Poster: study of protein-ligand binding geometries using a scalable and accurate octree-based algorithm in mapReduce

Authors:
Trilce Estrada;Boyu Zhang;Pietro Cicotti;Roger Armen;Michela Taufer
Affiliations:
University of Delaware, Newark, DE, USA;University of Delaware, Newark, DE, USA;San Diego Supercomputer Center, San Diego, CA, USA;Thomas Jefferson University School of Pharmacy, Philadelphia, PA, USA;University of Delaware, Newark, DE, USA
Venue:
Proceedings of the 2011 companion on High Performance Computing Networking, Storage and Analysis Companion
Year:
2011

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Automatic selection of near-native protein-ligand conformations using a hierarchical clustering and volunteer computing

Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

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Abstract

We present a scalable and accurate method for classifying protein-ligand binding geometries in molecular docking. Our method is a three-step process: the first step encodes the geometry of a three-dimensional (3D) ligand conformation into a single 3D point in the 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 method for MapReduce and implement it in Hadoop. 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 docking and crossdocking for a series of HIV protease inhibitors. Our method demonstrates 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.