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High-Throughput Virtual Molecular Docking: Hadoop Implementation of AutoDock4 on a Private Cloud Sally R. Ellingson Graduate Research Assistant Center for Molecular Biophysics, UT/ORNL Department of Genome Science and Technology, UT Scalable Computing and Leading Edge Innovative Technologies (IGERT) Dr. Jerome Baudry PhD Advisor Center for Molecular Biophysics, UT/ORNL Department of BCMB, UT The Second International Emerging Computational Methods for the Life Sciences Workshop ACM International Symposium on High Performance Distributed Computing June 8, 2011, San Jose, CA
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Ultimate Goal: Reduce the time and cost of discovering novel drugs
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1.Virtual Molecular Docking a)Novel Drug Discovery b)Virtual high-throughput screenings (VHTS) 2.Cloud Computing a)Advantages for VHTS b)Kandinsky c)Hadoop (MapReduce) 3.AutoDockCloud a)Current Implementation b)Future Implementations
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Virtual Molecular Docking Given a receptor (protein) and ligand (small molecule), predict 1.Bound conformations Search algorithm to explore conformational space 2.Binding affinity Force field to evaluate energetics
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Novel Drug Discovery Human HDAC4 HA3 crystal structure ZINC03962325
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Virtual High-Throughput Screening (VHTS)
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VHTS with Autodock4
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Potential advantages of Cloud Computing for VHTS Affordable access to compute resources (especially for small labs and classrooms). Easy to use interface accessible through web for non-computer experts. Software maintained by experts. Scalable resources for size of screening.
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Kandinsky Private Cloud Platform at ORNL Kandinsky, the Systems Biology Knowledgebase Computer, Sponsored by the Office of Biological and Environmental Research in the DOE Office of Science 68 nodes X 16 cores/node = 1088 cores 20 Gbps Infiniband Interconnect Designed to support Hadoop applications and gain an understanding of the MapReduce paradigm. 57 nodes for MapReduce tasks 1 tasktracker per node 10 map and 6 reduce tasks per node (16 tasks per node) 570 map tasks and 342 reduce tasks can run simultaneously on Kandinsky
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Hadoop Scalable Economical Efficient Reliable http://hadoop.apache.org/common/docs/current/api/overview-summary.html
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MapReduce programming paradigm used by Hadoop people.apache.org
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Current AutoDockCloud Implementation input=file names needed for each docking map(input) { copy input to local working directory; run AutoDock4 locally; copy result file to HDFS; } *pre-docking set-up and post-docking analysis is currently done manually *no reduce function is currently being used
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Current AutoDockCloud Implementation Er Agonist screening from DUD as benchmark 450 speed-up with 570 available map slots on Kandinsky, private cloud at ORNL
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Current AutoDockCloud Implementation Docking enrichment plot for ER agonist using AutoDockCloud and DUD. Percent of known ligands found Percent of ranked database
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Future AutoDockCloud Implementation input=ligand file from chemical compound database map(input) { create pdbqt (AutoDock input file) from input; run AutoDock4 locally; find best scoring ligand structure; save structure to HDFS; return ; } reduce( ) { sort; return ranked_database; } *pre-docking and post-docking will be automated and distributed *less total I/O requirements
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Future Plans Incorporate additional docking engines – Autodock Vina Less I/O More efficient and accurate algorithm No charge information needed Deploy on Commercial Cloud (EC2) Develop web interface
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1.Virtual Molecular Docking a)Novel Drug Discovery b)Virtual high-throughput screenings (VHTS) 2.Cloud Computing a)Advantages for VHTS b)Kandinsky c)Hadoop (MapReduce) 3.AutoDockCloud a)Current Implementation b)Future Implementations
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Questions/Comments Acknowledgements Dr. Jerome Baudry (advisor) Center for Molecular Biophysics, UT/ORNL Genome Science and Technology, UT Scalable Computing and Leading Edge Innovative Technologies (IGERT) Avinash Kewalramani, ORNL ECMLS and HPDC organizers and participants
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