Faust: An algorithm for extracting functionally relevant templates from protein structures

0Citations
Citations of this article
10Readers
Mendeley users who have this article in their library.
Get full text

Abstract

FAUST(Functional Annotations Using Structural Templates) is an algorithm for: extraction of functionally relevant templates from protein structures and using such templates to annotate novel structures. Proteins and structural templates are represented as colored, undirected graphs with atoms as nodes and interatomic distances as edge weights. Node colors are based on chemical identities of atoms. Edge labels are equivalent if interatomic distances for corresponding nodes (atoms) differ less than a threshold value. We define FAUST structural template as a common subgraph of a set of graphs corresponding to two or more functionally related proteins. Pairs of functionally related protein structures are searched for sets of chemically equivalent atoms whose interatomic distances are conserved in both structures. Structural templates resulting from such pair wise searches are then combined to maximize classification performance on a training set of irredundant protein structures. The resulting structural template provides new language for description of structure—function relationship in proteins. These templates are used for active and binding site identification in protein structures. We are demonstrating here structural template extraction results for the highly divergent family of serine proteases. We compare FAUST templates to the standard description of the serine proteases active site pattern conservation and demonstrate depth of information captured in such description. Also, we present preliminary results of the high-throughput protein structure database annotations with a comprehensive library of FAUST templates.

Cite

CITATION STYLE

APA

Milik, M., Szalma, S., & Olszewski, K. A. (2002). Faust: An algorithm for extracting functionally relevant templates from protein structures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2452, pp. 172–184). Springer Verlag. https://doi.org/10.1007/3-540-45784-4_13

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free