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Abstract This review article discusses some common enhanced sampling methods in relation to the process of self-assembly of biomolecules. An introduction to self-assembly and its challenges is covered followed by a brief overview of the methods and analysis for replica-exchange molecular dynamics, umbrella sampling, metadynamics, and machine learning based techniques. Applications of select methods towards peptides, proteins, polymers, and nucleic acids are discussed. Finally, a short discussion of the future directions of some of these methods is provided. 

Soft materials are critical to the pharmaceutical industry for their role in formulations, delivery of active compounds or understanding relevant physiological processes. This chapter will focus on coarse-grained (CG) approaches and models that have been used in conjunction with the Molecular Dynamics (MD) simulation method to investigate soft materials of interest to various applications in pharmaceutical sciences. This chapter also discusses several examples of CG MD simulations used to scientifically probe molecules with different chemistries. 

Recent experiments have shown that complexation with a stabilizing compound can preserve enzyme activity in harsh environments. Such complexation is believed to be driven by noncovalent interactions at the enzyme surface, including hydrophobicity and electrostatics. Molecular modeling of these interactions is costly at the all-atom scale due to the long time scales and large particle counts needed to characterize binding. Protein structure at the scale of amino acid residues is parsimoniously represented by a coarse-grained model in which one particle represents several atoms, significantly reducing the cost of simulation. Coarse-grained models may then be used to generate reduced surface descriptions to underlie detailed theories of surface adhesion. In this study, we present two coarse-grained enzyme models—lipase and dehalogenase—that have been prepared using the Martini 3 top-down modeling framework. We simulate each enzyme in aqueous solution and calculate the statistics of protein surface features and shape descriptors. The values from the coarse-grained data are compared with the same calculations performed on all-atom reference systems, revealing key similarities of surface chemistry at the two scales. Structural measures are calculated from the all-atom reference systems and compared with estimates from small-angle x-ray scattering experiments, with good agreement between the two. The described procedures of modeling and analysis comprise a framework for the development of coarse-grained models of protein surfaces with validation to experiment. 

Recent experiments have shown that enzyme activity can preserved in harsh environments by complexing enzyme with polymer into a Protein Polymer Hybrid (PPH). In a successful PPH, heteropolymer strands bind to the enzyme surface and restrain the folded protein without adversely affecting the binding and active sites. It is believed that hybridization is driven by noncovalent interactions at the enzyme surface including hydrophobicity and electrostatics. Molecular modeling of these interactions is not practical at the all atom scale due to the long timescales and large particle counts needed to characterize binding. Protein structure at the scale of amino acid residues is parsimoniously represented by a coarse grained model in which one particle represents several atoms, significantly reducing the cost of simulation. In this study we present two coarse grained enzyme models, lipase and dehalogenase, prepared using a top down modeling strategy. We simulate each enzyme in aqueous solution and calculate statistics of protein surface features and shape descriptors. The values from the coarse grained data are compared with the same calculations performed on all atom reference systems, revealing key similarities of surface chemistry at the two scales. Structural measures are calculated from the all-atom reference systems and compared with estimates from small angle X ray scattering (SAXS) experiments, with good agreement between the two. The described procedures of modeling and analysis comprise a framework for the development of coarse-grained models of protein surfaces with validation to experiment. 

The process of self-assembly of biomolecules underlies the formation of macromolecular assemblies, biomolecular materials and protein folding, and thereby is critical in many disciplines and related applications. This process typically spans numerous spatiotemporal scales and hence, is well suited for scientific interrogation via coarse-grained (CG) models used in conjunction with a suitable computational approach. This perspective provides a discussion on different coarse-graining approaches which have been used to develop CG models that resolve the process of self-assembly of biomolecules. 

Abstract The formation of biomolecular materials via dynamical interfacial processes, such as self-assembly and fusion, for diverse compositions and external conditions can be efficiently probed using ensemble Molecular Dynamics (MD). However, this approach requires many simulations when investigating a large composition phase space. In addition, there is difficulty in predicting whether each simulation will yield biomolecular materials with the desired properties or outcomes and how long each simulation will run. These difficulties can be overcome by rules-based management systems, including intermittent inspection, variable sampling, and premature termination or extension of the individual MD simulations. Automating such a management system can significantly improve runtime efficiency and reduce the burden of organizing large ensembles of MD simulations. To this end, a computational framework, the Pipelines for Automating Compliance-based Elimination and Extension (PACE²), is proposed for high-throughput ensemble biomolecular materials simulations. The PACE²framework encompasses Candidate pipelines, where each pipeline includes temporally separated simulation and analysis tasks. When a MD simulation is completed, an analysis task is triggered, which evaluates the MD trajectory for compliance. Compliant simulations are extended to the next MD phase with a suitable sample rate to allow additional, detailed analysis. Non-compliant simulations are eliminated, and their computational resources are reallocated or released. The framework is designed to run on local desktop computers and high-performance computing resources. Preliminary scientific results enabled by the use of PACE²framework are presented, which demonstrate its potential and validates its function. In the future, the framework will be extended to address generalized workflows and investigate composition-structure-property relations for other classes of materials.