| | - Jian-Wei Lin
- Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, North Dongxia Road (Guangxia New Town), Shantou, Guangdong 515041, China
| | - Yao Yao
- Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, North Dongxia Road (Guangxia New Town), Shantou, Guangdong 515041, China
- Shantou University Medical College, 22 Xinling Road, Shantou, Guangdong 515041, China
| | - Rucui Yang
- Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, North Dongxia Road (Guangxia New Town), Shantou, Guangdong 515041, China
- Shantou University Medical College, 22 Xinling Road, Shantou, Guangdong 515041, China
| | - Geng Dong
- Shantou University Medical College, 22 Xinling Road, Shantou, Guangdong 515041, China
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Shantou University Medical College, 22 Xinling Road, Shantou, Guangdong 515041, China
| | - Yingjie Cao
- Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, North Dongxia Road (Guangxia New Town), Shantou, Guangdong 515041, China
| | - Chongbo Chen
- Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, North Dongxia Road (Guangxia New Town), Shantou, Guangdong 515041, China
| | - Shi-Qi Yao
- Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, North Dongxia Road (Guangxia New Town), Shantou, Guangdong 515041, China
- Shantou University Medical College, 22 Xinling Road, Shantou, Guangdong 515041, China
| | - Wen-Ying Wang
- Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, North Dongxia Road (Guangxia New Town), Shantou, Guangdong 515041, China
- Shantou University Medical College, 22 Xinling Road, Shantou, Guangdong 515041, China
| | - Zijing Huang
- Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, North Dongxia Road (Guangxia New Town), Shantou, Guangdong 515041, China
| | - Kunliang Qiu
- Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, North Dongxia Road (Guangxia New Town), Shantou, Guangdong 515041, China
| | - Chi Pui Pang
- Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, North Dongxia Road (Guangxia New Town), Shantou, Guangdong 515041, China
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, 147K Argyle Street, KLN, Hong Kong
| | - Qingping Liu
- Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, North Dongxia Road (Guangxia New Town), Shantou, Guangdong 515041, China
- Shantou University Medical College, 22 Xinling Road, Shantou, Guangdong 515041, China
| | - Mingzhi Zhang
- Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, North Dongxia Road (Guangxia New Town), Shantou, Guangdong 515041, China
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27
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King JE, Koes DR. Interpreting forces as deep learning gradients improves quality of predicted protein structures. Biophys J 2023:S0006-3495(23)04149-8. [PMID: 38104241 DOI: 10.1016/j.bpj.2023.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/30/2023] [Accepted: 12/12/2023] [Indexed: 12/19/2023] Open
Abstract
Protein structure predictions from deep learning models like AlphaFold2, despite their remarkable accuracy, are likely insufficient for direct use in downstream tasks like molecular docking. The functionality of such models could be improved with a combination of increased accuracy and physical intuition. We propose a new method to train deep learning protein structure prediction models using molecular dynamics force fields to work toward these goals. Our custom PyTorch loss function, OpenMM-Loss, represents the potential energy of a predicted structure. OpenMM-Loss can be applied to any all-atom representation of a protein structure capable of mapping into our software package, SidechainNet. We demonstrate our method's efficacy by finetuning OpenFold. We show that subsequently predicted protein structures, both before and after a relaxation procedure, exhibit comparable accuracy while displaying lower potential energy and improved structural quality as assessed by MolProbity metrics.
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Affiliation(s)
- Jonathan Edward King
- Joint PhD Program in Computational Biology, Carnegie Mellon University-University of Pittsburgh, Pittsburgh, Pennsylvania
| | - David Ryan Koes
- Computational & Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.
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Li Y, Fan Z, Rao J, Chen Z, Chu Q, Zheng M, Li X. An overview of recent advances and challenges in predicting compound-protein interaction (CPI). MEDICAL REVIEW (2021) 2023; 3:465-486. [PMID: 38282802 PMCID: PMC10808869 DOI: 10.1515/mr-2023-0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 08/30/2023] [Indexed: 01/30/2024]
Abstract
Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.
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Affiliation(s)
- Yanbei Li
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhehuan Fan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingxin Rao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhiyi Chen
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qinyu Chu
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Mingyue Zheng
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
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Vani BP, Aranganathan A, Wang D, Tiwary P. AlphaFold2-RAVE: From Sequence to Boltzmann Ranking. J Chem Theory Comput 2023; 19:4351-4354. [PMID: 37171364 PMCID: PMC10524496 DOI: 10.1021/acs.jctc.3c00290] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
While AlphaFold2 is rapidly being adopted as a new standard in protein structure predictions, it is limited to single structures. This can be insufficient for the inherently dynamic world of biomolecules. In this Letter, we propose AlphaFold2-RAVE, an efficient protocol for obtaining Boltzmann-ranked ensembles from sequence. The method uses structural outputs from AlphaFold2 as initializations for artificial intelligence-augmented molecular dynamics. We release the method as an open-source code and demonstrate results on different proteins.
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Affiliation(s)
- Bodhi P. Vani
- Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, USA
| | - Akashnathan Aranganathan
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park 20742, USA
| | - Dedi Wang
- Biophysics Program and Institute for Physical Science and Technology, University of Maryland, College Park 20742, USA
| | - Pratyush Tiwary
- Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, College Park 20742, USA
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30
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Bolz SN, Schroeder M. Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances. Expert Opin Drug Discov 2023; 18:973-985. [PMID: 37489516 DOI: 10.1080/17460441.2023.2239700] [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: 06/09/2023] [Accepted: 07/19/2023] [Indexed: 07/26/2023]
Abstract
INTRODUCTION Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks. AREAS COVERED This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets. EXPERT OPINION Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.
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Affiliation(s)
- Sarah Naomi Bolz
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
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31
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Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature 2023; 616:673-685. [PMID: 37100941 DOI: 10.1038/s41586-023-05905-z] [Citation(s) in RCA: 158] [Impact Index Per Article: 158.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 03/01/2023] [Indexed: 04/28/2023]
Abstract
Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.
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Affiliation(s)
- Anastasiia V Sadybekov
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA
| | - Vsevolod Katritch
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA.
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32
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Yang Z, Zeng X, Zhao Y, Chen R. AlphaFold2 and its applications in the fields of biology and medicine. Signal Transduct Target Ther 2023; 8:115. [PMID: 36918529 PMCID: PMC10011802 DOI: 10.1038/s41392-023-01381-z] [Citation(s) in RCA: 78] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/27/2022] [Accepted: 02/16/2023] [Indexed: 03/16/2023] Open
Abstract
AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.
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Affiliation(s)
- Zhenyu Yang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaoxi Zeng
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Yi Zhao
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Runsheng Chen
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- Pingshan Translational Medicine Center, Shenzhen Bay Laboratory, Shenzhen, 518118, China.
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33
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Velazhahan V, McCann BL, Bignell E, Tate CG. Developing novel antifungals: lessons from G protein-coupled receptors. Trends Pharmacol Sci 2023; 44:162-174. [PMID: 36801017 DOI: 10.1016/j.tips.2022.12.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/15/2022] [Accepted: 12/15/2022] [Indexed: 02/18/2023]
Abstract
Up to 1.5 million people die yearly from fungal disease, but the repertoire of antifungal drug classes is minimal and the incidence of drug resistance is rising rapidly. This dilemma was recently declared by the World Health Organization as a global health emergency, but the discovery of new antifungal drug classes remains excruciatingly slow. This process could be accelerated by focusing on novel targets, such as G protein-coupled receptor (GPCR)-like proteins, that have a high likelihood of being druggable and have well-defined biology and roles in disease. We discuss recent successes in understanding the biology of virulence and in structure determination of yeast GPCRs, and highlight new approaches that might pay significant dividends in the urgent search for novel antifungal drugs.
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Affiliation(s)
- Vaithish Velazhahan
- Medical Research Council (MRC) Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK
| | - Bethany L McCann
- MRC Centre for Medical Mycology, Stocker Road, University of Exeter, Exeter EX4 4QD, UK
| | - Elaine Bignell
- MRC Centre for Medical Mycology, Stocker Road, University of Exeter, Exeter EX4 4QD, UK.
| | - Christopher G Tate
- Medical Research Council (MRC) Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK.
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34
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Zhao H, Zhang H, She Z, Gao Z, Wang Q, Geng Z, Dong Y. Exploring AlphaFold2's Performance on Predicting Amino Acid Side-Chain Conformations and Its Utility in Crystal Structure Determination of B318L Protein. Int J Mol Sci 2023; 24:ijms24032740. [PMID: 36769074 PMCID: PMC9916901 DOI: 10.3390/ijms24032740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 02/04/2023] Open
Abstract
Recent technological breakthroughs in machine-learning-based AlphaFold2 (AF2) are pushing the prediction accuracy of protein structures to an unprecedented level that is on par with experimental structural quality. Despite its outstanding structural modeling capability, further experimental validations and performance assessments of AF2 predictions are still required, thus necessitating the development of integrative structural biology in synergy with both computational and experimental methods. Focusing on the B318L protein that plays an essential role in the African swine fever virus (ASFV) for viral replication, we experimentally demonstrate the high quality of the AF2 predicted model and its practical utility in crystal structural determination. Structural alignment implies that the AF2 model shares nearly the same atomic arrangement as the B318L crystal structure except for some flexible and disordered regions. More importantly, side-chain-based analysis at the individual residue level reveals that AF2's performance is likely dependent on the specific amino acid type and that hydrophobic residues tend to be more accurately predicted by AF2 than hydrophilic residues. Quantitative per-residue RMSD comparisons and further molecular replacement trials suggest that AF2 has a large potential to outperform other computational modeling methods in terms of structural determination. Additionally, it is numerically confirmed that the AF2 model is accurate enough so that it may well potentially withstand experimental data quality to a large extent for structural determination. Finally, an overall structural analysis and molecular docking simulation of the B318L protein are performed. Taken together, our study not only provides new insights into AF2's performance in predicting side-chain conformations but also sheds light upon the significance of AF2 in promoting crystal structural determination, especially when the experimental data quality of the protein crystal is poor.
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Affiliation(s)
- Haifan Zhao
- School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Heng Zhang
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Zhun She
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Zengqiang Gao
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Qi Wang
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhi Geng
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (Z.G.); (Y.D.)
| | - Yuhui Dong
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (Z.G.); (Y.D.)
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35
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Nicoli A, Haag F, Marcinek P, He R, Kreißl J, Stein J, Marchetto A, Dunkel A, Hofmann T, Krautwurst D, Di Pizio A. Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1. J Chem Inf Model 2023; 63:2014-2029. [PMID: 36696962 PMCID: PMC10091413 DOI: 10.1021/acs.jcim.2c00752] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
With approximately 400 encoding genes in humans, odorant receptors (ORs) are the largest subfamily of class A G protein-coupled receptors (GPCRs). Despite its high relevance and representation, the odorant-GPCRome is structurally poorly characterized: no experimental structures are available, and the low sequence identity of ORs to experimentally solved GPCRs is a significant challenge for their modeling. Moreover, the receptive range of most ORs is unknown. The odorant receptor OR5K1 was recently and comprehensively characterized in terms of cognate agonists. Here, we report two additional agonists and functional data of the most potent compound on two mutants, L1043.32 and L2556.51. Experimental data was used to guide the investigation of the binding modes of OR5K1 ligands into the orthosteric binding site using structural information from AI-driven modeling, as recently released in the AlphaFold Protein Structure Database, and from homology modeling. Induced-fit docking simulations were used to sample the binding site conformational space for ensemble docking. Mutagenesis data guided side chain residue sampling and model selection. We obtained models that could better rationalize the different activity of active (agonist) versus inactive molecules with respect to starting models and also capture differences in activity related to minor structural differences. Therefore, we provide a model refinement protocol that can be applied to model the orthosteric binding site of ORs as well as that of GPCRs with low sequence identity to available templates.
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Affiliation(s)
- Alessandro Nicoli
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Franziska Haag
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Patrick Marcinek
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Ruiming He
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany.,Department of Chemistry, Technical University of Munich, 85748 Garching, Germany
| | - Johanna Kreißl
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Jörg Stein
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Alessandro Marchetto
- Computational Biomedicine, Institute for Advanced Simulations (IAS)-5/Institute for Neuroscience and Medicine (INM)-9, Forschungszentrum Jülich, 52428 Jülich, Germany.,Department of Biology, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, 52074 Aachen, Germany
| | - Andreas Dunkel
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Thomas Hofmann
- Chair of Food Chemistry and Molecular Sensory Science, Technical University of Munich, 85354 Freising, Germany
| | - Dietmar Krautwurst
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
| | - Antonella Di Pizio
- Leibniz Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany
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36
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Cary BP, Zhang X, Cao J, Johnson RM, Piper SJ, Gerrard EJ, Wootten D, Sexton PM. New insights into the structure and function of class B1 GPCRs. Endocr Rev 2022; 44:492-517. [PMID: 36546772 PMCID: PMC10166269 DOI: 10.1210/endrev/bnac033] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/07/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022]
Abstract
G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors. Class B1 GPCRs constitute a subfamily of 15 receptors that characteristically contain large extracellular domains (ECDs) and respond to long polypeptide hormones. Class B1 GPCRs are critical regulators of homeostasis, and as such, many are important drug targets. While most transmembrane proteins, including GPCRs, are recalcitrant to crystallization, recent advances in electron cryo-microscopy (cryo-EM) have facilitated a rapid expansion of the structural understanding of membrane proteins. As a testament to this success, structures for all the class B1 receptors bound to G proteins have been determined by cryo-EM in the past five years. Further advances in cryo-EM have uncovered dynamics of these receptors, ligands, and signalling partners. Here, we examine the recent structural underpinnings of the class B1 GPCRs with an emphasis on structure-function relationships.
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Affiliation(s)
- Brian P Cary
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia.,ARC Centre for Cryo-electron Microscopy of Membrane Proteins, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia
| | - Xin Zhang
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia.,ARC Centre for Cryo-electron Microscopy of Membrane Proteins, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia
| | - Jianjun Cao
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia.,ARC Centre for Cryo-electron Microscopy of Membrane Proteins, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia
| | - Rachel M Johnson
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia.,ARC Centre for Cryo-electron Microscopy of Membrane Proteins, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia
| | - Sarah J Piper
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia.,ARC Centre for Cryo-electron Microscopy of Membrane Proteins, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia
| | - Elliot J Gerrard
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia.,ARC Centre for Cryo-electron Microscopy of Membrane Proteins, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia
| | - Denise Wootten
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia.,ARC Centre for Cryo-electron Microscopy of Membrane Proteins, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia
| | - Patrick M Sexton
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia.,ARC Centre for Cryo-electron Microscopy of Membrane Proteins, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia
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37
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Lee S, Kim S, Lee GR, Kwon S, Woo H, Seok C, Park H. Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction. Comput Struct Biotechnol J 2022; 21:158-167. [PMID: 36544468 PMCID: PMC9747351 DOI: 10.1016/j.csbj.2022.11.057] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/27/2022] [Accepted: 11/27/2022] [Indexed: 12/03/2022] Open
Abstract
While deep learning (DL) has brought a revolution in the protein structure prediction field, still an important question remains how the revolution can be transferred to advances in structure-based drug discovery. Because the lessons from the recent GPCR dock challenge were inconclusive primarily due to the size of the dataset, in this work we further elaborated on 70 diverse GPCR complexes bound to either small molecules or peptides to investigate the best-practice modeling and docking strategies for GPCR drug discovery. From our quantitative analysis, it is shown that substantial improvements in docking and virtual screening have been possible by the advance in DL-based protein structure predictions with respect to the expected results from the combination of best pre-DL tools. The success rate of docking on DL-based model structures approaches that of cross-docking on experimental structures, showing over 30% improvement from the best pre-DL protocols. This amount of performance could be achieved only when two modeling points were considered properly: 1) correct functional-state modeling of receptors and 2) receptor-flexible docking. Best-practice modeling strategies and the model confidence estimation metric suggested in this work may serve as a guideline for future computer-aided GPCR drug discovery scenarios.
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Key Words
- AF, AlphaFold
- CAPRI, critical assessment of predicted interactions, DOF, Degree-of-freedom
- DL, deep learning
- Deep learning
- Drug discovery
- GALD, Rosetta GA LigandDock
- GD3, GalaxyDock3
- GDT, global distance test
- GPCR
- Ligand docking
- MD, molecular dynamics
- Protein structure prediction
- RMSD, root-mean-squared deviation
- SBDD, Structure-based drug design
- TBM, template-based modeling or template-based model
- p-lDDT, predicted local distance difference test
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Affiliation(s)
- Sumin Lee
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Republic of Korea
| | - Seeun Kim
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Gyu Rie Lee
- Department of Biochemistry, University of Washington, WA, USA
| | - Sohee Kwon
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea,Corresponding authors.
| | - Hahnbeom Park
- Brain Science Institute, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea,Corresponding authors.
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38
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Intrinsically Disordered Proteins: An Overview. Int J Mol Sci 2022; 23:ijms232214050. [PMID: 36430530 PMCID: PMC9693201 DOI: 10.3390/ijms232214050] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/16/2022] Open
Abstract
Many proteins and protein segments cannot attain a single stable three-dimensional structure under physiological conditions; instead, they adopt multiple interconverting conformational states. Such intrinsically disordered proteins or protein segments are highly abundant across proteomes, and are involved in various effector functions. This review focuses on different aspects of disordered proteins and disordered protein regions, which form the basis of the so-called "Disorder-function paradigm" of proteins. Additionally, various experimental approaches and computational tools used for characterizing disordered regions in proteins are discussed. Finally, the role of disordered proteins in diseases and their utility as potential drug targets are explored.
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Hinge Region Mediates Signal Transmission of Luteinizing Hormone and Chorionic Gonadotropin Receptor. Comput Struct Biotechnol J 2022; 20:6503-6511. [DOI: 10.1016/j.csbj.2022.11.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/19/2022] [Accepted: 11/20/2022] [Indexed: 11/23/2022] Open
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