Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools
| Autor: | Ramya Yarlagadda, Charles C. Reed, Lei Jia |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Models
Molecular Protein Folding Computer science In silico Science Protein design Mutant Machine learning computer.software_genre Protein Structure Secondary Machine Learning Protein structure Point Mutation Computer Simulation Thermostability chemistry.chemical_classification Multidisciplinary Artificial neural network Models Genetic business.industry Protein Stability Point mutation Mutagenesis Proteins Protein engineering Protein structure prediction Amino acid Support vector machine chemistry Regression Analysis Thermodynamics Medicine Artificial intelligence business computer Research Article |
| Zdroj: | PLoS ONE, Vol 10, Iss 9, p e0138022 (2015) PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | 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, naive 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. |
| Databáze: | OpenAIRE |
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