Abstract
Molecular dynamics simulations represent a powerful tool to gain insights into structural and dynamical features of biomolecular systems. Nevertheless, their recognized limitation in terms of achievable timescales becomes particularly severe when dealing with slow processes. In such cases, the employment of enhanced sampling methods, which allow accelerating the characterization of rare events in a timeframe consistent with conventional computational resources, results as crucial. In particular, such advanced techniques have proven highly valuable in the context of protein folding and, specifically, to explore the conformational ensemble spanned by intrinsically disordered proteins (IDPs). Here, we describe how to set up molecular dynamics simulations with one of these enhanced sampling approaches (namely, Parallel Tempering Metadynamics in the Well-Tempered Ensemble) using the NTAIL peptide as a test case.
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References
Wright PE, Dyson HJ (1999) Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm. J Mol Biol 293:321–331
Uversky VN (2011) Intrinsically disordered proteins from A to Z. Int J Biochem Cell Biol 43:1090–1103
Gibbs EB, Showalter SA (2015) Quantitative biophysical characterization of intrinsically disordered proteins. Biochemistry 54:1314–1326
Tompa P (2012) Intrinsically disordered proteins: a 10-year recap. Trends Biochem Sci 37:509–516
Uversky VN, Oldfield CJ, Midic U et al (2009) Unfoldomics of human diseases: linking protein intrinsic disorder with diseases. BMC Genomics 10:S1–S7
Habchi J, Tompa P, Longhi S et al (2014) Introducing protein intrinsic disorder. Chem Rev 114:6561–6588
Varadi M, Vranken W, Guharoy M et al (2015) Computational approaches for inferring the functions of intrinsically disordered proteins. Front Mol Biosci 2:45
Dyson HJ, Wright PE (2005) Intrinsically unstructured proteins and their functions. Nat Rev Mol Cell Biol 6:197–208
Gunasekaran K, Tsai C-J, Kumar S et al (2003) Extended disordered proteins: targeting function with less scaffold. Trends Biochem Sci 28:81–85
Uversky VN, Oldfield CJ, Dunker AK (2008) Intrinsically disordered proteins in human diseases: introducing the D2 concept. Annu Rev Biophys 37:215–246
Joshi P, Vendruscolo M (2015) Druggability of intrinsically disordered proteins BT. In: Felli IC, Pierattelli R (eds) Intrinsically disordered proteins studied by NMR spectroscopy. Advances in experimental medicine and biology, vol 870. Springer, Cham, p 383
Recanatini M (2018) How dynamic docking simulations can help to tackle tough drug targets. Future Med Chem 10:2763–2765
Tompa P, Varadi M (2014) Predicting the predictive power of IDP ensembles. Structure 22:177–178
Masetti M, Rocchia W (2014) Molecular mechanics and dynamics: numerical tools to sample the configuration space. Front Biosci (Landmark Ed) 19:578–604
De Vivo M, Masetti M, Bottegoni G et al (2016) Role of molecular dynamics and related methods in drug discovery. J Med Chem 59:4035–4061
Nettels D, Müller-Späth S, Küster F et al (2009) Single-molecule spectroscopy of the temperature-induced collapse of unfolded proteins. Proc Natl Acad Sci U S A 106:20740–20745
Merchant KA, Best RB, Louis JM et al (2007) Characterizing the unfolded states of proteins using single-molecule FRET spectroscopy and molecular simulations. Proc Natl Acad Sci U S A 104:1528–1533
Voelz VA, Jäger M, Yao S et al (2012) Slow unfolded-state structuring in acyl-CoA binding protein folding revealed by simulation and experiment. J Am Chem Soc 134:12565–12577
Best RB, Mittal J (2010) Protein simulations with an optimized water model: cooperative helix formation and temperature-induced unfolded state collapse. J Phys Chem B 114:14916–14923
Piana S, Donchev AG, Robustelli P et al (2015) Water dispersion interactions strongly influence simulated structural properties of disordered protein states. J Phys Chem B 119:5113–5123
Ye W, Ji D, Wang W et al (2015) Test and evaluation of ff99IDPs force field for intrinsically disordered proteins. J Chem Inf Model 55:1021–1029
Palazzesi F, Prakash MK, Bonomi M et al (2015) Accuracy of current all-atom force-fields in modeling protein disordered states. J Chem Theory Comput 11:2–7
Huang J, MacKerell AD (2018) Force field development and simulations of intrinsically disordered proteins. Curr Opin Struct Biol 48:40–48
Liu H, Song D, Lu H et al (2018) Intrinsically disordered protein-specific force field CHARMM36IDPSFF. Chem Biol Drug Des 92:1722–1735
Robustelli P, Piana S, Shaw DE (2018) Develo** a molecular dynamics force field for both folded and disordered protein states. Proc Natl Acad Sci U S A 115:4758–4766
Best RB, Zheng W, Mittal J (2014) Balanced protein–water interactions improve properties of disordered proteins and non-specific protein association. J Chem Theory Comput 10:5113–5124
Abrams C, Bussi G (2014) Enhanced sampling in molecular dynamics using Metadynamics, replica-exchange, and temperature-acceleration. Entropy 16:163
Camilloni C, Pietrucci F (2018) Advanced simulation techniques for the thermodynamic and kinetic characterization of biological systems. Adv Phys X 3:1477531
Laio A, Parrinello M (2002) Esca** free-energy minima. Proc Natl Acad Sci U S A 99:12562–12566
Barducci A, Bussi G, Parrinello M (2008) Well-tempered metadynamics: a smoothly converging and tunable free-energy method. Phys Rev Lett 100:020603
Barducci A, Bonomi M, Parrinello M (2011) Metadynamics. Wiley Interdiscip Rev Comput Mol Sci 1:826–843
Bussi G, Branduardi D (2015) Free-energy calculations with Metadynamics: theory and practice. In: Parrill AL, Lipkowitz KB (eds) Reviews in computational chemistry, vol 28. Springer, New York, p 1
Elvati P, Violi A (2012) Free energy calculation of Permeant–membrane interactions using molecular dynamics simulations. In: Reineke J (ed) Nanotoxicity: methods and protocols. Humana Press, Totowa, NJ, p 189
Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314:141–151
Fukunishi H, Watanabe O, Takada S (2002) On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: application to protein structure prediction. J Chem Phys 116:9058–9067
Liu P, Kim B, Friesner RA et al (2005) Replica exchange with solute tempering: a method for sampling biological systems in explicit water. Proc Natl Acad Sci U S A 102:13749–13754
Bussi G (2014) Hamiltonian replica exchange in GROMACS: a flexible implementation. Mol Phys 112:379–384
Bussi G, Gervasio FL, Laio A et al (2006) Free-energy landscape for β hairpin folding from combined parallel tempering and Metadynamics. J Am Chem Soc 128:13435–13441
Bonomi M, Parrinello M (2010) Enhanced sampling in the well-tempered ensemble. Phys Rev Lett 104:190601
Deighan M, Bonomi M, Pfaendtner J (2012) Efficient simulation of explicitly solvated proteins in the well-tempered ensemble. J Chem Theory Comput 8:2189–2192
Bernetti M, Masetti M, Pietrucci F et al (2017) Structural and kinetic characterization of the intrinsically disordered protein SeV NTAIL through enhanced sampling simulations. J Phys Chem B 121:9572–9582
Skiadopoulos MH, Surman SR, Riggs JM et al (2002) Sendai virus, a murine Parainfluenza virus type 1, replicates to a level similar to human PIV1 in the upper and lower respiratory tract of African green monkeys and chimpanzees. Virology 297:153–160
Jensen MR, Houben K, Lescop E et al (2008) Quantitative conformational analysis of partially folded proteins from residual dipolar couplings: application to the molecular recognition element of Sendai virus nucleoprotein. J Am Chem Soc 130:8055–8061
Barducci A, Bonomi M, Parrinello M (2010) Linking well-tempered Metadynamics simulations with experiments. Biophys J 98:44–46
Barducci A, Bonomi M, Prakash MK et al (2013) Free-energy landscape of protein oligomerization from atomistic simulations. Proc Natl Acad Sci U S A 110:4708–4713
Palazzesi F, Barducci A, Tollinger M et al (2013) The allosteric communication pathways in KIX domain of CBP. Proc Natl Acad Sci U S A 110:14237–14242
Sutto L, Gervasio FL (2013) Effects of oncogenic mutations on the conformational free-energy landscape of EGFR kinase. Proc Natl Acad Sci U S A 110:10616–10621
Lovera S, Morando M, Pucheta-Martinez E et al (2015) Towards a molecular understanding of the link between Imatinib resistance and kinase conformational dynamics. PLoS Comput Biol 11:e1004578
Kuzmanic A, Sutto L, Saladino G et al (2017) Changes in the free-energy landscape of p38α MAP kinase through its canonical activation and binding events as studied by enhanced molecular dynamics simulations. Elife 6:e22175
Granata D, Baftizadeh F, Habchi J et al (2015) The inverted free energy landscape of an intrinsically disordered peptide by simulations and experiments. Sci Rep 5:15449
Rossetti G, Musiani F, Abad E et al (2016) Conformational ensemble of human α-synuclein physiological form predicted by molecular simulations. Phys Chem Chem Phys 18:5702–5706
Bellucci L, Bussi G, Di Felice R et al (2017) Fibrillation-prone conformations of the amyloid-β-42 peptide at the gold/water interface. Nanoscale 9:2279–2290
Salomon-Ferrer R, Götz AW, Poole D et al (2013) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J Chem Theory Comput 9:3878–3888
Abraham MJ, Murtola T, Schulz R et al (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1:19–25
Tribello GA, Bonomi M, Branduardi D et al (2014) PLUMED 2: new feathers for an old bird. Comput Phys Commun 185:604–613
Humphrey W, Dalke A, Schulten K (1996) VMD: Visual molecular dynamics. J Mol Graph 14:33–38
Racine J (2006) Gnuplot 4.0: a portable interactive plotting utility. J Appl Econ 21:133–141
Best RB, Hummer G (2009) Optimized molecular dynamics force fields applied to the helix−coil transition of polypeptides. J Phys Chem B 113:9004–9015
Hornak V, Abel R, Okur A et al (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65:712–725
Lindorff-Larsen K, Piana S, Palmo K et al (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 78:1950–1958
Hess B, Bekker H, Berendsen HJC et al (1998) LINCS: a linear constraint solver for molecular simulations. J Comput Chem 18:1463–1472
Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N·log(N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092
Hockney RW, Goel SP, Eastwood JW (1974) Quiet high-resolution computer models of a plasma. J Comput Phys 14:148–158
Bussi G, Donadio D, Parrinello M (2007) Canonical sampling through velocity rescaling. J Chem Phys 126:14101
Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 52:7182–7190
Pietrucci F, Laio A (2009) A collective variable for the efficient exploration of protein Beta-sheet structures: application to SH3 and GB1. J Chem Theory Comput 5:2197–2201
Vymětal J, Vondrášek J (2011) Gyration- and inertia-tensor-based collective coordinates for Metadynamics. Application on the conformational behavior of Polyalanine peptides and Trp-cage folding. J Phys Chem A 115:11455–11465
Huang J, Rauscher S, Nawrocki G et al (2016) CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat Methods 14:71–73
Joung IS, Cheatham TE (2008) Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations. J Phys Chem B 112:9020–9041
Nadler W, Hansmann UHE (2008) Optimized explicit-solvent replica exchange molecular dynamics from scratch. J Phys Chem B 112:10386–10387
Bonomi M, Bussi G, Camilloni C et al (2019) Promoting transparency and reproducibility in enhanced molecular simulations. Nat Methods 16:670–673
Bonomi M, Barducci A, Parrinello M (2009) Reconstructing the equilibrium Boltzmann distribution from well-tempered metadynamics. J Comput Chem 30:1615–1621
Acknowledgments
The authors wish to thank Fabio Pietrucci and Giovanni Bussi for their invaluable feedback and useful discussion.
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Masetti, M., Bernetti, M., Cavalli, A. (2020). Enhanced Molecular Dynamics Simulations of Intrinsically Disordered Proteins. In: Kragelund, B.B., Skriver, K. (eds) Intrinsically Disordered Proteins. Methods in Molecular Biology, vol 2141. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0524-0_19
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DOI: https://doi.org/10.1007/978-1-0716-0524-0_19
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