Prodepth: predict residue depth by support vector regression approach from protein sequences only.

ABSTRACT: Residue depth (RD) is a solvent exposure measure that complements the information provided by conventional accessible surface area (ASA) and describes to what extent a residue is buried in the protein structure space. Previous studies have established that RD is correlated with several protein properties, such as protein stability, residue conservation and amino acid types. Accurate prediction of RD has many potentially important applications in the field of structural bioinformatics, for example, facilitating the identification of functionally important residues, or residues in the folding nucleus, or enzyme active sites from sequence information. In this work, we introduce an efficient approach that uses support vector regression to quantify the relationship between RD and protein sequen

SUBMITTER: Song J 

PROVIDER: S-EPMC2742725 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature