1
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Xia Y, Pan X, Shen HB. A comprehensive survey on protein-ligand binding site prediction. Curr Opin Struct Biol 2024; 86:102793. [PMID: 38447285 DOI: 10.1016/j.sbi.2024.102793] [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: 12/20/2023] [Revised: 02/18/2024] [Accepted: 02/18/2024] [Indexed: 03/08/2024]
Abstract
Protein-ligand binding site prediction is critical for protein function annotation and drug discovery. Biological experiments are time-consuming and require significant equipment, materials, and labor resources. Developing accurate and efficient computational methods for protein-ligand interaction prediction is essential. Here, we summarize the key challenges associated with ligand binding site (LBS) prediction and introduce recently published methods from their input features, computational algorithms, and ligand types. Furthermore, we investigate the specificity of allosteric site identification as a particular LBS type. Finally, we discuss the prospective directions for machine learning-based LBS prediction in the near future.
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Affiliation(s)
- Ying Xia
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
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2
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Sinha K, Basu I, Shah Z, Shah S, Chakrabarty S. Leveraging Bidirectional Nature of Allostery To Inhibit Protein-Protein Interactions (PPIs): A Case Study of PCSK9-LDLR Interaction. J Chem Inf Model 2024; 64:3923-3932. [PMID: 38615325 DOI: 10.1021/acs.jcim.4c00294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
The protein PCSK9 (proprotein convertase subtilisin/Kexin type 9) negatively regulates the recycling of LDLR (low-density lipoprotein receptor), leading to an elevated plasma level of LDL. Inhibition of PCSK9-LDLR interaction has emerged as a promising therapeutic strategy to manage hypercholesterolemia. However, the large interaction surface area between PCSK9 and LDLR makes it challenging to identify a small molecule competitive inhibitor. An alternative strategy would be to identify distal cryptic sites as targets for allosteric inhibitors that can remotely modulate PCSK9-LDLR interaction. Using several microseconds long molecular dynamics (MD) simulations, we demonstrate that on binding with LDLR, there is a significant conformational change (population shift) in a distal loop (residues 211-222) region of PCSK9. Consistent with the bidirectional nature of allostery, we establish a clear correlation between the loop conformation and the binding affinity with LDLR. Using a thermodynamic argument, we establish that the loop conformations predominantly present in the apo state of PCSK9 would have lower LDLR binding affinity, and they would be potential targets for designing allosteric inhibitors. We elucidate the molecular origin of the allosteric coupling between this loop and the LDLR binding interface in terms of the population shift in a set of salt bridges and hydrogen bonds. Overall, our work provides a general strategy toward identifying allosteric hotspots: compare the conformational ensemble of the receptor between the apo and bound states of the protein and identify distal conformational changes, if any. The inhibitors should be designed to bind and stabilize the apo-specific conformations.
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Affiliation(s)
- Krishnendu Sinha
- Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Kolkata 700 106, India
| | - Ipsita Basu
- Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Kolkata 700 106, India
| | - Zacharia Shah
- Hingez Therapeutics Inc., 8000 Towers Crescent Drive, STE 1331, Vienna, Virginia 22182, United States
| | - Salim Shah
- Hingez Therapeutics Inc., 8000 Towers Crescent Drive, STE 1331, Vienna, Virginia 22182, United States
| | - Suman Chakrabarty
- Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Kolkata 700 106, India
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3
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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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4
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Nerín-Fonz F, Cournia Z. Machine learning approaches in predicting allosteric sites. Curr Opin Struct Biol 2024; 85:102774. [PMID: 38354652 DOI: 10.1016/j.sbi.2024.102774] [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: 10/08/2023] [Revised: 12/29/2023] [Accepted: 01/02/2024] [Indexed: 02/16/2024]
Abstract
Allosteric regulation is a fundamental biological mechanism that can control critical cellular processes via allosteric modulator binding to protein distal functional sites. The advantages of allosteric modulators over orthosteric ones have sparked the development of numerous computational approaches, such as the identification of allosteric binding sites, to facilitate allosteric drug discovery. Building on the success of machine learning (ML) models for solving complex problems in biology and chemistry, several ML models for predicting allosteric sites have been developed. In this review, we provide an overview of these models and discuss future perspectives powered by the field of artificial intelligence such as protein language models.
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Affiliation(s)
- Francho Nerín-Fonz
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephesiou, Athens 11527, Greece; Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria.
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephesiou, Athens 11527, Greece; Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria.
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5
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Tee WV, Berezovsky IN. Allosteric drugs: New principles and design approaches. Curr Opin Struct Biol 2024; 84:102758. [PMID: 38171188 DOI: 10.1016/j.sbi.2023.102758] [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: 09/29/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024]
Abstract
Focusing on an important biomedical implication of allostery - design of allosteric drugs, we describe characteristics of allosteric sites, effectors, and their modes of actions distinguishing them from the orthosteric counterparts and calling for new principles and protocols in the quests for allosteric drugs. We show the importance of considering both binding affinity and allosteric signaling in establishing the structure-activity relationships (SARs) toward design of allosteric effectors, arguing that pairs of allosteric sites and their effector ligands - the site-effector pairs - should be generated and adjusted simultaneously in the framework of what we call directed design protocol. Key ideas and approaches for designing allosteric effectors including reverse perturbation, targeted and agnostic analysis are also discussed here. Several promising computational approaches are highlighted, along with the need for and potential advantages of utilizing generative models to facilitate discovery/design of new allosteric drugs.
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Affiliation(s)
- Wei-Ven Tee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A∗STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671.
| | - Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A∗STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671; Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117579, Singapore.
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6
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He J, Liu X, Zhu C, Zha J, Li Q, Zhao M, Wei J, Li M, Wu C, Wang J, Jiao Y, Ning S, Zhou J, Hong Y, Liu Y, He H, Zhang M, Chen F, Li Y, He X, Wu J, Lu S, Song K, Lu X, Zhang J. ASD2023: towards the integrating landscapes of allosteric knowledgebase. Nucleic Acids Res 2024; 52:D376-D383. [PMID: 37870448 PMCID: PMC10767950 DOI: 10.1093/nar/gkad915] [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: 08/21/2023] [Revised: 09/22/2023] [Accepted: 10/06/2023] [Indexed: 10/24/2023] Open
Abstract
Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66 589 potential allosteric sites, covering > 80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leads and (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic exploration and drug discovery.
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Affiliation(s)
- Jixiao He
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xinyi Liu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chunhao Zhu
- College of Pharmacy, Ningxia Medical University, 1160 Shengli Street, Yinchuan, Ningxia 750004, China
| | - Jinyin Zha
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qian Li
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Mingzhu Zhao
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jiacheng Wei
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Mingyu Li
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chengwei Wu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Junyuan Wang
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Yonglai Jiao
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shaobo Ning
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jiamin Zhou
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Yue Hong
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yonghui Liu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hongxi He
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Mingyang Zhang
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Feiying Chen
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yanxiu Li
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xinheng He
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jing Wu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shaoyong Lu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Kun Song
- Nutshell Therapeutics, Shanghai 201210, China
| | - Xuefeng Lu
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Jian Zhang
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- College of Pharmacy, Ningxia Medical University, 1160 Shengli Street, Yinchuan, Ningxia 750004, China
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, China
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7
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Li M, Lan X, Lu X, Zhang J. A Structure-Based Allosteric Modulator Design Paradigm. HEALTH DATA SCIENCE 2023; 3:0094. [PMID: 38487194 PMCID: PMC10904074 DOI: 10.34133/hds.0094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 10/11/2023] [Indexed: 03/17/2024]
Abstract
Importance: Allosteric drugs bound to topologically distal allosteric sites hold a substantial promise in modulating therapeutic targets deemed undruggable at their orthosteric sites. Traditionally, allosteric modulator discovery has predominantly relied on serendipitous high-throughput screening. Nevertheless, the landscape has undergone a transformative shift due to recent advancements in our understanding of allosteric modulation mechanisms, coupled with a significant increase in the accessibility of allosteric structural data. These factors have extensively promoted the development of various computational methodologies, especially for machine-learning approaches, to guide the rational design of structure-based allosteric modulators. Highlights: We here presented a comprehensive structure-based allosteric modulator design paradigm encompassing 3 critical stages: drug target acquisition, allosteric binding site, and modulator discovery. The recent advances in computational methods in each stage are encapsulated. Furthermore, we delve into analyzing the successes and obstacles encountered in the rational design of allosteric modulators. Conclusion: The structure-based allosteric modulator design paradigm holds immense potential for the rational design of allosteric modulators. We hope that this review would heighten awareness of the use of structure-based computational methodologies in advancing the field of allosteric drug discovery.
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Affiliation(s)
- Mingyu Li
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiaobin Lan
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xun Lu
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jian Zhang
- College of Pharmacy,
Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital,
Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center,
Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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8
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Lu X, Lan X, Lu S, Zhang J. Progressive computational approaches to facilitate decryption of allosteric mechanism and drug discovery. Curr Opin Struct Biol 2023; 83:102701. [PMID: 37716092 DOI: 10.1016/j.sbi.2023.102701] [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: 07/12/2023] [Revised: 08/17/2023] [Accepted: 08/21/2023] [Indexed: 09/18/2023]
Abstract
Allostery is a ubiquitous biological phenomenon where perturbation at topologically distal areas of a protein serves as a trigger to fine-tune the orthosteric site and thus regulate protein function. The investigation of allosteric regulation greatly enhances our understanding of human diseases and broadens avenue for drug discovery. For decades, owing to the difficulty in allostery characterization through serendipitous experimental screening, researchers have developed several innovative computational approaches, which proves to accelerate the elucidation of allostery. Herein, we review the state-of-the-art advance of computational methodologies for allostery study, with particular emphasis on promising trends emerging over the past two years. We expect this review will outline the latest landscape of allostery study and inspire researchers to further facilitate this field.
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Affiliation(s)
- Xun Lu
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiaobing Lan
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, China
| | - Shaoyong Lu
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jian Zhang
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, China; State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.
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9
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Verkhivker G, Alshahrani M, Gupta G. Exploring Conformational Landscapes and Cryptic Binding Pockets in Distinct Functional States of the SARS-CoV-2 Omicron BA.1 and BA.2 Trimers: Mutation-Induced Modulation of Protein Dynamics and Network-Guided Prediction of Variant-Specific Allosteric Binding Sites. Viruses 2023; 15:2009. [PMID: 37896786 PMCID: PMC10610873 DOI: 10.3390/v15102009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/23/2023] [Accepted: 09/26/2023] [Indexed: 10/29/2023] Open
Abstract
A significant body of experimental structures of SARS-CoV-2 spike trimers for the BA.1 and BA.2 variants revealed a considerable plasticity of the spike protein and the emergence of druggable binding pockets. Understanding the interplay of conformational dynamics changes induced by the Omicron variants and the identification of cryptic dynamic binding pockets in the S protein is of paramount importance as exploring broad-spectrum antiviral agents to combat the emerging variants is imperative. In the current study, we explore conformational landscapes and characterize the universe of binding pockets in multiple open and closed functional spike states of the BA.1 and BA.2 Omicron variants. By using a combination of atomistic simulations, a dynamics network analysis, and an allostery-guided network screening of binding pockets in the conformational ensembles of the BA.1 and BA.2 spike conformations, we identified all experimentally known allosteric sites and discovered significant variant-specific differences in the distribution of binding sites in the BA.1 and BA.2 trimers. This study provided a structural characterization of the predicted cryptic pockets and captured the experimentally known allosteric sites, revealing the critical role of conformational plasticity in modulating the distribution and cross-talk between functional binding sites. We found that mutational and dynamic changes in the BA.1 variant can induce the remodeling and stabilization of a known druggable pocket in the N-terminal domain, while this pocket is drastically altered and may no longer be available for ligand binding in the BA.2 variant. Our results predicted the experimentally known allosteric site in the receptor-binding domain that remains stable and ranks as the most favorable site in the conformational ensembles of the BA.2 variant but could become fragmented and less probable in BA.1 conformations. We also uncovered several cryptic pockets formed at the inter-domain and inter-protomer interface, including functional regions of the S2 subunit and stem helix region, which are consistent with the known role of pocket residues in modulating conformational transitions and antibody recognition. The results of this study are particularly significant for understanding the dynamic and network features of the universe of available binding pockets in spike proteins, as well as the effects of the Omicron-variant-specific modulation of preferential druggable pockets. The exploration of predicted druggable sites can present a new and previously underappreciated opportunity for therapeutic interventions for Omicron variants through the conformation-selective and variant-specific targeting of functional sites involved in allosteric changes.
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Affiliation(s)
- Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
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10
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Xiao S, Alshahrani M, Gupta G, Tao P, Verkhivker G. Markov State Models and Perturbation-Based Approaches Reveal Distinct Dynamic Signatures and Hidden Allosteric Pockets in the Emerging SARS-Cov-2 Spike Omicron Variant Complexes with the Host Receptor: The Interplay of Dynamics and Convergent Evolution Modulates Allostery and Functional Mechanisms. J Chem Inf Model 2023; 63:5272-5296. [PMID: 37549201 PMCID: PMC11162552 DOI: 10.1021/acs.jcim.3c00778] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
The new generation of SARS-CoV-2 Omicron variants displayed a significant growth advantage and increased viral fitness by acquiring convergent mutations, suggesting that the immune pressure can promote convergent evolution leading to the sudden acceleration of SARS-CoV-2 evolution. In the current study, we combined structural modeling, microsecond molecular dynamics simulations, and Markov state models to characterize conformational landscapes and identify specific dynamic signatures of the SARS-CoV-2 spike complexes with the host receptor ACE2 for the recently emerged highly transmissible XBB.1, XBB.1.5, BQ.1, and BQ.1.1 Omicron variants. Microsecond simulations and Markovian modeling provided a detailed characterization of the functional conformational states and revealed the increased thermodynamic stabilization of the XBB.1.5 subvariant, which can be contrasted to more dynamic BQ.1 and BQ.1.1 subvariants. Despite considerable structural similarities, Omicron mutations can induce unique dynamic signatures and specific distributions of the conformational states. The results suggested that variant-specific changes of the conformational mobility in the functional interfacial loops of the receptor-binding domain in the SARS-CoV-2 spike protein can be fine-tuned through crosstalk between convergent mutations which could provide an evolutionary path for modulation of immune escape. By combining atomistic simulations and Markovian modeling analysis with perturbation-based approaches, we determined important complementary roles of convergent mutation sites as effectors and receivers of allosteric signaling involved in modulation of conformational plasticity and regulation of allosteric communications. This study also revealed hidden allosteric pockets and suggested that convergent mutation sites could control evolution and distribution of allosteric pockets through modulation of conformational plasticity in the flexible adaptable regions.
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Affiliation(s)
- Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Grace Gupta
- Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75275, United States
| | - Gennady Verkhivker
- Keck Center for Science and Engineering, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States
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11
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Xiao S, Alshahrani M, Gupta G, Tao P, Verkhivker G. Markov State Models and Perturbation-Based Approaches Reveal Distinct Dynamic Signatures and Hidden Allosteric Pockets in the Emerging SARS-Cov-2 Spike Omicron Variants Complexes with the Host Receptor: The Interplay of Dynamics and Convergent Evolution Modulates Allostery and Functional Mechanisms. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.20.541592. [PMID: 37292827 PMCID: PMC10245745 DOI: 10.1101/2023.05.20.541592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The new generation of SARS-CoV-2 Omicron variants displayed a significant growth advantage and the increased viral fitness by acquiring convergent mutations, suggesting that the immune pressure can promote convergent evolution leading to the sudden acceleration of SARS-CoV-2 evolution. In the current study, we combined structural modeling, extensive microsecond MD simulations and Markov state models to characterize conformational landscapes and identify specific dynamic signatures of the SARS-CoV-2 spike complexes with the host receptor ACE2 for the recently emerged highly transmissible XBB.1, XBB.1.5, BQ.1, and BQ.1.1 Omicron variants. Microsecond simulations and Markovian modeling provided a detailed characterization of the conformational landscapes and revealed the increased thermodynamic stabilization of the XBB.1.5 subvariant which is contrasted to more dynamic BQ.1 and BQ.1.1 subvariants. Despite considerable structural similarities, Omicron mutations can induce unique dynamic signatures and specific distributions of conformational states. The results suggested that variant-specific changes of conformational mobility in the functional interfacial loops of the spike receptor binding domain can be fine-tuned through cross-talk between convergent mutations thereby providing an evolutionary path for modulation of immune escape. By combining atomistic simulations and Markovian modeling analysis with perturbation-based approaches, we determined important complementary roles of convergent mutation sites as effectors and receivers of allosteric signaling involved in modulating conformational plasticity at the binding interface and regulating allosteric responses. This study also characterized the dynamics-induced evolution of allosteric pockets in the Omicron complexes that revealed hidden allosteric pockets and suggested that convergent mutation sites could control evolution and distribution of allosteric pockets through modulation of conformational plasticity in the flexible adaptable regions. Through integrative computational approaches, this investigation provides a systematic analysis and comparison of the effects of Omicron subvariants on conformational dynamics and allosteric signaling in the complexes with the ACE2 receptor. For Table of Contents Use Only
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12
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Xiao S, Verkhivker GM, Tao P. Machine learning and protein allostery. Trends Biochem Sci 2023; 48:375-390. [PMID: 36564251 PMCID: PMC10023316 DOI: 10.1016/j.tibs.2022.12.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/16/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022]
Abstract
The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric mechanisms will have an increasingly important role in bridging a wide spectrum of data-intensive experimental and theoretical technologies.
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Affiliation(s)
- Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75205, USA.
| | - Gennady M Verkhivker
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75205, USA.
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13
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Agajanian S, Alshahrani M, Bai F, Tao P, Verkhivker GM. Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches. J Chem Inf Model 2023; 63:1413-1428. [PMID: 36827465 PMCID: PMC11162550 DOI: 10.1021/acs.jcim.2c01634] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Abstract
Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.
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Affiliation(s)
- Steve Agajanian
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Fang Bai
- Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology and Information Science and Technology, Shanghai Tech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75205, United States
| | - Gennady M Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States
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14
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Tan ZW, Tee WV, Guarnera E, Berezovsky IN. AlloMAPS 2: allosteric fingerprints of the AlphaFold and Pfam-trRosetta predicted structures for engineering and design. Nucleic Acids Res 2022; 51:D345-D351. [PMID: 36169226 PMCID: PMC9825619 DOI: 10.1093/nar/gkac828] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/26/2022] [Accepted: 09/15/2022] [Indexed: 01/29/2023] Open
Abstract
AlloMAPS 2 is an update of the Allosteric Mutation Analysis and Polymorphism of Signalling database, which contains data on allosteric communication obtained for predicted structures in the AlphaFold database (AFDB) and trRosetta-predicted Pfam domains. The data update contains Allosteric Signalling Maps (ASMs) and Allosteric Probing Maps (APMs) quantifying allosteric effects of mutations and of small probe binding, respectively. To ensure quality of the ASMs and APMs, we performed careful and accurate selection of protein sets containing high-quality predicted structures in both databases for each organism/structure, and the data is available for browsing and download. The data for remaining structures are available for download and should be used at user's discretion and responsibility. We believe these massive data can facilitate both diagnostics and drug design within the precision medicine paradigm. Specifically, it can be instrumental in the analysis of allosteric effects of pathological and rescue mutations, providing starting points for fragment-based design of allosteric effectors. The exhaustive character of allosteric signalling and probing fingerprints will be also useful in future developments of corresponding machine learning applications. The database is freely available at: http://allomaps.bii.a-star.edu.sg.
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Affiliation(s)
- Zhen Wah Tan
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Wei-Ven Tee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Enrico Guarnera
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Igor N Berezovsky
- To whom correspondence should be addressed. Tel: +65 6478 8269; Fax: +65 6478 9047;
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15
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Strömich L, Wu N, Barahona M, Yaliraki SN. Allosteric Hotspots in the Main Protease of SARS-CoV-2. J Mol Biol 2022; 434:167748. [PMID: 35843284 PMCID: PMC9288249 DOI: 10.1016/j.jmb.2022.167748] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/30/2022] [Accepted: 07/11/2022] [Indexed: 02/06/2023]
Abstract
Inhibiting the main protease of SARS-CoV-2 is of great interest in tackling the COVID-19 pandemic caused by the virus. Most efforts have been centred on inhibiting the binding site of the enzyme. However, considering allosteric sites, distant from the active or orthosteric site, broadens the search space for drug candidates and confers the advantages of allosteric drug targeting. Here, we report the allosteric communication pathways in the main protease dimer by using two novel fully atomistic graph-theoretical methods: Bond-to-bond propensity, which has been previously successful in identifying allosteric sites in extensive benchmark data sets without a priori knowledge, and Markov transient analysis, which has previously aided in finding novel drug targets in catalytic protein families. Using statistical bootstrapping, we score the highest ranking sites against random sites at similar distances, and we identify four statistically significant putative allosteric sites as good candidates for alternative drug targeting.
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Affiliation(s)
- Léonie Strömich
- Department of Chemistry Imperial College London, United Kingdom
| | - Nan Wu
- Department of Chemistry Imperial College London, United Kingdom
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16
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Berezovsky IN, Nussinov R. Multiscale Allostery: Basic Mechanisms and Versatility in Diagnostics and Drug Design. J Mol Biol 2022; 434:167751. [PMID: 35863488 DOI: 10.1016/j.jmb.2022.167751] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671, Singapore; Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117579, Singapore.
| | - Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboraory, National Cancer Institute, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
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17
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Wu N, Yaliraki SN, Barahona M. Prediction of Protein Allosteric Signalling Pathways and Functional Residues Through Paths of Optimised Propensity. J Mol Biol 2022; 434:167749. [PMID: 35841931 DOI: 10.1016/j.jmb.2022.167749] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 07/09/2022] [Accepted: 07/11/2022] [Indexed: 11/28/2022]
Abstract
Allostery commonly refers to the mechanism that regulates protein activity through the binding of a molecule at a different, usually distal, site from the orthosteric site. The omnipresence of allosteric regulation in nature and its potential for drug design and screening render the study of allostery invaluable. Nevertheless, challenges remain as few computational methods are available to effectively predict allosteric sites, identify signalling pathways involved in allostery, or to aid with the design of suitable molecules targeting such sites. Recently, bond-to-bond propensity analysis has been shown successful at identifying allosteric sites for a large and diverse group of proteins from knowledge of the orthosteric sites and its ligands alone by using network analysis applied to energy-weighted atomistic protein graphs. To address the identification of signalling pathways, we propose here a method to compute and score paths of optimised propensity that link the orthosteric site with the identified allosteric sites, and identifies crucial residues that contribute to those paths. We showcase the approach with three well-studied allosteric proteins: h-Ras, caspase-1, and 3-phosphoinositide-dependent kinase-1 (PDK1). Key residues in both orthosteric and allosteric sites were identified and showed agreement with experimental results, and pivotal signalling residues along the pathway were also revealed, thus providing alternative targets for drug design. By using the computed path scores, we were also able to differentiate the activity of different allosteric modulators.
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Affiliation(s)
- Nan Wu
- Department of Chemistry Imperial College London, United Kingdom
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18
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Wah Tan Z, Tee WV, Berezovsky IN. Learning about allosteric drugs and ways to design them. J Mol Biol 2022; 434:167692. [PMID: 35738428 DOI: 10.1016/j.jmb.2022.167692] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 05/23/2022] [Accepted: 06/15/2022] [Indexed: 11/16/2022]
Abstract
While the accelerating quest for precision medicine requires new individually targeting and selective drugs, and the ability to work with so-called undruggable targets, the realm of allosteric drugs meeting this need remains largely uncharted. Generalizing the observations on two major drug targets with widely observed inherent allostery, GPCRs and kinases, we describe and discuss basic allosteric modes of action that are universally applicable in all types of structures and functions. Using examples of Class A GPCRs and CMGC protein kinases, we show how Allosteric Signalling and Probing Fingerprints can be used to identify potential allosteric sites and reveal effector-leads that may serve as a starting point for the development of allosteric drugs targeting these regulatory sites. A set of distinct characteristics of allosteric ligands was established, which highlights the versatility of their design and make them advantageous before their orthosteric counterparts in personalized medicine. We argue that rational design of allosteric drugs should begin with the search for latent sites or design of non-natural binding sites followed by fragment-based design of allosteric ligands and by the mutual adjustment of the site-ligand pair in order to achieve required effects. On the basis of the perturbative nature and reversibility of allosteric communication, we propose a generic protocol for computational design of allosteric effectors, enabling also the allosteric tuning of biologics, in obtaining allosteric control over protein functions.
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Affiliation(s)
- Zhen Wah Tan
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671
| | - Wei-Ven Tee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671
| | - Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671; Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117579, Singapore.
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19
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Casadó-Anguera V, Casadó V. Unmasking allosteric binding sites: Novel targets for GPCR drug discovery. Expert Opin Drug Discov 2022; 17:897-923. [PMID: 35649692 DOI: 10.1080/17460441.2022.2085684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
INTRODUCTION Unexpected non-apparent and hidden allosteric binding sites are non-classical and non-apparent allosteric centers in 3-D X-ray protein structures until orthosteric or allosteric ligands bind to them. The orthosteric center of one protomer that modulates binding centers of the other protomers within an oligomer is also an unexpected allosteric site. Furthermore, another partner protein can also produce these effects, acting as an unexpected allosteric modulator. AREAS COVERED This review summarizes both classical and non-classical allosterism. The authors focus on G protein-coupled receptor (GPCR) oligomers as a paradigm of allosteric molecules. Moreover, they show several examples of unexpected allosteric sites such as hidden allosteric sites in a protomer that appear after the interaction with other molecules and the allosterism exerted between orthosteric sites within GPCR oligomer, emphasizing on the allosteric modulations that can occur between binding sites. EXPERT OPINION The study of these new non-classical allosteric sites will expand the diversity of allosteric control on the function of orthosteric sites within proteins, whether GPCRs or other receptors, enzymes or transporters. Moreover, the design of new drugs targeting these hidden allosteric sites or already known orthosteric sites acting as allosteric sites in protein homo- or hetero-oligomers will increase the therapeutic potential of allosterism.
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Affiliation(s)
- Verònica Casadó-Anguera
- Laboratory of Molecular Neuropharmacology, Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, and Institute of Biomedicine of the Universitat de Barcelona, Barcelona, Spain.,Laboratory of Neuropharmacology-Neurophar, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Vicent Casadó
- Laboratory of Molecular Neuropharmacology, Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, and Institute of Biomedicine of the Universitat de Barcelona, Barcelona, Spain
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20
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Mattice JR, Shisler KA, DuBois JL, Peters JW, Bothner B. A catalytic dyad modulates conformational change in the CO 2-fixing flavoenzyme 2-ketopropyl coenzyme M oxidoreductase/carboxylase. J Biol Chem 2022; 298:101884. [PMID: 35367206 PMCID: PMC9062435 DOI: 10.1016/j.jbc.2022.101884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 03/21/2022] [Accepted: 03/24/2022] [Indexed: 11/09/2022] Open
Abstract
2-Ketopropyl-coenzyme M oxidoreductase/carboxylase (2-KPCC) is a member of the flavin and cysteine disulfide containing oxidoreductase family (DSOR) that catalyzes the unique reaction between atmospheric CO2 and a ketone/enolate nucleophile to generate acetoacetate. However, the mechanism of this reaction is not well understood. Here, we present evidence that 2-KPCC, in contrast to the well-characterized DSOR enzyme glutathione reductase, undergoes conformational changes during catalysis. Using a suite of biophysical techniques including limited proteolysis, differential scanning fluorimetry, and native mass spectrometry in the presence of substrates and inhibitors, we observed conformational differences between different ligand-bound 2-KPCC species within the catalytic cycle. Analysis of site-specific amino acid variants indicated that 2-KPCC-defining residues, Phe501-His506, within the active site are important for transducing these ligand induced conformational changes. We propose that these conformational changes promote substrate discrimination between H+ and CO2 to favor the metabolically preferred carboxylation product, acetoacetate.
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Affiliation(s)
- Jenna R Mattice
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, Montana, USA
| | - Krista A Shisler
- Institute of Biological Chemistry, Washington State University, Pullman, Washington, USA
| | - Jennifer L DuBois
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, Montana, USA
| | - John W Peters
- Institute of Biological Chemistry, Washington State University, Pullman, Washington, USA
| | - Brian Bothner
- Department of Chemistry and Biochemistry, Montana State University, Bozeman, Montana, USA.
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21
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Tee WV, Wah Tan Z, Guarnera E, Berezovsky IN. Conservation and diversity in allosteric fingerprints of proteins for evolutionary-inspired engineering and design. J Mol Biol 2022; 434:167577. [PMID: 35395233 DOI: 10.1016/j.jmb.2022.167577] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 03/30/2022] [Accepted: 03/30/2022] [Indexed: 11/26/2022]
Abstract
Hand-in-hand work of physics and evolution delivered protein universe with diversity of forms, sizes, and functions. Pervasiveness and advantageous traits of allostery made it an important component of the protein function regulation, calling for thorough investigation of its structural determinants and evolution. Learning directly from nature, we explored here allosteric communication in several major folds and repeat proteins, including α/β and β-barrels, β-propellers, Ig-like fold, ankyrin and α/β leucine-rich repeat proteins, which provide structural platforms for many different enzymatic and signalling functions. We obtained a picture of conserved allosteric communication characteristic in different fold types, modifications of the structure-driven signalling patterns via sequence-determined divergence to specific functions, as well as emergence and potential diversification of allosteric regulation in multi-domain proteins and oligomeric assemblies. Our observations will be instrumental in facilitating the engineering and de novo design of proteins with allosterically regulated functions, including development of therapeutic biologics. In particular, results described here may guide the identification of the optimal structural platforms (e.g. fold type, size, and oligomerization states) and the types of diversifications/perturbations, such as mutations, effector binding, and order-disorder transition. The tunable allosteric linkage across distant regions can be used as a pivotal component in the design/engineering of modular biological systems beyond the traditional scaffolding function.
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Affiliation(s)
- Wei-Ven Tee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671
| | - Zhen Wah Tan
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671
| | - Enrico Guarnera
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671
| | - Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671; Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, Singapore 117597.
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22
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Mozumder S, Bej A, Sengupta J. Ligand-Dependent Modulation of the Dynamics of Intracellular Loops Dictates Functional Selectivity of 5-HT 2AR. J Chem Inf Model 2022; 62:2522-2537. [PMID: 35324173 DOI: 10.1021/acs.jcim.2c00118] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The serotonin 2A receptor (5-HT2AR) subtype of the G protein-coupled receptor (GPCR) family is involved in a plethora of neuromodulatory functions (e.g., neurogenesis, sleep, and cognitive processes). 5-HT2AR is the target of pharmacologically distinct classes of ligands, binding of which either activate or inactivate the receptor. Although high-resolution structures of 5-HT2AR as well as several other 5-HT GPCRs provided snapshots of both active and inactive conformational states, these structures, representing a truncated form of the receptor, cannot fully explain the mechanism of conformational transitions during their function. Importantly, biochemical studies have suggested the importance of intracellular loops in receptor functions. In our previous study, a model of the ligand-free form of 5-HT2AR with the third intracellular loop (ICL3) has been meticulously built. Here, we have investigated the functional regulation of 5-HT2AR with intact intracellular loops in ligand-free and five distinct ligand-bound configurations using unbiased atomistic molecular dynamics (MD) simulations. The selected ligands belong to either of the full, partial, or inverse agonist classes, which exert distinct pharmacological responses. We have observed significant structural, dynamic, and thermodynamic differences within ligand-bound complexes. Our results revealed, for the first time, that either activation or inactivation of the receptor upon specific ligand binding is primarily achieved through conformational transitions of its second and third intracellular loops (ICL2 and ICL3). A remarkable allosteric cross-talk between the ligand-binding site and the distal intracellular parts of the receptor, where binding of a specific ligand thermodynamically controls (either stabilizes or destabilizes) the intracellular region, consisting of crucial dynamic elements ICL2 and ICL3, and differential conformational transitions of these loops determine ligand-dependent functional selectivity.
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Affiliation(s)
- Sukanya Mozumder
- Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology, 4, Raja S.C. Mullick Road, Jadavpur, Kolkata 700032, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Aritra Bej
- Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology, 4, Raja S.C. Mullick Road, Jadavpur, Kolkata 700032, India
| | - Jayati Sengupta
- Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology, 4, Raja S.C. Mullick Road, Jadavpur, Kolkata 700032, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
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Ni D, Liu Y, Kong R, Yu Z, Lu S, Zhang J. Computational elucidation of allosteric communication in proteins for allosteric drug design. Drug Discov Today 2022; 27:2226-2234. [DOI: 10.1016/j.drudis.2022.03.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 01/22/2022] [Accepted: 03/17/2022] [Indexed: 02/07/2023]
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24
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Zha J, Li M, Kong R, Lu S, Zhang J. Explaining and Predicting Allostery with Allosteric Database and Modern Analytical Techniques. J Mol Biol 2022; 434:167481. [DOI: 10.1016/j.jmb.2022.167481] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/25/2022] [Accepted: 01/31/2022] [Indexed: 12/17/2022]
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25
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Wu N, Strömich L, Yaliraki SN. Prediction of allosteric sites and signaling: Insights from benchmarking datasets. PATTERNS (NEW YORK, N.Y.) 2022; 3:100408. [PMID: 35079717 PMCID: PMC8767309 DOI: 10.1016/j.patter.2021.100408] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/06/2021] [Accepted: 11/15/2021] [Indexed: 12/12/2022]
Abstract
Allostery is a pervasive mechanism that regulates protein activity through ligand binding at a site different from the orthosteric site. The universality of allosteric regulation complemented by the benefits of highly specific and potentially non-toxic allosteric drugs makes uncovering allosteric sites invaluable. However, there are few computational methods to effectively predict them. Bond-to-bond propensity analysis has successfully predicted allosteric sites in 19 of 20 cases using an energy-weighted atomistic graph. We here extended the analysis onto 432 structures of 146 proteins from two benchmarking datasets for allosteric proteins: ASBench and CASBench. We further introduced two statistical measures to account for the cumulative effect of high-propensity residues and the crucial residues in a given site. The allosteric site is recovered for 127 of 146 proteins (407 of 432 structures) knowing only the orthosteric sites or ligands. The quantitative analysis using a range of statistical measures enables better characterization of potential allosteric sites and mechanisms involved.
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Affiliation(s)
- Nan Wu
- Department of Chemistry, Imperial College London, London W12 0BZ, UK
| | - Léonie Strömich
- Department of Chemistry, Imperial College London, London W12 0BZ, UK
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26
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Tan ZW, Tee WV, Samsudin F, Guarnera E, Bond PJ, Berezovsky IN. Allosteric perspective on the mutability and druggability of the SARS-CoV-2 Spike protein. Structure 2022; 30:590-607.e4. [PMID: 35063064 PMCID: PMC8772014 DOI: 10.1016/j.str.2021.12.011] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 11/03/2021] [Accepted: 12/22/2021] [Indexed: 12/22/2022]
Abstract
Recent developments in the SARS-CoV-2 pandemic point to its inevitable transformation into an endemic disease, urging both refinement of diagnostics for emerging variants of concern (VOCs) and design of variant-specific drugs in addition to vaccine adjustments. Exploring the structure and dynamics of the SARS-CoV-2 Spike protein, we argue that the high-mutability characteristic of RNA viruses coupled with the remarkable flexibility and dynamics of viral proteins result in a substantial involvement of allosteric mechanisms. While allosteric effects of mutations should be considered in predictions and diagnostics of new VOCs, allosteric drugs advantageously avoid escape mutations via non-competitive inhibition originating from alternative distal locations. The exhaustive allosteric signaling and probing maps presented herein provide a comprehensive picture of allostery in the spike protein, making it possible to locate potential mutations that could work as new VOC “drivers” and to determine binding patches that may be targeted by newly developed allosteric drugs.
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Fan J, Liu Y, Kong R, Ni D, Yu Z, Lu S, Zhang J. Harnessing Reversed Allosteric Communication: A Novel Strategy for Allosteric Drug Discovery. J Med Chem 2021; 64:17728-17743. [PMID: 34878270 DOI: 10.1021/acs.jmedchem.1c01695] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Allostery is a fundamental and extensive mechanism of intramolecular signal transmission. Allosteric drugs possess several unique pharmacological advantages over traditional orthosteric drugs, including greater selectivity, better physicochemical properties, and lower off-target toxicity. However, owing to the complexity of allosteric regulation, experimental approaches for the development of allosteric modulators are traditionally serendipitous. Recently, the reversed allosteric communication theory has been proposed, providing a feasible tool for the unbiased detection of allosteric sites. Herein, we review the latest research on the reversed allosteric communication effect using the examples of sirtuin 6, epidermal growth factor receptor, 3-phosphoinositide-dependent protein kinase 1, and Related to A and C kinases (RAC) serine/threonine protein kinase B and recapitulate the methodologies of reversed allosteric communication strategy. The novel reversed allosteric communication strategy greatly expands the horizon of allosteric site identification and allosteric mechanism exploration and is expected to accelerate an end-to-end framework for drug discovery.
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Affiliation(s)
- Jigang Fan
- College of Pharmacy, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region 750004, China.,State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.,Zhiyuan Innovative Research Center, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yaqin Liu
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Duan Ni
- The Charles Perkins Centre, University of Sydney, Sydney, New South Wales 2006, Australia
| | | | - Shaoyong Lu
- College of Pharmacy, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region 750004, China.,State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.,Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Jian Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region 750004, China.,State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.,Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.,School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, China
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28
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Rehman AU, Lu S, Khan AA, Khurshid B, Rasheed S, Wadood A, Zhang J. Hidden allosteric sites and De-Novo drug design. Expert Opin Drug Discov 2021; 17:283-295. [PMID: 34933653 DOI: 10.1080/17460441.2022.2017876] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Hidden allosteric sites are not visible in apo-crystal structures, but they may be visible in holo-structures when a certain ligand binds and maintains the ligand intended conformation. Several computational and experimental techniques have been used to investigate these hidden sites but identifying them remains a challenge. AREAS COVERED This review provides a summary of the many theoretical approaches for predicting hidden allosteric sites in disease-related proteins. Furthermore, promising cases have been thoroughly examined to reveal the hidden allosteric site and its modulator. EXPERT OPINION In the recent past, with the development in scientific techniques and bioinformatics tools, the number of drug targets for complex human diseases has significantly increased but unfortunately most of these targets are undruggable due to several reasons. Alternative strategies such as finding cryptic (hidden) allosteric sites are an attractive approach for exploitation of the discovery of new targets. These hidden sites are difficult to recognize compared to allosteric sites, mainly due to a lack of visibility in the crystal structure. In our opinion, after many years of development, MD simulations are finally becoming successful for obtaining a detailed molecular description of drug-target interaction.
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Affiliation(s)
- Ashfaq Ur Rehman
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Renji Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Shaoyong Lu
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Renji Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China
| | - Abdul Aziz Khan
- Bio-X Institutes, Key Laboratory for the Genetics of Development and Neuropsychiatric Disorders (Ministry of Education), Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Institute of Psychology and Behavioral Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - Beenish Khurshid
- Department of Biochemistry, Abdul Wali Khan University Mardan, Mardan, Pakistan
| | - Salman Rasheed
- National Center for Bioinformatics, Quaid-e-Azam University, Islamabad, Pakistan
| | - Abdul Wadood
- Department of Biochemistry, Abdul Wali Khan University Mardan, Mardan, Pakistan
| | - Jian Zhang
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Renji Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China.,School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
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29
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Celebi M, Inan T, Kurkcuoglu O, Akten ED. Potential allosteric sites captured in glycolytic enzymes via residue-based network models: Phosphofructokinase, glyceraldehyde-3-phosphate dehydrogenase and pyruvate kinase. Biophys Chem 2021; 280:106701. [PMID: 34736071 DOI: 10.1016/j.bpc.2021.106701] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/12/2021] [Accepted: 10/19/2021] [Indexed: 01/05/2023]
Abstract
Likelihood of new allosteric sites for glycolytic enzymes, phosphofructokinase (PFK), glyceraldehyde-3-phosphate dehydrogenase (GADPH) and pyruvate kinase (PK) was evaluated for bacterial, parasitic and human species. Allosteric effect of a ligand binding at a site was revealed on the basis of low-frequency normal modes via Cα-harmonic residue network model. In bacterial PFK, perturbation of the proposed allosteric site outperformed the known allosteric one, producing a high amount of stabilization or reduced dynamics, on all catalytic regions. Another proposed allosteric spot at the dimer interface in parasitic PFK exhibited major stabilization effect on catalytic regions. In parasitic GADPH, the most desired allosteric response was observed upon perturbation of its tunnel region which incorporated key residues for functional regulation. Proposed allosteric site in bacterial PK produced a satisfactory allosteric response on all catalytic regions, whereas in human and parasitic PKs, a partial inhibition was observed. Residue network model based solely on contact topology identified the 'hub residues' with high betweenness tracing plausible allosteric communication pathways between distant functional sites. For both bacterial PFK and PK, proposed sites accommodated hub residues twice as much as the known allosteric site. Tunnel region in parasitic GADPH with the strongest allosteric effect among species, incorporated the highest number of hub residues. These results clearly suggest a one-to-one correspondence between the degree of allosteric effect and the number of hub residues in that perturbation site, which increases the likelihood of its allosteric nature.
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Affiliation(s)
- Metehan Celebi
- Graduate Program of Computational Biology and Bioinformatics, Graduate School of Science and Engineering, Kadir Has University, Istanbul, Turkey
| | - Tugce Inan
- Department of Chemical Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Ozge Kurkcuoglu
- Department of Chemical Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Ebru Demet Akten
- Department of Bioinformatics and Genetics, Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul, Turkey.
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30
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Ni D, Chai Z, Wang Y, Li M, Yu Z, Liu Y, Lu S, Zhang J. Along the allostery stream: Recent advances in computational methods for allosteric drug discovery. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1585] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Duan Ni
- College of Pharmacy Ningxia Medical University Yinchuan China
- The Charles Perkins Centre University of Sydney Sydney New South Wales Australia
| | - Zongtao Chai
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital Second Military Medical University Shanghai China
| | - Ying Wang
- State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Mingyu Li
- State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education Shanghai Jiao Tong University School of Medicine Shanghai China
| | | | - Yaqin Liu
- Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Shaoyong Lu
- College of Pharmacy Ningxia Medical University Yinchuan China
- State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education Shanghai Jiao Tong University School of Medicine Shanghai China
- Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong University School of Medicine Shanghai China
| | - Jian Zhang
- College of Pharmacy Ningxia Medical University Yinchuan China
- State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education Shanghai Jiao Tong University School of Medicine Shanghai China
- Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong University School of Medicine Shanghai China
- School of Pharmaceutical Sciences Zhengzhou University Zhengzhou China
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31
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Okeke CJ, Musyoka TM, Sheik Amamuddy O, Barozi V, Tastan Bishop Ö. Allosteric pockets and dynamic residue network hubs of falcipain 2 in mutations including those linked to artemisinin resistance. Comput Struct Biotechnol J 2021; 19:5647-5666. [PMID: 34745456 PMCID: PMC8545671 DOI: 10.1016/j.csbj.2021.10.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 09/30/2021] [Accepted: 10/03/2021] [Indexed: 10/29/2022] Open
Abstract
Continually emerging resistant strains of malarial parasites to current drugs present challenges. Understanding the underlying resistance mechanisms, especially those linked to allostery is, thus, highly crucial for drug design. This forms the main concern of the paper through a case study of falcipain 2 (FP-2) and its mutations, some of which are linked to artemisinin (ART) drug resistance. Here, we applied a variety of in silico approaches and tools that we developed recently, together with existing computational tools. This included novel essential dynamics and dynamic residue network (DRN) analysis algorithms. We identified six pockets demonstrating dynamic differences in the presence of some mutations. We observed striking allosteric effects in two mutant proteins. In the presence of M245I, a cryptic pocket was detected via a unique mechanism in which Pocket 2 fused with Pocket 6. In the presence of the A353T mutation, which is located at Pocket 2, the pocket became the most rigid among all protein systems analyzed. Pocket 6 was also highly stable in all cases, except in the presence of M245I mutation. The effect of ART linked mutations was more subtle, and the changes were at residue level. Importantly, we identified an allosteric communication path formed by four unique averaged BC hubs going from the mutated residue to the catalytic site and passing through the interface of three identified pockets. Collectively, we established and demonstrated that we have robust tools and a pipeline that can be applicable to the analysis of mutations.
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Affiliation(s)
| | | | - Olivier Sheik Amamuddy
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda 6140, South Africa
| | - Victor Barozi
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda 6140, South Africa
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda 6140, South Africa
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32
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Mersmann S, Strömich L, Song FJ, Wu N, Vianello F, Barahona M, Yaliraki S. ProteinLens: a web-based application for the analysis of allosteric signalling on atomistic graphs of biomolecules. Nucleic Acids Res 2021; 49:W551-W558. [PMID: 33978752 PMCID: PMC8661402 DOI: 10.1093/nar/gkab350] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/16/2021] [Accepted: 04/22/2021] [Indexed: 11/28/2022] Open
Abstract
The investigation of allosteric effects in biomolecular structures is of great current interest in diverse areas, from fundamental biological enquiry to drug discovery. Here we present ProteinLens, a user-friendly and interactive web application for the investigation of allosteric signalling based on atomistic graph-theoretical methods. Starting from the PDB file of a biomolecule (or a biomolecular complex) ProteinLens obtains an atomistic, energy-weighted graph description of the structure of the biomolecule, and subsequently provides a systematic analysis of allosteric signalling and communication across the structure using two computationally efficient methods: Markov Transients and bond-to-bond propensities. ProteinLens scores and ranks every bond and residue according to the speed and magnitude of the propagation of fluctuations emanating from any site of choice (e.g. the active site). The results are presented through statistical quantile scores visualised with interactive plots and adjustable 3D structure viewers, which can also be downloaded. ProteinLens thus allows the investigation of signalling in biomolecular structures of interest to aid the detection of allosteric sites and pathways. ProteinLens is implemented in Python/SQL and freely available to use at: www.proteinlens.io.
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Affiliation(s)
- Sophia F Mersmann
- Department of Mathematics, Imperial College London, Huxley Building, 180 Queen’s Gate, London SW7 2AZ, UK
| | - Léonie Strömich
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, 82 Wood Lane, London W12 0BZ, UK
| | - Florian J Song
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, 82 Wood Lane, London W12 0BZ, UK
| | - Nan Wu
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, 82 Wood Lane, London W12 0BZ, UK
| | - Francesca Vianello
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, 82 Wood Lane, London W12 0BZ, UK
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, Huxley Building, 180 Queen’s Gate, London SW7 2AZ, UK
| | - Sophia N Yaliraki
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, 82 Wood Lane, London W12 0BZ, UK
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33
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Chatzigoulas A, Cournia Z. Rational design of allosteric modulators: Challenges and successes. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1529] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Alexios Chatzigoulas
- Biomedical Research Foundation Academy of Athens Athens Greece
- Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece
| | - Zoe Cournia
- Biomedical Research Foundation Academy of Athens Athens Greece
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34
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He XL, Du LF, Zhang J, Liang Y, Wu YD, Su JG, Li QM. The functional motions and related key residues behind the uncoating of coxsackievirus A16. Proteins 2021; 89:1365-1375. [PMID: 34085313 DOI: 10.1002/prot.26157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 05/09/2021] [Accepted: 06/01/2021] [Indexed: 11/05/2022]
Abstract
The coxsackievirus A16 (CVA16) is a highly contagious virus that causes the hand, foot, and mouth disease, which seriously threatens the health of children. At present, there are still no available antiviral drugs or effective treatments against the infection of CVA16, and thus it is of great significance to develop anti-CVA16 vaccines. However, the intrinsic uncoating property of the capsid may destroy the neutralizing epitopes and influence its immunogenicity, which hinders the vaccine developments. In the present work, the functional-quantity-based elastic network model analysis method developed by our group was extended to combine with group theory to investigate the uncoating motions of the CVA16 capsid, and then the functionally key residues controlling the uncoating motions were identified by our functional-quantity-based perturbation method. Several motion modes encoded in the topological structure of the capsid were revealed to be responsible for the uncoating of CVA16 particle. These modes predominantly contribute to the fluctuation of the gyration radius of the capsid. Then, by using the perturbation method, four clusters of key sites involved in the uncoating motions were identified, whose perturbations induce significant changes in the fluctuation of the gyration radius. These key residues are mainly located at the 2-fold channels, the quasi 3-fold channels, the bottom of the canyons, and the inter-subunit interfaces around the 3-fold axes. Our studies are helpful for better understanding the uncoating mechanism of the CVA16 capsid and provide potential target sites to prevent the uncoating motions, which is valuable for the vaccine design against CVA16.
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Affiliation(s)
- Xing Long He
- Key Laboratory for Microstructural Material Physics of Hebei Province, School of Science, Yanshan University, Qinhuangdao, China
| | - Li Fang Du
- The Sixth Laboratory, National Vaccine and Serum Institute, Beijing, China
| | - Jing Zhang
- The Sixth Laboratory, National Vaccine and Serum Institute, Beijing, China
| | - Yu Liang
- The Sixth Laboratory, National Vaccine and Serum Institute, Beijing, China
| | - Yi Dong Wu
- Key Laboratory for Microstructural Material Physics of Hebei Province, School of Science, Yanshan University, Qinhuangdao, China
| | - Ji Guo Su
- Key Laboratory for Microstructural Material Physics of Hebei Province, School of Science, Yanshan University, Qinhuangdao, China.,The Sixth Laboratory, National Vaccine and Serum Institute, Beijing, China
| | - Qi Ming Li
- The Sixth Laboratory, National Vaccine and Serum Institute, Beijing, China
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35
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Tee WV, Tan ZW, Lee K, Guarnera E, Berezovsky IN. Exploring the Allosteric Territory of Protein Function. J Phys Chem B 2021; 125:3763-3780. [PMID: 33844527 DOI: 10.1021/acs.jpcb.1c00540] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
While the pervasiveness of allostery in proteins is commonly accepted, we further show the generic nature of allosteric mechanisms by analyzing here transmembrane ion-channel viroporin 3a and RNA-dependent RNA polymerase (RdRp) from SARS-CoV-2 along with metabolic enzymes isocitrate dehydrogenase 1 (IDH1) and fumarate hydratase (FH) implicated in cancers. Using the previously developed structure-based statistical mechanical model of allostery (SBSMMA), we share our experience in analyzing the allosteric signaling, predicting latent allosteric sites, inducing and tuning targeted allosteric response, and exploring the allosteric effects of mutations. This, yet incomplete list of phenomenology, forms a complex and unique allosteric territory of protein function, which should be thoroughly explored. We propose a generic computational framework, which not only allows one to obtain a comprehensive allosteric control over proteins but also provides an opportunity to approach the fragment-based design of allosteric effectors and drug candidates. The advantages of allosteric drugs over traditional orthosteric compounds, complemented by the emerging role of the allosteric effects of mutations in the expansion of the cancer mutational landscape and in the increased mutability of viral proteins, leave no choice besides further extensive studies of allosteric mechanisms and their biomedical implications.
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Affiliation(s)
- Wei-Ven Tee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore.,Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117597, Singapore
| | - Zhen Wah Tan
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Keene Lee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Enrico Guarnera
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore.,Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117597, Singapore
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36
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Mechanical coupling in the nitrogenase complex. PLoS Comput Biol 2021; 17:e1008719. [PMID: 33661889 PMCID: PMC7963043 DOI: 10.1371/journal.pcbi.1008719] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 03/16/2021] [Accepted: 01/18/2021] [Indexed: 11/19/2022] Open
Abstract
The enzyme nitrogenase reduces dinitrogen to ammonia utilizing electrons, protons, and energy obtained from the hydrolysis of ATP. Mo-dependent nitrogenase is a symmetric dimer, with each half comprising an ATP-dependent reductase, termed the Fe Protein, and a catalytic protein, known as the MoFe protein, which hosts the electron transfer P-cluster and the active-site metal cofactor (FeMo-co). A series of synchronized events for the electron transfer have been characterized experimentally, in which electron delivery is coupled to nucleotide hydrolysis and regulated by an intricate allosteric network. We report a graph theory analysis of the mechanical coupling in the nitrogenase complex as a key step to understanding the dynamics of allosteric regulation of nitrogen reduction. This analysis shows that regions near the active sites undergo large-scale, large-amplitude correlated motions that enable communications within each half and between the two halves of the complex. Computational predictions of mechanically regions were validated against an analysis of the solution phase dynamics of the nitrogenase complex via hydrogen-deuterium exchange. These regions include the P-loops and the switch regions in the Fe proteins, the loop containing the residue β-188Ser adjacent to the P-cluster in the MoFe protein, and the residues near the protein-protein interface. In particular, it is found that: (i) within each Fe protein, the switch regions I and II are coupled to the [4Fe-4S] cluster; (ii) within each half of the complex, the switch regions I and II are coupled to the loop containing β-188Ser; (iii) between the two halves of the complex, the regions near the nucleotide binding pockets of the two Fe proteins (in particular the P-loops, located over 130 Å apart) are also mechanically coupled. Notably, we found that residues next to the P-cluster (in particular the loop containing β-188Ser) are important for communication between the two halves.
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37
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Zhu Y, Huang C, Su M, Ge Z, Gao L, Shi Y, Wang X, Chen J. Characterization of amino acid residues of T-cell receptors interacting with HLA-A*02-restricted antigen peptides. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:495. [PMID: 33850892 PMCID: PMC8039679 DOI: 10.21037/atm-21-835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background The present study aimed to explore residues’ properties interacting with HLA-A*02-restricted peptides on T-cell receptors (TCRs) and their effects on bond types of interaction and binding free energy. Methods We searched the crystal structures of HLA-A*02-restricted peptide-TCR complexes from the Protein Data Bank (PDB) database and subsequently collected relevant parameters. We then employed Schrodinger to analyze the bond types of interaction and Gromacs 2019 to evaluate the TCR-antigen peptide complex’s molecular dynamics simulation. Finally, we compared the changes of bond types of interaction and binding free energy before and after residue substitution to ensure consistency of the conditions before and after residue substitution. Results The main sites on the antigen peptides that formed the intermolecular interaction [hydrogen bond (HB) and pi stack] with TCRs were P4, P8, P2, and P6. The hydrophobicity of the amino acids inside or outside the disulfide bond of TCRs may be related to the intermolecular interaction and binding free energy between TCRs and peptides. Residues located outside the disulfide bond of TCR α or β chains and forming pi stack force played favorable roles in the complex intermolecular interaction and binding free energy. The residues of the TCR α or β chains that interacted with peptides were replaced by alanine (Ala) or glycine (Gly), and their intermolecular binding free energy of the complex had been improved. However, it had nothing to do with the formation of HB. Conclusions The findings of this study suggest that the hydrophobic nature of the amino acids inside or outside the disulfide bonds on the TCR may be associated with the intermolecular interaction and binding between the TCR and polypeptide. The residues located outside the TCR α or β single-chain disulfide bond and forming the pi-stack force showed a beneficial effect on the intermolecular interaction and binding of the complex. In addition, the part of the residues on the TCR α or β single chain that produced bond types of interaction with the polypeptide after being replaced by Ala or Gly, the intermolecular binding free energy of the complex was increased, regardless of whether HB was formed.
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Affiliation(s)
- Ying Zhu
- Department of Oncology, First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Changxin Huang
- Department of Oncology, Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Meng Su
- Master Class, Zhejiang Chinese Medical University, Fourth School of Clinical Medicine, Hangzhou, China
| | - Zuanmin Ge
- Master Class, Hangzhou Normal University, School of Medicine, Hangzhou, China
| | - Lanlan Gao
- Master Class, Hangzhou Normal University, School of Medicine, Hangzhou, China
| | - Yanfei Shi
- Master Class, Hangzhou Normal University, School of Medicine, Hangzhou, China
| | - Xuechun Wang
- Master Class, Zhejiang Chinese Medical University, Fourth School of Clinical Medicine, Hangzhou, China
| | - Jianfeng Chen
- Department of Proctology, Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
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Abstract
Allosteric regulation in proteins is fundamental to many important biological processes. Allostery has been employed to control protein functions by regulating protein activity. Engineered allosteric regulation allows controlling protein activity in subsecond time scale and has a broad range of applications, from dissecting spatiotemporal dynamics in biochemical cascades to applications in biotechnology and medicine. Here, we review the concept of allostery in proteins and various approaches to identify allosteric sites and pathways. We then provide an overview of strategies and tools used in allosteric protein regulation and their utility in biological applications. We highlight various classes of proteins, where regulation is achieved through allostery. Finally, we analyze the current problems, critical challenges, and future prospective in achieving allosteric regulation in proteins.
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Affiliation(s)
| | - Jiaxing Chen
- Department of Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania 17033-0850, United States
| | - Nikolay V Dokholyan
- Department of Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania 17033-0850, United States
- Departments of Biochemistry & Molecular Biology, Penn State College of Medicine, Hershey, Pennsylvania 17033-0850, United States
- Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, United States
- Department of Biomedical Engineering, Penn State University, University Park, Pennsylvania 16802, United States
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39
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Birgül Iyison N, Shahraki A, Kahveci K, Düzgün MB, Gün G. Are insect GPCRs ideal next‐generation pesticides: opportunities and challenges. FEBS J 2021; 288:2727-2745. [DOI: 10.1111/febs.15708] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 12/27/2020] [Accepted: 01/06/2021] [Indexed: 12/22/2022]
Affiliation(s)
- Necla Birgül Iyison
- Department of Molecular Biology and Genetics Institute of Graduate Studies in Science and Engineering Boğaziçi University Istanbul Turkey
| | - Aida Shahraki
- Department of Molecular Biology and Genetics Institute of Graduate Studies in Science and Engineering Boğaziçi University Istanbul Turkey
| | - Kübra Kahveci
- Department of Molecular Biology and Genetics Institute of Graduate Studies in Science and Engineering Boğaziçi University Istanbul Turkey
| | - Mustafa Barbaros Düzgün
- Department of Molecular Biology and Genetics Institute of Graduate Studies in Science and Engineering Boğaziçi University Istanbul Turkey
| | - Gökhan Gün
- Department of Molecular Biology and Genetics Institute of Graduate Studies in Science and Engineering Boğaziçi University Istanbul Turkey
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40
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Tan ZW, Guarnera E, Tee WV, Berezovsky IN. AlloSigMA 2: paving the way to designing allosteric effectors and to exploring allosteric effects of mutations. Nucleic Acids Res 2020; 48:W116-W124. [PMID: 32392302 PMCID: PMC7319554 DOI: 10.1093/nar/gkaa338] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 04/09/2020] [Accepted: 04/22/2020] [Indexed: 12/21/2022] Open
Abstract
The AlloSigMA 2 server provides an interactive platform for exploring the allosteric signaling caused by ligand binding and/or mutations, for analyzing the allosteric effects of mutations and for detecting potential cancer drivers and pathogenic nsSNPs. It can also be used for searching latent allosteric sites and for computationally designing allosteric effectors for these sites with required agonist/antagonist activity. The server is based on the implementation of the Structure-Based Statistical Mechanical Model of Allostery (SBSMMA), which allows one to evaluate the allosteric free energy as a result of the perturbation at per-residue resolution. The Allosteric Signaling Map (ASM) providing a comprehensive residue-by-residue allosteric control over the protein activity can be obtained for any structure of interest. The Allosteric Probing Map (APM), in turn, allows one to perform the fragment-based-like computational design experiment aimed at finding leads for potential allosteric effectors. The server can be instrumental in elucidating of allosteric mechanisms and actions of allosteric mutations, and in the efforts on design of new elements of allosteric control. The server is freely available at: http://allosigma.bii.a-star.edu.sg.
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Affiliation(s)
- Zhen Wah Tan
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Enrico Guarnera
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Wei-Ven Tee
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore.,Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117579, Singapore
| | - Igor N Berezovsky
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671, Singapore.,Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117579, Singapore
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41
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Ghode A, Gross LZF, Tee WV, Guarnera E, Berezovsky IN, Biondi RM, Anand GS. Synergistic Allostery in Multiligand-Protein Interactions. Biophys J 2020; 119:1833-1848. [PMID: 33086047 PMCID: PMC7677135 DOI: 10.1016/j.bpj.2020.09.019] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 08/31/2020] [Accepted: 09/15/2020] [Indexed: 02/07/2023] Open
Abstract
Amide hydrogen-deuterium exchange mass spectrometry is powerful for describing combinatorial coupling effects of a cooperative ligand pair binding at noncontiguous sites: adenosine at the ATP-pocket and a docking peptide (PIFtide) at the PIF-pocket, on a model protein kinase PDK1. Binding of two ligands to PDK1 reveal multiple hotspots of synergistic allostery with cumulative effects greater than the sum of individual effects mediated by each ligand. We quantified this synergism and ranked these hotspots using a difference in deuteration-based approach, which showed that the strongest synergistic effects were observed at three of the critical catalytic loci of kinases: the αB-αC helices, and HRD-motif loop, and DFG-motif. Additionally, we observed weaker synergistic effects at a distal GHI-subdomain locus. Synergistic changes in deuterium exchange observed at a distal site but not at the intermediate sites of the large lobe of the kinase reveals allosteric propagation in proteins to operate through two modes. Direct electrostatic interactions between polar and charged amino acids that mediate targeted relay of allosteric signals, and diffused relay of allosteric signals through soft matter-like hydrophobic core amino acids. Furthermore, we provide evidence that the conserved β-3 strand lysine of protein kinases (Lys111 of PDK1) functions as an integrator node to coordinate allosteric coupling of the two ligand-binding sites. It maintains indirect interactions with the ATP-pocket and mediates a critical salt bridge with a glutamate (Glu130) of αC helix, which is conserved across all kinases. In summary, allosteric propagation in cooperative, dual-liganded enzyme targets is bidirectional and synergistic and offers a strategy for combinatorial drug development.
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Affiliation(s)
- Abhijeet Ghode
- Department of Biological Sciences, National University of Singapore, Singapore
| | - Lissy Z F Gross
- Instituto de Investigación en Biomedicina de Buenos Aires - CONICET - Partner Institute of the Max Planck Society, Buenos Aires, Argentina
| | - Wei-Ven Tee
- Department of Biological Sciences, National University of Singapore, Singapore; Bioinformatics Institute, Agency for Science, Technology and Research, Matrix, Singapore
| | - Enrico Guarnera
- Bioinformatics Institute, Agency for Science, Technology and Research, Matrix, Singapore
| | - Igor N Berezovsky
- Department of Biological Sciences, National University of Singapore, Singapore; Bioinformatics Institute, Agency for Science, Technology and Research, Matrix, Singapore
| | - Ricardo M Biondi
- Instituto de Investigación en Biomedicina de Buenos Aires - CONICET - Partner Institute of the Max Planck Society, Buenos Aires, Argentina
| | - Ganesh S Anand
- Department of Biological Sciences, National University of Singapore, Singapore.
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42
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Tee WV, Guarnera E, Berezovsky IN. Disorder driven allosteric control of protein activity. Curr Res Struct Biol 2020; 2:191-203. [PMID: 34235479 PMCID: PMC8244471 DOI: 10.1016/j.crstbi.2020.09.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 08/27/2020] [Accepted: 09/02/2020] [Indexed: 12/23/2022] Open
Abstract
Studies of protein allostery increasingly reveal an involvement of the back and forth order-disorder transitions in this mechanism of protein activity regulation. Here, we investigate the allosteric mechanisms mediated by structural disorder using the structure-based statistical mechanical model of allostery (SBSMMA) that we have previously developed. We show that SBSMMA accounts for the energetics and causality of allosteric communication underlying dimerization of the BirA biotin repressor, activation of the sortase A enzyme, and inhibition of the Rac1 GTPase. Using the SBSMMA, we also show that introducing structural order or disorder in various regions of esterases can originate tunable allosteric modulation of the catalytic triad. On the basis of obtained results, we propose that operating with the order-disorder continuum allows one to establish an allosteric control scale for achieving desired modulation of the protein activity. Back and forth order-disorder transitions can induce allosteric signaling. Allosteric signaling originated by order/disorder follow universal rules. Allosteric control scale facilitates engineering of the protein activity regulation.
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Affiliation(s)
- Wei-Ven Tee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A∗STAR), 30 Biopolis Street, #07-01, Matrix 138671, Singapore.,Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive 117597, Singapore
| | - Enrico Guarnera
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A∗STAR), 30 Biopolis Street, #07-01, Matrix 138671, Singapore
| | - Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A∗STAR), 30 Biopolis Street, #07-01, Matrix 138671, Singapore.,Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive 117597, Singapore
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43
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Allosteric drugs and mutations: chances, challenges, and necessity. Curr Opin Struct Biol 2020; 62:149-157. [DOI: 10.1016/j.sbi.2020.01.010] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 01/16/2020] [Indexed: 12/22/2022]
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44
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Tan ZW, Tee WV, Guarnera E, Booth L, Berezovsky IN. AlloMAPS: allosteric mutation analysis and polymorphism of signaling database. Nucleic Acids Res 2020; 47:D265-D270. [PMID: 30365033 PMCID: PMC6323965 DOI: 10.1093/nar/gky1028] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 10/16/2018] [Indexed: 01/06/2023] Open
Abstract
AlloMAPS database provides data on the causality and energetics of allosteric communication obtained with the structure-based statistical mechanical model of allostery (SBSMMA). The database contains data on allosteric signaling in three sets of proteins and protein chains: (i) 46 proteins with comprehensively annotated functional and allosteric sites; (ii) 1908 protein chains from PDBselect set of chains with low (<25%) sequence identity; (iii) 33 proteins with more than 50 known pathological SNPs in each molecule. In addition to energetics of allosteric signaling between known functional and regulatory sites, allosteric modulation caused by the binding to these sites, by SNPs, and by mutations designated by the user can be explored. Allosteric Signaling Maps (ASMs), which are produced via the exhaustive computational scanning for stabilizing and destabilizing mutations and for the modulation range caused by the sequence position are available for each protein/protein chain in the database. We propose to use this database for evaluating the effects of allosteric signaling in the search for latent regulatory sites and in the design of allosteric sites and effectors. The database is freely available at: http://allomaps.bii.a-star.edu.sg.
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Affiliation(s)
- Zhen Wah Tan
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671 Singapore
| | - Wei-Ven Tee
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671 Singapore.,Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117579 Singapore
| | - Enrico Guarnera
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671 Singapore
| | - Lauren Booth
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671 Singapore.,Research School of Chemistry, The Australian National University, Canberra, ACT 2601, Australia
| | - Igor N Berezovsky
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, 138671 Singapore.,Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, 117579 Singapore
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45
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Essential site scanning analysis: A new approach for detecting sites that modulate the dispersion of protein global motions. Comput Struct Biotechnol J 2020; 18:1577-1586. [PMID: 32637054 PMCID: PMC7330491 DOI: 10.1016/j.csbj.2020.06.020] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 06/08/2020] [Accepted: 06/10/2020] [Indexed: 12/14/2022] Open
Abstract
Despite the wealth of methods developed for exploring the molecular basis of allostery in biomolecular systems, there is still a need for structure-based predictive tools that can efficiently detect susceptible sites for triggering allosteric responses. Toward this goal, we introduce here an elastic network model (ENM)-based method, Essential Site Scanning Analysis (ESSA). Essential sites are here defined as residues that would significantly alter the protein's global dynamics if bound to a ligand. To mimic the crowding induced upon substrate binding, the heavy atoms of each residue are incorporated as additional network nodes into the α-carbon-based ENM, and the resulting shifts in soft mode frequencies are used as a metric for evaluating the essentiality of each residue. Results on a dataset of monomeric proteins indicate the enrichment of allosteric and orthosteric binding sites, as well as global hinge regions among essential residues, highlighting the significant role of these sites in controlling the overall structural dynamics. Further integration of ESSA with information on predicted pockets and their local hydrophobicity density enables successful predictions of allosteric pockets for both ligand-bound and -unbound structures. ESSA can be efficiently applied to large multimeric systems. Three case studies, namely (i) G-protein binding to a GPCR, (ii) heterotrimeric assembly of the Ser/Thr protein phosphatase PP2A, and (iii) allo-targeting of AMPA receptor, demonstrate the utility of ESSA for identifying essential sites and narrowing down target allosteric sites identified by druggability simulations.
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46
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Zhang Y, Doruker P, Kaynak B, Zhang S, Krieger J, Li H, Bahar I. Intrinsic dynamics is evolutionarily optimized to enable allosteric behavior. Curr Opin Struct Biol 2019; 62:14-21. [PMID: 31785465 DOI: 10.1016/j.sbi.2019.11.002] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 10/31/2019] [Accepted: 11/04/2019] [Indexed: 12/13/2022]
Abstract
Allosteric behavior is central to the function of many proteins, enabling molecular machinery, metabolism, signaling and regulation. Recent years have shown that the intrinsic dynamics of allosteric proteins defined by their 3-dimensional architecture or by the topology of inter-residue contacts favors cooperative motions that bear close similarity to structural changes they undergo during their allosteric actions. These conformational motions are usually driven by energetically favorable or soft modes at the low frequency end of the mode spectrum, and they are evolutionarily conserved among orthologs. These observations brought into light evolutionary adaptation mechanisms that help maintain, optimize or regulate allosteric behavior as the evolution from bacterial to higher organisms introduces sequential heterogeneities and structural complexities.
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Affiliation(s)
- Yan Zhang
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - Pemra Doruker
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - Burak Kaynak
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - She Zhang
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - James Krieger
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA
| | - Hongchun Li
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA; Research Center for Computer-Aided Drug Discovery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Suite 3064 BST3, Pittsburgh, PA 15260, USA.
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47
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Hayatshahi HS, Ahuactzin E, Tao P, Wang S, Liu J. Probing Protein Allostery as a Residue-Specific Concept via Residue Response Maps. J Chem Inf Model 2019; 59:4691-4705. [PMID: 31589429 DOI: 10.1021/acs.jcim.9b00447] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Allosteric regulation is a well-established phenomenon defined as a distal conformational or dynamical change of the protein upon allosteric effector binding. Here, we developed a novel approach to delineate allosteric effects in proteins. In this approach, we applied robust machine learning methods, including deep neural network and random forest, on extensive molecular dynamics (MD) simulations to distinguish otherwise similar allosteric states of proteins. Using the PDZ3 domain of PDS-95 as a model protein, we demonstrated that the allosteric effects could be represented as residue-specific properties through two-dimensional property-residue maps, which we refer to as "residue response maps". These maps were constructed through two machine learning methods and could accurately describe how different properties of various residues are affected upon allosteric perturbation on protein. Based on the "residue response maps", we propose allostery as a residue-specific concept, suggesting that all residues could be considered as allosteric residues because each residue "senses" the allosteric events through changing its single or multiple attributes in a quantitatively unique way. The "residue response maps" could be used to fingerprint a protein based on the unique patterns of residue responses upon binding events, providing a novel way to systematically describe the protein allosteric effects of each residue upon perturbation.
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Affiliation(s)
- Hamed S Hayatshahi
- Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy , University of North Texas Health Science Center , 3500 Camp Bowie Blvd. , Fort Worth , Texas 76107 , United States
| | - Emilio Ahuactzin
- Harmony School of Innovation-Fort Worth , 8100 S. Hulen St. , Fort Worth , Texas 76123 , United States
| | - Peng Tao
- Department of Chemistry, Center for Drug Discovery, Design, and Delivery (CD4), Center for Scientific Computation , Southern Methodist University , Dallas , Texas 75275 , United States
| | - Shouyi Wang
- Department of Industrial, Manufacturing, & Systems Engineering, College of Engineering , University of Texas at Arlington , 701 S. Nedderman Dr. , Arlington , Texas 76019 , United States
| | - Jin Liu
- Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy , University of North Texas Health Science Center , 3500 Camp Bowie Blvd. , Fort Worth , Texas 76107 , United States
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48
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Tee WV, Guarnera E, Berezovsky IN. On the Allosteric Effect of nsSNPs and the Emerging Importance of Allosteric Polymorphism. J Mol Biol 2019; 431:3933-3942. [PMID: 31306666 DOI: 10.1016/j.jmb.2019.07.012] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 06/11/2019] [Accepted: 07/04/2019] [Indexed: 12/19/2022]
Abstract
The molecular mechanisms of pathological non-synonymous single-nucleotide polymorphisms are still the object of intensive research. To this end, we explore here whether non-synonymous single-nucleotide polymorphisms can work via allosteric mechanisms. Using structure-based statistical mechanical model of allostery and analyzing energetics of the effects of mutations in a set of 27 proteins with at least 50 pathological SNPs in each molecule, we found that, indeed, some SNPs can work allosterically. We illustrate the molecular basis of disease phenotypes caused by allosteric SNPs with the case studies of human galactose 1-phosphate uridyltransferase (GALT) and glucose-6-phosphate dehydrogenase (G6PD). We also found that mutations of a number of other residues in the protein may cause modulation comparable to those observed for known pathological SNPs. In order to explain this, we propose a notion of allosteric polymorphism, which implies the presence of a number of critical positions in the protein sequence, whose mutations can allosterically disrupt the protein function and result in a disease phenotype. We conclude that the emerging importance of allosteric polymorphism calls for the development of computational framework for analyzing the allosteric effects of mutations and their role in the modulation of protein activity.
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Affiliation(s)
- Wei-Ven Tee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671; Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, Singapore 117597
| | - Enrico Guarnera
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671
| | - Igor N Berezovsky
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01, Matrix, Singapore 138671; Department of Biological Sciences (DBS), National University of Singapore (NUS), 8 Medical Drive, Singapore 117597.
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49
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Song K, Li Q, Gao W, Lu S, Shen Q, Liu X, Wu Y, Wang B, Lin H, Chen G, Zhang J. AlloDriver: a method for the identification and analysis of cancer driver targets. Nucleic Acids Res 2019; 47:W315-W321. [PMID: 31069394 PMCID: PMC6602569 DOI: 10.1093/nar/gkz350] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2019] [Revised: 04/23/2019] [Accepted: 04/25/2019] [Indexed: 12/16/2022] Open
Abstract
Identifying the variants that alter protein function is a promising strategy for deciphering the biological consequences of somatic mutations during tumorigenesis, which could provide novel targets for the development of cancer therapies. Here, based on our previously developed method, we present a strategy called AlloDriver that identifies cancer driver genes/proteins as possible targets from mutations. AlloDriver utilizes structural and dynamic features to prioritize potentially functional genes/proteins in individual cancers via mapping mutations generated from clinical cancer samples to allosteric/orthosteric sites derived from three-dimensional protein structures. This strategy exhibits desirable performance in the reemergence of known cancer driver mutations and genes/proteins from clinical samples. Significantly, the practicability of AlloDriver to discover novel cancer driver proteins in head and neck squamous cell carcinoma (HNSC) was tested in a real case of human protein tyrosine phosphatase, receptor type K (PTPRK) through a L1143F driver mutation located at the allosteric site of PTPRK, which was experimentally validated by cell proliferation assay. AlloDriver is expected to help to uncover innovative molecular mechanisms of tumorigenesis by perturbing proteins and to discover novel targets based on cancer driver mutations. The AlloDriver is freely available to all users at http://mdl.shsmu.edu.cn/ALD.
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MESH Headings
- Algorithms
- Allosteric Regulation
- Allosteric Site
- Antineoplastic Agents/chemistry
- Antineoplastic Agents/therapeutic use
- Carcinogenesis/drug effects
- Carcinogenesis/genetics
- Carcinogenesis/metabolism
- Carcinogenesis/pathology
- Carcinoma, Squamous Cell/chemistry
- Carcinoma, Squamous Cell/drug therapy
- Carcinoma, Squamous Cell/genetics
- Carcinoma, Squamous Cell/pathology
- Cell Line, Tumor
- Cell Proliferation
- Drug Discovery
- Head and Neck Neoplasms/chemistry
- Head and Neck Neoplasms/drug therapy
- Head and Neck Neoplasms/genetics
- Head and Neck Neoplasms/pathology
- Humans
- Internet
- Molecular Targeted Therapy
- Mutation
- Neoplasm Proteins/antagonists & inhibitors
- Neoplasm Proteins/chemistry
- Neoplasm Proteins/genetics
- Neoplasm Proteins/metabolism
- Protein Binding
- Protein Conformation, alpha-Helical
- Protein Conformation, beta-Strand
- Protein Interaction Domains and Motifs
- Protein Tyrosine Phosphatase, Non-Receptor Type 11/antagonists & inhibitors
- Protein Tyrosine Phosphatase, Non-Receptor Type 11/chemistry
- Protein Tyrosine Phosphatase, Non-Receptor Type 11/genetics
- Protein Tyrosine Phosphatase, Non-Receptor Type 11/metabolism
- Receptor-Like Protein Tyrosine Phosphatases, Class 2/antagonists & inhibitors
- Receptor-Like Protein Tyrosine Phosphatases, Class 2/chemistry
- Receptor-Like Protein Tyrosine Phosphatases, Class 2/genetics
- Receptor-Like Protein Tyrosine Phosphatases, Class 2/metabolism
- Software
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Affiliation(s)
- Kun Song
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Department of Pharmacy, Renji Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200127, China
- Research Center for Marine Drugs, State Key Laboratory of Oncogenes and Related Genes, Department of Pharmacy, Renji Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200127, China
| | - Qian Li
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Department of Pharmacy, Renji Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200127, China
- Medicinal Bioinformatics Center, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Wei Gao
- Shanxi Key Laboratory of Otorhinolaryngology Head and Neck Cancer, Department of Otolaryngology Head & Neck Surgery, the First Hospital, Shanxi Medical University, Taiyuan, Shanxi 030001, China
| | - Shaoyong Lu
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Department of Pharmacy, Renji Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200127, China
| | - Qiancheng Shen
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Department of Pharmacy, Renji Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200127, China
- Medicinal Bioinformatics Center, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Xinyi Liu
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Department of Pharmacy, Renji Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200127, China
- Research Center for Marine Drugs, State Key Laboratory of Oncogenes and Related Genes, Department of Pharmacy, Renji Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200127, China
| | - Yongyan Wu
- Shanxi Key Laboratory of Otorhinolaryngology Head and Neck Cancer, Department of Otolaryngology Head & Neck Surgery, the First Hospital, Shanxi Medical University, Taiyuan, Shanxi 030001, China
| | - Binquan Wang
- Shanxi Key Laboratory of Otorhinolaryngology Head and Neck Cancer, Department of Otolaryngology Head & Neck Surgery, the First Hospital, Shanxi Medical University, Taiyuan, Shanxi 030001, China
| | - Houwen Lin
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Department of Pharmacy, Renji Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200127, China
- Research Center for Marine Drugs, State Key Laboratory of Oncogenes and Related Genes, Department of Pharmacy, Renji Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200127, China
| | - Guoqiang Chen
- Research Center for Marine Drugs, State Key Laboratory of Oncogenes and Related Genes, Department of Pharmacy, Renji Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200127, China
| | - Jian Zhang
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Department of Pharmacy, Renji Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200127, China
- Research Center for Marine Drugs, State Key Laboratory of Oncogenes and Related Genes, Department of Pharmacy, Renji Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200127, China
- Medicinal Bioinformatics Center, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
- Department of Pathophysiology, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
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Bongini P, Niccolai N, Bianchini M. Glycine-induced formation and druggability score prediction of protein surface pockets. J Bioinform Comput Biol 2019; 17:1950026. [PMID: 31744363 DOI: 10.1142/s0219720019500264] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Nowadays, it is well established that most of the human diseases which are not related to pathogen infections have their origin from DNA disorders. Thus, DNA mutations, waiting for the availability of CRISPR-like remedies, will propagate into proteomics, offering the possibility to select natural or synthetic molecules to fight against the effects of malfunctioning proteins. Drug discovery, indeed, is a flourishing field of biotechnological research to improve human health, even though the development of a new drug is increasingly more expensive in spite of the massive use of informatics in Medicinal Chemistry. CRISPR technology adds new alternatives to cure diseases by removing DNA defects responsible of genome-related pathologies. In principle, the same technology, however, could also be exploited to induce protein mutations whose effects are controlled by the presence of suitable ligands. In this paper, a new idea is proposed for the realization of mutated proteins, on the surface of which more spacious transient pockets are formed and, therefore, are more suitable for hosting drugs. In particular, new allosteric sites are obtained by replacing amino-acids with bulky side chains with glycine, Gly, the smallest natural amino-acid. We also present a machine learning approach to evaluate the druggability score of new (or enlarged) pockets. Preliminary experimental results are very promising, showing that 10% of the sites created by the Gly-pipe software are druggable.
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Affiliation(s)
- Pietro Bongini
- Department of Information Engineering, University of Florence, via S. Marta 3, 50139, Florence, Italy.,Department of Information Engineering and Mathematics, University of Siena, via Roma 56, 53100, Siena, Italy
| | - Neri Niccolai
- Department of Biotechnologies, Chemistry and Pharmacy, University of Siena, via Aldo Moro 2, 53100, Siena, Italy
| | - Monica Bianchini
- Department of Information Engineering and Mathematics, University of Siena, via Roma 56, 53100, Siena, Italy
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