Abstract
SeqFEATURE, a tool for protein function annotation, models protein functions described by sequence motifs using a structural representation. The tool shows significantly improved performance over other methods when sequence and structural similarity are low.
Highlights
With the complete genomes sequenced for an increasing number of organisms, emphasis is shifting from identifying genes and gene products to understanding protein function and the interactions between biological entities on a systems level
We built a library of 3D functional site models using the FEATURE algorithm applied to training sets extracted automatically through sequence motifs found in the Protein Data Bank (PDB)
We show the (a) true positive (TP), (b) false negative (FN), and (c) false positive (FP) prediction rates for SeqFEATURE and PROSITE on test sites derived from the corresponding PROSITE patterns
Summary
With the complete genomes sequenced for an increasing number of organisms, emphasis is shifting from identifying genes and gene products to understanding protein function and the interactions between biological entities on a systems level. Molecular-level descriptions of cellular physiology are critical for elucidating biological processes and manipulating them for medical or industrial purposes, such as bioremediation or drug design. With protein structure determination becoming more efficient, the number of available structures is growing rapidly. The emergence of structural genomics [1], which aims to solve a representative set of proteins covering the entire space of naturally occurring structural folds, has spurred this growth, and depositions in the Protein Data Bank (PDB) [2] from structural genomics projects accounted for 16% of new structures in 2006, almost double the percentage in 2003 [3]. Structural genomics data and targets from almost all structural genomics centers are stored centrally in TargetDB [4], a database accessible from the PDB
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