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

Ultimate Goal: Reduce the time and cost of discovering novel drugs

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

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

Novel Drug Discovery Human HDAC4 HA3 crystal structure ZINC

Virtual High-Throughput Screening (VHTS)

VHTS with Autodock4

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.

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

Hadoop Scalable Economical Efficient Reliable

MapReduce programming paradigm used by Hadoop people.apache.org

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

Current AutoDockCloud Implementation Er Agonist screening from DUD as benchmark 450 speed-up with 570 available map slots on Kandinsky, private cloud at ORNL

Current AutoDockCloud Implementation Docking enrichment plot for ER agonist using AutoDockCloud and DUD. Percent of known ligands found Percent of ranked database

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

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

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

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