Classification of proteins based on similarity of two-dimensional protein maps.
Data reduction techniques are now a vital part of numerical analysis and principal component analysis is often used to identify important molecular features from a set of descriptors. We now take a different approach and apply data reduction techniques directly to protein structure. With this we can reduce the three-dimensional structural data into two-dimensions while preserving the correct relationships. With two-dimensional representations, structural comparisons between proteins are accelerated significantly. This means that protein-protein similarity comparisons are now feasible on a large scale. We show how the approach can help to predict the function of kinase structures according to the Hanks' classification based on their structural similarity to different kinase classes.
Albrecht
Birgit
B
Department of Chemistry, University of Oxford, Central Chemistry Laboratory, South Parks Road, Oxford OX13QH, UK.
Grant
Guy H
GH
Sisu
Cristina
C
Richards
W Graham
WG
D019295:Computational Biology; D030562:Databases, Protein / Q000145:classification; D008954:Models, Biological; D010770:Phosphotransferases / Q000737:chemistry*; D011487:Protein Conformation; D017510:Protein Folding; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D040681:Structural Homology, Protein
D011506:Proteins; D010770:Phosphotransferases