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Wang A, Lin X, Chau KN, Onuchic JN, Levine H, George JT. RACER-m leverages structural features for sparse T cell specificity prediction. SCIENCE ADVANCES 2024; 10:eadl0161. [PMID: 38748791 PMCID: PMC11095454 DOI: 10.1126/sciadv.adl0161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 04/10/2024] [Indexed: 05/19/2024]
Abstract
Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.
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Affiliation(s)
- Ailun Wang
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
| | - Xingcheng Lin
- Department of Physics, North Carolina State University, Raleigh, NC, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Kevin Ng Chau
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
| | - José N. Onuchic
- Departments of Physics and Astronomy, Chemistry, and Biosciences, Rice University, Houston, TX, USA
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
| | - Herbert Levine
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
| | - Jason T. George
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
- Department of Biomedical Engineering, Texas A&M University, Houston, TX, USA
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Mészáros B, Park E, Malinverni D, Sejdiu BI, Immadisetty K, Sandhu M, Lang B, Babu MM. Recent breakthroughs in computational structural biology harnessing the power of sequences and structures. Curr Opin Struct Biol 2023; 80:102608. [PMID: 37182396 DOI: 10.1016/j.sbi.2023.102608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 05/16/2023]
Abstract
Recent advances in computational approaches and their integration into structural biology enable tackling increasingly complex questions. Here, we discuss several key areas, highlighting breakthroughs and remaining challenges. Theoretical modeling has provided tools to accurately predict and design protein structures on a scale currently difficult to achieve using experimental approaches. Molecular Dynamics simulations have become faster and more precise, delivering actionable information inaccessible by current experimental methods. Virtual screening workflows allow a high-throughput approach to discover ligands that bind and modulate protein function, while Machine Learning methods enable the design of proteins with new functionalities. Integrative structural biology combines several of these approaches, pushing the frontiers of structural and functional characterization to ever larger systems, advancing towards a complete understanding of the living cell. These breakthroughs will accelerate and significantly impact diverse areas of science.
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Affiliation(s)
- Bálint Mészáros
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
| | - Electa Park
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
| | - Duccio Malinverni
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/DucMalinverni
| | - Besian I Sejdiu
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/bisejdiu
| | - Kalyan Immadisetty
- Department of Bone Marrow Transplantation & Cellular Therapy, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/k_immadisetty
| | - Manbir Sandhu
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/M5andhu
| | - Benjamin Lang
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA. https://twitter.com/langbnj
| | - M Madan Babu
- Department of Structural Biology and Center of Excellence for Data Driven Discovery, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
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Zheng S, Zou M, Shao Y, Wu H, Wu H, Wang X. Two-dimensional measurements of receptor-ligand interactions. Front Mol Biosci 2023; 10:1154074. [PMID: 36876050 PMCID: PMC9981951 DOI: 10.3389/fmolb.2023.1154074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 02/08/2023] [Indexed: 02/19/2023] Open
Abstract
Gaining insight into the two-dimensional receptor-ligand interactions, which play a significant role in various pivotal biological processes such as immune response and cancer metastasis, will deepen our understanding of numerous physiological and pathological mechanisms and contribute to biomedical applications and drug design. A central issue involved is how to measure the in situ receptor-ligand binding kinetics. Here, we review several representative mechanical-based and fluorescence-based methods, and briefly discuss the strengths and weaknesses for each method. In addition, we emphasize the great importance of the combination of experimental and computational methods in studying the receptor-ligand interactions, and further studies should focus on the synergistic development of experimental and computational methods.
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Affiliation(s)
- Songjie Zheng
- Institute of Mechanics, Chinese Academy of Sciences, Beijing, China.,School of Engineering Science, University of Chinese Academy of Sciences, Beijing, China
| | - Min Zou
- Institute of Mechanics, Chinese Academy of Sciences, Beijing, China.,School of Engineering Science, University of Chinese Academy of Sciences, Beijing, China
| | - Yingfeng Shao
- Institute of Mechanics, Chinese Academy of Sciences, Beijing, China
| | - Huaping Wu
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Helong Wu
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xiaohuan Wang
- Department of Rehabilitation Medicine, Peking University Third Hospital, Beijing, China
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