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Understanding Molecular Recognition by Kinetic Network Models Constructed from Molecular Dynamics Simulations

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Book cover An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 797))

Abstract

Molecular recognition, the process by which biological macromolecules selectively bind, plays an important role in many biological processes. Molecular simulations hold great potential to reveal the chemical details of molecular recognition and to complement experiments. However, it is challenging to reconstruct the binding process for two-body systems like protein-ligand complexes because the system’s dynamics occurs on significantly different timescales due to several physical processes involved, such as diffusion, local interactions and conformational changes. In this chapter, we review some recent progress on applying Markov state models (MSMs) to two-body systems. Emphasis is placed on the value of projecting dynamics onto collective reaction coordinates and treating the ligand dynamics with different resolution models depending on the proximity of the protein and ligand. We also discuss some future directions on constructing MSMs to investigate molecular recognition processes.

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References

  1. Koshland DE (1958) Application of a theory of enzyme specificity to protein synthesis. Proc Natl Acad Sci USA 44(2):98–104

    Article  PubMed  CAS  Google Scholar 

  2. Kumar S, Ma B, Tsai CJ, Sinha N, Nussinov R (2000) Folding and binding cascades: dynamic landscapes and population shifts. Protein Sci 9(1):10–19

    Article  PubMed  CAS  Google Scholar 

  3. Ma B, Kumar S, Tsai CJ, Nussinov R (1999) Folding funnels and binding mechanisms. Protein Eng 12(9):713–720

    Article  PubMed  CAS  Google Scholar 

  4. Ma B, Shatsky M, Wolfson HJ, Nussinov R (2002) Multiple diverse ligands binding at a single protein site: a matter of pre-existing populations. Protein Sci 11(2):184–197

    Article  PubMed  CAS  Google Scholar 

  5. Tsai CJ, Kumar S, Ma B, Nussinov R (1999) Folding funnels, binding funnels, and protein function. Protein Sci 8(6):1181–1190

    Article  PubMed  CAS  Google Scholar 

  6. Tsai CJ, Ma B, Nussinov R (1999) Folding and binding cascades: shifts in energy landscapes. Proc Natl Acad Sci USA 96(18):9970–9972

    Article  PubMed  CAS  Google Scholar 

  7. Arora K, Brooks CL (2007) Large-scale allosteric conformational transitions of adenylate kinase appear to involve a population-shift mechanism. Proc Natl Acad Sci USA 104(47):18496–18501

    Article  PubMed  CAS  Google Scholar 

  8. Bahar I, Chennubhotla C, Tobi D (2007) Intrinsic dynamics of enzymes in the unbound state and relation to allosteric regulation. Curr Opin Struct Biol 17(6):633–640

    Article  PubMed  CAS  Google Scholar 

  9. Oh BH, Ames GF, Kim SH (1994) Structural basis for multiple ligand specificity of the periplasmic lysine-, arginine-, ornithine-binding protein. J Biol Chem 269(42):26323–26330

    PubMed  CAS  Google Scholar 

  10. Ames GF (1986) Bacterial periplasmic transport systems: structure, mechanism, and evolution. Annu Rev Biochem 55:397–425

    Article  PubMed  CAS  Google Scholar 

  11. Pang A, Arinaminpathy Y, Sansom MS, Biggin PC (2005) Comparative molecular dynamics-similar folds and similar motions? Proteins 61(4):809–822

    Article  PubMed  CAS  Google Scholar 

  12. Stockner T, Vogel H, Tieleman D (2005) A salt-bridge motif involved in ligand binding and large-scale domain motions of the maltose-binding protein. Biophys J 89(5):3362–3371

    Article  PubMed  CAS  Google Scholar 

  13. Buchete NV, Hummer G (2008) Coarse master equations for peptide folding dynamics. J Phys Chem B 112(19):6057–6069

    Article  PubMed  CAS  Google Scholar 

  14. Noe F, Schutte C, Vanden-Eijnden E, Reich L, Weikl TR (2009) Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations. Proc Natl Acad Sci USA 106(45):19011–19016

    Article  PubMed  CAS  Google Scholar 

  15. Huang X, Yao Y, Bowman GR, Sun J, Guibas LJ, Carlsson G, Pande VS (2010) Constructing multi-resolution markov state models (msms) to elucidate RNA hairpin folding mechanisms. Pac Symp Biocomput, 228–239

    Google Scholar 

  16. Voelz VA, Bowman GR, Beauchamp K, Pande VS. Molecular simulation of ab initio protein folding for a millisecond folder NTL9(1-39). J Am Chem Soc 132(5):1526–1528

    Google Scholar 

  17. Morcos F, Chatterjee S, McClendon CL, Brenner PR, Lopez-Rendon R, Zintsmaster J, Ercsey-Ravasz M, Sweet CR, Jacobson MP, Peng JW, Izaguirre JA (2010) Modeling conformational ensembles of slow functional motions in Pin1-WW. PLoS Comput Biol 6(12):e1001015

    Article  PubMed  Google Scholar 

  18. Buch I, Sadiq SK, De Fabritiis G (2011) Optimized potential of mean force calculations for standard binding free energies. J Chem Theory Comput 7(6):1765–1772

    Article  CAS  Google Scholar 

  19. Buch I, Giorgino T, De Fabritiis G (2011) Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proc Natl Acad Sci USA 108(25):10184–10189

    Article  PubMed  CAS  Google Scholar 

  20. Bowman GR, Huang X, Pande VS (2009) Using generalized ensemble simulations and Markov state models to identify conformational states. Methods 49(2):197–201

    Article  PubMed  CAS  Google Scholar 

  21. Huang X, Bowman GR, Bacallado S, Pande VS (2009) Rapid equilibrium sampling initiated from nonequilibrium data. Proc Natl Acad Sci USA 106(47):19765–19769

    Article  PubMed  CAS  Google Scholar 

  22. Oh BH, Pandit J, Kang CH, Nikaido K, Gokcen S, Ames GF, Kim SH (1993) Three-dimensional structures of the periplasmic lysine/arginine/ornithine-binding protein with and without a ligand. J Biol Chem 268(15):11348–11355

    PubMed  CAS  Google Scholar 

  23. Harvey MJ, Giupponi G, De Fabritiis G (2009) ACEMD: accelerating biomolecular dynamics in the microsecond time scale. J Chem Theory Comput 5(6):1632–1639

    Article  CAS  Google Scholar 

  24. Buch I, Harvey MJ, Giorgino T, Anderson DP, De Fabritiis G (2010) High-throughput all-atom molecular dynamics simulations using distributed computing. J Chem Inf Model 50(3):397–403

    Article  PubMed  CAS  Google Scholar 

  25. Bucher D, Grant BJ, Markwick PR, McCammon JA (2011) Accessing a hidden conformation of the maltose binding protein using accelerated molecular dynamics. PLoS Comput Biol 7(4):e1002034

    Article  PubMed  CAS  Google Scholar 

  26. Quiocho FA, Ledvina PS (1996) Atomic structure and specificity of bacterial periplasmic receptors for active transport and chemotaxis: variation of common themes. Mol Microbiol 20(1):17–25

    Article  PubMed  CAS  Google Scholar 

  27. Tang C, Schwieters CD, Clore GM (2007) Open-to-closed transition in apo maltose-binding protein observed by paramagnetic NMR. Nature 449(7165):1078–1082

    Article  PubMed  CAS  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Chemistry, Division of Biomedical Engineering, Center of Systems Biology and Human Health, Institute for Advance Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

    Xuhui Huang

  2. Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C. Doctor Aiguader 88, 08003, Barcelona, Spain

    Gianni De Fabritiis

Authors
  1. Xuhui Huang
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  2. Gianni De Fabritiis
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Corresponding author

Correspondence to Xuhui Huang .

Editor information

Editors and Affiliations

  1. University of California, Berkeley, California, USA

    Gregory R. Bowman

  2. Department of Chemistry, Stanford University, Stanford, California, USA

    Vijay S. Pande

  3. Freie Universität Berlin, Berlin, Germany

    Frank Noé

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© 2014 Springer Science+Business Media Dordrecht

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Huang, X., De Fabritiis, G. (2014). Understanding Molecular Recognition by Kinetic Network Models Constructed from Molecular Dynamics Simulations. In: Bowman, G., Pande, V., Noé, F. (eds) An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation. Advances in Experimental Medicine and Biology, vol 797. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7606-7_9

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