Data mining the protein data bank: residue interactions.
The protein databank contains a vast wealth of structural and functional information. The analysis of this macromolecular information has been the subject of considerable work in order to advance knowledge beyond the collection of molecular coordinates. This article presents a method that determines local structural information within proteins using mathematical data mining techniques. The mine program described returns many known configurations of residues such as the catalytic triad, metal binding sites and the N-linked glycosylation site; as well as many other multiple residue interactions not previously categorized. Because mathematical constructs are used as targets, this method can identify new information not previously known, and also provide unbiased results of typical structure and their expected deviations. Because the results are defined mathematically, they cannot indicate the biological implications of the results. Therefore two support programs are described that provide insight into the biological context for the mine results. The first allows a weighted RMSD search between a template set of coordinates and a list of PDB files, and the second allows the labeling of a protein with the template results from mining to aid in the classification of this protein.
Oldfield
T J
TJ
Accelrys Inc., Department of Chemistry, University of York, Heslington, York, Yorkshire, United Kingdom. oldfield@ebi.ac.uk
D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D019295:Computational Biology / Q000295:instrumentation / Q000379:methods*; D003201:Computers; D030562:Databases, Protein; D006031:Glycosylation; D008670:Metals / Q000378:metabolism; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000378:metabolism*; D016415:Sequence Alignment; D012984:Software; D013997:Time Factors
D008670:Metals; D011506:Proteins