Advertisement

Google-Accelerated Biomolecular Simulations

  • Kai J. Kohlhoff
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2022)

Abstract

Biomolecular simulations rely heavily on the availability of suitable compute infrastructure for data-driven tasks like modeling, sampling, and analysis. These resources are typically available on a per-lab and per-facility basis, or through dedicated national supercomputing centers. In recent years, cloud computing has emerged as an alternative by offering an abundance of on-demand, specialist-maintained resources that enable efficiency and increased turnaround through rapid scaling.

Scientific computations that take the shape of parallel workloads using large datasets are commonplace, making them ideal candidates for distributed computing in the cloud. Recent developments have greatly simplified the task for the experimenter to configure the cloud for use and job submission. This chapter will show how to use Google’s Cloud Platform for biomolecular simulations by example of the molecular dynamics package GROningen MAchine for Chemical Simulations (GROMACS). The instructions readily transfer to a large variety of other tasks, allowing the reader to use the cloud for their specific purposes.

Importantly, by using Docker containers, a popular light-weight virtualization solution, and cloud storage, key issues in scientific research are addressed: reproducibility of results, record keeping, and the possibility for other researchers to obtain copies and directly build upon previous work for further experimentation and hypothesis testing.

Key words

Cloud computing Large-scale simulation Distributed computing 

Notes

Acknowledgments

This work was performed on Google infrastructure. The author thanks Jojo Dijamco for many detailed discussions and careful review of the manuscript, and members of the Google Accelerated Science team for helpful feedback.

References

  1. 1.
    Shaw DE, Deneroff MM, Dror RO, Kuskin JS, Larson RH, Salmon JK, Young C, Batson B, Bowers KJ, Chao JC, Eastwood MP, Gagliardo J, Grossman JP, Ho CR, Ierardi DJ, Kolossváry I, Klepeis JL, Layman T, McLeavey C, Moraes MA, Mueller R, Priest EC, Shan Y, Spengler J, Theobald M, Towles B, Wang SC (2008) Anton, a special-purpose machine for molecular dynamics simulation. Commun ACM 51(7):91–97CrossRefGoogle Scholar
  2. 2.
    Shaw DE, Grossman JP, Bank JA, Batson B, Butts JA, Chao JC, Deneroff MM, Dror RO, Even A, Fenton CH, Forte A, Gagliardo J, Gill G, Greskamp B, Ho CR, Ierardi DJ, Iserovich L, Kuskin JS, Larson RH, Layman T, Lee L, Lerer AK, Li C, Killebrew D, Mackenzie KM, Mok SY, Moraes MA, Mueller R, Nociolo LJ, Peticolas JL, Quan T, Ramot D, Salmon JK, Scarpazza DP, Schafer UB, Siddique N, Snyder CW, Spengler J, Tang PTP, Theobald M, Toma H, Towles B, Vitale B, Wang SC, Young C (2014) Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer. In: Kellenberger P (ed) SC’14 proc. int. conf. high performance computing, networking, storage and analysis, New Orleans, 2014Google Scholar
  3. 3.
    Shirts M, Pande VS (2000) Screensavers of the world, unite! Science 290:1903–1904CrossRefGoogle Scholar
  4. 4.
    Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y, Beauchamp KA, Wang L-P, Simmonett AC, Harrigan MP, Stern CD, Wiewiora RP, Brooks BR, Pande VS (2017) OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol 13(7):e1005659CrossRefGoogle Scholar
  5. 5.
    Bowman GR, Pande VS, Noé F (eds) (2014) An introduction to Markov state models and their application to long timescale molecular simulation. Springer, DordrechtGoogle Scholar
  6. 6.
    Dellago C, Bolhuis PG (2009) Transition path sampling and other advanced simulation techniques for rare events. Adv Polym Sci 221:167–233Google Scholar
  7. 7.
    Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4(3):435–447CrossRefGoogle Scholar
  8. 8.
    Abraham MJ, Murtola T, Schulz R, Pall S, Smith JC, Hess B, Lindahl E (2015) GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25CrossRefGoogle Scholar
  9. 9.
    Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossváry I, Moraes MA, Sacerdoti FD, Salmon JK, Shan Y, Shaw DE (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters. In: SC’06 proc. ACM/IEEE conf. supercomputing, Tampa, 2006Google Scholar
  10. 10.
    Conway P, Tyka MD, DiMaio F, Konerding DE, Baker D (2013) Relaxation of backbone bond geometry improves protein energy landscape modeling. Protein Sci 23(1):47–55CrossRefGoogle Scholar
  11. 11.
    Kohlhoff KJ, Shukla D, Lawrenz M, Bowman GR, Konerding DE, Belov D, Altman RB, Pande SB (2014) Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways. Nat Chem 6:15–21CrossRefGoogle Scholar
  12. 12.
    Poplin R, Newburger D, Dijamco J, Nguyen N, Loy D, Gross SS, McLean CY, DePristo MA (2017) Creating a universal SNP and small indel variant caller with deep neural networks, biorxiv.  https://doi.org/10.1101/092890
  13. 13.
    Mak HC (2017) Unhidden figures. Cell Syst 5(6):533CrossRefGoogle Scholar
  14. 14.
    Hykes S (2013) The future of Linux containers. In: PyCon’13 lightning talksGoogle Scholar
  15. 15.
    Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118(45):11225–11236CrossRefGoogle Scholar
  16. 16.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935CrossRefGoogle Scholar
  17. 17.
    Google Cloud Platform (2018) Running a dsub pipeline. https://cloud.google.com/genomics/tutorials/dsub. Accessed 26 Aug 2018

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Kai J. Kohlhoff
    • 1
  1. 1.ResearchGoogleMountain ViewUSA

Personalised recommendations