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A knowledge‐based scale for amino acid membrane propensity
Author(s) -
Punta Marco,
Maritan Amos
Publication year - 2002
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.10247
Subject(s) - transmembrane protein , transmembrane domain , scale (ratio) , set (abstract data type) , membrane protein , sequence (biology) , amino acid , biological system , peptide sequence , chemistry , membrane , computational biology , computer science , biochemistry , biology , physics , gene , receptor , quantum mechanics , programming language
Abstract In this article, a membrane‐propensity scale for amino acids is derived using only two ingredients: (i) a set of transmembrane helices segments from membrane protein crystal structures and (ii) the request that each component of the set has a free energy lower than that of a typical soluble protein sequence of the same length. Although the most widely used hydropathy scales satisfy this request, we use an optimization procedure that allows for extraction of an optimal scale, which correlates equally well with those scales. We show that, if the choice of the sequence database is accurate, significant knowledge‐based scales, which are robust with respect to changes in the learning set, can be easily derived. The obtained scales can be used for transmembrane helices prediction. The predictive power of one of these scales is tested on membrane proteins, soluble proteins, and signal peptides databases, finding that its performances is comparable with those of the hydropathy scales. Proteins 2003;50:114–121. © 2002 Wiley‐Liss, Inc.