ArticleBioinformatics (Oxford, England)2023
Predicting protein stability changes upon mutation using a simple orientational potential.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 30 citations in OpenAlex.
- Bridging Algorithms and Biocatalysis: Perspectives on AI-Supported Enzyme Engineering.Molecules (Basel, Switzerland) · 2026Review
- OrgNet+: towards robust protein stability prediction with convolutional neural networks.Bioinformatics (Oxford, England) · 2026Article
- JanusDDG: a physics-informed neural network for sequence-based protein stability via two-fronts attention.Communications biology · 2026Article
- Generalizable and scalable protein stability prediction with rewired protein generative models.Nature communications · 2025Article
- AbAgym: a well-curated dataset for the mutational analysis of antibody-antigen complexes.mAbs · 2025Article
- Molecular Consequences ofInternational journal of molecular sciences · 2025Article
- Predicting protein stability changes upon mutations with dual-view ensemble learning from single sequence.Briefings in bioinformatics · 2025Article
- Mass balance approximation of unfolding boosts potential-based protein stability predictions.Protein science : a publication of the Protein Society · 2025Article
- MAST Kinases' Function and Regulation: Insights from Structural Modeling and Disease Mutations.Biomedicines · 2025Article
- Exploring Evolution to Uncover Insights Into Protein Mutational Stability.Molecular biology and evolution · 2025Article
- Exploring Evolution to Uncover Insights Into Protein Mutational Stability.Molecular biology and evolution · 2025Article
- Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method.BMC bioinformatics · 2024Article
- An end-to-end framework for the prediction of protein structure and fitness from single sequence.Nature communications · 2024Article
- Influence of Model Structures on Predictors of Protein Stability Changes from Single-Point Mutations.Genes · 2023Article
- Machine Learning-Guided Protein Engineering.ACS catalysis · 2023Review
- Unbiased curriculum learning enhanced global-local graph neural network for protein thermodynamic stability prediction.Bioinformatics (Oxford, England) · 2023Article
- Intra-Frontiers in genetics · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
motivationStructure-based stability prediction upon mutation is crucial for protein engineering and design, and for understanding genetic diseases or drug resistance events. For this task, we adopted a simple residue-based orientational potential that considers only three backbone atoms, previously applied in protein modeling. Its application to stability prediction only requires parametrizing 12 amino acid-dependent weights using cross-validation strategies on a curated dataset in which we tried to reduce the mutations that belong to protein-protein or protein-ligand interfaces, extreme conditions and the alanine over-representation.
resultsOur method, called KORPM, accurately predicts mutational effects on an independent benchmark dataset, whether the wild-type or mutated structure is used as starting point. Compared with state-of-the-art methods on this balanced dataset, our approach obtained the lowest root mean square error (RMSE) and the highest correlation between predicted and experimental ΔΔG measures, as well as better receiver operating characteristics and precision-recall curves. Our method is almost anti-symmetric by construction, and it performs thus similarly for the direct and reverse mutations with the corresponding wild-type and mutated structures. Despite the strong limitations of the available experimental mutation data in terms of size, variability, and heterogeneity, we show competitive results with a simple sum of energy terms, which is more efficient and less prone to overfitting. AVAILABILITY AND IMPLEMENTATION: https://github.com/chaconlab/korpm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.