Paper Info
Title
Poster: study of protein-ligand binding geometries using a scalable and accurate octree-based algorithm in mapReduce
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.
Year
DOI
Venue
2011
10.1145/2148600.2148621
SC Companion
Keywords
Field
DocType
ligand conformation,large conformation space,native ligand geometries,octant identifier,reduced conformation space,accurate method,protein-ligand binding geometries,traditional clustering method,accurate octree-based algorithm,accurate identification,molecular docking,dense octant,load balance,ligand binding,drug design,fault tolerant,three dimensional
Protein ligand,Docking (molecular),Identifier,Docking (dog),Computer science,Parallel computing,Cluster analysis,Octant (instrument),Octree,Scalability
Conference
Citations 
PageRank 
References 
0
0.34
1
Authors
5
Name
Order
Citations
PageRank
Trilce Estrada112018.27
boyu zhang27117.54
Pietro Cicotti310114.52
Roger Armen4262.87
michela taufer535253.04