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Barletta GP, Tandiana R, Soler M, Fortuna S, Rocchia W. Locuaz: an in silico platform for protein binders optimization. Bioinformatics 2024; 40:btae492. [PMID: 39107888 PMCID: PMC11324344 DOI: 10.1093/bioinformatics/btae492] [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: 03/30/2024] [Revised: 07/28/2024] [Accepted: 08/03/2024] [Indexed: 08/16/2024] Open
Abstract
MOTIVATION Engineering high-affinity binders targeting specific antigenic determinants remains a challenging and often daunting task, requiring extensive experimental screening. Computational methods have the potential to accelerate this process, reducing costs and time, but only if they demonstrate broad applicability and efficiency in exploring mutations, evaluating affinity, and pruning unproductive mutation paths. RESULTS In response to these challenges, we introduce a new computational platform for optimizing protein binders towards their targets. The platform is organized as a series of modules, performing mutation selection and application, molecular dynamics simulations to sample conformations around interaction poses, and mutation prioritization using suitable scoring functions. Notably, the platform supports parallel exploration of different mutation streams, enabling in silico high-throughput screening on High Performance Computing (HPC) systems. Furthermore, the platform is highly customizable, allowing users to implement their own protocols. AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/pgbarletta/locuaz and documentation is at https://locuaz.readthedocs.io/. The data underlying this article are available at https://github.com/pgbarletta/suppl_info_locuaz.
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Affiliation(s)
- German P Barletta
- CONCEPT, Istituto Italiano di Tecnologia, Via Enrico Melen, 83 Genova Liguria 16152, Italy
- The Abdus Salam International Centre for Theoretical Physics (ICTP), Str. Costiera, 11, Trieste, Friuli-Venezia Giulia, 34151, Italy
| | - Rika Tandiana
- CONCEPT, Istituto Italiano di Tecnologia, Via Enrico Melen, 83 Genova Liguria 16152, Italy
| | - Miguel Soler
- CONCEPT, Istituto Italiano di Tecnologia, Via Enrico Melen, 83 Genova Liguria 16152, Italy
- Dipartimento di Scienze Matematiche, Informatiche e Fisiche (DMIF), University of Udine, Via delle Scienze, 206, Udine, Friuli-Venezia Giulia, 33100, Italy
| | - Sara Fortuna
- CONCEPT, Istituto Italiano di Tecnologia, Via Enrico Melen, 83 Genova Liguria 16152, Italy
- Cresset, New Cambridge House, Litlington, Royston, SG8-0SS, United Kingdom
| | - Walter Rocchia
- CONCEPT, Istituto Italiano di Tecnologia, Via Enrico Melen, 83 Genova Liguria 16152, Italy
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Chi LA, Barnes JE, Suresh Patel J, Ytreberg FM. Exploring the ability of the MD+FoldX method to predict SARS-CoV-2 antibody escape mutations using large-scale data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.22.595230. [PMID: 38826284 PMCID: PMC11142147 DOI: 10.1101/2024.05.22.595230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Antibody escape mutations pose a significant challenge to the effectiveness of vaccines and antibody-based therapies. The ability to predict these escape mutations with computer simulations would allow us to detect threats early and develop effective countermeasures, but a lack of large-scale experimental data has hampered the validation of these calculations. In this study, we evaluate the ability of the MD+FoldX molecular modeling method to predict escape mutations by leveraging a large deep mutational scanning dataset, focusing on the SARS-CoV-2 receptor binding domain. Our results show a positive correlation between predicted and experimental data, indicating that mutations with reduced predicted binding affinity correlate moderately with higher experimental escape fractions. We also demonstrate that better performance can be achieved using affinity cutoffs tailored to distinct antibody-antigen interactions rather than a one-size-fits-all approach. We find that 70% of the systems surpass the 50% precision mark, and demonstrate success in identifying mutations present in significant variants of concern and variants of interest. Despite promising results for some systems, our study highlights the challenges in comparing predicted and experimental values. It also emphasizes the need for new binding affinity methods with improved accuracy that are fast enough to estimate hundreds to thousands of antibody-antigen binding affinities.
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Affiliation(s)
- L. América Chi
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID 83843, USA
| | - Jonathan E. Barnes
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID 83843, USA
| | - Jagdish Suresh Patel
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID 83843, USA
- Department of Chemical and Biological Engineering, University of Idaho, Moscow, ID 83843, USA
| | - F. Marty Ytreberg
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID 83843, USA
- Department of Physics, University of Idaho, Moscow, ID 83843, USA
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Nurrohman DT, Chiu NF. Unraveling the Dynamics of SARS-CoV-2 Mutations: Insights from Surface Plasmon Resonance Biosensor Kinetics. BIOSENSORS 2024; 14:99. [PMID: 38392018 PMCID: PMC10887047 DOI: 10.3390/bios14020099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 02/08/2024] [Accepted: 02/11/2024] [Indexed: 02/24/2024]
Abstract
Surface Plasmon Resonance (SPR) technology is known to be a powerful tool for studying biomolecular interactions because it offers real-time and label-free multiparameter analysis with high sensitivity. This article summarizes the results that have been obtained from the use of SPR technology in studying the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations. This paper will begin by introducing the working principle of SPR and the kinetic parameters of the sensorgram, which include the association rate constant (ka), dissociation rate constant (kd), equilibrium association constant (KA), and equilibrium dissociation constant (KD). At the end of the paper, we will summarize the kinetic data on the interaction between angiotensin-converting enzyme 2 (ACE2) and SARS-CoV-2 obtained from the results of SPR signal analysis. ACE2 is a material that mediates virus entry. Therefore, understanding the kinetic changes between ACE2 and SARS-CoV-2 caused by the mutation will provide beneficial information for drug discovery, vaccine development, and other therapeutic purposes.
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Affiliation(s)
- Devi Taufiq Nurrohman
- Laboratory of Nano-Photonics and Biosensors, Institute of Electro-Optical Engineering, National Taiwan Normal University, Taipei 11677, Taiwan;
| | - Nan-Fu Chiu
- Laboratory of Nano-Photonics and Biosensors, Institute of Electro-Optical Engineering, National Taiwan Normal University, Taipei 11677, Taiwan;
- Department of Life Science, National Taiwan Normal University, Taipei 11677, Taiwan
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Tonouchi K, Adachi Y, Suzuki T, Kuroda D, Nishiyama A, Yumoto K, Takeyama H, Suzuki T, Hashiguchi T, Takahashi Y. Structural basis for cross-group recognition of an influenza virus hemagglutinin antibody that targets postfusion stabilized epitope. PLoS Pathog 2023; 19:e1011554. [PMID: 37556494 PMCID: PMC10411744 DOI: 10.1371/journal.ppat.1011554] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 07/11/2023] [Indexed: 08/11/2023] Open
Abstract
Plasticity of influenza virus hemagglutinin (HA) conformation increases an opportunity to generate conserved non-native epitopes with unknown functionality. Here, we have performed an in-depth analysis of human monoclonal antibodies against a stem-helix region that is occluded in native prefusion yet exposed in postfusion HA. A stem-helix antibody, LAH31, provided IgG Fc-dependent cross-group protection by targeting a stem-helix kinked loop epitope, with a unique structure emerging in the postfusion state. The structural analysis and molecular modeling revealed key contact sites responsible for the epitope specificity and cross-group breadth that relies on somatically mutated light chain. LAH31 was inaccessible to the native prefusion HA expressed on cell surface; however, it bound to the HA structure present on infected cells with functional linkage to the Fc-mediated clearance. Our study uncovers a novel non-native epitope that emerges in the postfusion HA state, highlighting the utility of this epitope for a broadly protective antigen design.
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Affiliation(s)
- Keisuke Tonouchi
- Research Center for Drug and Vaccine Development, National Institute of Infectious Diseases, Shinjuku, Tokyo, Japan
- Department of Life Science and Medical Bioscience, Waseda University, Shinjuku, Tokyo, Japan
| | - Yu Adachi
- Research Center for Drug and Vaccine Development, National Institute of Infectious Diseases, Shinjuku, Tokyo, Japan
| | - Tateki Suzuki
- Laboratory of Medical Virology, Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan
| | - Daisuke Kuroda
- Research Center for Drug and Vaccine Development, National Institute of Infectious Diseases, Shinjuku, Tokyo, Japan
| | - Ayae Nishiyama
- Research Center for Drug and Vaccine Development, National Institute of Infectious Diseases, Shinjuku, Tokyo, Japan
- Laboratory of Precision Immunology, Center for Intractable Diseases and ImmunoGenomics research, National Institutes of Biomedical Innovation, Health and Nutrition; Saito-Asagi, Ibaraki City, Osaka, Japan
| | - Kohei Yumoto
- Research Center for Drug and Vaccine Development, National Institute of Infectious Diseases, Shinjuku, Tokyo, Japan
| | - Haruko Takeyama
- Department of Life Science and Medical Bioscience, Waseda University, Shinjuku, Tokyo, Japan
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology, Shinjuku, Tokyo, Japan
- Research Organization for Nano and Life Innovation, Waseda University, Shinjuku, Tokyo, Japan
- Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, Waseda University, Shinjuku, Tokyo, Japan
| | - Tadaki Suzuki
- Department of Pathology, National Institute of Infectious Diseases, Shinjuku, Tokyo, Japan
| | - Takao Hashiguchi
- Laboratory of Medical Virology, Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan
| | - Yoshimasa Takahashi
- Research Center for Drug and Vaccine Development, National Institute of Infectious Diseases, Shinjuku, Tokyo, Japan
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