Přehled o publikaci
2022
SoluProtMutDB: A manually curated database of protein solubility changes upon mutations
VELECKÝ, Jan, Marie HAMŠÍKOVÁ, Jan ŠTOURAČ, Miloš MUSIL, Jiří DAMBORSKÝ et. al.Basic information
Original name
SoluProtMutDB: A manually curated database of protein solubility changes upon mutations
Authors
VELECKÝ, Jan (203 Czech Republic, belonging to the institution), Marie HAMŠÍKOVÁ (203 Czech Republic, belonging to the institution), Jan ŠTOURAČ (203 Czech Republic, belonging to the institution), Miloš MUSIL (203 Czech Republic, belonging to the institution), Jiří DAMBORSKÝ (203 Czech Republic, belonging to the institution), David BEDNÁŘ (203 Czech Republic, guarantor, belonging to the institution) and Stanislav MAZURENKO (643 Russian Federation, belonging to the institution)
Edition
Computational and Structural Biotechnology Journal, Amsterdam, Elsevier, 2022, 2001-0370
Other information
Language
English
Type of outcome
Article in a journal
Country of publisher
Netherlands
Confidentiality degree
is not subject to a state or trade secret
References:
RIV identification code
RIV/00216224:14310/22:00130114
Organization
Přírodovědecká fakulta – Repository – Repository
UT WoS
001043880900004
EID Scopus
2-s2.0-85142188313
Keywords in English
Mutational database; Protein engineering; Soluble expression; Protein yield; Machine learning; Protein aggregation
Links
EF15_003/0000469, research and development project. EF17_043/0009632, research and development project. FW03010208, research and development project. GJ20-15915Y, research and development project. LM2018121, research and development project. LX22NPO5102, research and development project. ELIXIR-CZ II, large research infrastructures.
Changed: 27/2/2025 00:50, RNDr. Daniel Jakubík
Abstract
V originále
Protein solubility is an attractive engineering target primarily due to its relation to yields in protein production and manufacturing. Moreover, better knowledge of the mutational effects on protein solubility could connect several serious human diseases with protein aggregation. However, we have limited understanding of the protein structural determinants of solubility, and the available data have mostly been scattered in the literature. Here, we present SoluProtMutDB – the first database containing data on protein solubility changes upon mutations. Our database accommodates 33 000 measurements of 17 000 protein variants in 103 different proteins. The database can serve as an essential source of information for the researchers designing improved protein variants or those developing machine learning tools to predict the effects of mutations on solubility. The database comprises all the previously published solubility datasets and thousands of new data points from recent publications, including deep mutational scanning experiments. Moreover, it features many available experimental conditions known to affect protein solubility. The datasets have been manually curated with substantial corrections, improving suitability for machine learning applications. The database is available at loschmidt.chemi.muni.cz/soluprotmutdb.