ArticleProteins2019
iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations.
Article in Proteins, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 84 citations in OpenAlex.
- Deep Contrastive Learning for High-Throughput Prediction of Drug Resistance Mutations from Sequences.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Prot-ΔΔG: Prediction of protein-protein binding affinity changes upon mutations with pre-training strategies.Journal of computer-aided molecular design · 2026Article
- PPAC: Predicting Protein-Protein Affinity Changes Induced by Amino Acid Mutations Using Protein Large Language Models.ACS omega · 2025Article
- CDKL5 Deficiency Disorder: Revealing the Molecular Mechanism of Pathogenic Variants.International journal of molecular sciences · 2025Article
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- Rapid response to fast viral evolution using AlphaFold 3-assisted topological deep learning.Virus evolution · 2025Article
- Rapid response to fast viral evolution using AlphaFold 3-assisted topological deep learning.ArXiv · 2024Article
- Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions.Communications biology · 2024Article
- Evaluation of an Affinity-Enhanced Anti-SARS-CoV2 Nanobody Design Workflow Using Machine Learning and Molecular Dynamics.Journal of chemical information and modeling · 2024Article
- Decoding Missense Variants by Incorporating Phase Separation via Machine Learning.Nature communications · 2024Article
- MPA-MutPred: a novel strategy for accurately predicting the binding affinity change upon mutation in membrane protein complexes.Briefings in bioinformatics · 2024Article
- SAAMBE-MEM: a sequence-based method for predicting binding free energy change upon mutation in membrane protein-protein complexes.Bioinformatics (Oxford, England) · 2024Article
- DDMut-PPI: predicting effects of mutations on protein-protein interactions using graph-based deep learning.Nucleic acids research · 2024Article
- DeepPPAPredMut: deep ensemble method for predicting the binding affinity change in protein-protein complexes upon mutation.Bioinformatics (Oxford, England) · 2024Article
- Cold Spot SCANNER: Colab Notebook for predicting cold spots in protein-protein interfaces.BMC bioinformatics · 2024Article
- MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein-protein interactions.Briefings in bioinformatics · 2023Article
- Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations.International journal of molecular sciences · 2023Article
- Deep Local Analysis deconstructs protein-protein interfaces and accurately estimates binding affinity changes upon mutation.Bioinformatics (Oxford, England) · 2023Article
- Characterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning.PLoS computational biology · 2023Article
- Beyond sequence: Structure-based machine learning.Computational and structural biotechnology journal · 2023Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution, and Energy-based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kcal/mol on a diverse training dataset consisting of 1102 mutations in 57 protein-protein complexes. It competes with existing state-of-the-art methods on two blind test datasets. Predictions for a new dataset of 487 mutations in 56 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC < 0.42), although their combination does improve the predictions. Feature analysis for iSEE underlines the significance of evolutionary conservations for quantitative prediction of mutation effects. As an application example, we perform a full mutation scanning of the interface residues in the MDM2-p53 complex.
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Registered trials
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 OpenQuestion graph.