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Gomes DEB, Yang B, Vanella R, Nash MA, Bernardi RC. Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions. J Am Chem Soc 2024; 146:23842-23853. [PMID: 39146039 DOI: 10.1021/jacs.4c05869] [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: 08/17/2024]
Abstract
Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure are long-standing goals with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost-intensive. Computational methods have potential to accelerate epitope predictions; however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologues. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis were ineffective in distinguishing between two proposed binding models, parallel and perpendicular. However, our integrated approach, utilizing dynamic network analysis, demonstrated that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols, including cross-linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Conversely, AlphaFold3 failed to predict a structure bound in the perpendicular pose, highlighting the necessity for exploratory research in the search for binding epitopes and challenging the notion that AI-generated protein structures can be accepted without scrutiny. Our research underscores the potential of employing dynamic network analysis to enhance AI-based structure predictions for more accurate identification of protein-protein interaction interfaces.
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Affiliation(s)
- Diego E B Gomes
- Department of Physics, Auburn University, Auburn, Alabama 36849, United States
| | - Byeongseon Yang
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, Basel 4058, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Rosario Vanella
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, Basel 4058, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Michael A Nash
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, Basel 4058, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
| | - Rafael C Bernardi
- Department of Physics, Auburn University, Auburn, Alabama 36849, United States
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2
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Miller JM, Carlson AE. Locking hERG channels into place: Using photoreactive unnatural amino acids to study voltage gating. Biophys J 2024; 123:2358-2359. [PMID: 39033327 PMCID: PMC11365217 DOI: 10.1016/j.bpj.2024.07.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 07/01/2024] [Accepted: 07/17/2024] [Indexed: 07/23/2024] Open
Affiliation(s)
- Jennifer M Miller
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Anne E Carlson
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania.
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3
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Li J, Zhou Y, Chen SJ. Embracing exascale computing in nucleic acid simulations. Curr Opin Struct Biol 2024; 87:102847. [PMID: 38815519 PMCID: PMC11283969 DOI: 10.1016/j.sbi.2024.102847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 04/18/2024] [Accepted: 05/09/2024] [Indexed: 06/01/2024]
Abstract
This mini-review reports the recent advances in biomolecular simulations, particularly for nucleic acids, and provides the potential effects of the emerging exascale computing on nucleic acid simulations, emphasizing the need for advanced computational strategies to fully exploit this technological frontier. Specifically, we introduce recent breakthroughs in computer architectures for large-scale biomolecular simulations and review the simulation protocols for nucleic acids regarding force fields, enhanced sampling methods, coarse-grained models, and interactions with ligands. We also explore the integration of machine learning methods into simulations, which promises to significantly enhance the predictive modeling of biomolecules and the analysis of complex data generated by the exascale simulations. Finally, we discuss the challenges and perspectives for biomolecular simulations as we enter the dawning exascale computing era.
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Affiliation(s)
- Jun Li
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, 223 Physics Bldg., Columbia, 65211, MO, USA
| | - Yuanzhe Zhou
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, 223 Physics Bldg., Columbia, 65211, MO, USA
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, 223 Physics Bldg., Columbia, 65211, MO, USA.
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4
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Yang H, Zhou D, Zhou Z, Duan M, Yu H. Mechanistic Insight into the Mechanical Unfolding of the Integral Membrane Diacylglycerol Kinase. JACS AU 2024; 4:1422-1435. [PMID: 38665647 PMCID: PMC11040704 DOI: 10.1021/jacsau.3c00829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 04/28/2024]
Abstract
The essential forces stabilizing membrane proteins and governing their folding and unfolding are difficult to decipher. Single-molecule atomic force spectroscopy mechanically unfolds individual membrane proteins and quantifies their dynamics and energetics. However, it remains challenging to structurally assign unfolding intermediates precisely and to deduce dominant interactions between specific residues that facilitate either the localized stabilization of these intermediates or the global assembly of membrane proteins. Here, we performed force spectroscopy experiments and multiscale molecular dynamics simulations to study the unfolding pathway of diacylglycerol kinase (DGK), a small trimeric multispan transmembrane enzyme. The remarkable agreement between experiments and simulations allowed precise structural assignment and interaction analysis of unfolding intermediates, bypassing existing limitations on structural mapping, and thus provided mechanistic explanations for the formation of these states. DGK unfolding was found to proceed with structural segments varying in size that do not correlate with its secondary structure. We identified intermolecular side-chain packing interactions as one of the major contributions to the stability of unfolding intermediates. Mutagenesis creating packing defects induced a dramatic decrease in the mechano-stability of corresponding intermediates and also in the thermo-stability of DGK trimer, in good agreement with predictions from simulations. Hence, the molecular determinants of the mechano- and thermo-stability of a membrane protein can be identified at residue resolution. The accurate structural assignment established and microscopic mechanism revealed in this work may substantially expand the scope of single-molecule studies of membrane proteins.
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Affiliation(s)
- Huiying Yang
- School
of Physics, Huazhong University of Science
and Technology, Wuhan 430074, China
| | - Daihong Zhou
- School
of Physics, Huazhong University of Science
and Technology, Wuhan 430074, China
| | - Zhangyi Zhou
- School
of Physics, Huazhong University of Science
and Technology, Wuhan 430074, China
| | - Mojie Duan
- Innovation
Academy for Precision Measurement Science and Technology, Chinese
Academy of Sciences, Wuhan 430071, China
| | - Hao Yu
- School
of Physics, Huazhong University of Science
and Technology, Wuhan 430074, China
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5
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Singh PK, Stan RC. ThermoPCD: a database of molecular dynamics trajectories of antibody-antigen complexes at physiologic and fever-range temperatures. Database (Oxford) 2024; 2024:baae015. [PMID: 38502609 PMCID: PMC10950042 DOI: 10.1093/database/baae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/12/2024] [Accepted: 02/22/2024] [Indexed: 03/21/2024]
Abstract
Progression of various cancers and autoimmune diseases is associated with changes in systemic or local tissue temperatures, which may impact current therapies. The role of fever and acute inflammation-range temperatures on the stability and activity of antibodies relevant for cancers and autoimmunity is unknown. To produce molecular dynamics (MD) trajectories of immune complexes at relevant temperatures, we used the Research Collaboratory for Structural Bioinformatics (RCSB) database to identify 50 antibody:antigen complexes of interest, in addition to single antibodies and antigens, and deployed Groningen Machine for Chemical Simulations (GROMACS) to prepare and run the structures at different temperatures for 100-500 ns, in single or multiple random seeds. MD trajectories are freely available. Processed data include Protein Data Bank outputs for all files obtained every 50 ns, and free binding energy calculations for some of the immune complexes. Protocols for using the data are also available. Individual datasets contain unique DOIs. We created a web interface, ThermoPCD, as a platform to explore the data. The outputs of ThermoPCD allow the users to relate thermally-dependent changes in epitopes:paratopes interfaces to their free binding energies, or against own experimentally derived binding affinities. ThermoPCD is a free to use database of immune complexes' trajectories at different temperatures that does not require registration and allows for all the data to be available for download. Database URL: https://sites.google.com/view/thermopcd/home.
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Affiliation(s)
- Puneet K Singh
- Department of Basic Medical Science, Chonnam National University, Hwasun 58128, Republic of Korea
| | - Razvan C Stan
- Department of Basic Medical Science, Chonnam National University, Hwasun 58128, Republic of Korea
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Gomes DEB, Yang B, Vanella R, Nash MA, Bernardi RC. Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.08.579577. [PMID: 38370725 PMCID: PMC10871313 DOI: 10.1101/2024.02.08.579577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure is a long standing goal with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost intensive. Computational methods have potential to accelerate epitope predictions, however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologs. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2 Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis alone each proved ineffectual in differentiating between two putative binding models referred to as parallel and perpendicular. However, our integrated approach based on dynamic network analysis showed that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols including cross linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Our research highlights the potential of deploying dynamic network analysis to refine AI-based structure predictions for precise predictions of protein-protein interaction interfaces.
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7
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Singharoy A, Pérez A, Chipot C. Biophysics at the dawn of exascale computers. Biophys J 2023; 122:E1-E2. [PMID: 37419113 PMCID: PMC10398342 DOI: 10.1016/j.bpj.2023.06.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 06/22/2023] [Accepted: 06/22/2023] [Indexed: 07/09/2023] Open
Affiliation(s)
| | - Alberto Pérez
- Department of Chemistry, University of Florida, Gainesville, Florida
| | - Chris Chipot
- UMR 7019, Université de Lorraine, Laboratoire International Associé CNRS, Vandœuvre-lès-Nancy, France; Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois; Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois
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