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Predicting protein stability and solubility changes upon mutations: data perspective
Author(s) -
Mazurenko Stanislav
Publication year - 2020
Publication title -
chemcatchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.497
H-Index - 106
eISSN - 1867-3899
pISSN - 1867-3880
DOI - 10.1002/cctc.202000933
Subject(s) - stability (learning theory) , computer science , in silico , protein stability , quality (philosophy) , solubility , biochemical engineering , machine learning , computational biology , data science , data mining , artificial intelligence , chemistry , biology , engineering , genetics , biochemistry , philosophy , epistemology , gene , organic chemistry
Understanding mutational effects on protein stability and solubility is of particular importance for creating industrially relevant biocatalysts, resolving mechanisms of many human diseases, and producing efficient biopharmaceuticals, to name a few. For in silico predictions, the complexity of the underlying processes and increasing computational capabilities favor the use of machine learning. However, this approach requires sufficient training data of reasonable quality for making precise predictions. This minireview aims to summarize and scrutinize available mutational datasets commonly used for training predictors. We analyze their structure and discuss the possible directions of improvement in terms of data size, quality, and availability. We also present perspectives on the development of mutational data for accelerating the design of efficient predictors, introducing two new manually curated databases FireProt DB and SoluProtMut DB for protein stability and solubility, respectively.