Predicting metal-binding site residues in low-resolution structural models.
The accurate prediction of the biochemical function of a protein is becoming increasingly important, given the unprecedented growth of both structural and sequence databanks. Consequently, computational methods are required to analyse such data in an automated manner to ensure genomes are annotated accurately. Protein structure prediction methods, for example, are capable of generating approximate structural models on a genome-wide scale. However, the detection of functionally important regions in such crude models, as well as structural genomics targets, remains an extremely important problem. The method described in the current study, MetSite, represents a fully automatic approach for the detection of metal-binding residue clusters applicable to protein models of moderate quality. The method involves using sequence profile information in combination with approximate structural data. Several neural network classifiers are shown to be able to distinguish metal sites from non-sites with a mean accuracy of 94.5%. The method was demonstrated to identify metal-binding sites correctly in LiveBench targets where no obvious metal-binding sequence motifs were detectable using InterPro. Accurate detection of metal sites was shown to be feasible for low-resolution predicted structures generated using mGenTHREADER where no side-chain information was available. High-scoring predictions were observed for a recently solved hypothetical protein from Haemophilus influenzae, indicating a putative metal-binding site.
Bioinformatics Unit, Department of Computer Science, University College London, Gower Street, WC1E 6BT, UK.
Jaspreet Singh
JS
Sodhi
Kevin
K
Bryson
Liam J
LJ
McGuffin
Jonathan J
JJ
Ward
Lorenz
L
Wernisch
David T
DT
Jones
D001665:Binding Sites; D003198:Computer Simulation; D016208:Databases, Factual; D023281:Genomics; D008670:Metals / Q000737:chemistry*; D008958:Models, Molecular; D008962:Models, Theoretical; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry / Q000378:metabolism
D008670:Metals; D011506:Proteins