ArticleJournal of cheminformatics2023
PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity.
Article in Journal of cheminformatics, 2023. 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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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- Computational alanine scanning with GBSA and interaction entropy: a review of methodologies and applications in protein-protein and protein-ligand binding.Chemical science · 2026Review
- Enhancing mutation impact prediction in protein-protein interactions through interpretable graph-based multi-level feature interactions.Bioinformatics (Oxford, England) · 2026Article
- 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
- Structural pharmacogenomics of drug-associated SNPs in oral squamous cell carcinoma.Frontiers in genetics · 2026Article
- Targeted Drug Delivery Strategies in Overcoming Antimicrobial Resistance: Advances and Future Directions.Pharmaceutics · 2025Review
- AFToolkit: a framework for molecular modeling of proteins with AlphaFold-derived representations.Briefings in bioinformatics · 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
- Evaluating data partitioning strategies for accurate prediction of protein-ligand binding free energy changes in mutated proteins.Computational and structural biotechnology journal · 2025Article
- Molecular Study ofJournal of fungi (Basel, Switzerland) · 2024Article
- A Benchmark Study of Protein-Fragment Complex Structure Calculations withInternational journal of molecular sciences · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol
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