ELISA: Structure-Function Inferences Based on Statistically Significant and Evolutionarily Inspired Observations
Shakhnovich, Boris E.
Harvey, John M.
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Citation (published version)Shakhnovich, Boris E, John M Harvey, Steve Comeau, David Lorenz, Charles DeLisi, Eugene Shakhnovich. "ELISA: Structure-Function Inferences based on statistically significant and evolutionarily inspired observations" BMC Bioinformatics 4:34. (2003)
The problem of functional annotation based on homology modeling is primary to current bioinformatics research. Researchers have noted regularities in sequence, structure and even chromosome organization that allow valid functional cross-annotation. However, these methods provide a lot of false negatives due to limited specificity inherent in the system. We want to create an evolutionarily inspired organization of data that would approach the issue of structure-function correlation from a new, probabilistic perspective. Such organization has possible applications in phylogeny, modeling of functional evolution and structural determination. ELISA (Evolutionary Lineage Inferred from Structural Analysis, ) is an online database that combines functional annotation with structure and sequence homology modeling to place proteins into sequence-structure-function "neighborhoods". The atomic unit of the database is a set of sequences and structural templates that those sequences encode. A graph that is built from the structural comparison of these templates is called PDUG (protein domain universe graph). We introduce a method of functional inference through a probabilistic calculation done on an arbitrary set of PDUG nodes. Further, all PDUG structures are mapped onto all fully sequenced proteomes allowing an easy interface for evolutionary analysis and research into comparative proteomics. ELISA is the first database with applicability to evolutionary structural genomics explicitly in mind. Availability: The database is available at.
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