FireProtDB: Database of Manually Curated Protein Stability Data

FireProtDB: Databáze manuálně zkontrolovaných dat proteinových stabilit

journal article

English

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

• https://academic.oup.com/nar/article/49/D1/D319/5964070

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

10.1093/nar/gkaa981

000608437800041

2-s2.0-85099428147