Predicting protein stability and solubility changes upon mutations: data perspective

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F20%3A00117450" target="_blank" >RIV/00216224:14310/20:00117450 - isvavai.cz</a>

Výsledek na webu

<a href="https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/cctc.202000933" target="_blank" >https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/cctc.202000933</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/cctc.202000933" target="_blank" >10.1002/cctc.202000933</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting protein stability and solubility changes upon mutations: data perspective

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Predicting protein stability and solubility changes upon mutations: data perspective

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10403 - Physical chemistry

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

ChemCatChem

ISSN

1867-3880

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

22

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

9

Strana od-do

5590-5598

Kód UT WoS článku

000565378700001

EID výsledku v databázi Scopus

2-s2.0-85090208505