J 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.

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