title: The SeqFEATURE library of 3D functional site models: comparison to existing methods and applications to protein function annotation.
abstract: Structural genomics efforts have led to increasing numbers of novel, uncharacterized protein structures with low sequence identity to known proteins, resulting in a growing need for structure-based function recognition tools. Our method, SeqFEATURE, robustly models protein functions described by sequence motifs using a structural representation. We built a library of models that shows good performance compared to other methods. In particular, SeqFEATURE demonstrates significant improvement over other methods when sequence and structural similarity are low.
authors.affiliation: Program in Biomedical Informatics, Stanford University, Stanford, CA, 94305 USA.
authors.forename: Shirley
authors.initials: S
authors.lastname: Wu
authors.forename: Mike P
authors.initials: MP
authors.lastname: Liang
authors.forename: Russ B
authors.initials: RB
authors.lastname: Altman
mesh_terms: D000595:Amino Acid Sequence; D030562:Databases, Protein; D008722:Methods; D011506:Proteins / Q000737:chemistry* / Q000502:physiology; D040681:Structural Homology, Protein
chemical_list: D011506:Proteins