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WIREs Comput Mol Sci
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Biomolecular simulations: From dynamics and mechanisms to computational assays of biological activity

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Biomolecular simulation is increasingly central to understanding and designing biological molecules and their interactions. Detailed, physics‐based simulation methods are demonstrating rapidly growing impact in areas as diverse as biocatalysis, drug delivery, biomaterials, biotechnology, and drug design. Simulations offer the potential of uniquely detailed, atomic‐level insight into mechanisms, dynamics, and processes, as well as increasingly accurate predictions of molecular properties. Simulations can now be used as computational assays of biological activity, for example, in predictions of drug resistance. Methodological and algorithmic developments, combined with advances in computational hardware, are transforming the scope and range of calculations. Different types of methods are required for different types of problem. Accurate methods and extensive simulations promise quantitative comparison with experiments across biochemistry. Atomistic simulations can now access experimentally relevant timescales for large systems, leading to a fertile interplay of experiment and theory and offering unprecedented opportunities for validating and developing models. Coarse‐grained methods allow studies on larger length‐ and timescales, and theoretical developments are bringing electronic structure calculations into new regimes. Multiscale methods are another key focus for development, combining different levels of theory to increase accuracy, aiming to connect chemical and molecular changes to macroscopic observables. In this review, we outline biomolecular simulation methods and highlight examples of its application to investigate questions in biology. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Free Energy Methods
Trypsin conformations with benzamidine bound and the binding mode of benzamidine. The seven conformational states shown are equivalent to the six apo states shown in Figure , plus the yellow conformation that is only found with benzamidine bound. The binding pocket conformation is defined by three loops: the yellow loop (residues 187–194) with Asp189, the green loop (residues 215–221) with Trp215, and the orange loop (residues 225–230). The circles have an area proportional to the equilibrium probability of the respective conformation, given that benzamidine is bound, πi. Their respective relative free energies G = –kBT ln πi and the unbinding times tunbind (mean first passage time to unbinding) are given. The arrows indicate the transition probabilities for direct transitions between the different states. The binding mode (pocket 1 or 1*) is indicated by the green square with “1” or “1*”. (Reprinted with permission from Reference . Copyright 2015 Nature Publishing Group)
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Example of a typical Markov‐state model, in this case, describing the apo state of trypsin (a) structural features, equilibrium distribution, and kinetics of six unbound (apo) protein conformations. Transitions between them occur at timescales on order of tens of microseconds. The three slowest relaxation timescales and their corresponding transition process are indicated (dashed lines). The circles have an area proportional to the equilibrium probability πi. Their respective free energy differences ΔGb of binding a ligand to this conformation and the binding time tbind (mean first passage time to binding) are given. The arrows indicate the transition probabilities for direct transitions between the different states (see legend). The most important structural differences concerning ligand binding are shown in (b–e), and the structures are classified with respect to these features by green/orange/red bullets in (a). The structures are classified by the state of S1 or S1*: open (green circle with “1” or “1*”), half‐open (orange circle), or closed (red circle) and by the S1* pocket conformational switch: favorable for binding (green circle with “Sw”) or unfavorable for binding (red circle with “Sw”). (Reprinted with permission from Reference . Copyright 2015 Nature Publishing Group)
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A coarse‐grained model of the envelope of dengue virus shown as (a) a cross‐section of the entire virion envelope model is shown, along with (b) a zoomed‐in view. Protein (orange) and lipid (dark green) components of the envelope are shown
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The viral envelope membrane of influenza A modeled at coarse‐grained resolution. The hemagglutinin, neuraminidase, and M2 proteins are shown in orange, yellow, and pink, respectively. The glycolipid molecules are shown in cyan, and other lipids in gray. Overall (a) and zoomed‐in (b) views are shown. (c) Model of an influenza A virion (with the red sphere indicating the approximate location of the genome within the virion, not currently modeled) docked against a simple model of a mammalian cell membrane with glycolipids in pale green, other lipids in darker green, and cell membrane proteins in orange
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The outer membrane protein OmpA is shown in its dimeric form in red and blue. Braun's lipoprotein, which is covalently bound to peptidoglycan is shown in orange. Peptidoglycan is shown in green. The lipids of the outer membrane of Escherichia coli are shown in gray and red
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Example of quantum mechanics/molecular mechanics (QM/MM) modeling of an enzyme‐catalyzed reaction. The enzyme (ketosteroid isomerase) is divided into an MM region and a QM region. The transition‐state structure of the first proton transfer in the mechanism is shown
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Examples of the variety of DNA structures simulated atomistically. Left: A 2:1 complex between the nucleic acid stain Hoechst 33258 and a DNA dodecamer. Center: A supercoiled 336‐base pair DNA microcircle. Right: The 2:1 complex between the shelterin protein TRF1 and a 19 base pair DNA duplex
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Comparison of the hierarchical organisation of protein structure (panel a) with the structure of DNA in the cell nucleus (panel b). (Reprinted with permission from Reference . Copyright 2015 Cell Press)
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Illustration of a free energy surface obtained using quantum mechanics/molecular mechanics (QM/MM) umbrella sampling along two reaction coordinates, with energy contours shown (values in kcal/mol). Here, the rate‐limiting deacylation of the β‐lactamase inhibitor clavulanate by the β‐lactamase KPC‐2 is shown, with QM/MM energies indicated on the surface plot. Active‐site structures of three states are depicted: the acyl‐enzyme (AC), the transition state (TS), and the tetrahedral intermediate (TI)
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Molecular and Statistical Mechanics > Free Energy Methods
Structure and Mechanism > Computational Biochemistry and Biophysics
Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods

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