An Efficient Low Storage and Memory Treatment of Gridded Interaction Fields for Simulations of Macromolecular Association.

Computer simulations of molecular systems often make use of regular rectangular grids with equidistant spacing to store information on their molecular interaction fields, e.g., electrostatic potential. These grids provide an easy way to store the data as they do not require any particular specification of the structure of the data. However, such grids may easily become large, and the storage and memory demands may become so high that calculations become infeasible. To overcome this problem, we show here how the data structure DT-Grid can be adapted and applied to efficiently represent macromolecular interaction grids by exploiting the nonuniformity of information on the grid; at the same time, this data structure ensures fast random data access. We demonstrate use of the DT-Grid data structure for potential of mean force and Brownian dynamics simulations of protein-surface binding and macromolecular association with the SDA software. We further demonstrate that the DT-Grid structure enables systems of large size, such as a viral capsid, and high resolution grids to be handled that are beyond current computational feasibility.

Modeling and simulation of protein-surface interactions: achievements and challenges.

Understanding protein-inorganic surface interactions is central to the rational design of new tools in biomaterial sciences, nanobiotechnology and nanomedicine. Although a significant amount of experimental research on protein adsorption onto solid substrates has been reported, many aspects of the recognition and interaction mechanisms of biomolecules and inorganic surfaces are still unclear. Theoretical modeling and simulations provide complementary approaches for experimental studies, and they have been applied for exploring protein-surface binding mechanisms, the determinants of binding specificity towards different surfaces, as well as the thermodynamics and kinetics of adsorption. Although the general computational approaches employed to study the dynamics of proteins and materials are similar, the models and force-fields (FFs) used for describing the physical properties and interactions of material surfaces and biological molecules differ. In particular, FF and water models designed for use in biomolecular simulations are often not directly transferable to surface simulations and vice versa. The adsorption events span a wide range of time- and length-scales that vary from nanoseconds to days, and from nanometers to micrometers, respectively, rendering the use of multi-scale approaches unavoidable. Further, changes in the atomic structure of material surfaces that can lead to surface reconstruction, and in the structure of proteins that can result in complete denaturation of the adsorbed molecules, can create many intermediate structural and energetic states that complicate sampling. In this review, we address the challenges posed to theoretical and computational methods in achieving accurate descriptions of the physical, chemical and mechanical properties of protein-surface systems. In this context, we discuss the applicability of different modeling and simulation techniques ranging from quantum mechanics through all-atom molecular mechanics to coarse-grained approaches. We examine uses of different sampling methods, as well as free energy calculations. Furthermore, we review computational studies of protein-surface interactions and discuss the successes and limitations of current approaches.

SDA 7: A modular and parallel implementation of the simulation of diffusional association software.

The simulation of diffusional association (SDA) Brownian dynamics software package has been widely used in the study of biomacromolecular association. Initially developed to calculate bimolecular protein-protein association rate constants, it has since been extended to study electron transfer rates, to predict the structures of biomacromolecular complexes, to investigate the adsorption of proteins to inorganic surfaces, and to simulate the dynamics of large systems containing many biomacromolecular solutes, allowing the study of concentration-dependent effects. These extensions have led to a number of divergent versions of the software. In this article, we report the development of the latest version of the software (SDA 7). This release was developed to consolidate the existing codes into a single framework, while improving the parallelization of the code to better exploit modern multicore shared memory computer architectures. It is built using a modular object-oriented programming scheme, to allow for easy maintenance and extension of the software, and includes new features, such as adding flexible solute representations. We discuss a number of application examples, which describe some of the methods available in the release, and provide benchmarking data to demonstrate the parallel performance.

Kar1 binding to Sfi1 C-terminal regions anchors the SPB bridge to the nuclear envelope.

The yeast spindle pole body (SPB) is the functional equivalent of the mammalian centrosome. The half bridge is a SPB substructure on the nuclear envelope (NE), playing a key role in SPB duplication. Its cytoplasmic components are the membrane-anchored Kar1, the yeast centrin Cdc31, and the Cdc31-binding protein Sfi1. In G1, the half bridge expands into the bridge through Sfi1 C-terminal (Sfi1-CT) dimerization, the licensing step for SPB duplication. We exploited photo-activated localization microscopy (PALM) to show that Kar1 localizes in the bridge center. Binding assays revealed direct interaction between Kar1 and C-terminal Sfi1 fragments. kar1Δ cells whose viability was maintained by the dominant CDC31-16 showed an arched bridge, indicating Kar1's function in tethering Sfi1 to the NE. Cdc31-16 enhanced Cdc31-Cdc31 interactions between Sfi1-Cdc31 layers, as suggested by binding free energy calculations. In our model, Kar1 binding is restricted to Sfi1-CT and Sfi1 C-terminal centrin-binding repeats, and centrin and Kar1 provide cross-links, while Sfi1-CT stabilizes the bridge and ensures timely SPB separation.

Molecular recognition of H3/H4 histone tails by the tudor domains of JMJD2A: a comparative molecular dynamics simulations study.

BACKGROUND: Histone demethylase, JMJD2A, specifically recognizes and binds to methylated lysine residues at histone H3 and H4 tails (especially trimethylated H3K4 (H3K4me3), trimethylated H3K9 (H3K9me3) and di,trimethylated H4K20 (H4K20me2, H4K20me3)) via its tandem tudor domains. Crystal structures of JMJD2A-tudor binding to H3K4me3 and H4K20me3 peptides are available whereas the others are not. Complete picture of the recognition of the four histone peptides by the tandem tudor domains yet remains to be clarified. METHODOLOGY/PRINCIPAL FINDINGS: We report a detailed molecular dynamics simulation and binding energy analysis of the recognition of JMJD2A-tudor with four different histone tails. 25 ns fully unrestrained molecular dynamics simulations are carried out for each of the bound and free structures. We investigate the important hydrogen bonds and electrostatic interactions between the tudor domains and the peptide molecules and identify the critical residues that stabilize the complexes. Our binding free energy calculations show that H4K20me2 and H3K9me3 peptides have the highest and lowest affinity to JMJD2A-tudor, respectively. We also show that H4K20me2 peptide adopts the same binding mode with H4K20me3 peptide, and H3K9me3 peptide adopts the same binding mode with H3K4me3 peptide. Decomposition of the enthalpic and the entropic contributions to the binding free energies indicate that the recognition of the histone peptides is mainly driven by favourable van der Waals interactions. Residue decomposition of the binding free energies with backbone and side chain contributions as well as their energetic constituents identify the hotspots in the binding interface of the structures. CONCLUSION: Energetic investigations of the four complexes suggest that many of the residues involved in the interactions are common. However, we found two receptor residues that were related to selective binding of the H3 and H4 ligands. Modifications or mutations on one of these residues can selectively alter the recognition of the H3 tails or the H4 tails.