Accurate Estimation of Solvent Accessible Surface Area for Coarse-Grained Biomolecular Structures with Deep Learning.

Hydrogen-induced failure of TiNi based alloy with coarse-grained and ultrafine-grained structure

Removed due to plagiarism The original research was published as: MODELING OF ULTRASONIC PROPAGATION IN A COARSE GRAIN STRUCTURE <https://apps.webofknowledge.com/full_record.do?product=UA&search_mode=GeneralSearch&qid=2&SID=Z1pJDVNJsjgeYkWdsdi&page=2&doc=15> By: Jenson, F.; Fortuna, T.; Doudet, L. Edited by: Thompson, DO; Chimenti, DE Conference: 35th Annual Review of Progress in Quantitative Nondestructive Evaluation Location: Univ Chicago, Chicago, IL Date: JUL 20-25, 2008 REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION, VOLS 28A AND 28B Book Series:AIP Conference Proceedings Volume: 1096 Pages: 1201-1208 Published: 2009

BackgroundThere are many different methods for estimating solvent accessible surface area for proteins in their unfolded states. In this article, we compare eight methods, assessing whether or not they lead to different estimates of total accessible surface area as well as their impact on relationships with thermodynamic variables.FindingsOur results demonstrate that most pairs of compared methods do result in different unfolded estimates of accessible surface area (only four pairs of methods do not yield significantly different estimates). However, we do not see a significant impact on the relationship between accessible surface area and thermodynamic parameters across the different methods.ConclusionsWe advocate the use of the Gong and Rose transition midpoint method for computing solvent accessible surface area due to its computational ease, physical basis, and performance in terms of relationships with thermodynamic parameters.

A tight and consistent link between resolutions is crucial to further expand the impact of multiscale modeling for complex materials. We herein tackle the generation of condensed molecular structures as a refinement—backmapping—of a coarse-grained (CG) structure. Traditional schemes start from a rough coarse-to-fine mapping and perform further energy minimization and molecular dynamics simulations to equilibrate the system. In this study we introduce DeepBackmap: A deep neural network based approach to directly predict equilibrated molecular structures for condensed-phase systems. We use generative adversarial networks to learn the Boltzmann distribution from training data and realize reverse mapping by using the CG structure as a conditional input. We apply our method to a challenging condensed-phase polymeric system. We observe that the model trained in a melt has remarkable transferability to the crystalline phase. The combination of data-driven and physics-based aspects of our architecture help reach temperature transferability with only limited training data.

Solvent accessible surface area (ASA) of simulated phospholipid membranes

The MArtini Database (MAD - https://mad.ibcp.fr) is a web server designed for the sharing of structures and topologies of molecules parametrized with the Martini coarse-grained (CG) force field. MAD can also convert atomistic structures into CG structures and prepare complex systems (including proteins, lipids, etc.) for molecular dynamics (MD) simulations at the CG level. It is dedicated to the generation of input files for Martini 3, the most recent version of this popular CG force field. Specifically, the MAD server currently includes tools to submit or retrieve CG models of a wide range of molecules (lipids, carbohydrates, nanoparticles, etc.), transform atomistic protein structures into CG structures and topologies, with fine control on the process and assemble biomolecules into large systems, and deliver all files necessary to start simulations in the GROMACS MD engine.

Coarse-grained (CG) molecular dynamics simulation has become an increasingly powerful approach for the rational design of drug delivery systems, providing access to spatial and temporal scales beyond the reach of atomistic simulations. This review synthesises recent progress in applying CG methodologies to diverse delivery platforms, including micelles, liposomes, dendrimers, polymeric nanoparticles, hydrogels, metal-organic frameworks (MOFs), and amorphous solid dispersions (ASDs). We examine how CG simulations elucidate self-assembly mechanisms, nanostructural organisation, drug encapsulation and release behaviour, and carrier stability under physiological conditions. Particular attention is given to the Martini and related CG force fields for predicting key descriptors such as aggregation number, solvent-accessible surface area (SASA), release kinetics, and morphological transitions. By integrating computational insights with experimental evidence, CG approaches have shown remarkable potential to accelerate formulation development, optimise carrier performance, and enable predictive screening of drug delivery materials. Collectively, this review underscores the translational relevance of CG simulations and their expanding role in advancing pharmaceutical nanotechnology.

Prediction of a common neutralizing epitope of H5N1 avian influenza virus by in silico molecular docking

The knowledge of the free energy of binding of small molecules to a macromolecular target is crucial in drug design as is the ability to predict the functional consequences of binding. We highlight how a molecular dynamics (MD)-based approach can be used to predict the free energy of small molecules, and to provide priorities for the synthesis and the validation via in vitro tests. Here, we study the dynamics and energetics of the nuclear receptor REV-ERBα with its co-repressor NCoR and 35 novel agonists. Our in silico approach combines molecular docking, molecular dynamics (MD), solvent-accessible surface area (SASA) and molecular mechanics poisson boltzmann surface area (MMPBSA) calculations. While docking yielded initial hints on the binding modes, their stability was assessed by MD. The SASA calculations revealed that the presence of the ligand led to a higher exposure of hydrophobic REV-ERB residues for NCoR recruitment. MMPBSA was very successful in ranking ligands by potency in a retrospective and prospective manner. Particularly, the prospective MMPBSA ranking-based validations for four compounds, three predicted to be active and one weakly active, were confirmed experimentally.

We characterise the hydrophobic collapse of single polystyrene chains in water using molecular dynamics simulations. Specifically, we calculate the potential of mean force for the collapse of a single polystyrene chain in water using metadynamics, comparing the results between all atomistic with coarse-grained (CG) molecular simulation. We next explore the scaling behaviour of the collapsed globular shape at the minimum energy configuration, characterised by the radius of gyration, as a function of chain length. The exponent is close to one third, consistent with that predicted for a polymer chain in bad solvent. We also explore the scaling behaviour of the solvent accessible surface area (SASA) as a function of chain length, finding a similar exponent for both all atomistic and CG simulations. Furthermore, calculation of the local water density as a function of chain length near the minimum energy configuration suggests that intermediate chain lengths are more likely to form dewetted states, as compared to shorter or longer chain lengths.

Molecular dynamics simulations based on coarse-grained (CG) models are used to accelerate conformational dynamics of biomolecules and other chemical systems with reduced computational costs. CG models achieve this by discarding atomic information necessary for downstream structural analysis. Recovering the atomic detail from CG structures, i.e., backmapping, remains a fundamental challenge in multiscale modeling, especially for proteins and complex biomolecular assemblies. Recent machine learning methods have shown promise in reconstructing atomistic details, but most approaches only target simple systems at a small scale. In particular, conventional backmapping pipelines often fail to preserve stereochemistry, induce high-energy configurations, or require extensive minimization. Here we present CGBack, a backmapping framework that employs a denoising diffusion probabilistic model to reconstruct all-atom molecular structures from CG representations. CGBack consists of backmapping and refinement procedures and accurately recovers atomic coordinates across diverse protein systems from small to large scales. We show that CGBack is capable of accurately backmapping both single-chain and multichain molecular systems, including densely packed intrinsically disordered proteins in condensates. These results suggest that CGBack can be a powerful tool for multiscale molecular simulation pipelines. We anticipate that CGBack will enable more efficient workflows for protein modeling, as well as for other biomolecules across different CG models.

The hydrogen effect on the inelastic properties and plastic strain development after electrolythical hydrogenation in physiological solution was investigated. This effect virtually results in a failure under torsion of Ti49.1Ni50.9 (atom per cent) alloy specimens with coarse-grained (CG) and submicrocrystalline (SMC) structures. It is shown that hydrogen embrittlement (HE) phenomenon occurs irrespective of the grain size in the studied specimens at approximately equal strain values. However, compared to the specimens with CG structure, those with SMC structure accumulate two to three times more hydrogen for the same hydrogenation time. It is found that hydrogen has a much smaller effect on the inelastic properties of specimens with SMC structure as compared to those with CG structure.

The purpose of this work was to study the hot-corrosion behavior of dissimilar weldments of AISI 304 stainless steel and AISI 1015 low carbon steel assembled with consumable-309L stainless steel wires at 750 °C in static air with 2 mg/cm^2 mixture of various ratios of NaCl-Na_2SO_4 deposits. It was found that the HAZ of weldments revealed the coarse grain structure and the around 304 HAZ. Moreover, the carbon migrated from the substrate of low carbon steel of the higher chromium weld metal side and formed chromium carbide adjacent to the interface between the low carbon steel HAZ and weld metal. The results of hot corrosion showed that both 304 and 309L appeared the subscale attack while low carbon steel revealed the uniform corrosion. The depth of attack increased with increasing exposure duration of time. The 309L weld metal had the smallest metal loss because of its high chromium content. Due to sensitized reaction the 304 HAZ had the highest metal loss and the total depth of attack. The galvanic effect to the low carbon steel resulted in a serious corrosion with sufficient molten salts at low carbon steel HAZ/WM interface.

Recent years have seen the initial success of a variational implicit-solvent model (VISM), implemented with a robust level-set method, in capturing efficiently different hydration states and providing quantitatively good estimation of solvation free energies of biomolecules. The level-set minimization of the VISM solvation free-energy functional of all possible solute-solvent interfaces or dielectric boundaries predicts an equilibrium biomolecular conformation that is often close to an initial guess. In this work, we develop a theory in the form of Langevin geometrical flow to incorporate solute-solvent interfacial fluctuations into the VISM. Such fluctuations are crucial to biomolecular conformational changes and binding process. We also develop a stochastic level-set method to numerically implement such a theory. We describe the interfacial fluctuation through the "normal velocity" that is the solute-solvent interfacial force, derive the corresponding stochastic level-set equation in the sense of Stratonovich so that the surface representation is independent of the choice of implicit function, and develop numerical techniques for solving such an equation and processing the numerical data. We apply our computational method to study the dewetting transition in the system of two hydrophobic plates and a hydrophobic cavity of a synthetic host molecule cucurbit[7]uril. Numerical simulations demonstrate that our approach can describe an underlying system jumping out of a local minimum of the free-energy functional and can capture dewetting transitions of hydrophobic systems. In the case of two hydrophobic plates, we find that the wavelength of interfacial fluctuations has a strong influence to the dewetting transition. In addition, we find that the estimated energy barrier of the dewetting transition scales quadratically with the inter-plate distance, agreeing well with existing studies of molecular dynamics simulations. Our work is a first step toward the inclusion of fluctuations into the VISM and understanding the impact of interfacial fluctuations on biomolecular solvation with an implicit-solvent approach.

The K+(aq) ion is an integral component of many cellular processes, amongst which the most important, perhaps, is its role in transmitting electrical impulses along the nerve. Understanding its hydration structure and thermodynamics is crucial in dissecting its role in such processes. Here we address these questions using both the statistical mechanical quasi-chemical theory of solutions and ab initio molecular dynamics simulations. Simulations predict an interesting hydration structure for K+(aq): the population of about six (6) water molecules within the initial minimum of the observed gKO(r) at infinite dilution involves four (4) innermost molecules that the quasi-chemical theory suggests should be taken as the theoretical inner shell. The contribution of the fifth and sixth closest water molecules is observable as a distinct shoulder on the principal maximum of the gKO(r). The quasi-chemical estimate of solvation free energy for the neutral pair KOH is also in good agreement with experiments.