Comput Biol Chem - Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins.

Tópicos

{ sequenc(1873) structur(1644) protein(1328) }
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{ sampl(1606) size(1419) use(1276) }
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{ patient(2837) hospit(1953) medic(668) }
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{ method(2212) result(1239) propos(1039) }

Resumo

Understanding of proteins adaptive to hypersaline environment and identifying them is a challenging task and would help to design stable proteins. Here, we have systematically analyzed the normalized amino acid compositions of 2121 halophilic and 2400 non-halophilic proteins. The results showed that halophilic protein contained more Asp at the expense of Lys, Ile, Cys and Met, fewer small and hydrophobic residues, and showed a large excess of acidic over basic amino acids. Then, we introduce a support vector machine method to discriminate the halophilic and non-halophilic proteins, by using a novel Pearson VII universal function based kernel. In the three validation check methods, it achieved an overall accuracy of 97.7%, 91.7% and 86.9% and outperformed other machine learning algorithms. We also address the influence of protein size on prediction accuracy and found the worse performance for small size proteins might be some significant residues (Cys and Lys) were missing in the proteins.

Resumo Limpo

understand protein adapt hypersalin environ identifi challeng task help design stabl protein systemat analyz normal amino acid composit halophil nonhalophil protein result show halophil protein contain asp expens lys ile cys met fewer small hydrophob residu show larg excess acid basic amino acid introduc support vector machin method discrimin halophil nonhalophil protein use novel pearson vii univers function base kernel three valid check method achiev overal accuraci outperform machin learn algorithm also address influenc protein size predict accuraci found wors perform small size protein might signific residu cys lys miss protein

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