NDLI: Multiple Graph Alignment for the Structural Analysis of Protein Active Sites

Content Provider	ACM Digital Library
Author	Klebe, Gerhard Weskamp, Nils Hullermeier, Eyke Kuhn, Daniel
Abstract	Graphs are frequently used to describe the geometry and also the physicochemical composition of protein active sites. Here, the concept of graph alignment as a novel method for the structural analysis of protein binding pockets is presented. Using inexact graph-matching techniques, one is able to identify both conserved areas and regions of difference among different binding pockets. Thus, using multiple graph alignments, it is possible to characterize functional protein families and to examine differences among related protein families independent of sequence or fold homology. Optimized algorithms are described for the efficient calculation of multiple graph alignments for the analysis of physicochemical descriptors representing protein binding pockets. Additionally, it is shown how the calculated graph alignments can be analyzed to identify structural features that are characteristic for a given protein family and also features that are discriminative among related families. The methods are applied to a substantial high-quality subset of the PDB database and their ability to successfully characterize and classify 10 highly populated functional protein families is shown. Additionally, two related protein families from the group of serine proteases are examined and important structural differences are detected automatically and efficiently.
Starting Page	310
Ending Page	320
Page Count	11
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2007.358301
Volume Number	4
Issue Number	2
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-04-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Knowledge discovery in databases, structural pattern discovery, fuzzy patterns, graph mining, drug design.
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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