ArticlePharmaceuticals (Basel, Switzerland)2021
NbX: Machine Learning-Guided Re-Ranking of Nanobody-Antigen Binding Poses.
Article in Pharmaceuticals (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- AI-Driven Design Platforms of Next-Generation Antibody Therapeutics.Topics in current chemistry (Cham) · 2026Review
- NanoBinder: a machine learning assisted nanobody binding prediction tool using Rosetta energy scores.Journal of cheminformatics · 2025Article
- Nanobodies: From Discovery to AI-Driven Design.Biology · 2025Review
- Unveiling the new chapter in nanobody engineering: advances in traditional construction and AI-driven optimization.Journal of nanobiotechnology · 2025Review
- Molecular dynamics and machine learning stratify motion-dependent activity profiles of S-layer destabilizing nanobodies.PNAS nexus · 2024Article
- NABP-BERT: NANOBODY®-antigen binding prediction based on bidirectional encoder representations from transformers (BERT) architecture.Briefings in bioinformatics · 2024Article
- Targeting Ras-binding domain of ELMO1 by computational nanobody design.Communications biology · 2023Article
- VInternational journal of molecular sciences · 2022Review
- Review
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Authors and funding
3 authors.
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Abstract
Modeling the binding pose of an antibody is a prerequisite to structure-based affinity maturation and design. Without knowing a reliable binding pose, the subsequent structural simulation is largely futile. In this study, we have developed a method of machine learning-guided re-ranking of antigen binding poses of nanobodies, the single-domain antibody which has drawn much interest recently in antibody drug development. We performed a large-scale self-docking experiment of nanobody-antigen complexes. By training a decision tree classifier through mapping a feature set consisting of energy, contact and interface property descriptors to a measure of their docking quality of the refined poses, significant improvement in the median ranking of native-like nanobody poses by was achieved eightfold compared with ClusPro and an established deep 3D CNN classifier of native protein-protein interaction. We further interpreted our model by identifying features that showed relatively important contributions to the prediction performance. This study demonstrated a useful method in improving our current ability in pose prediction of nanobodies.
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