title: Towards site-based protein functional annotations.
abstract: The exact relationship between protein active centres and protein functions is unclear even after decades of intensive study. To improve functional prediction ability based on the local structures, we proposed three different methods. 1. We used Markov Random Field (MRF) to describe protein active region. 2. We developed filtering method that considers the local environment around the active sites. 3. We created multiple structure motifs by extending the motif to neighbouring residues. Our experiment results with enzyme families < 40% sequence identity demonstrated that our methods reduced random matches and could improve up to 70% of the functional annotation ability (using area under curve).
authors.affiliation: School of Electrical Engineering and Computer Science, University of Kansas, Lawrence, Kansas 66045, USA. slei@eecs.ku.edu
authors.forename: Seak Fei
authors.initials: SF
authors.lastname: Lei
authors.forename: Jun
authors.initials: J
authors.lastname: Huan
mesh_terms: D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D020134:Catalytic Domain; D008390:Markov Chains; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
chemical_list: D011506:Proteins
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