Bioinformatics Advance Access originally published online on March 17, 2006
Bioinformatics 2006 22(12):1456-1463; doi:10.1093/bioinformatics/btl102
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
A machine learning information retrieval approach to protein fold recognition
Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, CA, USA
*To whom correspondence should be addressed.
Motivation: Recognizing proteins that have similar tertiary structure is the key step of template-based protein structure prediction methods. Traditionally, a variety of alignment methods are used to identify similar folds, based on sequence similarity and sequence-structure compatibility. Although these methods are complementary, their integration has not been thoroughly exploited. Statistical machine learning methods provide tools for integrating multiple features, but so far these methods have been used primarily for protein and fold classification, rather than addressing the retrieval problem of fold recognition-finding a proper template for a given query protein.
Results: Here we present 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. Compared with 11 other fold recognition methods, FOLDpro yields the best results in almost all standard categories on a comprehensive benchmark dataset. Using predictions of the top-ranked template, the sensitivity is 
85, 56, and 27% at the family, superfamily and fold levels respectively. Using the 5 top-ranked templates, the sensitivity increases to 90, 70, and 48%.
Availability: The FOLDpro server is available with the SCRATCH suite through http://www.igb.uci.edu/servers/psss.html.
Contact: pfbaldi{at}ics.uci.edu
Supplementary information: Supplementary data are available at http://mine5.ics.uci.edu:1026/gain.html
Received on January 17, 2006; revised on March 4, 2006; accepted on March 15, 2006
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  Q. Dong, S. Zhou, and J. Guan A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation Bioinformatics, October 15, 2009; 25(20): 2655 - 2662. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 X. Guo and X. Gao A novel hierarchical ensemble classifier for protein fold recognition Protein Eng. Des. Sel., November 1, 2008; 21(11): 659 - 664. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Song, H. Tan, K. Takemoto, and T. Akutsu HSEpred: predict half-sphere exposure from protein sequences Bioinformatics, July 1, 2008; 24(13): 1489 - 1497. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Poleksic and M. Fienup Optimizing the size of the sequence profiles to increase the accuracy of protein sequence alignments generated by profile-profile algorithms Bioinformatics, May 1, 2008; 24(9): 1145 - 1153. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. T. A. Shamim, M. Anwaruddin, and H.A. Nagarajaram Support Vector Machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs Bioinformatics, December 15, 2007; 23(24): 3320 - 3327. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Song, Z. Yuan, H. Tan, T. Huber, and K. Burrage Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure Bioinformatics, December 1, 2007; 23(23): 3147 - 3154. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Cheng DOMAC: an accurate, hybrid protein domain prediction server Nucleic Acids Res., July 13, 2007; 35(suppl_2): W354 - W356. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 L. S. Z. Larsen, M. Zhang, N. Beliakova-Bethell, V. Bilanchone, A. Lamsa, K. Nagashima, R. Najdi, K. Kosaka, V. Kovacevic, J. Cheng, et al. Ty3 Capsid Mutations Reveal Early and Late Functions of the Amino-Terminal Domain J. Virol., July 1, 2007; 81(13): 6957 - 6972. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. Fariselli, I. Rossi, E. Capriotti, and R. Casadio The WWWH of remote homolog detection: The state of the art Brief Bioinform, March 1, 2007; 8(2): 78 - 87. [Abstract] [Full Text] [PDF]
|
|