ArticleScientific reports2020
Predicting antibody affinity changes upon mutations by combining multiple predictors.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Predicting antibody-antigen affinity with a dual-level representation model.Bioinformatics (Oxford, England) · 2026Article
- Review
- Article
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- Predicting Antibody Affinity Changes upon Mutation Based on Unbound Protein Structures.International journal of molecular sciences · 2025Article
- Applying computational protein design to therapeutic antibody discovery - current state and perspectives.Frontiers in immunology · 2025Review
- Epitope mapping via in vitro deep mutational scanning methods and its applications.The Journal of biological chemistry · 2025Review
- A comprehensive overview of recent advances in generative models for antibodies.Computational and structural biotechnology journal · 2024Review
- Toward real-world automated antibody design with combinatorial Bayesian optimization.Cell reports methods · 2023Article
- VariBench, new variation benchmark categories and data sets.Frontiers in bioinformatics · 2023Article
- The impact of COVID-19 and related containment measures on Bangkok's public transport ridership.Transportation research interdisciplinary perspectives · 2023Article
- Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies.Computational and structural biotechnology journal · 2022Article
- Review
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antibodies are proteins working in our immune system with high affinity and specificity for target antigens, making them excellent tools for both biotherapeutic and bioengineering applications. The prediction of antibody affinity changes upon mutations ([Formula: see text]) is important for antibody engineering. Numerous computational methods have been proposed based on different approaches including molecular mechanics and machine learning. However, the accuracy by each individual predictor is not enough for efficient antibody development. In this study, we develop a new prediction method by combining multiple predictors based on machine learning. Our method was tested on the SiPMAB database, evaluating the Pearson's correlation coefficient between predicted and experimental [Formula: see text]. Our method achieved higher accuracy (R = 0.69) than previous molecular mechanics or machine-learning based methods (R = 0.59) and the previous method using the average of multiple predictors (R = 0.64). Feature importance analysis indicated that the improved accuracy was obtained by combining predictors with different importance, which have different protocols for calculating energies and for generating mutant and unbound state structures. This study demonstrates that machine learning is a powerful framework for combining different approaches to predict antibody affinity changes.
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Registered trials
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