SOLpro: accurate sequence-based prediction of protein solubility | Zendy

Chr̀istophe Magnan | Zendy; Arlo Randall | Zendy; Pierre Baldi | Zendy

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SOLpro: accurate sequence-based prediction of protein solubility

Author(s) -

Chr̀istophe Magnan,

Arlo Randall,

Pierre Baldi

Publication year - 2009

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/btp386

Subject(s) - computer science , support vector machine , sequence (biology) , data mining , proteomics , set (abstract data type) , solubility , machine learning , chemistry , biochemistry , organic chemistry , gene , programming language

Protein insolubility is a major obstacle for many experimental studies. A sequence-based prediction method able to accurately predict the propensity of a protein to be soluble on overexpression could be used, for instance, to prioritize targets in large-scale proteomics projects and to identify mutations likely to increase the solubility of insoluble proteins.

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