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Study of protein-ligand binding geometries using a scalable and accurate octree-based algorithm in MapReduce
SESSION: Research Poster Reception
EVENT TYPE: ACM Student Research Competition Poster, Poster, Electronic Poster
TIME: 5:15PM - 7:00PM
SESSION CHAIR: Bernd Mohr
AUTHOR(S):Trilce Estrada, Boyu Zhang, Roger Armen, Michela Taufer
ROOM:WSCC North Galleria 2nd/3rd Floors
ABSTRACT: We present a scalable and accurate algorithm for classifying
protein-ligand binding geometries in molecular docking. Our
algorithm is a three-step algorithm: the first step encodes
the geometry of a 3D ligand conformation
into a single 3D point in space; the second step
builds an octree by assigning an octant identifier to every
single point in the space under consideration; and the third
step performs an octree-based clustering on the reduced conformation
space and identifies the most dense octant. We
adapt our algorithm for MapReduce and implement it in
Hadoop. The load-balancing, fault-tolerance, and scalability
in MapReduce allows screening of very large conformation
spaces not approachable with traditional clustering methods.
We analyze results for a dataset of HIV protease inhibitors and demonstrate significant improvement over “energy-only” scoring
for the accurate identification of native ligand geometries.
The advantages of this approach make it attractive for complex
applications in real-world drug design efforts.
Bernd Mohr (Chair) - Juelich Supercomputing Centre
Trilce Estrada - University of Delaware
Boyu Zhang - University of Delaware
Roger Armen - Thomas Jefferson University School of Pharmacy