A kernel framework for protein residue annotation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Over the last decade several prediction methods have been developed for determining structural and functional properties of individual protein residues using sequence and sequence-derived information. Most of these methods are based on support vector machines as they provide accurate and generalizable prediction models. We developed a general purpose protein residue annotation toolkit (ProSAT) to allow biologists to formulate residue-wise prediction problems. ProSAT formulates annotation problem as a classification or regression problem using support vector machines. For every residue ProSAT captures local information (any sequence-derived information) around the reside to create fixed length feature vectors. ProSAT implements accurate and fast kernel functions, and also introduces a flexible window-based encoding scheme that allows better capture of signals for certain prediction problems. In this work we evaluate the performance of ProSAT on the disorder prediction and contact order estimation problems, studying the effect of the different kernels introduced here. ProSAT shows better or at least comparable performance to state-of-the-art prediction systems. In particular ProSAT has proven to be the best performing transmembrane-helix predictor on an independent blind benchmark.

Original languageEnglish (US)
Title of host publication13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Number of pages13
StatePublished - Jul 23 2009
Event13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, Thailand
Duration: Apr 27 2009Apr 30 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5476 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009


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