Applications of Molecular Dynamics Simulations in Computational Toxicology

Computational toxicology is a discipline seeking to computationally model and predict toxicity of chemicals including drugs, food additives, and other environmental chemicals. Risk assessment of chemicals using current in vitro or in vivo experimental methods is at best time-consuming and expensive. Computational toxicology seeks to accelerate this process and decrease the cost by predicting the risk of chemicals to humans and animals. Molecular dynamics (MD) simulation, an emerging computational toxicology technique, characterizes the interactions of chemicals with biomolecules such as proteins and nucleic acids. This chapter will give a brief review both of available software tools for MD simulations and also how to apply these software tools to computational toxicology challenges. We also summarize key protocols to run MD simulations.

[1]  T. Poulos,et al.  Using molecular dynamics to probe the structural basis for enhanced stability in thermal stable cytochromes P450. , 2010, Biochemistry.

[2]  Martin Zacharias,et al.  Structural flexibility of the nucleosome core particle at atomic resolution studied by molecular dynamics simulation. , 2007, Biopolymers.

[3]  M C Nicklaus,et al.  Discovery of HIV-1 integrase inhibitors by pharmacophore searching. , 1997, Journal of medicinal chemistry.

[4]  Chris Oostenbrink,et al.  A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force‐field parameter sets 53A5 and 53A6 , 2004, J. Comput. Chem..

[5]  Martin Karplus,et al.  Molecular dynamics of biological macromolecules: A brief history and perspective , 2003, Biopolymers.

[6]  Hwangseo Park,et al.  Structural and dynamical basis of broad substrate specificity, catalytic mechanism, and inhibition of cytochrome P450 3A4. , 2005, Journal of the American Chemical Society.

[7]  Laxmikant V. Kalé,et al.  Scalable molecular dynamics with NAMD , 2005, J. Comput. Chem..

[8]  Weida Tong,et al.  QSAR Models at the US FDA/NCTR. , 2016, Methods in molecular biology.

[9]  W. Tong,et al.  Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs , 2017, Scientific Reports.

[10]  Jennifer L. Johnson,et al.  Transcriptome Analysis in Domesticated Species: Challenges and Strategies , 2015, Bioinformatics and biology insights.

[11]  Weida Tong,et al.  The Accurate Prediction of Protein Family from Amino Acid Sequence by Measuring Features of Sequence Fragments , 2009, J. Comput. Biol..

[12]  Weida Tong,et al.  Multiclass Decision Forest--a novel pattern recognition method for multiclass classification in microarray data analysis. , 2004, DNA and cell biology.

[13]  S. Ulam,et al.  Studies of nonlinear problems i , 1955 .

[14]  Andreas Bender,et al.  Computational Prediction of Metabolism: Sites, Products, SAR, P450 Enzyme Dynamics, and Mechanisms , 2012, J. Chem. Inf. Model..

[15]  Weida Tong,et al.  Toward predictive models for drug-induced liver injury in humans: are we there yet? , 2014, Biomarkers in medicine.

[16]  Emilio Benfenati,et al.  Predicting toxicity through computers: a changing world , 2007, Chemistry Central journal.

[17]  H. C. Andersen Molecular dynamics simulations at constant pressure and/or temperature , 1980 .

[18]  Mark A. Wilson,et al.  Intrinsic motions along an enzymatic reaction trajectory , 2007, Nature.

[19]  Jianpeng Ma,et al.  CHARMM: The biomolecular simulation program , 2009, J. Comput. Chem..

[20]  Y. Pommier,et al.  Identification of HIV-1 Integrase Inhibitors Based on a Four-Point Pharmacophore , 1998, Antiviral chemistry & chemotherapy.

[21]  R. Dror,et al.  How Fast-Folding Proteins Fold , 2011, Science.

[22]  W. L. Jorgensen,et al.  Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids , 1996 .

[23]  B. Alder,et al.  Studies in Molecular Dynamics. I. General Method , 1959 .

[24]  Weida Tong,et al.  Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents , 2018, Oncotarget.

[25]  P. Kollman,et al.  A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules , 1995 .

[26]  J. Ponder,et al.  An efficient newton‐like method for molecular mechanics energy minimization of large molecules , 1987 .

[27]  Holger Gohlke,et al.  The Amber biomolecular simulation programs , 2005, J. Comput. Chem..

[28]  Ron O Dror,et al.  The midpoint method for parallelization of particle simulations. , 2006, The Journal of chemical physics.

[29]  Federico D. Sacerdoti,et al.  Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters , 2006, ACM/IEEE SC 2006 Conference (SC'06).

[30]  Weida Tong,et al.  Homology Model and Ligand Binding Interactions of the Extracellular Domain of the Human α4β2 Nicotinic Acetylcholine Receptor , 2016 .

[31]  M. Levitt,et al.  Theoretical studies of enzymic reactions: dielectric, electrostatic and steric stabilization of the carbonium ion in the reaction of lysozyme. , 1976, Journal of molecular biology.

[32]  Weida Tong,et al.  Estrogenic activity data extraction and in silico prediction show the endocrine disruption potential of bisphenol A replacement compounds. , 2015, Chemical research in toxicology.

[33]  Nan Hu,et al.  Decision Forest Analysis of 61 Single Nucleotide Polymorphisms in a Case-Control Study of Esophageal Cancer; a novel method , 2005, BMC Bioinformatics.

[34]  Aneesur Rahman,et al.  Correlations in the Motion of Atoms in Liquid Argon , 1964 .

[35]  Michael P Eastwood,et al.  A common, avoidable source of error in molecular dynamics integrators. , 2007, The Journal of chemical physics.

[36]  Weida Tong,et al.  Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products , 2016, International journal of environmental research and public health.

[37]  W Smith,et al.  DL_POLY_2.0: a general-purpose parallel molecular dynamics simulation package. , 1996, Journal of molecular graphics.

[38]  Relly Brandman,et al.  Two-dimensional NMR and All-atom Molecular Dynamics of Cytochrome P450 CYP119 Reveal Hidden Conformational Substates* , 2010, The Journal of Biological Chemistry.

[39]  P. Kollman,et al.  Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. , 2000, Accounts of chemical research.

[40]  B. Alder,et al.  Phase Transition for a Hard Sphere System , 1957 .

[41]  J Devillers,et al.  Prediction of biological activity profiles of cyanobacterial secondary metabolites , 2007, SAR and QSAR in environmental research.

[42]  N. Van Mau,et al.  Protein structural changes induced by their uptake at interfaces. , 2002, Biochimica et biophysica acta.

[43]  R. Dror,et al.  Microsecond molecular dynamics simulation shows effect of slow loop dynamics on backbone amide order parameters of proteins. , 2008, The journal of physical chemistry. B.

[44]  M. Karplus,et al.  Dynamics of folded proteins , 1977, Nature.

[45]  Weida Tong,et al.  Decision Forest: Combining the Predictions of Multiple Independent Decision Tree Models , 2003, J. Chem. Inf. Comput. Sci..

[46]  Weida Tong,et al.  Development and Validation of Decision Forest Model for Estrogen Receptor Binding Prediction of Chemicals Using Large Data Sets. , 2015, Chemical research in toxicology.

[47]  Steve Plimpton,et al.  Fast parallel algorithms for short-range molecular dynamics , 1993 .

[48]  Huixiao Hong,et al.  Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure. , 2015, Chemical research in toxicology.

[49]  Weida Tong,et al.  A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals , 2016, International journal of environmental research and public health.

[50]  Michal Otyepka,et al.  Flexibility of human cytochromes P450: molecular dynamics reveals differences between CYPs 3A4, 2C9, and 2A6, which correlate with their substrate preferences. , 2008, The journal of physical chemistry. B.

[51]  Y. Pommier,et al.  Potent inhibitors of human immunodeficiency virus type 1 integrase: identification of a novel four-point pharmacophore and tetracyclines as novel inhibitors. , 1997, Molecular pharmacology.

[52]  Oliver F. Lange,et al.  Recognition Dynamics Up to Microseconds Revealed from an RDC-Derived Ubiquitin Ensemble in Solution , 2008, Science.

[53]  G. Vineyard,et al.  THE DYNAMICS OF RADIATION DAMAGE , 1960 .

[54]  H Hong,et al.  An in silico ensemble method for lead discovery: decision forest , 2005, SAR and QSAR in environmental research.

[55]  M. Karplus,et al.  Conformational sampling using high‐temperature molecular dynamics , 1990, Biopolymers.

[56]  Enrico Clementi,et al.  A theoretical study on the water structure for nucleic acids bases and base pairs in solution at T=300 K , 1980 .

[57]  Weida Tong,et al.  Rat α-Fetoprotein binding affinities of a large set of structurally diverse chemicals elucidated the relationships between structures and binding affinities. , 2012, Chemical research in toxicology.

[58]  Robert J Kavlock,et al.  Computational toxicology--a state of the science mini review. , 2008, Toxicological sciences : an official journal of the Society of Toxicology.

[59]  Heng Luo,et al.  Molecular Docking for Identification of Potential Targets for Drug Repurposing. , 2016, Current topics in medicinal chemistry.

[60]  Laxmikant V. Kalé,et al.  NAMD: a Parallel, Object-Oriented Molecular Dynamics Program , 1996, Int. J. High Perform. Comput. Appl..

[61]  Weida Tong,et al.  Homology modeling, molecular docking, and molecular dynamics simulations elucidated α-fetoprotein binding modes , 2013, BMC Bioinformatics.

[62]  Leming Shi,et al.  Molecular docking to identify associations between drugs and class I human leukocyte antigens for predicting idiosyncratic drug reactions. , 2015, Combinatorial chemistry & high throughput screening.

[63]  Weida Tong,et al.  Mold2, Molecular Descriptors from 2D Structures for Chemoinformatics and Toxicoinformatics , 2008, J. Chem. Inf. Model..

[64]  Michal Otyepka,et al.  Flexibility of human cytochrome P450 enzymes: molecular dynamics and spectroscopy reveal important function-related variations. , 2011, Biochimica et biophysica acta.

[65]  Weida Tong,et al.  Machine Learning Methods for Predicting HLA–Peptide Binding Activity , 2015, Bioinformatics and biology insights.

[66]  M. Parrinello,et al.  Crystal structure and pair potentials: A molecular-dynamics study , 1980 .

[67]  F. Stillinger,et al.  Improved simulation of liquid water by molecular dynamics , 1974 .

[68]  Weida Tong,et al.  Experimental Data Extraction and in Silico Prediction of the Estrogenic Activity of Renewable Replacements for Bisphenol A , 2016, International journal of environmental research and public health.

[69]  Ruth Nussinov,et al.  Structural dynamics of the cooperative binding of organic molecules in the human cytochrome P450 3A4. , 2007, Journal of the American Chemical Society.

[70]  Gerrit Groenhof,et al.  GROMACS: Fast, flexible, and free , 2005, J. Comput. Chem..

[71]  T R Burke,et al.  Salicylhydrazine-containing inhibitors of HIV-1 integrase: implication for a selective chelation in the integrase active site. , 1998, Journal of medicinal chemistry.

[72]  Keith Refson,et al.  Moldy: a portable molecular dynamics simulation program for serial and parallel computers , 2000 .

[73]  Feng Ding,et al.  Multiscale modeling of nucleosome dynamics. , 2007, Biophysical journal.