Volume 2, Issue 5, September 2016, Page: 34-47
Towards Physical Understanding of Molecular Recognition in the Cell: Recent Evolution of Molecular Dynamics Techniques and Free Energy Theories
Takefumi Yamashita, Laboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, the University of Tokyo, Tokyo, Japan
Received: Sep. 29, 2016;       Accepted: Oct. 19, 2016;       Published: Nov. 21, 2016
DOI: 10.11648/j.bs.20160205.11      View  6364      Downloads  584
Abstract
In cells, molecules do not arbitrarily interact with others; interact only with molecules of a particular type. This molecular recognition is a very important molecular function as one of the molecular bases on which the cells sustain their lives. Recently, it has been found that molecular recognition, which occurs not only between protein and protein but also between RNA and protein, plays important roles in the cell. Understanding of the molecular recognition at the atomic level is one of the challenging problems in the field of molecular biology and biochemistry. In this review, we address the theoretical and practical aspects of molecular dynamics simulation, which has become an important tool for studying the molecular recognition. From the theoretical viewpoint, many free energy calculation methods based on statistical mechanics have been developed. As for the practical aspects, it is important that the evolution of the computing technique not only enabled long-time simulations, but also enhanced prediction accuracy of simulations with developing new reliable force fields. By the recent development of theory and technology, the challenging tasks such as analysis and prediction of conformational distribution, structural change, and free energy of protein and/or nucleic acid systems are becoming possible.
Keywords
Molecular Dynamics, Binding Free Energy, Simulation, Molecular Recognition, Protein, RNA
To cite this article
Takefumi Yamashita, Towards Physical Understanding of Molecular Recognition in the Cell: Recent Evolution of Molecular Dynamics Techniques and Free Energy Theories, Biomedical Sciences. Vol. 2, No. 5, 2016, pp. 34-47. doi: 10.11648/j.bs.20160205.11
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Andreani J and Guerois R: Evolution of protein interactions: from interactomes to interfaces. Arch Biochem Biophys 2014, 554:65-75.
[2]
Jankowsky E and Harris ME: Specificity and nonspecificity in RNA-protein interactions. Nat Rev Mol Cell Biol 2015, 16:533-544.
[3]
Mortier J, et al.: The impact of molecular dynamics on drug design: applications for the characterization of ligand-macromolecule complexes. Drug Discov Today 2015, 20:686-702.
[4]
Shaw DE, et al.: Atomic-level characterization of the structural dynamics of proteins. Science 2010, 330:341-346.
[5]
Shuman CF, Hamalainen MD, and Danielson UH: Kinetic and thermodynamic characterization of HIV-1 protease inhibitors. J Mol Recognit 2004, 17:106-119.
[6]
Mobley DL, Chodera JD, and Dill KA: On the use of orientational restraints and symmetry corrections in alchemical free energy calculations. J Chem Phys 2006, 125:084902.
[7]
Chodera JD, et al.: Alchemical free energy methods for drug discovery: progress and challenges. Curr Opin Struct Biol 2011, 21:150-160.
[8]
Jorgensen WL, et al.: Efficient Computation of Absolute Free-Energies of Binding by Computer-Simulations - Application to the Methane Dimer in Water. J Chem Phys 1988, 89:3742-3746.
[9]
Frenkel D and Smit B: Understanding Molecular Simulation (2nd Ed.). 2002: Academic Press, San Diego.
[10]
Pearlman DA and Kollman PA: The Lag between the Hamiltonian and the System Configuration in Free-Energy Perturbation Calculations. J Chem Phys 1989, 91:7831-7839.
[11]
Zwanzig RW: High-Temperature Equation of State by a Perturbation Method.1. Nonpolar Gases. J Chem Phys 1954, 22:1420-1426.
[12]
Jarzynski C: Nonequilibrium equality for free energy differences. Phys Rev Lett 1997, 78:2690-2693.
[13]
Jarzynski C: Nonequilibrium work theorem for a system strongly coupled to a thermal environment. J Stat Mech-Theory E 2004:P09005.
[14]
Bennett CH: Efficient Estimation of Free-Energy Differences from Monte-Carlo Data. J Comput Phys 1976, 22:245-268.
[15]
Shirts MR, et al.: Equilibrium free energies from nonequilibrium measurements using maximum-likelihood methods. Phys Rev Lett 2003, 91:140601.
[16]
Crooks GE: Path-ensemble averages in systems driven far from equilibrium. Phys Rev E 2000, 61:2361-2366.
[17]
Yamashita T and Fujitani H: On accurate calculation of the potential of mean force between antigen. and antibody: A case of the HyHEL-10-hen egg white lysozyme system. Chem Phys Lett 2014, 609:50-53.
[18]
Fujitani H, et al.: Direct calculation of the binding free energies of FKBP ligands. J Chem Phys 2005, 123:084108.
[19]
Yamashita T, et al.: The Feasibility of an Efficient Drug Design Method with High-Performance Computers. Chem Pharm Bull 2015, 63:147-155.
[20]
Yamashita T, et al.: Molecular Dynamics Simulation-Based Evaluation of the Binding Free Energies of Computationally Designed Drug Candidates: Importance of the Dynamical Effects. Chem Pharm Bull 2014, 62:661-667.
[21]
Fujitani H, et al.: High performance computing for drug development on K computer. J Phys Conf Ser 2013, 454:012018.
[22]
Yamashita T: Improvement in Empirical Potential Functions for Increasing the Utility of Molecular Dynamics Simulations. JPS Conf Proc 2015, 5:010003.
[23]
Yamashita T and Kato S: Regularity in highly excited vibrational dynamics of NOCl (X(1)A(')): Quantum mechanical calculations on a new potential energy surface. J Chem Phys 2003, 119:4251-4261.
[24]
Yamashita T and Kato S: Excited state electronic structures and dynamics of NOCl: A new potential function set, absorption spectrum, and photodissociation mechanism. J Chem Phys 2004, 121:2105-2116.
[25]
Yamashita T and Kato S: Resonance Raman spectra of NOCl: Quantum dynamics study. Chem Phys Lett 2005, 405:142-147.
[26]
Lindorff-Larsen K, et al.: How Fast-Folding Proteins Fold. Science 2011, 334:517-520.
[27]
Grdadolnik J, et al.: Populations of the three major backbone conformations in 19 amino acid dipeptides. Proc Natl Acad Sci USA 2011, 108:1794-1798.
[28]
Fujitani H, et al.: High-Level ab Initio Calculations To Improve Protein Backbone Dihedral Parameters. J Chem Theory Comput 2009, 5:1155-1165.
[29]
Yamashita T: Effects of side chain on short biopolymer conformation. unpublished.
[30]
Oldfield CJ and Dunker AK: Intrinsically Disordered Proteins and Intrinsically Disordered Protein Regions. Annu Rev Biochem 2014, 83:553-584.
[31]
Steiner M and Neri D: Antibody-Radionuclide Conjugates for Cancer Therapy: Historical Considerations and New Trends. Clin Cancer Res 2011, 17:6406-6416.
[32]
Nakayama T, et al.: Structural features of interfacial tyrosine residue in ROBO1 fibronectin domain-antibody complex: Crystallographic, thermodynamic, and molecular dynamic analyses. Protein Sci 2015, 24:328-340.
[33]
Yamashita T: On the Accurate Molecular Dynamics Analysis of Biological Molecules. AIP Conf. Proc. (in press).
[34]
Hu WQ, Alvarez-Dominguez JR, and Lodish HF: Regulation of mammalian cell differentiation by long non-coding RNAs. Embo Rep 2012, 13:971-983.
[35]
Wang X, et al.: Induced ncRNAs allosterically modify RNA-binding proteins in cis to inhibit transcription. Nature 2008, 454:126-130.
[36]
Perez A, et al.: Refinement of the AMBER force field for nucleic acids: improving the description of alpha/gamma conformers. Biophys J 2007, 92:3817-3829.
[37]
Hamada M and Asai K: A Classification of Bioinformatics Algorithms from the Viewpoint of Maximizing Expected Accuracy (MEA). J Comput Biol 2012, 19:532-549.
[38]
Okamoto Y: Generalized-ensemble algorithms: enhanced sampling techniques for Monte Carlo and molecular dynamics simulations. J Mol Graph Model 2004, 22:425-439.
[39]
Kastner J: Umbrella sampling. Comput Mol Sci 2011, 1:932-942.
[40]
Lane TJ, et al.: Markov State Model Reveals Folding and Functional Dynamics in Ultra-Long MD Trajectories. J Am Chem Soc 2011, 133:18413-18419.
[41]
Sakano T, et al.: Molecular dynamics analysis to evaluate docking pose prediction. Biophys Physicobiol 2016, 13:181–194.
[42]
Dror RO, et al.: Biomolecular Simulation: A Computational Microscope for Molecular Biology. Annu Rev Biophys 2012, 41:429-452.
[43]
Perilla JR, et al.: Molecular dynamics simulations of large macromolecular complexes. Curr Opin Struct Biol 2015, 31:64-74.
[44]
Yamashita T and Voth GA: Properties of Hydrated Excess Protons near Phospholipid Bilayers. J Phys Chem B 2010, 114:592-603.
[45]
Yamashita T: Properties of a Hydrated Excess Proton Near the Cholesterol-Containing Phospholipid Bilayer. JPS Conf Proc 2014, 1:013086.
[46]
Yamashita T and Voth GA: Insights into the Mechanism of Proton Transport in Cytochrome c Oxidase. J Am Chem Soc 2012, 134:1147-1152.
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