pGenTHREADER and pDomTHREADER: new methods for improved protein fold recognition and superfamily discrimination.
BACKGROUND
Generation of structural models and recognition of homologous relationships for unannotated protein sequences are fundamental problems in bioinformatics. Improving the sensitivity and selectivity of methods designed for these two tasks therefore has downstream benefits for many other bioinformatics applications.


RESULTS
We describe the latest implementation of the GenTHREADER method for structure prediction on a genomic scale. The method combines profile-profile alignments with secondary-structure specific gap-penalties, classic pair- and solvation potentials using a linear combination optimized with a regression SVM model. We find this combination significantly improves both detection of useful templates and accuracy of sequence-structure alignments relative to other competitive approaches. We further present a second implementation of the protocol designed for the task of discriminating superfamilies from one another. This method, pDomTHREADER, is the first to incorporate both sequence and structural data directly in this task and improves sensitivity and selectivity over the standard version of pGenTHREADER and three other standard methods for remote homology detection.
Department of Computer Science, University College London, UK.
Anna
A
Lobley
Michael I
MI
Sadowski
David T
DT
Jones
D019295:Computational Biology / Q000379:methods*; D017510:Protein Folding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry / Q000145:classification*; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software
D011506:Proteins