ArticleComputational and structural biotechnology journal2020
Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review on active sites and functions of PIM-1 protein.Human cell · 2022Pooled it
- Structural Dynamics and Susceptibility of Aminonaphthoquinone-Chalcone Hybrids Against Human Tyrosinase.International journal of molecular sciences · 2026Article
- Sequence-based drug-target binding site pre-training enables cryptic pocket detection and improves binding affinity and kinetics prediction.Journal of cheminformatics · 2026Article
- Sequence-based Drug-Target Binding Site Pretraining Enables Cryptic Pocket Detection and Improves Binding Affinity and Kinetics Prediction.bioRxiv : the preprint server for biology · 2026Article
- Active subspace learning for coarse-grained molecular dynamics.The Journal of chemical physics · 2026Article
- CDK4/6 Inhibitors for Breast Cancer Therapy-A Review of Clinical Trials, Structural and Computational Approaches.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Systematic evaluation of computational tools to predict the effects of mutations on protein-ligand binding affinity in the absence of experimental structures.Briefings in bioinformatics · 2026Article
- PLXFPred: interpretable cross-attention networks with hierarchical fusion of multi-modal features for predicting protein-ligand interactions and affinities.Bioinformatics (Oxford, England) · 2026Article
- Active subspace learning for coarse-grained molecular dynamics.bioRxiv : the preprint server for biology · 2025Article
- PackPPI: An integrated framework for protein-protein complex side-chain packing and ΔΔG prediction based on diffusion model.Protein science : a publication of the Protein Society · 2025Article
- Evaluation of Structure Prediction and Molecular Docking Tools for Therapeutic Peptides in Clinical Use and Trials Targeting Coronary Artery Disease.International journal of molecular sciences · 2025Article
- Edge-enhanced interaction graph network for protein-ligand binding affinity prediction.PloS one · 2025Article
- Article
- Evaluating data partitioning strategies for accurate prediction of protein-ligand binding free energy changes in mutated proteins.Computational and structural biotechnology journal · 2025Article
- Alantolactone Inhibits Double Expression Lymphoma via Dual-Targeted Glycogen Synthase Kinase 3 Beta and B-Cell Lymphoma2.Blood and lymphatic cancer : targets and therapy · 2025Article
- Anilino-1,4-naphthoquinones as potent mushroom tyrosinase inhibitors:Journal of enzyme inhibition and medicinal chemistry · 2024Article
- Ensembling methods for protein-ligand binding affinity prediction.Scientific reports · 2024Article
- Catalytic mechanism and kinetics of malate dehydrogenase.Essays in biochemistry · 2024Review
- Structural investigation of Trypanosoma cruzi Akt-like kinase as drug target against Chagas disease.Scientific reports · 2024Article
- Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants.Journal of chemical information and modeling · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeMutation-induced variation of protein-ligand binding affinity is the key to many genetic diseases and the emergence of drug resistance, and therefore predicting such mutation impacts is of great importance. In this work, we aim to predict the mutation impacts on protein-ligand binding affinity using efficient structure-based, computational methods.
methodsRelying on consolidated databases of experimentally determined data we characterize the affinity change upon mutation based on a number of local geometrical features and monitor such feature differences upon mutation during molecular dynamics (MD) simulations. The differences are quantified according to average difference, trajectory-wise distance or time-vary differences. Machine-learning methods are employed to predict the mutation impacts using the resulting conventional or time-series features. Predictions based on estimation of energy and based on investigation of molecular descriptors were conducted as benchmarks.
resultsOur method (machine-learning techniques using time-series features) outperformed the benchmark methods, especially in terms of the balanced F1 score. Particularly, deep-learning models led to the best prediction performance with distinct improvements in balanced F1 score and a sustained accuracy.
conclusionOur work highlights the effectiveness of the characterization of affinity change upon mutations. Furthermore, deep-learning techniques are well designed for handling the extracted time-series features. This study can lead to a deeper understanding of mutation-induced diseases and resistance, and further guide the development of innovative drug design.
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