ArticlePloS one2015
Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Computational Ligand-Binding Site Prediction.Advances in experimental medicine and biology · 2026Review
- Energetic and structural control of polyspecificity in a multidrug transporter.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Recent approaches in the application of antimicrobial peptides in food preservation.World journal of microbiology & biotechnology · 2024Review
- Machine-learning-guided Directed Evolution for AAV Capsid Engineering.Current pharmaceutical design · 2024Review
- Machine Learning for Protein Engineering.ArXiv · 2023Article
- Characterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning.PLoS computational biology · 2023Article
- Inferring the Effects of Protein Variants on Protein-Protein Interactions with Interpretable Transformer Representations.Research (Washington, D.C.) · 2023Article
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- NMR Structure and Biophysical Characterization of Thermophilic Single-Stranded DNA Binding Protein fromInternational journal of molecular sciences · 2022Article
- Step-by-step design of proteins for small molecule interaction: A review on recent milestones.Protein science : a publication of the Protein Society · 2021Review
- Recent advancements in enzyme-mediated crosslinkable hydrogels:APL bioengineering · 2021Review
- Application of Machine Learning in Translational Medicine: Current Status and Future Opportunities.The AAPS journal · 2021Article
- Protein structure, amino acid composition and sequence determine proteome vulnerability to oxidation-induced damage.The EMBO journal · 2020Article
- Large scale analyses of genotype-phenotype relationships of glycine decarboxylase mutations and neurological disease severity.PLoS computational biology · 2020Article
- Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset.ACS omega · 2020Article
- Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis.Scientific reports · 2020Article
- Prediction of Protein Mutational Free Energy: Benchmark and Sampling Improvements Increase Classification Accuracy.Frontiers in bioengineering and biotechnology · 2020Article
- Robust Prediction of Single and Multiple Point Protein Mutations Stability Changes.Biomolecules · 2019Article
- Machine-learning-guided directed evolution for protein engineering.Nature methods · 2019Review
- Sequence Tolerance of a Single-Domain Antibody with a High Thermal Stability: Comparison of Computational and Experimental Fitness Profiles.ACS omega · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Thermostability issue of protein point mutations is a common occurrence in protein engineering. An application which predicts the thermostability of mutants can be helpful for guiding decision making process in protein design via mutagenesis. An in silico point mutation scanning method is frequently used to find "hot spots" in proteins for focused mutagenesis. ProTherm (http://gibk26.bio.kyutech.ac.jp/jouhou/Protherm/protherm.html) is a public database that consists of thousands of protein mutants' experimentally measured thermostability. Two data sets based on two differently measured thermostability properties of protein single point mutations, namely the unfolding free energy change (ddG) and melting temperature change (dTm) were obtained from this database. Folding free energy change calculation from Rosetta, structural information of the point mutations as well as amino acid physical properties were obtained for building thermostability prediction models with informatics modeling tools. Five supervised machine learning methods (support vector machine, random forests, artificial neural network, naïve Bayes classifier, K nearest neighbor) and partial least squares regression are used for building the prediction models. Binary and ternary classifications as well as regression models were built and evaluated. Data set redundancy and balancing, the reverse mutations technique, feature selection, and comparison to other published methods were discussed. Rosetta calculated folding free energy change ranked as the most influential features in all prediction models. Other descriptors also made significant contributions to increasing the accuracy of the prediction models.
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