High-performance molecular dynamics simulations
The Advantages of Molecular Dynamics Simulations
Molecular and condensed-phase systems are dynamic in nature; therefore analyzing their motions at the molecular and atomistic level is essential to understanding key physicochemical phenomena. For decades, there has been keen interest in modeling the dynamic aspects of various systems in both life science and materials science. Such systems include protein-ligand complexes, small molecules in mixed solvents, organic solids, and synthetic macromolecular complexes. Molecular dynamics (MD) simulation stands alone as the fundamental computational tool for capturing dynamic events of scientific interest in all these applications.
Recently in life science applications, static structure-based approaches, such as docking and virtual screening, have made important strides in advancing drug discovery. MD, especially when coupled with these other computational tools, will open the door to addressing the many drug discovery problems for which the dynamic nature of proteins cannot be ignored, as in the mechanisms of highly mobile membrane proteins and in ligand-induced conformational changes of active sites.
Another crucial area that can benefit from MD simulation is in the analysis of existing systems, and development of new materials for a wide variety of applications. Predicting dynamic properties such as thermophysical and mechanical properties of materials systems reliably using MD simulation is an enabling capability required to establish a “Materials by Design” framework in electronics, energy, and aerospace industries.
Many physicochemical phenomena of scientific interest both in life science and materials science occur on time scales that are computationally demanding to simulate. A high-performance MD code, together with continuously advancing computer hardware technologies, can be used to perform simulations on time scales that illuminate these important dynamic processes. Desmond, a newly developed MD code created by D. E. Shaw Research, provides an unprecedented combination of parallel scalability, simulation throughput, and scientific accuracy to achieve these goals.
Desmond achieves exceptional scalability on commodity Linux clusters with both typical and high-end networks.
State-of-the-art GPU acceleration technology:
With the latest graphics processing unit (GPU) technology implemented in Desmond, MD simulation can run up to 200 times faster than on CPU, which can bring up the time scale of interest by orders of magnitude.
Desmond excels in numerical accuracy, which helps to ensure proper modeling of certain thermodynamic relationships that depend on detailed balance. Time-reversible simulations can also be performed. Numerical rigor helps to maintain low energy drift throughout each simulation.
Desmond provides a robust framework for the calculation of energies and forces for various force field models and is compatible with those models commonly used in both biomolecular and condensed-matter research, including CHARMM, AMBER, and OPLS.
Desmond performs explicit solvent simulations with periodic boundary conditions using cubic, orthorhombic, truncated octahedron, rhombic dodecahedron, and arbitrary triclinic simulation boxes, and can be used to model explicit membrane systems under various conditions.
Desmond computes both absolute and relative solvation free energies as well as relative free energies of binding.
Desmond supports automated simulation setup, including highly complex Free Energy Perturbation (FEP) calculations, multistage MD simulations with built-in simulation protocols, prediction of equation of states (EOS) at multiple temperatures, and prediction of dynamic responses at non-equilibrium states. An intuitive interface provides intelligent default settings and allows for rapid setup of computational experiments. Powerful analysis tools make it possible to visualize and examine computed results within the same Maestro modeling environment.
Citations and Acknowledgements
Schrödinger Release 2017-1: Desmond Molecular Dynamics System, D. E. Shaw Research, New York, NY, 2017. Maestro-Desmond Interoperability Tools, Schrödinger, New York, NY, 2017.
ö Shivakumar, D.; Williams, J.; Wu, Y.; Damm, W.; Shelley, J.; Sherman, W., "Prediction of Absolute Solvation Free Energies using Molecular Dynamics Free Energy Perturbation and the OPLS Force Field," J. Chem. Theory Comput., 2010, 6, 1509–1519
ö Guo, Z.; Mohanty, U.; Noehre, J.; Sawyer, T. K.; Sherman, W.; Krilov, G., "Probing the α-Helical Structural Stability of Stapled p53 Peptides: Molecular Dynamics Simulations and Analysis," Chem. Biol. Drug Des., 2010, 75, 348-359
Kevin J. Bowers, Edmond Chow, Huafeng Xu, Ron O. Dror, Michael P. Eastwood, Brent A. Gregersen, John L. Klepeis, Istvan Kolossvary, Mark A. Moraes, Federico D. Sacerdoti, John K. Salmon, Yibing Shan, and David E. Shaw, "Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters," Proceedings of the ACM/IEEE Conference on Supercomputing (SC06), Tampa, Florida, 2006, November 11-17
ö "Predicting the Effect of Amino Acid Single-Point Mutations on Protein Stability—Large-Scale Validation of MD-Based Relative Free Energy Calculations"Steinbrecher, T.; Zhu, C.; Wang, L.; Abel, A.; Negron, C.; Pealman, D.; Feyfant, E.; Duan, J.; Sherman, W., J. Mol. Biol. , 2017, 429 (7), 948-963
ö "Free Energy Perturbation Calculation of Relative Binding Free Energy between Broadly Neutralizing Antibodies and the gp120 Glycoprotein of HIV-1"Clark, A.J.; Gindin, T.; Zhang, B.; Wang, L.; Abel, R.; Murret, C.S.; Xu, F.; Bao, A.; Lu, N.J.; Zhou, T.; Kwong, P.D.; Shapiro, L.; Honig, B.; Friesner, R.A. , J. Mol. Biol., 2016, 16, 30516-2
"Influence of electron acceptors on the kinetics of metoprolol photocatalytic degradation in TiO2 suspension. A combined experimental and theoretical study"Armaković, S.J.; Armaković, S.; Finčur, N.L.; Šibul, F.; Vione, D.; Šetrajčić, J.P.; Abramović, B., RSC Advances, 2016, 5, 54589
ö "Relative Binding Free Energy Calculations Applied to Protein Homology Models"Cappel, D.; Hall, M.L.; Lenselink, E.B.; Beuming, T.; Qi, J.; Bradner, J.; Sherman, W., J. Chem. Inf. Model., 2016, 56 (12), 2388–2400
"Kosmotropism of Newly Synthesized 1-butyl-3-methylimidazolium Taurate Ionic Liquid: Experimental and Computational Study"Tot, A.; Armaković, S.; Armaković, S.J.; Gadžurić, S.; Vraneš, M. , The Journal of Chemical Thermodynamics , 2016, 94, 85
Schrödinger has made available the set of 239 molecules used for Absolute Solvation Free Energy calculations from the following publication:
Shivakumar, D.; Williams, J.; Wu, Y.; Damm, W.; Shelley, J.; Sherman, W., "Prediction of Absolute Solvation Free Energies using Molecular Dynamics Free Energy Perturbation and the OPLS Force Field," J. Chem. Theory Comput., 2010, 6, 1509–1519.
Note: The downloaded file contains the set of 239 molecules in Maestro, SMILES and SMARTS format along with the experimental and calculated absolute solvation free energies.
Schrödinger has also made available parameters for the set of 239 molecules used for Absolute Solvation Free Energy calculations from the following publication:
Shivakumar, D.; Harder, E.; Damm, W.; Friesner, R.A.; Sherman, W., "Improving the Prediction of Absolute Solvation Free Energies using the Next Generation OPLS Force Field," J. Chem. Theory Comput., 2012, 8, 2553-2558.
Note: The OPLS 2.0 parameters used in the above study were generated using Schrödinger Suite 2011. Parameters and structures for acenaphthylene (compound m78) and acenaphthene (compound m78_new) are also available for download below.
Accelerating Molecular Dynamics Simulations with GPUs
Molecular Dynamics (MD) and Free Energy Perturbation (FEP) calculations occur on time scales that are computationally demanding to simulate. A key factor in determining whether a simulation will take days, hours, or minutes to run is the hardware being used. The advent of GPU computing, however, has opened the door to a new world of computationally intensive simulations that would not have been possible even a few years ago. Desmond's high-performance Molecular Dynamics code, together with continuously improving computer hardware technologies are helping scientists push the boundaries of discovery further than ever before.
Figure 1: Comparison of GPU and CPU performance (ns/day) vs number of atoms (thousands) in sample simulations using the average of NVT and NPT ensembles. In general, using GPUs improve performance by 1-2 orders of magnitude compared to CPUs.
Schrödinger MD Compatible Systems by Exxact Corporation
Schrödinger has teamed up with Exxact Corporation to design a series of GPU computing systems that meet or exceed the requirements for Desmond MD and FEP calculations. Exxact works closely with the NVIDIA Tesla GPU team and is both a leading supplier and a Tesla Preferred Partner with NVIDIA. It offers both Workstations and Servers specifically tailored to run MD and FEP simulations with Schrödinger software.
|MD/FEP+ Workstations||MD/FEP+ Servers|
Schrödinger MD Compatible Systems by Exxact Corporation
- Designed to meet the requirements for Molecular Dynamics GPU Computing or highly complex Free Energy Perturbation calculations
- Optimized to meet or exceed the published performance benchmarks
- Preinstalled Desmond GPU computing solutions designed in collaboration with Schrödinger
- Every system is optimized and validated for your computing environment
- Fully customizable to meet your budget
- Tesla Preferred Partner: As a leading Tesla Preferred Partner with NVIDIA, Exxact Corporation works closely with the NVIDIA Tesla GPU team to ensure seamless factory development and support. Exxact prides itself on providing value-added service standards unmatched by any competitors.
- Scalable to Your Changing Needs: Exxact also offers multiple form factors should your computing needs change down the road, minimizing growing pains due to unpredictability.
- In-House Engineering and Design: Exxact GPU systems are built by in-house engineers and individually customizable for peak performance tailored to solving your unique and complex computing challenges.
- 3 Year Warranty: Each GPU system is engineer built and meticulously tested for absolute reliability and performance. Exxact stands behind its products by offering a 3-year warranty on its Tesla GPU systems, including parts and labor.
- Onsite Support: Besides remote assistance, Exxact also offers on-site technical support including 8x5 next business day, 24x7 next business day, and 24x7x4hr.