The Associative memory, Water mediated, Structure and Energy Model (AWSEM) is a coarse-grained protein force field. AWSEM contains physically motivated terms, such as hydrogen bonding, as well as a bioinformatically based local structure biasing term, which efficiently takes into account many-body effects that are modulated by the local sequence. When combined with appropriate local or global alignments to choose memories, AWSEM can be used to perform de novo protein structure prediction. Herein we present structure prediction results for a particular choice of local sequence alignment method based on short residue sequences called fragments. We demonstrate the model’s structure prediction capabilities for three levels of global homology between the target sequence and those proteins used for local structure biasing, all of which assume that the structure of the target sequence is not known. When there are no homologs in the database of structures used for local structure biasing, AWSEM calculations produce structural predictions that are somewhat improved compared with prior works using related approaches. The inclusion of a small number of structures from homologous sequences improves structure prediction only marginally but when the fragment search is restricted to only homologous sequences, AWSEM can perform high resolution structure prediction and can be used for kinetics and dynamics studies.

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Proteins have evolved to use water to help guide folding. A physically motivated, nonpairwise-additive model of water-mediated interactions added to a protein structure prediction Hamiltonian yields marked improvement in the quality of structure prediction for larger proteins. Free energy profile analysis suggests that long-range water-mediated potentials guide folding and smooth the underlying folding funnel. Analyzing simulation trajectories gives direct evidence that water-mediated interactions facilitate native-like packing of supersecondary structural elements. Long-range pairing of hydrophilic groups is an integral part of protein architecture. Specific water-mediated interactions are a universal feature of biomolecular recognition landscapes in both folding and binding. W ater is intimately involved in protein folding (1-4). That proteins denature both on heating and cooling strongly implicates the involvement of water degrees of freedom. Kauzmann (5) correctly inferred from thermodynamics the hydrophobic layering characteristic of protein structure before protein structures were determined crystallographically. The kinetics of water exclusion is often considered in discussing mechanisms of protein folding, but again it is the avoidance of water in the final folded structure that is emphasized (1). Hydrophobicity patterns have long been a dominant consideration in predicting protein structure by using sequence data (6) and are basic in synthetic protein design (7). Nevertheless, the structured character of water has not been a paramount factor in most existing algorithms for structure prediction (8). These usually rely on effective pair potentials (9) or buried surface area terms to account for the free energy of burying hydrophobic residues (10).In this article, we hypothesize that specific water-mediated interactions help guide the folding process even before native contacts form. Using this idea we develop a bioinformatic, nonpairwise-additive interaction model accounting for water and show that it greatly improves the efficiency and accuracy of structure prediction for ␣-helical proteins. Analysis of folding trajectories with this potential strongly implicates the guiding role of long-range water-mediated interactions. Interestingly, we find here that long-range hydrophilic interactions, as distinct from hydrophobic interactions, also take center stage.The bioinformatic route to water-mediated potentials is difficult in several ways (for more directly physical approaches see ref. 11). Although bound water is visible in structures, localizing waters is more difficult than localizing main-chain atoms. Monomeric protein structures also have relatively few visible watermediated interactions. Our path to a water-mediated potential started with an energy landscape analysis of protein-protein interactions and a bioinformatic survey of interfaces in dimer structures (12, 13). We found that the often-used contact potentials (9) worked well to describe hydrophobic binding interfaces; however, hydrophilic interf...

Chemisorbed hydrogen and various intermediate hydrocarbon fragments play an important role in the important reaction of ethylene hydrogenation to ethane, which is catalyzed by Pt(111). As a first step toward building a theoretical mechanism of the ethylene hydrogenation process, binding site preferences and geometries of chemisorbed hydrogen, methyl, and ethyl on the Pt(111) surface are presented and rationalized. State-of-the-art Pseudopotential Planewave Density Functional Theory is employed for calculating accurate binding energies and geometries for the adsorbates. A comprehensive theory of hydrogen and methyl chemisorption on Pt (111) is developed with the help of Crystal Orbital Hamilton Population formalism within the extended Hückel molecular orbital theory. The symmetry properties of the surface Pt orbitals as well as the mixing of Pt s, p, and d orbitals in pure Pt is shown to be crucial in determining the strength of subsequent interaction with an adsorbate. It is suggested that hydrogen moves freely on the Pt(111) surface while the methyl and ethyl groups are essentially pinned on the atop position. Strong agostic interactions between C-H bonds and surface Pt are proposed for methyl and ethyl on higher symmetry sites. The different nature of chemisorption on Pt and Ni surfaces is speculated. Theoretical results presented in this paper are generally consistent with the available experimental data.

Active matter systems, and in particular the cell cytoskeleton, exhibit complex mechanochemical dynamics that are still not well understood. While prior computational models of cytoskeletal dynamics have lead to many conceptual insights, an important niche still needs to be filled with a high-resolution structural modeling framework, which includes a minimally-complete set of cytoskeletal chemistries, stochastically treats reaction and diffusion processes in three spatial dimensions, accurately and efficiently describes mechanical deformations of the filamentous network under stresses generated by molecular motors, and deeply couples mechanics and chemistry at high spatial resolution. To address this need, we propose a novel reactive coarse-grained force field, as well as a publicly available software package, named the Mechanochemical Dynamics of Active Networks (MEDYAN), for simulating active network evolution and dynamics (available at www.medyan.org). This model can be used to study the non-linear, far from equilibrium processes in active matter systems, in particular, comprised of interacting semi-flexible polymers embedded in a solution with complex reaction-diffusion processes. In this work, we applied MEDYAN to investigate a contractile actomyosin network consisting of actin filaments, alpha-actinin cross-linking proteins, and non-muscle myosin IIA mini-filaments. We found that these systems undergo a switch-like transition in simulations from a random network to ordered, bundled structures when cross-linker concentration is increased above a threshold value, inducing contraction driven by myosin II mini-filaments. Our simulations also show how myosin II mini-filaments, in tandem with cross-linkers, can produce a range of actin filament polarity distributions and alignment, which is crucially dependent on the rate of actin filament turnover and the actin filament’s resulting super-diffusive behavior in the actomyosin-cross-linker system. We discuss the biological implications of these findings for the arc formation in lamellipodium-to-lamellum architectural remodeling. Lastly, our simulations produce force-dependent accumulation of myosin II, which is thought to be responsible for their mechanosensation ability, also spontaneously generating myosin II concentration gradients in the solution phase of the simulation volume.

Condensation of monovalent counterions around DNA influences polymer properties of the DNA chain. For example, the Na(+) ions show markedly stronger propensity to induce multiple DNA chains to assemble into compact structures compared with the K(+) ions. To investigate the similarities and differences in the sodium and potassium ion condensation around DNA, we carried out a number of extensive all-atom molecular dynamics simulations of a DNA oligomer consisting of 16 base pairs, [d(CGAGGTTTAAACCTCG)](2), in explicit water. We found that the Na(+) ions penetrate the DNA interior and condense around the DNA exterior to a significantly larger degree compared with the K(+) ions. We have provided a microscopic explanation for the larger Na(+) affinity toward DNA that is based on a combination of steric, electrostatic, and hydration effects. Unexpectedly, we found that the Cl(-) co-ions provide more efficient electrostatic screening for the K(+) ions than for the Na(+) ions, contributing to the larger Na(+) condensation around DNA. To examine the importance of the discrete nature of water and ions, we also computed the counterion distributions from the mean-field electrostatic theory, demonstrating significant disagreements with the all-atom simulations. Prior experimental results on the relative extent of the Na(+) and K(+) condensation around DNA were somewhat contradictory. Recent DNA compaction experiments may be interpreted to suggest stronger Na(+) condensation around DNA compared to K(+), which is consistent with our simulations. We also provide a simple interpretation for the experimentally observed increase in DNA electrophoretic mobility in the alkali metal series, Li(+) < Na(+) < K(+) < Rb(+). We compare the DNA segment conformational preferences in various buffers with the proposed NMR models.

Coarse-grained (CG) modeling approaches are widely used to simulate many important biological processes involving DNA, including chromatin folding and genomic packaging. The bending propensity of a semiflexible DNA molecule critically influences these processes. However, existing CG DNA models do not retain a sufficient fidelity of the important local chain motions, whose propagation at larger length scales would generate correct DNA persistent lengths, in particular when the solution's ionic strength is widely varied. Here we report on a development of an accurate CG model for the double-stranded DNA chain, with explicit treatment of mobile ions, derived systematically from all-atom molecular dynamics simulations. Our model generates complex local motions of the DNA chain, similar to fully atomistic dynamics, leading also to a quantitative agreement of our simulation results with the experimental data on the dependence of the DNA persistence length on the solution ionic strength. We also predict a structural transition in a torsionally stressed DNA nanocircle as the buffer ionic strength is increased beyond a threshold value. T o successfully model many biological processes involving DNA, atomistic or coarse-grained (CG) DNA force fields should reproduce the bending rigidity of a semiflexible DNA chain, which is a large-scale polymer property of critical significance (1, 2). In particular, the response of the DNA persistence length to the change in the surrounding ionic environment is of major biological importance. For example, even a slight change in the persistence length of a linker DNA segment connecting adjacent nucleosomes in the chromatin fiber, induced by the variation of the ionic strength of the solution, may result in significant conformational changes of a chromatin in a compact state (3). Current structure-based DNA models (4, 5) do not generate DNA persistence length values that are fully consistent with those measured experimentally in a wide range of ionic concentrations, c ∼ ½0.1-100 mM (6-8). These and other DNA models differ in resolution in terms of representing both the DNA structure and also the surrounding ionic environment.The discrete nature of mobile ions and spatial correlations among them may significantly affect structure, dynamics, and the electrostatic atmosphere of many biomolecules, such as DNA and RNA (9-15). Thus, explicit inclusion of the monovalent mobile ions into the CG model of DNA is desirable from the physical standpoint. Another vital aspect of DNA modeling is a choice of the parametrization protocol. Many prior one-bead DNA models were based on use of the phenomenological wormlike chain Hamiltonian (16,17). In more detailed structure-based models, parameters are often derived by combining the statistical information from the available crystal structures with some specific experimental data (4). However, this parametrization approach, which is focused on matching a small number of integral experimental characteristics (for example, the melting temperature or free ener...

The energy landscape picture of protein folding and binding is employed to optimize a number of pair potentials for direct and water-mediated interactions in protein complex interfaces. We find that water-mediated interactions greatly complement direct interactions in discriminating against various types of trap interactions that model those present in the cell. We highlight the context dependent nature of knowledge-based binding potentials, as contrasted with the situation for autonomous folding. By performing a Principal Component Analysis (PCA) of the corresponding interaction matrixes, we rationalize the strength of the recognition signal for each combination of the contact type and reference trap states using the differential in the idealized "canonical" amino acid compositions of native and trap layers. The comparison of direct and water-mediated contact potential matrixes emphasizes the importance of partial solvation in stabilizing charged groups in the protein interfaces. Specific water-mediated interresidue interactions are expected to influence significantly the kinetics as well as thermodynamics of protein association.