SCooP: an accurate and fast predictor of protein stability curves as a function of temperature
Author(s) -
Fabrizio Pucci,
Jean Marc Kwasigroch,
Marianne Rooman
Publication year - 2017
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btx417
Subject(s) - scoop , stability (learning theory) , function (biology) , computer science , biology , machine learning , programming language , evolutionary biology
The molecular bases of protein stability remain far from elucidated even though substantial progress has been made through both computational and experimental investigations. One of the most challenging goals is the development of accurate prediction tools of the temperature dependence of the standard folding free energy ΔG(T). Such predictors have an enormous series of potential applications, which range from drug design in the biopharmaceutical sector to the optimization of enzyme activity for biofuel production. There is thus an important demand for novel, reliable and fast predictors.
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