FOLDpro | Fold recognition : Protein structure analysis

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A two-stage machine learning, information retrieval, approach to fold recognition. First, we use alignment methods to derive pairwise similarity features for query-template protein pairs. We also use global profile-profile alignments in combination with predicted secondary structure, relative solvent accessibility, contact map and beta-strand pairing to extract pairwise structural compatibility features. Second, we apply support vector machines to these features to predict the structural relevance (i.e. in the same fold or not) of the query-template pairs. For each query, the continuous relevance scores are used to rank the templates. The FOLDpro approach is modular, scalable and effective.

FOLDpro specifications

Unique identifier:: OMICS_10827
Restrictions to use:: None
Input format:: Plain sequence
Stability:: Stable

Interface:: Web user interface
Input data:: Protein sequence
Computer skills:: Basic
Maintained:: Yes

FOLDpro support

Maintainer

Pierre Baldi <>

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Credits

Publications

(Cheng and Baldi, 2006) A machine learning information retrieval approach to protein fold recognition. Bioinformatics.
PMID: 16547073

Institution(s)

Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California, Irvine, CA, USA

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