Descriptor-based Fold Recognition System
biointelligence
biointelligence 23rd DEC 2009
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Machine learning-based methods have been proven to be powerful in developing new fold recognition tools.
DescFold(Descriptor-based Fold Recognition System) is a web server for protein fold recognition,which can predict a protein's fold type from its amino acid sequence. The server combines six effictive descriptors : a profile-sequence-alignment-based descriptor using Psi-blast e-values and bit scores, a sequence-profile-alignment-based descriptor using Rps-blast e-values and bit scores,a descriptor based on secondary structure element alignment (SSEA), a descriptor based on the occurrence of PROSITE functional motifs,
a descriptor based on profile-profile-alignment(PPA) and a descriptor based on Profile-structural-profile-alignment (PSPA) .
When the PPA and PSPA descriptors were introduced, the new DescFold boosts the performance of fold recognition substantially. Using the SCOP_1.73_40% dataset as the fold library, the DescFold web server based on the trained SVM models was further constructed. To provide a large-scale test for the new DescFold, a stringent test set of 1,866 proteins were selected from the SCOP 1.75 version. At a less than 5% false positive rate control, the new DescFold is able to correctly recognize structural homologs at the fold level for nearly 46% test proteins. Additionally, we also benchmarked the DescFold method against several well-established fold recognition algorithms through the LiveBench targets and Lindahl dataset.
The DESC server is freely available at:http://202.112.170.199/DescFold/index.html
Team Biointelligence
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Biointelligence
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