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Study of Highly Accurate and Fast Protein-Ligand Docking Method Based on Molecular Dynamics

Reporter: Yu Lun KuoE-mail: sscc6991@gmail.comDate: November 21, 2006

M. Taufer, M. Crowley, D. J. Price, A. A. Chien‡ and C. L. Brooks III ,∗†

Department of Molecular Biology (TPC6), The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, U.S.A.

Published online 24 June 2005 in Wiley InterScience

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Outline

• Introduction

• MD-based Docking Method

• Algorithm Evaluation

• Metrics

• MD vs. Other Methods

• Conclusion & Future work

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Introduction (1/3)

• Drug development• Use of small molecules (ligand) to turn on or off a protei

n function

• Protein-ligand docking• Computational methods for the prediction of ligand-prot

ein structure information

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Introduction (2/3)

• Exiting docking method– Current docking algorithms are fast and use simplified

scoring function to direct conformational search and select the best structure

– Methods based on molecular dynamics (MD) and atomically detailed force field (e.g., CDOCKER) are more accurate but time- and resource-expensive.

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Introduction (3/3)

• Desktop grids– By scavenging for available and idle cycles

– Provide computing power at a significant cost saving

• Our algorithm– Parallel and each simulation attempt is decomposable

into independent sub-jobs

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MD-based Docking Method (1/2)

• Goals– Assure accuracy

• Benefit from the molecular mechanics force fields

– Guarantee performance • Return docking results in a short turnaround time using

cost-effective platforms

• Approach– Docking method based on CHARMM molecular

dynamics simulations and with a highly flexible computational granularity

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MD-based Docking Method (2/2)

MD SimulationHeating & Cooling

phase (300K700K300K)

Scoring function to rankLowest energy structure

20 Docking trial

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Algorithm Evaluation (1/2)

• Characterization of the docking method:– Does the MD length affect the docking accuracy?

– Does the number of trials affect the docking accuracy?

• Comparing algorithm with other well-known docking methods– AutoDock 、 DOCK 、 FlexX 、 ICM 、 GOLD

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Algorithm Evaluation (2/2)

• Experimental testbed– Platform

• SGI R10000 – Single 195MHz IP2 processor– 128MB memory

• A cluster of 64 dual-processor nodes at the SDSC (San Diego Supercomputer Center)

– Data set: 31 protein-ligand complexes• 10 proteins• 31 ligands with different levels of complexity

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Metrics

• Docking Accuracy (DA)– DA = fRMSD<2 + 0.5(fRMSD<3 - fRMSD<2)

– fRMSD<a fraction of predicted ligands docked into a given protein with RMSD lesser or equal to a Ǻ

• Computational Time– Time to complete a set of docking trials

– Report CPU time for sets of 1, 10 and 20 trials

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Four Different MD Simulations

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Docking Accuracy (DA)

Ten trials per attempt ensure

enough accuracy(T10)

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Average Time with Different Number of MD Steps

Increase of number of MD steps

Almost linear increase of the simulation time

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MD vs. Other Methods

• Other Methods– AutoDock 、 DOCK 、 FlexX 、 ICM 、 GOLD

• Comparison Metrics– Docking Accuracy (DA)

– RMSD of predicted ligands

– CPU time per attempt

• Definition of attempt– Consider CASE B and 10 trials per attempt (T10)

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Comparison of Docking Accuracy

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Best RMSD of Predicted Ligands

RMSD: Root-mean-square-deviation

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Time ComparisonIf enough processors are available, the time for completing a protein-ligand docking is competitive with the other methods

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Conclusion & Future work

• The MD-based docking method– Reach an average accuracy of 71%

• Still a lot of exciting research has to be addressed both at the application and system levels– Number of ligand orientations per trial based on r

esources and node reliability

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Conclusion & Future work

• Future work– Plan to make a more detailed study of MD and

Monte Carlo simulations for the docking process in the near future.

• ICM running multiple Monte Carlo minimizations

• Our docking protocol to desktop grids– Proportionally decreases the time to solution

– Fine-grained parallel algorithm for docking trial

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Thanks for your attention