TRIFORCE: Tessellated Semianalytical Solvent Exposed Surface Areas and Derivatives

Spotlight onto surfactant-steam-bitumen interfacial behavior via molecular dynamics simulation

Heavy oil and bitumen play a vital role in the global energy supply, and to unlock such resources, thermal methods, e.g., steam injection, are applied. To improve the performance of these methods, different additives, such as air, solvents, and chemicals, can be used. As a subset of chemicals, surfactants are one of the potential additives for steam-based bitumen recovery methods. Molecular interactions between surfactant/steam/bitumen have not been addressed in the literature. This paper investigates molecular interactions between anionic surfactants, steam, and bitumen in high-temperature and high-pressure conditions. For this purpose, a real Athabasca oil sand composition is employed to assess the phase behavior of surfactant/steam/bitumen under in-situ steam-based bitumen recovery. Two different asphaltene architectures, archipelago and Island, are used to examine the effect of asphaltene type on bitumen's interfacial behavior. The influence of having sulfur heteroatoms in a resin structure and a benzene ring's effect in an anionic surfactant structure on surfactant-steam-bitumen interactions are investigated systematically. The outputs are supported by different analyses, including radial distribution functions (RDFs), mean squared displacement (MSD), radius of gyration, self-diffusion coefficient, solvent accessible surface area (SASA), interfacial thickness, and interaction energies. According to MD outputs, adding surfactant molecules to the steam phase improved the interaction energy between steam and bitumen. Moreover, surfactants can significantly improve steam emulsification capability by decreasing the interfacial tension (IFT) between bitumen and the steam phase. Asphaltene architecture has a considerable effect on the interfacial behavior in such systems. This study provides a better and more in-depth understanding of surfactant-steam-bitumen systems and spotlights the interactions between bitumen fractions and surfactant molecules under thermal recovery conditions.

Predicting the Antigenic Relationship of Foot-and-Mouth Disease Virus for Vaccine Selection Through a Computational Model

Foot-and-mouth disease virus (FMDV) is an antigenic-variable RNA virus that is responsible for the recurrence of foot-and-mouth disease in livestock and can be prevented and controlled using a vaccine with broad-spectrum protection. Current anti-genicity evaluation methods, which involve animal immunity experiments and serum preparation, are unable to fulfill the needs of high-throughput antigenicity measurements. This study designed an antigenicity scoring model to rapidly predict the antigenicity of FMDV. Antigenic-dominant sites were initially determined on the VP1 protein, a position-specific scoring matrix and physical chemical indexes were integrated to generate antigenicity descriptors. Independent tests showed a high accuracy of 0.848 and an AUC value of 0.889, indicating the good performance of the model in antigenicity measurement. When applying this model to historical data, annual antigenicity coverage of widely used vaccine strains was successfully evaluated, this was also supported by previous experiments. Furthermore, the utility of this model was extended to select potential broad-spectrum vaccines among 1,201 historical non-redundant strains to recommend potential univalent, bivalent and trivalent vaccine candidates. The results suggested that the computational model designed in this study could be used for the high-throughput antigenicity measurement of FMDV and could aid in vaccine development for preventing FMDV epidemics.

Analytical calculation of the solvent-accessible surface area and its nuclear gradients by stereographic projection: A general approach for molecules, polymers, nanotubes, helices, and surfaces

In this article, we explore an alternative to the analytical Gauss-Bonnet approach for computing the solvent-accessible surface area (SASA) and its nuclear gradients. These two key quantities are required to evaluate the nonelectrostatic contribution to the solvation energy and its nuclear gradients in implicit solvation models. We extend a previously proposed analytical approach for finite systems based on the stereographic projection technique to infinite periodic systems such as polymers, nanotubes, helices, or surfaces and detail its implementation in the Crystal code. We provide the full derivation of the SASA nuclear gradients, and introduce an iterative perturbation scheme of the atomic coordinates to stabilize the gradients calculation for certain difficult symmetric systems. An excellent agreement of computed SASA with reference analytical values is found for finite systems, while the SASA size-extensivity is verified for infinite periodic systems. In addition, correctness of the analytical gradients is confirmed by the excellent agreement obtained with numerical gradients and by the translational invariance achieved, both for finite and infinite periodic systems. Overall therefore, the stereographic projection approach appears as a general, simple, and efficient technique to compute the key quantities required for the calculation of the nonelectrostatic contribution to the solvation energy and its nuclear gradients in implicit solvation models applicable to both finite and infinite periodic systems.

Computational Signaling Protein Dynamics and Geometric Mass Relations in Biomolecular Diffusion

We present an atomistic level computational investigation of the dynamics of a signaling protein, monocyte chemoattractant protein-1 (MCP-1), that explores how simulation geometry and solution ionic strength affect the calculated diffusion coefficient. Using a simple extension of noncubic finite size diffusion correction expressions, it is possible to calculate experimentally comparable diffusion coefficients that are fully consistent with those determined from cubic box simulations. Additionally, increasing the concentration of salt in the solvent environment leads to changes in protein dynamics that are not explainable through changes in solvent viscosity alone. This work in accurate computational determination of protein diffusion coefficients led us to investigate molecular-weight-based predictors for biomolecular diffusion. By introducing protein volume- and protein surface-area-based extensions of traditional statistical relations connecting particle molecular weight to diffusion, we find that protein solvent-excluded surface area rather than volume works as a better geometric property for estimating biomolecule Stokes radii. This work highlights the considerations necessary for accurate computational determination of biomolecule diffusivity and presents insight into molecular weight relations for diffusion that could lead to new routes for estimating protein diffusion beyond the traditional approaches.

Efficient gaussian density formulation of volume and surface areas of macromolecules on graphical processing units

We present an algorithm to efficiently compute accurate volumes and surface areas of macromolecules on graphical processing unit (GPU) devices using an analytic model which represents atomic volumes by continuous Gaussian densities. The volume of the molecule is expressed by means of the inclusion-exclusion formula, which is based on the summation of overlap integrals among multiple atomic densities. The surface area of the molecule is obtained by differentiation of the molecular volume with respect to atomic radii. The many-body nature of the model makes a port to GPU devices challenging. To our knowledge, this is the first reported full implementation of this model on GPU hardware. To accomplish this, we have used recursive strategies to construct the tree of overlaps and to accumulate volumes and their gradients on the tree data structures so as to minimize memory contention. The algorithm is used in the formulation of a surface area-based non-polar implicit solvent model implemented as an open source plug-in (named GaussVol) for the popular OpenMM library for molecular mechanics modeling. GaussVol is 50 to 100 times faster than our best optimized implementation for the CPUs, achieving speeds in excess of 100 ns/day with 1 fs time-step for protein-sized systems on commodity GPUs. © 2017 Wiley Periodicals, Inc.

FreeSASA: An open source C library for solvent accessible surface area calculations

Calculating solvent accessible surface areas (SASA) is a run-of-the-mill calculation in structural biology. Although there are many programs available for this calculation, there are no free-standing, open-source tools designed for easy tool-chain integration. FreeSASA is an open source C library for SASA calculations that provides both command-line and Python interfaces in addition to its C API. The library implements both Lee and Richards’ and Shrake and Rupley’s approximations, and is highly configurable to allow the user to control molecular parameters, accuracy and output granularity. It only depends on standard C libraries and should therefore be easy to compile and install on any platform. The library is well-documented, stable and efficient. The command-line interface can easily replace closed source legacy programs, with comparable or better accuracy and speed, and with some added functionality.