[IEEE-bhpjobs] Wednesday, October 12, 2005, 11:00am Schiciano Auditorium,

[Walter Heger]

Duke Institute for Genome Sciences & Policy Center for Bioinformatics & 
Computational Biology Seminar Series
Wednesday, October 12, 2005, 11:00am Schiciano Auditorium,
Fitzpatrick Center - CIEMAS Bldg.
Wei Wang, PhD Department of Computer Science University of North Carolina at 
Chapel Hill
“Mining Patterns from Protein Structures”
Protein structure can be described as complex three-dimensional network of 
contacts between amino acid residues where specific subnetworks of contacts 
(or packing patterns) can be responsible for structural integrity or 
function. We employ a novel representation of this contact residue networks 
in the form of connected labeled graphs in which vertices correspond to 
amino acid residues and edges encode connectivity and physical distance 
between vertices. This representation provides ample opportunity to apply 
innovative graph mining techniques to explore protein structure graphs and 
graph families (corresponding to protein structural and functional families) 
to detect structurally and functionally specific subgraphs (residue 
patterns) and help elucidate the relationships between protein structure and 
function. The complexity of protein structures and corresponding graphs 
poses significant computational challenges. The kernel of our approach is an 
efficient subgraph mining algorithm that detects all (maximal) frequent 
subgraphs from a graph database with a user specified minimal frequency. Our 
algorithm uses the pattern growth paradigm with an efficient depth-first 
enumeration scheme, searching through the graph space for frequent 
subgraphs. The recent algorithm incorporates several improvements by taking 
into account the properties of protein 3D structural graphs.

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