2006 | OriginalPaper | Buchkapitel
Discovering Sequence-Structure Patterns in Proteins with Variable Secondary Structure
verfasst von : Tom Milledge, Gaolin Zheng, Giri Narasimhan
Erschienen in: Computational Science – ICCS 2006
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Proteins that share a similar function often exhibit conserved sequence patterns. Sequence patterns help to classify proteins into families where the exact function may or may not be known. Research has shown that these domain signatures often exhibit specific three-dimensional structures. We have previously shown that sequence patterns combined with structural information, in general, have superior discrimination ability than those derived without structural information. However in some cases, divergent backbone configurations and/or variable secondary structure in otherwise well-aligned proteins make identification of conserved regions of sequence and structure problematic. In this paper, we describe improvements in our method of designing biologically meaningful sequence-structure patterns (SSPs) starting from a seed sequence pattern from any of the existing sequence pattern databases. Improved pattern precision is achieved by including conserved residues from coil regions that are not readily apparent from examination of multiple sequence alignments alone. Pattern recall is improved by systematically comparing the structure of all known true family members and to include all the allowable variations in the pattern residues.