ArticleProceedings of the National Academy of Sciences of the United States of America2024
Data-driven classification of ligand unbinding pathways.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 22 citations in OpenAlex.
- Unveiling hidden intermediate states in protein folding with AI-based conditional transition clustering.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Unsupervised synchronization of molecular dynamics trajectories via graph embedding and time warping.Bioinformatics (Oxford, England) · 2026Article
- Data-driven enhanced sampling of mechanistic pathways.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Topological Analysis Reveals Multiple Pathways in Molecular Dynamics.Journal of chemical theory and computation · 2025Article
- More Sophisticated Is Not Always Better: A Comparison of Similarity Measures for Unsupervised Learning of Pathways in Biomolecular Simulations.The journal of physical chemistry. B · 2025Article
- PathInHydro, a Set of Machine Learning Models to Identify Unbinding Pathways of Gas Molecules in [NiFe] Hydrogenases.Journal of chemical information and modeling · 2025Article
- Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?International journal of molecular sciences · 2024Review
- Calculating Protein-Ligand Residence Times through State Predictive Information Bottleneck Based Enhanced Sampling.Journal of chemical theory and computation · 2024Article
- Calculating Protein-Ligand Residence Times Through State Predictive Information Bottleneck based Enhanced Sampling.bioRxiv : the preprint server for biology · 2024Article
- Data-driven classification of ligand unbinding pathways.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Studying the pathways of ligand-receptor binding is essential to understand the mechanism of target recognition by small molecules. The binding free energy and kinetics of protein-ligand complexes can be computed using molecular dynamics (MD) simulations, often in quantitative agreement with experiments. However, only a qualitative picture of the ligand binding/unbinding paths can be obtained through a conventional analysis of the MD trajectories. Besides, the higher degree of manual effort involved in analyzing pathways limits its applicability in large-scale drug discovery. Here, we address this limitation by introducing an automated approach for analyzing molecular transition paths with a particular focus on protein-ligand dissociation. Our method is based on the dynamic time-warping algorithm, originally designed for speech recognition. We accurately classified molecular trajectories using a very generic descriptor set of contacts or distances. Our approach outperforms manual classification by distinguishing between parallel dissociation channels, within the pathways identified by visual inspection. Most notably, we could compute exit-path-specific ligand-dissociation kinetics. The unbinding timescale along the fastest path agrees with the experimental residence time, providing a physical interpretation to our entirely data-driven protocol. In combination with appropriate enhanced sampling algorithms, this technique can be used for the initial exploration of ligand-dissociation pathways as well as for calculating path-specific thermodynamic and kinetic properties.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.