MSMBuilder: Statistical Models for Biomolecular Dynamics

Biophysical Journal

Volumn 112, Issue 1, 2017, Pages 10-15

  Harrigan, Matthew P ^a   Sultan, Mohammad M ^a   Hernández, Carlos X ^b   Husic, Brooke E ^a   Eastman, Peter ^a   Schwantes, Christian R ^a   Beauchamp, Kyle A ^c   McGibbon, Robert T ^a   Pande, Vijay S ^a,b,d  

^a STANFORD UNIVERSITY   (United States)

^b Memorial Sloan Kettering Cancer Center ^*

^c MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

^d Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CSK TYROSINE-PROTEIN KINASE; PROTEIN TYROSINE KINASE;

CHEMISTRY; MARKOV CHAIN; METABOLISM; MOLECULAR DYNAMICS; PROTEIN CONFORMATION; SOFTWARE; STATISTICAL MODEL;

MARKOV CHAINS; MODELS, STATISTICAL; MOLECULAR DYNAMICS SIMULATION; PROTEIN CONFORMATION; SOFTWARE; SRC-FAMILY KINASES;

EID: 85008895438     PISSN: 00063495     EISSN: 15420086     Source Type: Journal    
DOI: 10.1016/j.bpj.2016.10.042     Document Type: Article

References (36)

1. 1 Shaw, D.E., Deneroff, M.M., et al., Wang, S.C., Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM, 51, 2008, 91.
2. 2 Friedrichs, M.S., Eastman, P., et al., Pande, V.S., Accelerating molecular dynamic simulation on graphics processing units. J. Comput. Chem. 30 (2009), 864–872.
3. 3 Shirts, M., Pande, V.S., COMPUTING: screen savers of the world unite!. Science 290 (2000), 1903–1904.
4. 4 Buch, I., Harvey, M.J., et al., De Fabritiis, G., High-throughput all-atom molecular dynamics simulations using distributed computing. J. Chem. Inf. Model. 50 (2010), 397–403.
5. 5 Kohlhoff, K.J., Shukla, D., et al., Pande, V.S., Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways. Nat. Chem. 6 (2014), 15–21.
6. 6 Schwantes, C.R., McGibbon, R.T., Pande, V.S., Perspective: Markov models for long-timescale biomolecular dynamics. J. Chem. Phys., 141, 2014, 090901.
7. 7 Pande, V.S., Beauchamp, K., Bowman, G.R., Everything you wanted to know about Markov state models but were afraid to ask. Methods 52 (2010), 99–105.
8. 8 Chodera, J.D., Noé, F., Markov state models of biomolecular conformational dynamics. Curr. Opin. Struct. Biol. 25 (2014), 135–144.
9. 9 Bowman, G.R., Pande, V.S., Noé, F., (eds.) An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation, 2014, Springer, New York.
10. 10 Bowman, G.R., Huang, X., Pande, V.S., Using generalized ensemble simulations and Markov state models to identify conformational states. Methods 49 (2009), 197–201.
11. 11 Beauchamp, K.A., Bowman, G.R., et al., Pande, V.S., MSMBuilder2: modeling conformational dynamics at the picosecond to millisecond scale. J. Chem. Theory Comput. 7 (2011), 3412–3419.
12. 12 Senne, M., Trendelkamp-Schroer, B., et al., Noé, F., EMMA: a software package for Markov model building and analysis. J. Chem. Theory Comput. 8 (2012), 2223–2238.
13. 13 Scherer, M.K., Trendelkamp-Schroer, B., et al., Noé, F., PyEMMA 2: a software package for estimation, validation, and analysis of Markov models. J. Chem. Theory Comput. 11 (2015), 5525–5542.
14. 14 Doerr, S., Harvey, M.J., et al., De Fabritiis, G., HTMD: high-throughput molecular dynamics for molecular discovery. J. Chem. Theory Comput. 12 (2016), 1845–1852.
15. 15 Taylor, S.S., Kornev, A.P., Protein kinases: evolution of dynamic regulatory proteins. Trends Biochem. Sci. 36 (2011), 65–77.
16. 16 Shukla, D., Meng, Y., et al., Pande, V.S., Activation pathway of Src kinase reveals intermediate states as targets for drug design. Nat. Commun., 5, 2014, 3397.
17. 17 McGibbon, R.T., Beauchamp, K.A., et al., Pande, V.S., MDTraj: a modern open library for the analysis of molecular dynamics trajectories. Biophys. J. 109 (2015), 1528–1532.
18. 18 Flocco, M.M., Mowbray, S.L., C alpha-based torsion angles: a simple tool to analyze protein conformational changes. Protein Sci. 4 (1995), 2118–2122.
19. 19 Zhou, T., Caflisch, A., Distribution of reciprocal of interatomic distances: a fast structural metric. J. Chem. Theory Comput. 8 (2012), 2930–2937.
20. 20 Schwantes, C.R., Pande, V.S., Improvements in Markov state model construction reveal many non-native interactions in the folding of NTL9. J. Chem. Theory Comput. 9 (2013), 2000–2009.
21. 21 Pérez-Hernández, G., Paul, F., et al., Noé, F., Identification of slow molecular order parameters for Markov model construction. J. Chem. Phys., 139, 2013, 015102.
22. 22 McGibbon, R. T., and V. S. Pande. 2016. Identification of simple reaction coordinates from complex dynamics. arXiv:1602.08776.
23. 23 Sculley, D. 2010. Web-scale K-means clustering. Proc.19th Int. Conf. World Wide Web. Association for Computing Machinery.
24. 24 McGibbon, R.T., Pande, V.S., Efficient maximum likelihood parameterization of continuous-time Markov processes. J. Chem. Phys., 143, 2015, 034109.
25. 25 McGibbon, R. T., B. Ramsundar, …, V. S. Pande. 2014. Understanding protein dynamics with L1-regularized reversible hidden Markov models. Proc. 31st Int. Conf. Machine Learning. 1197–1205.
26. 26 Pérez, F., Granger, B.E., IPython: a system for interactive scientific computing. Comput. Sci. Eng., 9, 2007, 21.
27. 27 Humphrey, W., Dalke, A., Schulten, K., VMD: visual molecular dynamics. J. Mol. Graph. 14 (1996), 33–38 27–28.
28. 28 Deuflhard, P., Weber, M., Robust Perron cluster analysis in conformation dynamics. Linear Algebra Appl. 398 (2005), 161–184.
29. 29 Metzner, P., Schütte, C., Vanden-Eijnden, E., Transition path theory for Markov jump processes. Multiscale Model. Simul. 7 (2009), 1192–1219.
30. 30 Berezhkovskii, A., Hummer, G., Szabo, A., Reactive flux and folding pathways in network models of coarse-grained protein dynamics. J. Chem. Phys., 130, 2009, 205102.
31. 31 Noé, F., Schütte, C., et al., Weikl, T.R., Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations. Proc. Natl. Acad. Sci. USA 106 (2009), 19011–19016.
32. 32 Nüske, F., Keller, B.G., et al., Noé, F., Variational approach to molecular kinetics. J. Chem. Theory Comput. 10 (2014), 1739–1752.
33. 33 McGibbon, R.T., Pande, V.S., Variational cross-validation of slow dynamical modes in molecular kinetics. J. Chem. Phys., 142, 2015, 124105.
34. 34 McGibbon, R. T. 2014. Fs MD trajectories. https://figshare.com/articles/Fs_MD_Trajectories/1030363. http://dx.doi.org/10.6084/m9.figshare.1030363.v1.
35. 35 McGibbon, R. T., C. X. Hernández, …, V. S. Pande. 2016. Osprey 1.0.0. https://zenodo.org/record/56251. http://dx.doi.org/10.5281/zenodo.56251.
36. 36 McGibbon, R. T., M. Harrigan, …, G. Kiss. 2016. MSMBuilder 3.5. https://zenodo.org/record/55601. http://dx.doi.org/10.5281/zenodo.55601.