Rapid detection of similarity in protein structure and function through contact metric distances.
The characterization of biological function among newly determined protein structures is a central challenge in structural genomics. One class of computational solutions to this problem is based on the similarity of protein structure. Here, we implement a simple yet efficient measure of protein structure similarity, the contact metric. Even though its computation avoids structural alignments and is therefore nearly instantaneous, we find that small values correlate with geometrical root mean square deviations obtained from structural alignments. To test whether the contact metric detects functional similarity, as defined by Gene Ontology (GO) terms, it was compared in large-scale computational experiments to four other measures of structural similarity, including alignment algorithms as well as alignment independent approaches. The contact metric was the fastest method and its sensitivity, at any given specificity level, was a close second only to Fast Alignment and Search Tool--a structural alignment method that is slower by three orders of magnitude. Critically, nearly 40% of correct functional inferences by the contact metric were not identified by any other approach, which shows that the contact metric is complementary and computationally efficient in detecting functional relationships between proteins. A public 'Contact Metric Internet Server' is provided.
Lisewski
Andreas Martin
AM
Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.
Lichtarge
Olivier
O
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D005796:Genes; D020407:Internet; D008958:Models, Molecular; D015394:Molecular Structure; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry / Q000235:genetics / Q000502:physiology; D012984:Software; D040681:Structural Homology, Protein; D018875:Vocabulary, Controlled
D011506:Proteins