ArticleProtein science : a publication of the Protein Society2016
AB-Bind: Antibody binding mutational database for computational affinity predictions.
Article in Protein science : a publication of the Protein Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers.
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Who cites it
100 citing papers in PubMed.
- Article
- ASD: antigen-specific antibody database.mAbs · 2026Article
- Computational design and translational development of TCR-mimic scFv antibodies for peptide-HLA neoantigen targeting.npj drug discovery · 2026Review
- The Evolution of Artificial Intelligence in Antibody Design: From Structure-Based Engineering to Generative Models.Antibodies (Basel, Switzerland) · 2026Review
- Aligning protein-generative models to experimental fitness with ProteinDPO.Nature methods · 2026Article
- AbTune: layer-wise selective fine-tuning of protein language models for antibodies.Briefings in bioinformatics · 2026Article
- A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era.Journal of chemical theory and computation · 2026Review
- Affinity enhancement of an anti-VEGF humanized nanobody as a superior candidate to bevacizumab: integrative in silico and in vitro studies.BMC biotechnology · 2026Article
- Predicting antibody-antigen affinity with a dual-level representation model.Bioinformatics (Oxford, England) · 2026Article
- MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via mutation-path-based data augmentation.Briefings in bioinformatics · 2026Article
- A Unified Dataset for Antibody and Nanobody Design Including Sequence, Structure, and Binding Affinity Data.Scientific data · 2026Article
- Amino Acids Frequency and Interaction Trends: Comprehensive Analysis of Experimentally Validated Viral Antigen-Antibody Complexes.Molecular biotechnology · 2026Article
- Machine Learning Approaches for Interpretable Antibody Property Prediction Using Structural Data.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Article
- SAAINT-DB: a comprehensive structural antibody database for antibody modeling and design.Acta pharmacologica Sinica · 2025Article
- AbAgym: a well-curated dataset for the mutational analysis of antibody-antigen complexes.mAbs · 2025Article
- PPAC: Predicting Protein-Protein Affinity Changes Induced by Amino Acid Mutations Using Protein Large Language Models.ACS omega · 2025Article
- Systematic evaluation of predictors for binding free energy changes upon mutations in protein complexes.Briefings in bioinformatics · 2025Article
- Sequence-only prediction of binding affinity changes: a robust and interpretable model for antibody engineering.Bioinformatics (Oxford, England) · 2025Article
- Investigating the volume and diversity of data needed for generalizable antibody-antigen ΔΔG prediction.Nature computational science · 2025Article
40 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Antibodies (Abs) are a crucial component of the immune system and are often used as diagnostic and therapeutic agents. The need for high-affinity and high-specificity antibodies in research and medicine is driving the development of computational tools for accelerating antibody design and discovery. We report a diverse set of antibody binding data with accompanying structures that can be used to evaluate methods for modeling antibody interactions. Our Antibody-Bind (AB-Bind) database includes 1101 mutants with experimentally determined changes in binding free energies (ΔΔG) across 32 complexes. Using the AB-Bind data set, we evaluated the performance of protein scoring potentials in their ability to predict changes in binding free energies upon mutagenesis. Numerical correlations between computed and observed ΔΔG values were low (r = 0.16-0.45), but the potentials exhibited predictive power for classifying variants as improved vs weakened binders. Performance was evaluated using the area under the curve (AUC) for receiver operator characteristic (ROC) curves; the highest AUC values for 527 mutants with |ΔΔG| > 1.0 kcal/mol were 0.81, 0.87, and 0.88 using STATIUM, FoldX, and Discovery Studio scoring potentials, respectively. Some methods could also enrich for variants with improved binding affinity; FoldX and Discovery Studio were able to correctly rank 42% and 30%, respectively, of the 80 most improved binders (those with ΔΔG < -1.0 kcal/mol) in the top 5% of the database. This modest predictive performance has value but demonstrates the continuing need to develop and improve protein energy functions for affinity prediction.
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