ArticleCommunications biology2022
Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein-ligand binding affinities.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
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- A Trichomonas vaginalis C2-XYPPX-repeat protein with a structured C2 domain displaying dampened flexibility upon binding calcium.Biochimie · 2026Article
- Development of substituted 2-(4-(sulfonyl)piperazin-1-yl)quinazoline molecular hybrids as a new class of antimalarials.RSC advances · 2025Article
- Activity of STAMP inhibitors inBlood neoplasia · 2025Article
- Protein-ligand affinity prediction via Jensen-Shannon divergence of molecular dynamics simulation trajectories.Biophysics and physicobiology · 2025Article
- Machine Learning in Enhancing Protein Binding Sites Predictions - What Has Changed Since Then?Combinatorial chemistry & high throughput screening · 2025Review
- In-layer inhomogeneity of molecular dynamics in quasi-liquid layers of ice.Communications chemistry · 2024Article
- Medicinal Aspects of PTP1B Inhibitors as Anti-Breast Cancer Agents: An Overview.Current medicinal chemistry · 2024Review
- Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 MRSC advances · 2023Article
- Statistical Improvement of rGILCC 1 and rPOXA 1B Laccases Activity Assay Conditions Supported by Molecular Dynamics.Molecules (Basel, Switzerland) · 2023Article
- Regulation of Tumor Apoptosis ofNutrients · 2023Article
- Rational design of small-molecule responsive protein switches.Protein science : a publication of the Protein Society · 2023Review
- DataDTA: a multi-feature and dual-interaction aggregation framework for drug-target binding affinity prediction.Bioinformatics (Oxford, England) · 2023Article
- Molecular Dynamics Simulation of the Complex of PDE5 and Evodiamine.Life (Basel, Switzerland) · 2023Article
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5 authors.
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Abstract
Prediction of protein-ligand binding affinity is a major goal in drug discovery. Generally, free energy gap is calculated between two states (e.g., ligand binding and unbinding). The energy gap implicitly includes the effects of changes in protein dynamics induced by ligand binding. However, the relationship between protein dynamics and binding affinity remains unclear. Here, we propose a method that represents ligand-binding-induced protein behavioral change with a simple feature that can be used to predict protein-ligand affinity. From unbiased molecular simulation data, an unsupervised deep learning method measures the differences in protein dynamics at a ligand-binding site depending on the bound ligands. A dimension reduction method extracts a dynamic feature that strongly correlates to the binding affinities. Moreover, the residues that play important roles in protein-ligand interactions are specified based on their contribution to the differences. These results indicate the potential for binding dynamics-based drug discovery.
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