Please use this identifier to cite or link to this item: https://doi.org/10.1002/prot.20221
Title: Remote homolog detection using local sequence-structure correlations
Authors: Hou, Y.
Hsu, W. 
Lee, M.L. 
Bystroff, C.
Keywords: Hidden Markov
HMHSTR
I-sites
Local structure
Model
Protein folding
Remote homology
Support vector machines
Issue Date: 2004
Source: Hou, Y., Hsu, W., Lee, M.L., Bystroff, C. (2004). Remote homolog detection using local sequence-structure correlations. Proteins: Structure, Function and Genetics 57 (3) : 518-530. ScholarBank@NUS Repository. https://doi.org/10.1002/prot.20221
Abstract: Remote homology detection refers to the detection of structural homology in proteins when there is little or no sequence similarity. In this article, we present a remote homolog detection method called SVM-HMMSTR that overcomes the reliance on detectable sequence similarity by transforming the sequences into strings of hidden Markov states that represent local folding motif patterns. These state strings are transformed into fixed-dimension feature vectors for input to a support vector machine. Two sets of features are defined: an order-independent feature set that captures the amino acid and local structure composition; and an order-dependent feature set that captures the sequential ordering of the local structures. Tests using the Structural Classification of Proteins (SCOP) 1.53 data set show that the SVM-HMMSTR gives a significant improvement over several current methods. © 2004 Wiley-Liss, Inc.
Source Title: Proteins: Structure, Function and Genetics
URI: http://scholarbank.nus.edu.sg/handle/10635/39376
ISSN: 08873585
DOI: 10.1002/prot.20221
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