Journal
CHEMCATCHEM
Volume 12, Issue 22, Pages 5590-5598Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/cctc.202000933
Keywords
Database; Machine learning; Protein design; Protein engineering; Protein modifications
Categories
Funding
- Czech ministry of Education, Youth and Sports [LM2015047, LM2018121, 02.1.01/0.0/0.0/18_046/0015975]
- European Union (CETOCOEN EXCELLENCE Teaming 2 project - Horizon2020) [857560]
- Operational Programme Research, Development and Education project MSCAfellow@MUNI [CZ.02.2.69/0.0/0.0/17_050/0008496]
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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. Forin silicopredictions, 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.
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