Bart M H Bruininks et al., A Practical View of the Martini Force Field., Methods In Molecular Biology (Clifton, N.J.), 2022 (2019) 105-127.
Martini is a coarse-grained (CG) force field suitable for molecular dynamics (MD) simulations of (bio)molecular systems. It is based on mapping of two to four heavy atoms to one CG particle. The effective interactions between the CG particles are parametrized to reproduce partitioning free energies of small chemical compounds between polar and apolar phases. In this chapter, a summary of the key elements of this CG force field is presented, followed by an example of practical application: a lipoplex-membrane fusion experiment. Formulated as hands-on practice, this chapter contains guidelines to build CG models of important biological systems, such as asymmetric bilayers and double-stranded DNA. Finally, a series of notes containing useful information, limitations, and tips are described in the last section.
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Gaia Camisasca et al., Structure and slow dynamics of protein hydration water, Journal Of Molecular Liquids, 268 (2018) 903-910.
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Cecilia Clementi et al., Topological and energetic factors: what determines the structural details of the transition state ensemble and “en-route” intermediates for protein folding? an investigation for small globular proteins, Journal Of Molecular Biology, 298 (2000) 937-953.
Journal of Molecular Biology, Vol. 298, No. 5, pp. 937-953 (2000)
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Timothy Matthew Fawcett et al., An Artificial Neural Network Based Approach for Identification of Native Protein Structures using an Extended ForceField, Arxiv.Org, q-bio.BM (2020) 500-505.
Current protein forcefields like the ones seen in CHARMM or Xplor-NIH have many terms that include bonded and non-bonded terms. Yet the forcefields do not take into account the use of hydrogen bonds which are important for secondary structure creation and stabilization of proteins. SCOPE is an open-source program that generates proteins from rotamer space. It then creates a forcefield that uses only non-bonded and hydrogen bond energy terms to create a profile for a given protein. The profiles can then be used in an artificial neural network to create a linear model that is funneled to the true protein conformation.
Published in: 2011 IEEE International Conference on Bioinformatics and Biomedicine, 500-505
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Samuel Genheden, Effect of solvent model when probing protein dynamics with molecular dynamics, Journal Of Molecular Graphics And Modelling, 71 (2017) 80-87.
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P Gkeka et al., Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems, Arxiv.Org, 2020.
Machine learning encompasses a set of tools and algorithms which are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of …
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David J Huggins, Studying the role of cooperative hydration in stabilizing folded protein states, Journal Of Structural Biology, 196 (2016) 394-406.
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Oliver F Lange et al., Scrutinizing Molecular Mechanics Force Fields on the Submicrosecond Timescale with NMR Data, Biophysj, 99 (2010) 647-655.
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Kresten Lindorff-Larsen et al., Systematic Validation of Protein Force Fields against Experimental Data, Plos One, 7 (2012) e32131-6.
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Pedro E M Lopes et al., Current Status of Protein Force Fields for Molecular Dynamics Simulations, , 1215 (2014) 47-71.
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Giuliano Malloci et al., A Database of Force-Field Parameters, Dynamics, and Properties of Antimicrobial Compounds, Molecules, 20 (2015) 13997-14021.
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Ali May et al., Coarse-grained versus atomistic simulations: realistic interaction free energies for real proteins., Bioinformatics (Oxford, England), 30 (2014) 326-334.
MOTIVATION:To assess whether two proteins will interact under physiological conditions, information on the interaction free energy is needed. Statistical learning techniques and docking methods for predicting protein-protein interactions cannot quantitatively estimate binding free energies. Full atomistic molecular simulation methods do have this potential, but are completely unfeasible for large-scale applications in terms of computational cost required. Here we investigate whether applying coarse-grained (CG) molecular dynamics simulations is a viable alternative for complexes of known structure.
RESULTS:We calculate the free energy barrier with respect to the bound state based on molecular dynamics simulations using both a full atomistic and a CG force field for the TCR-pMHC complex and the MP1-p14 scaffolding complex. We find that the free energy barriers from the CG simulations are of similar accuracy as those from the full atomistic ones, while achieving a speedup of >500-fold. We also observe that extensive sampling is extremely important to obtain accurate free energy barriers, which is only within reach for the CG models. Finally, we show that the CG model preserves biological relevance of the interactions: (i) we observe a strong correlation between evolutionary likelihood of mutations and the impact on the free energy barrier with respect to the bound state; and (ii) we confirm the dominant role of the interface core in these interactions. Therefore, our results suggest that CG molecular simulations can realistically be used for the accurate prediction of protein-protein interaction strength.
AVAILABILITY AND IMPLEMENTATION:The python analysis framework and data files are available for download at http://www.ibi.vu.nl/downloads/bioinformatics-2013-btt675.tgz.
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P E Smith and B Montgomery Pettitt, Efficient Ewald electrostatic calculations for large systems, Studies In History And Philosophy Of Science Part B, 91 (1995) 339-344.
A method is described which improves the efficiency of Ewald simulations of large condensed phase systems. This is achieved by partitioning the real space sum into a short and long range component. The long range component is calculated every time the pair list is generated and included in subsequent steps using a multiple time step algorithm. The corresponding increase in the effective cutoff distance results in an algorithm which is only slightly more expensive than a traditional cutoff simulation, but with fewer artifacts than …
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Pak K Yuet and Daniel Blankschtein, Molecular Dynamics Simulation Study of Water Surfaces: Comparison of Flexible Water Models, The Journal Of Physical Chemistry B, 114 (2010) 13786-13795.
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Djurre H de Jong et al., Improved Parameters for the Martini Coarse-Grained Protein Force Field, Journal Of Chemical Theory And Computation, 9 (2012) 687-697.
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