ArticlePLoS computational biology2015
Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- Article
- ProBASS-a language model with sequence and structural features for predicting the effect of mutations on binding affinity.Bioinformatics (Oxford, England) · 2025Article
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity.Journal of cheminformatics · 2023Article
- Repertoire-scale measures of antigen binding.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Predicting the mutation effects of protein-ligand interactions via end-point binding free energy calculations: strategies and analyses.Journal of cheminformatics · 2022Article
- Naturally Occurring 8ß,13ß-kaur-15-en-17-al and Anti-Malarial Activity fromPharmaceuticals (Basel, Switzerland) · 2022Article
- Generalized Methodology for the Quick Prediction of Variant SARS-CoV-2 Spike Protein Binding Affinities with Human Angiotensin-Converting Enzyme II.The journal of physical chemistry. B · 2022Article
- PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions.Communications biology · 2021Article
- Conjugated β-Cyclodextrin Enhances the Affinity of Folic Acid towards FRα: Molecular Dynamics Study.Molecules (Basel, Switzerland) · 2021Article
- Nabe: an energetic database of amino acid mutations in protein-nucleic acid binding interfaces.Database : the journal of biological databases and curation · 2021Article
- KEAP1 Cancer Mutants: A Large-Scale Molecular Dynamics Study of Protein Stability.International journal of molecular sciences · 2021Article
- SAAMBE-SEQ: a sequence-based method for predicting mutation effect on protein-protein binding affinity.Bioinformatics (Oxford, England) · 2021Article
- In silico studies on stilbenolignan analogues as SARS-CoV-2 Mpro inhibitors.Chemical physics letters · 2021Article
- Fast Prediction of Binding Affinities of the SARS-CoV-2 Spike Protein Mutant N501Y (UK Variant) with ACE2 and Miniprotein Drug Candidates.The journal of physical chemistry. B · 2021Article
- Heterocyclic Substitutions Greatly Improve Affinity and Stability of Folic Acid towards FRα. an In Silico Insight.Molecules (Basel, Switzerland) · 2021Article
- Mining the Protein Data Bank to improve prediction of changes in protein-protein binding.PloS one · 2021Article
- Computational Analysis of Residue-Specific Binding Free Energies of Androgen Receptor to Ligands.Frontiers in molecular biosciences · 2021Article
- Using collections of structural models to predict changes of binding affinity caused by mutations in protein-protein interactions.Protein science : a publication of the Protein Society · 2020Article
- SSIPe: accurately estimating protein-protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function.Bioinformatics (Oxford, England) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
A new methodology termed Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) was developed to predict the changes of the binding free energy caused by mutations. The method utilizes 3D structures of the corresponding protein-protein complexes and takes advantage of both approaches: sequence- and structure-based methods. The method has two components: a MM/PBSA-based component, and an additional set of statistical terms delivered from statistical investigation of physico-chemical properties of protein complexes. While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants. This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins. The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624) while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation).
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