ArticlePloS one2014
Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Computational Ligand-Binding Site Prediction.Advances in experimental medicine and biology · 2026Review
- A Family with Meester-Loeys Syndrome Caused by a Novel Missense Variant in theInternational journal of molecular sciences · 2025Article
- DDGWizard: Integration of feature calculation resources for analysis and prediction of changes in protein thermostability upon point mutations.PLoS computational biology · 2025Article
- Bioinformatic analysis of rare endothelial protein C receptor missense variants associated with coagulation and thrombophilia risk.Scientific reports · 2025Article
- State-of-the-Art and Future Directions in Structural Proteomics.Molecular & cellular proteomics : MCP · 2025Review
- AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network.Interdisciplinary sciences, computational life sciences · 2025Article
- Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions.Communications biology · 2024Article
- Topological deep learning based deep mutational scanning.Computers in biology and medicine · 2023Article
- Incorporating physics to overcome data scarcity in predictive modeling of protein function: A case study of BK channels.PLoS computational biology · 2023Article
- Incorporating physics to overcome data scarcity in predictive modeling of protein function: a case study of BK channels.bioRxiv : the preprint server for biology · 2023Article
- Characterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning.PLoS computational biology · 2023Article
- Article
- Beyond sequence: Structure-based machine learning.Computational and structural biotechnology journal · 2023Review
- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
- Computational approaches for predicting variant impact: An overview from resources, principles to applications.Frontiers in genetics · 2022Review
- Structural bioinformatics enhances mechanistic interpretation of genomic variation, demonstrated through the analyses of 935 distinct RAS family mutations.Bioinformatics (Oxford, England) · 2021Article
- Integrating structure-based machine learning and co-evolution to investigate specificity in plant sesquiterpene synthases.PLoS computational biology · 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
- Mechanistic Insights into Protein Stability and Self-aggregation in GLUT1 Genetic Variants Causing GLUT1-Deficiency Syndrome.The Journal of membrane biology · 2020Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.
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