High-resolution modeling of antibody structures by a combination of bioinformatics, expert knowledge, and molecular simulations
Abstract
In the second antibody modeling assessment, we used a semiautomated template-based structure modeling approach for 11 blinded antibody variable region (Fv) targets. The structural modeling method involved several steps, including template selection for framework and canonical structures of complementary determining regions (CDRs), homology modeling, energy minimization, and expert inspection. The submitted models for Fv modeling in Stage 1 had the lowest average backbone root mean square deviation (RMSD) (1.06 Å). Comparison to crystal structures showed the most accurate Fv models were generated for 4 out of 11 targets. We found that the successful modeling in Stage 1 mainly was due to expert-guided template selection for CDRs, especially for CDR-H3, based on our previously proposed empirical method (H3-rules) and the use of position specific scoring matrix-based scoring. Loop refinement using fragment assembly and multicanonical molecular dynamics (McMD) was applied to CDR-H3 loop modeling in Stage 2. Fragment assembly and McMD produced putative structural ensembles with low free energy values that were scored based on the OSCAR all-atom force field and conformation density in principal component analysis space, respectively, as well as the degree of consensus between the two sampling methods. The quality of 8 out of 10 targets improved as compared with Stage 1. For 4 out of 10 Stage-2 targets, our method generated top-scoring models with RMSD values of less than 1 Å. In this article, we discuss the strengths and weaknesses of our approach as well as possible directions for improvement to generate better predictions in the future.
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Antibody structures have conventionally been predicted using either physics-based modeling, such as MD simulations, or homology-based modeling, or a combined method, such as MODELLER.
“Antibody structures have conventionally been predicted using either physics-based modeling, such as MD simulations [38], or homologybased modeling [39], or a combined method, such as MODELLER [40].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [38]
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- Authors
- Shirai, H., Ikeda, K., Yamashita, K., Tsuchiya, Y., Sarmiento, J., Liang, S., Morokata, T., Mizuguchi, K., Higo, J., Standley, D. M., & Nakamura, H.
- Affiliations
- Astellas Pharma (Japan) (JP)
- National Institute of Biomedical Innovation (JP)
- Immunology Frontier Research Center, The University of Osaka (JP)
- Institute for Protein Research, The University of Osaka (JP)
- Corresponding author
- Shirai, Hiroki
- ORCID
- Venue
- Proteins Structure Function and Bioinformatics
- Publisher
- Wiley
- Published
- 2014-05-13
- Volume
- 82
- Issue
- 8
- Pages
- 1624-1635
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- OpenAlex
- W2124008432
- Cited by
- 51
- References
- 33
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 4 · 2022: 5 · 2021: 2 · 2020: 4 · 2019: 5 · 2018: 1 · 2017: 8 · 2016: 10 · 2015: 4 · 2014: 4
- FWCI
- 3.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Astellas Pharma
- Japan Society for the Promotion of Science (24118008, 23370071, 25430186)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Keywords
- antibody structure prediction, immunoglobulin variable region, homology modeling, fragment assembly, multicanonical molecular dynamics, conformational ensembles, structural ensembles, position weight matrices
- MeSH terms
- Amino Acid Sequence, Animals, Antibodies, Humans, Immunoglobulin Variable Region, Immunoglobulins, Molecular Sequence Data, Protein Conformation, Computational Biology, Complementarity Determining Regions, Databases, Protein, Molecular Dynamics Simulation
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