ArticleScientific reports2023
Enhanced antibody-antigen structure prediction from molecular docking using AlphaFold2.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Antigen-Detected NMR for Minimal Epitope Engineering and Structure-Guided Selection of a NaAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Efficient in vivo assembly of DNA encoded polyvalent BTEs for dual antigen targeting for broadening therapeutic impact in ovarian cancer.Molecular therapy : the journal of the American Society of Gene Therapy · 2026Article
- Comprehensive Tutorial for Computational Methods of Protein Structure Prediction Incorporating Mass Spectrometry Data.Mass spectrometry reviews · 2026Review
- DeepRank-Ab: a scoring function for antibody-antigen complexes based on geometric deep learning.Communications biology · 2026Article
- Antibody Affinity Maturation by Computational Design.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Modeling Protein-Protein and Protein-Ligand Interactions by the ClusPro Team in CASP16.Proteins · 2026Article
- Optimizing the breadth of SARS-CoV-2-neutralizing antibodies in vivo and in silico.Human vaccines & immunotherapeutics · 2025Review
- Structure-based Predictions of Conformational B Cell Epitopes by Protein Language Model and Deep Learning.bioRxiv : the preprint server for biology · 2025Article
- Exploring Experimental and In Silico Approaches for Antibody-Drug Conjugates in Oncology Therapies.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Evaluation of AlphaFold modeling for elucidation of nanobody-peptide epitope interactions.The Journal of biological chemistry · 2025Article
- AI-augmented physics-based docking for antibody-antigen complex prediction.Bioinformatics (Oxford, England) · 2025Article
- Evaluation of Alphafold modeling for elucidation of nanobody-peptide epitope interactions.bioRxiv : the preprint server for biology · 2025Article
- Unlocking the potential ofFrontiers in bioinformatics · 2025Review
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure.Interdisciplinary sciences, computational life sciences · 2024Review
- AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease.Frontiers in molecular biosciences · 2024Review
- AI Models for Protein Design are Driving Antibody Engineering.Current opinion in biomedical engineering · 2023Article
- Review
- PD-1 Targeted Antibody Discovery Using AI Protein Diffusion.Technology in cancer research & treatmentArticle
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Predicting the structure of antibody-antigen complexes has tremendous value in biomedical research but unfortunately suffers from a poor performance in real-life applications. AlphaFold2 (AF2) has provided renewed hope for improvements in the field of protein-protein docking but has shown limited success against antibody-antigen complexes due to the lack of co-evolutionary constraints. In this study, we used physics-based protein docking methods for building decoy sets consisting of low-energy docking solutions that were either geometrically close to the native structure (positives) or not (negatives). The docking models were then fed into AF2 to assess their confidence with a novel composite score based on normalized pLDDT and pTMscore metrics after AF2 structural refinement. We show benefits of the AF2 composite score for rescoring docking poses both in terms of (1) classification of positives/negatives and of (2) success rates with particular emphasis on early enrichment. Docking models of at least medium quality present in the decoy set, but not necessarily highly ranked by docking methods, benefitted most from AF2 rescoring by experiencing large advances towards the top of the reranked list of models. These improvements, obtained without any calibration or novel methodologies, led to a notable level of performance in antibody-antigen unbound docking that was never achieved previously.
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