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      Constant pH Replica Exchange Molecular Dynamics in Explicit Solvent Using Discrete Protonation States: Implementation, Testing, and Validation

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          Abstract

          By utilizing Graphics Processing Units, we show that constant pH molecular dynamics simulations (CpHMD) run in Generalized Born (GB) implicit solvent for long time scales can yield poor p K a predictions as a result of sampling unrealistic conformations. To address this shortcoming, we present a method for performing constant pH molecular dynamics simulations (CpHMD) in explicit solvent using a discrete protonation state model. The method involves standard molecular dynamics (MD) being propagated in explicit solvent followed by protonation state changes being attempted in GB implicit solvent at fixed intervals. Replica exchange along the pH-dimension (pH-REMD) helps to obtain acceptable titration behavior with the proposed method. We analyzed the effects of various parameters and settings on the titration behavior of CpHMD and pH-REMD in explicit solvent, including the size of the simulation unit cell and the length of the relaxation dynamics following protonation state changes. We tested the method with the amino acid model compounds, a small pentapeptide with two titratable sites, and hen egg white lysozyme (HEWL). The proposed method yields superior predicted p K a values for HEWL over hundreds of nanoseconds of simulation relative to corresponding predicted values from simulations run in implicit solvent.

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          Long-timescale molecular dynamics simulations of protein structure and function.

          Molecular dynamics simulations allow for atomic-level characterization of biomolecular processes such as the conformational transitions associated with protein function. The computational demands of such simulations, however, have historically prevented them from reaching the microsecond and greater timescales on which these events often occur. Recent advances in algorithms, software, and computer hardware have made microsecond-timescale simulations with tens of thousands of atoms practical, with millisecond-timescale simulations on the horizon. This review outlines these advances in high-performance molecular dynamics simulation and discusses recent applications to studies of protein dynamics and function as well as experimental validation of the underlying computational models.
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            Constant pH molecular dynamics in generalized Born implicit solvent.

            A new method is proposed for constant pH molecular dynamics (MD), employing generalized Born (GB) electrostatics. Protonation states are modeled with different charge sets, and titrating residues sample a Boltzmann distribution of protonation states as the simulation progresses, using Monte Carlo sampling based on GB-derived energies. The method is applied to four different crystal structures of hen egg-white lysozyme (HEWL). pK(a) predictions derived from the simulations have root-mean-square (RMS) error of 0.82 relative to experimental values. Similarity of results between the four crystal structures shows the method to be independent of starting crystal structure; this is in contrast to most electrostatics-only models. A strong correlation between conformation and protonation state is noted and quantitatively analyzed, emphasizing the importance of sampling protonation states in conjunction with dynamics. (c) 2004 Wiley Periodicals, Inc.
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              Is Open Access

              Crossing Over…Markov Meets Mendel

              Chromosomal crossover is a biological mechanism to combine parental traits. It is perhaps the first mechanism ever taught in any introductory biology class. The formulation of crossover, and resulting recombination, came about 100 years after Mendel's famous experiments. To a great extent, this formulation is consistent with the basic genetic findings of Mendel. More importantly, it provides a mathematical insight for his two laws (and corrects them). From a mathematical perspective, and while it retains similarities, genetic recombination guarantees diversity so that we do not rapidly converge to the same being. It is this diversity that made the study of biology possible. In particular, the problem of genetic mapping and linkage—one of the first efforts towards a computational approach to biology—relies heavily on the mathematical foundation of crossover and recombination. Nevertheless, as students we often overlook the mathematics of these phenomena. Emphasizing the mathematical aspect of Mendel's laws through crossover and recombination will prepare the students to make an early realization that biology, in addition to being experimental, IS a computational science. This can serve as a first step towards a broader curricular transformation in teaching biological sciences. I will show that a simple and modern treatment of Mendel's laws using a Markov chain will make this step possible, and it will only require basic college-level probability and calculus. My personal teaching experience confirms that students WANT to know Markov chains because they hear about them from bioinformaticists all the time. This entire exposition is based on three homework problems that I designed for a course in computational biology. A typical reader is, therefore, an instructional staff member or a student in a computational field (e.g., computer science, mathematics, statistics, computational biology, bioinformatics). However, other students may easily follow by omitting the mathematically more elaborate parts. I kept those as separate sections in the exposition.
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                Author and article information

                Journal
                J Chem Theory Comput
                J Chem Theory Comput
                ct
                jctcce
                Journal of Chemical Theory and Computation
                American Chemical Society
                1549-9618
                1549-9626
                05 February 2015
                05 February 2014
                11 March 2014
                : 10
                : 3
                : 1341-1352
                Affiliations
                []Quantum Theory Project, Chemistry Department, University of Florida , Gainesville, Florida 32611, United States
                []BioMaPS Institute for Quantitative Biology, Center for Integrative Proteomics Research, and Department of Chemistry and Chemical Biology, Rutgers University , Piscataway, New Jersey 08901, United States
                Author notes
                Article
                10.1021/ct401042b
                3985686
                24803862
                74397b39-9b2b-4007-af9d-915c2bcd374f
                Copyright © 2014 American Chemical Society
                History
                : 02 December 2013
                Funding
                National Institutes of Health, United States
                Categories
                Article
                Custom metadata
                ct401042b
                ct-2013-01042b

                Computational chemistry & Modeling
                Computational chemistry & Modeling

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