Scott C-H Pegg | Search PubMed

PubMed2006

Leveraging enzyme structure-function relationships for functional inference and experimental design: the structure-function linkage database.

The study of mechanistically diverse enzyme superfamilies-collections of enzymes that perform different overall reactions but share both a common fold and a distinct mechanistic step performed by key conserved residues-helps elucidate the structure-function relationships of enzymes. We have developed a resource, the structure-function linkage database (SFLD), to analyze these structure-function relationships. Unique to the SFLD is its hierarchical classification scheme based on linking the specific partial reactions (or other chemical capabilities) that are conserved at the superfamily, subgroup, and family levels with the conserved structural elements that mediate them. We present the results of analyses using the SFLD in correcting misannotations, guiding protein engineering experiments, and elucidating the function of recently solved enzyme structures from the structural genomics initiative. The SFLD is freely accessible at http://sfld.rbvi.ucsf.edu.

Animals↗

PubMed2003

Intersect: identification and visualization of overlaps in database search results.

UNLABELLED: The determination of distant evolutionary relationships remains an important biological problem, and distant homologs often appear in statistically insignificant regions of sequence similarity searches. Intersect is a computer program designed to identify and visualize the overlaps between sets of sequences reported by multiple database searches. This capability extends the usefulness of database search results and aids researchers in identifying the individual sequences that best bridge sequence families and superfamilies. AVAILABILITY: The Intersect program is available from the Babbitt laboratory website at http://www.babbittlab.ucsf.edu/software/intersect

Algorithms↗

PubMedDate not supplied

SitePrint: three-dimensional pharmacophore descriptors derived from protein binding sites for family based active site analysis, classification, and drug design.

Integrating biological and chemical information is one key task in drug discovery, and one approach to attaining this goal is via three-dimensional pharmacophore descriptors derived from protein binding sites. The SitePrint program generates, aligns, scores, and classifies three-dimensional pharmacophore descriptors, active site grids, and ligand surfaces. The descriptors are formed from molecular fragments that have been docked, minimized, filtered, and clustered in protein active sites. The descriptors have geometric coordinates derived from the fragment positions, and they capture the shape, electrostatics, locations, and angles of entry into pockets of the recognition sites: they also provide a direct link to databases of organic molecules. The descriptors have been shown to be robust with respect to small changes in protein structure observed when multiple compounds are cocrystallized in a protein. Five aligned thrombin cocrystals with an average core alpha-carbon RMSD of 0.7 A gave three-dimensional pharmacophore descriptors with an average RMSD of 1.1 A. On a larger test set, alignment and scoring of the descriptors using clique-based alignment, and a best first search strategy with an adapted forward-looking Ullmann heuristic was able to select the global minimum three-dimensional alignment in twenty-nine out of thirty cases in less than one CPU second on a workstation. A protein family based analysis was then performed to demonstrate the usefulness of the method in producing a correlation of active site pharmacophore descriptors to protein function. Each protein in a test set of thirty was assigned membership to a family based on computed active site similarity to the following families: kinases, nuclear receptors, the aspartyl, cysteine, serine, and metallo proteases. This method of classifying proteins is complementary to approaches based on sequence or fold homology. The values within protein families for correctly assigning membership of a protein to a family ranged from 25% to 80%.

Algorithms↗