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Zhu C, Zhang C, Shang T, Zhang C, Zhai S, Cao L, Xu Z, Su Z, Song Y, Su A, Li C, Duan H. GAPS: a geometric attention-based network for peptide binding site identification by the transfer learning approach. Brief Bioinform 2024; 25:bbae297. [PMID: 38990514 PMCID: PMC11238429 DOI: 10.1093/bib/bbae297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 04/28/2024] [Accepted: 06/07/2024] [Indexed: 07/12/2024] Open
Abstract
Protein-peptide interactions (PPepIs) are vital to understanding cellular functions, which can facilitate the design of novel drugs. As an essential component in forming a PPepI, protein-peptide binding sites are the basis for understanding the mechanisms involved in PPepIs. Therefore, accurately identifying protein-peptide binding sites becomes a critical task. The traditional experimental methods for researching these binding sites are labor-intensive and time-consuming, and some computational tools have been invented to supplement it. However, these computational tools have limitations in generality or accuracy due to the need for ligand information, complex feature construction, or their reliance on modeling based on amino acid residues. To deal with the drawbacks of these computational algorithms, we describe a geometric attention-based network for peptide binding site identification (GAPS) in this work. The proposed model utilizes geometric feature engineering to construct atom representations and incorporates multiple attention mechanisms to update relevant biological features. In addition, the transfer learning strategy is implemented for leveraging the protein-protein binding sites information to enhance the protein-peptide binding sites recognition capability, taking into account the common structure and biological bias between proteins and peptides. Consequently, GAPS demonstrates the state-of-the-art performance and excellent robustness in this task. Moreover, our model exhibits exceptional performance across several extended experiments including predicting the apo protein-peptide, protein-cyclic peptide and the AlphaFold-predicted protein-peptide binding sites. These results confirm that the GAPS model is a powerful, versatile, stable method suitable for diverse binding site predictions.
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Affiliation(s)
- Cheng Zhu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Chaowang Road, Gongshu District, Hangzhou 310014, China
| | - Chengyun Zhang
- AI Department, Shanghai Highslab Therapeutics. Inc, Zhangheng Road, Pudong New Area, Shanghai 201203, China
| | - Tianfeng Shang
- AI Department, Shanghai Highslab Therapeutics. Inc, Zhangheng Road, Pudong New Area, Shanghai 201203, China
| | - Chenhao Zhang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Chaowang Road, Gongshu District, Hangzhou 310014, China
| | - Silong Zhai
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Chaowang Road, Gongshu District, Hangzhou 310014, China
| | - Lujing Cao
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Chaowang Road, Gongshu District, Hangzhou 310014, China
| | - Zhenyu Xu
- AI Department, Shanghai Highslab Therapeutics. Inc, Zhangheng Road, Pudong New Area, Shanghai 201203, China
| | - Zhihao Su
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Chaowang Road, Gongshu District, Hangzhou 310014, China
| | - Ying Song
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Chaowang Road, Gongshu District, Hangzhou 310014, China
| | - An Su
- College of Chemical Engineering, Zhejiang University of Technology, Chaowang Road, Gongshu District, Hangzhou 310014, China
| | - Chengxi Li
- College of Chemical and Biological Engineering, Zhejiang University, Yuhangtang Road, Xihu District, Hangzhou 310027, China
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao 999078, China
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Xu T, Li Y, Gao X, Zhang L. Understanding the Fast-Triggering Unfolding Dynamics of FK-11 upon Photoexcitation of Azobenzene. J Phys Chem Lett 2024; 15:3531-3540. [PMID: 38526058 DOI: 10.1021/acs.jpclett.4c00091] [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: 03/26/2024]
Abstract
Photoswitchable molecules can control the activity and functions of biomolecules by triggering conformational changes. However, it is still challenging to fully understand such fast-triggering conformational evolution from nonequilibrium to equilibrium distribution at the molecular level. Herein, we successfully simulated the unfolding of the FK-11 peptide upon the photoinduced trans-to-cis isomerization of azobenzene based on the Markov state model. We found that the ensemble of FK-11 contains five conformational states, constituting two unfolding pathways. More intriguingly, we observed the microsecond-scale conformational propagation of the FK-11 peptide from the fully folded state to the equilibrium populations of the five states. The computed CD spectra match well with the experimental data, validating our simulation method. Overall, our study not only offers a protocol to study the photoisomerization-induced conformational changes of enzymes but also could orientate the rational design of a photoswitchable molecule to manipulate biological functions.
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Affiliation(s)
- Tiantian Xu
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yongfang Li
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, China
| | - Xin Gao
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Lu Zhang
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Fuzhou, Fujian 361005, China
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Yang S, Song C. Switching Go̅ -Martini for Investigating Protein Conformational Transitions and Associated Protein-Lipid Interactions. J Chem Theory Comput 2024; 20:2618-2629. [PMID: 38447049 DOI: 10.1021/acs.jctc.3c01222] [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: 03/08/2024]
Abstract
Proteins are dynamic biomolecules that can transform between different conformational states when exerting physiological functions, which is difficult to simulate using all-atom methods. Coarse-grained (CG) Go̅-like models are widely used to investigate large-scale conformational transitions, which usually adopt implicit solvent models and therefore cannot explicitly capture the interaction between proteins and surrounding molecules, such as water and lipid molecules. Here, we present a new method, named Switching Go̅-Martini, to simulate large-scale protein conformational transitions between different states, based on the switching Go̅ method and the CG Martini 3 force field. The method is straightforward and efficient, as demonstrated by the benchmarking applications for multiple protein systems, including glutamine binding protein (GlnBP), adenylate kinase (AdK), and β2-adrenergic receptor (β2AR). Moreover, by employing the Switching Go̅-Martini method, we can not only unveil the conformational transition from the E2Pi-PL state to E1 state of the type 4 P-type ATPase (P4-ATPase) flippase ATP8A1-CDC50 but also provide insights into the intricate details of lipid transport.
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Affiliation(s)
- Song Yang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Chen Song
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
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Wang X, Xu T, Yao Y, Cheung PPH, Gao X, Zhang L. SARS-CoV-2 RNA-Dependent RNA Polymerase Follows Asynchronous Translocation Pathway for Viral Transcription and Replication. J Phys Chem Lett 2023; 14:10119-10128. [PMID: 37922192 DOI: 10.1021/acs.jpclett.3c01249] [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: 11/05/2023]
Abstract
Translocation is one essential step for the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) to exert viral replication and transcription. Although cryo-EM structures of SARS-CoV-2 RdRp are available, the molecular mechanisms of dynamic translocation remain elusive. Herein, we constructed a Markov state model based on extensive molecular dynamics simulations to elucidate the translocation dynamics of the SARS-CoV-2 RdRp. We identified two intermediates that pinpoint the rate-limiting step of translocation and characterize the asynchronous movement of the template-primer duplex. The 3'-terminal nucleotide in the primer strand lags behind due to the uneven distribution of protein-RNA interactions, while the translocation of the template strand is delayed by the hurdle residue K500. Even so, the two strands share the same "ratchet" to stabilize the polymerase in the post-translocation state, suggesting a Brownian-ratchet model. Overall, our study provides intriguing insights into SARS-CoV-2 replication and transcription, which would open a new avenue for drug discoveries.
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Affiliation(s)
- Xiaowei Wang
- Department of Chemical and Biological Engineering and Department of Mathematics, Hong Kong University of Science and Technology Kowloon, Clear Water Bay, Hong Kong
| | - Tiantian Xu
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuan Yao
- Department of Chemical and Biological Engineering and Department of Mathematics, Hong Kong University of Science and Technology Kowloon, Clear Water Bay, Hong Kong
| | - Peter Pak-Hang Cheung
- Li Ka Shing Institute of Health Sciences, Department of Chemical Pathology, Chinese University of Hong Kong, 999077, Hong Kong
| | - Xin Gao
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Lu Zhang
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Fuzhou, Fujian 361005, China
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Wu S, Zhang W, Li W, Huang W, Kong Q, Chen Z, Wei W, Yan S. Dissecting the Protein Dynamics Coupled Ligand Binding with Kinetic Models and Single-Molecule FRET. Biochemistry 2022; 61:433-445. [PMID: 35226469 DOI: 10.1021/acs.biochem.1c00771] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Protein-ligand interactions are crucial to many biological processes. Ligand binding and dissociation are the basic steps that allow proteins to function. Protein conformational dynamics have been shown to play important roles in ligand binding and dissociation. However, it is challenging to determine the ligand binding kinetics of dynamic proteins. Here, we undertook comprehensive single-molecule FRET (smFRET) measurements and kinetic model analysis to characterize the conformational dynamics coupled ligand binding of glutamine-binding protein (GlnBP). We showed that hinge and T118A mutations of GlnBP affect its conformational dynamics as well as the ligand binding affinity. Based on smFRET measurements, the kinetic model of ligand-GlnBP interactions was constructed. Using experimentally measured parameters, we solved the rate equations of the model and obtained the undetectable parameters of the model which allowed us to understand the ligand binding kinetics fully. Our results demonstrate that modulation of the conformational dynamics of GlnBP affects the ligand binding and dissociation rates. This study provides insights into the binding kinetics of ligands, which are related to the protein function itself.
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Affiliation(s)
- Shaowen Wu
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, Guangdong, China
| | - Wenyang Zhang
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, Guangdong, China
| | - Wenyan Li
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, Guangdong, China
| | - Wenjie Huang
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, Guangdong, China
| | - Qian Kong
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, Guangdong, China
| | - Zhongjian Chen
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, Guangdong, China
| | - Wenkang Wei
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, Guangdong, China
| | - Shijuan Yan
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, Guangdong, China
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Abdelsattar AS, Mansour Y, Aboul-Ela F. The Perturbed Free-Energy Landscape: Linking Ligand Binding to Biomolecular Folding. Chembiochem 2021; 22:1499-1516. [PMID: 33351206 DOI: 10.1002/cbic.202000695] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/19/2020] [Indexed: 12/24/2022]
Abstract
The effects of ligand binding on biomolecular conformation are crucial in drug design, enzyme mechanisms, the regulation of gene expression, and other biological processes. Descriptive models such as "lock and key", "induced fit", and "conformation selection" are common ways to interpret such interactions. Another historical model, linked equilibria, proposes that the free-energy landscape (FEL) is perturbed by the addition of ligand binding energy for the bound population of biomolecules. This principle leads to a unified, quantitative theory of ligand-induced conformation change, building upon the FEL concept. We call the map of binding free energy over biomolecular conformational space the "binding affinity landscape" (BAL). The perturbed FEL predicts/explains ligand-induced conformational changes conforming to all common descriptive models. We review recent experimental and computational studies that exemplify the perturbed FEL, with emphasis on RNA. This way of understanding ligand-induced conformation dynamics motivates new experimental and theoretical approaches to ligand design, structural biology and systems biology.
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Affiliation(s)
- Abdallah S Abdelsattar
- Center for X-Ray Determination of the Structure of Matter, Zewail City of Science and Technology, Ahmed Zewail Road, October Gardens, 12578, Giza, Egypt
| | - Youssef Mansour
- Center for X-Ray Determination of the Structure of Matter, Zewail City of Science and Technology, Ahmed Zewail Road, October Gardens, 12578, Giza, Egypt
| | - Fareed Aboul-Ela
- Center for X-Ray Determination of the Structure of Matter, Zewail City of Science and Technology, Ahmed Zewail Road, October Gardens, 12578, Giza, Egypt
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