FireProtDB: Database of Manually Curated Protein Stability Data

FireProtDB: Databáze manuálně zkontrolovaných dat proteinových stabilit

journal article

english

Štourač Jan (LL)
Dúbrava Juraj Ondrej, Ing. (FIT BUT)
Musil Miloš, Ing., Ph.D. (DIFS FIT BUT)
Horáčková Jana, Mgr. (LL)
Mazurenko Stanislav, Ph.D. (LL)
Damborský Jiří, prof. Mgr., Dr. (LL)
Bednář David, Mgr. (LL)

stable data, protein engineering, protein stabilization, stable mutations

The majority of naturally occurring proteins have evolved to function under mild conditions inside the living organisms. One of the critical obstacles for the use of proteins in biotechnological applications is their insufficient stability at elevated temperatures or in the presence of salts. Since experimental screening for stabilizing mutations is typically laborious and expensive, in silico predictors are often used for narrowing down the mutational landscape. The recent advances in machine learning and artificial intelligence further facilitate the development of such computational tools. However, the accuracy of these predictors strongly depends on the quality and amount of data used for training and testing, which have often been reported as the current bottleneck of the approach. To address this problem, we present a novel database of experimental thermostability data for single-point mutants FireProtDB. The database combines the published datasets, data extracted manually from the recent literature, and the data collected in our laboratory. Its user interface is designed to facilitate both types of the expected use: (i) the interactive explorations of individual entries on the level of a protein or mutation and (ii) the construction of highly customized and machine learning-friendly datasets using advanced searching and filtering. The database is freely available at https://loschmidt.chemi.muni.cz/fireprotdb.

2020

319-324

Nucleic Acids Research, vol. 49, no. 1, ISSN 1362-4962

Oxford University Press

10.1093/nar/gkaa981

000608437800041

2-s2.0-85099428147