Structural Insights into Sphingosine-1-phosphate Receptor Activation

As a critical sphingolipid metabolite, sphingosine-1-phosphate (S1P) plays an essential role in immune and vascular systems. There are five S1P receptors, designated as S1PR1-5, encoded in the human genome, and their activities are governed by endogenous S1P, lipid-like S1P mimics, or non-lipid-like therapeutic molecules. Among S1PRs, S1PR1 stands out due to its non-redundant functions, such as the egress of T and B cells from the thymus and secondary lymphoid tissues, making it a potential therapeutic target. However, the structural basis of S1PR1 activation and regulation by various agonists remains unclear. Here we reported four atomic resolution cryo-EM structures of Gi-coupled human S1PR1 complexes: bound to endogenous agonist d18:1 S1P, benchmark lipid-like S1P mimic phosphorylated Fingolimod ((S)-FTY720-P), or non-lipid-like therapeutic molecule CBP-307 in two binding modes. Our results revealed the similarities and differences of activation of S1PR1 through distinct ligands binding to the amphiphilic orthosteric pocket. We also proposed a two-step "shallow to deep" transition process of CBP-307 for S1PR1 activation. Both binding modes of CBP-307 could activate S1PR1, but from shallow to deep transition may trigger the rotation of the N-terminal helix of Gαi and further stabilize the complex by increasing the Gαi interaction with the cell membrane. We combine with extensive biochemical analysis and molecular dynamic simulations to suggest key steps of S1P binding and receptor activation. The above results decipher the common feature of the S1PR1 agonist recognition and activation mechanism and will firmly promote the development of therapeutics targeting S1P receptors.

An Accelerated Thrombosis Model for Computational Fluid Dynamics Simulations in Rotary Blood Pumps

Purpose Thrombosis ranks among the major complications in blood-carrying medical devices and a better understanding to influence the design related contribution to thrombosis is desirable. Over the past years many computational models of thrombosis have been developed. However, numerically cheap models able to predict localized thrombus risk in complex geometries are still lacking. The aim of the study was to develop and test a computationally efficient model for thrombus risk prediction in rotary blood pumps. Methods We used a two-stage approach to calculate thrombus risk. The first stage involves the computation of velocity and pressure fields by computational fluid dynamic simulations. At the second stage, platelet activation by mechanical and chemical stimuli was determined through species transport with an Eulerian approach. The model was compared with existing clinical data on thrombus deposition within the HeartMate II. Furthermore, an operating point and model parameter sensitivity analysis was performed. Results Our model shows good correlation (R2 > 0.93) with clinical data and identifies the bearing and outlet stator region of the HeartMate II as the location most prone to thrombus formation. The calculation of thrombus risk requires an additional 10–20 core hours of computation time. Conclusion The concentration of activated platelets can be used as a surrogate and computationally low-cost marker to determine potential risk regions of thrombus deposition in a blood pump. Relative comparisons of thrombus risk are possible even considering the intrinsic uncertainty in model parameters and operating conditions.

Unveiling of Pyrimidindinones as Potential Anti-Norovirus Agents—A Pharmacoinformatic-Based Approach

The RNA-dependent RNA polymerase (RdRp) receptor is an attractive target for treating human norovirus (HNV). A computer-aided approach like e-pharmacophore, molecular docking, and single point energy calculations were performed on the compounds retrieved from the Development Therapeutics Program (DTP) AIDS Antiviral Screen Database to identify the antiviral agent that could target the HNV RdRp receptor. Induced-fit docking (IFD) results showed that compounds ZINC1617939, ZINC1642549, ZINC6425208, ZINC5887658 and ZINC32068149 bind with the residues in the active site-B of HNV RdRp receptor via hydrogen bonds, salt bridge, and electrostatic interactions. During the molecular dynamic simulations, compounds ZINC6425208, ZINC5887658 and ZINC32068149 displayed an unbalanced backbone conformation with HNV RdRp protein, while ZINC1617939 and ZINC1642549 maintained stability with the protein backbone when interacting with the residues. Hence, the two new concluding compounds discovered by the computational approach can be used as a chemotype to design promising antiviral agents aimed at HNV RdRp.

Competitive effect between roughness and mask pattern on charging phenomena during plasma etching

In plasma etching process, the edge roughness and mask pattern usually play a significant role in the deformation of holes under the influence of charging effect. The competitive effect between these two factors has been investigated, focusing on the surface charging in a hexagonal array, with various values of roughness parameters (amplitude (A) and wavelength (W)) and distances between holes (L). A series of classical particle dynamic simulations of surface charging, surface etching and profile evolution were used to investigate the effect of roughness and pattern on charging. This study showed that various roughness and patterns (represented by different values of L) can significantly influence surface distributions of the electric-field (E-field) and the etching rates on the mask surface. The simulations also showed that (1) the shape of the pattern array influences the mask hole profile during etching process, i.e. a hexagonal array pattern tends to deform the profile of a circular mask hole into a hexagonal hole; (2) pattern roughness is aggravated during etching process. These factors were found to be significant only at a small feature pitch and may be ignored at a large feature pitch. Possible mechanisms of these results during etching process are discussed. This work sheds light on the ways to maintain pattern integrity and further improve the quality of the pattern transfer onto the substrate.

NGIWY-Amide: A Bioinspired Ultrashort Self-Assembled Peptide Gelator for Local Drug Delivery Applications

Fibrillar structures derived from plant or animal origin have long been a source of inspiration for the design of new biomaterials. The Asn-Gly-Ile-Trp-Tyr-NH2 (NGIWY-amide) pentapeptide, isolated from the sea cucumber Apostichopus japonicus, which spontaneously self-assembles in water to form hydrogel, pertains to this category. In this study, we evaluated this ultra-short cosmetic bioinspired peptide as vector for local drug delivery applications. Combining nuclear magnetic resonance, circular dichroism, infrared spectroscopy, X-ray diffraction, and rheological studies, the synthesized pentapeptide formed a stiff hydrogel with a high β-sheet content. Molecular dynamic simulations aligned well with scanning electron and atomic-force microscopy studies, revealing a highly filamentous structure with the fibers adopting a helical-twisted morphology. Model dye localization within the supramolecular hydrogel provided insights on the preferential distribution of hydrophobic and hydrophilic compounds in the hydrogel network. That was further depicted in the diffusion kinetics of drugs differing in their aqueous solubility and molecular weight, namely, doxorubicin hydrochloride, curcumin, and octreotide acetate, highlighting its versatility as a delivery vector of both hydrophobic and hydrophilic compounds of different molecular weight. Along with the observed cytocompatibility of the hydrogel, the NGIWY-amide pentapeptide may offer new approaches for cell growth, drug delivery, and 3D bioprinting tissue-engineering applications.

An Experimental and Theoretical Investigation of the Efficacy of Pantoprazole as a Corrosion Inhibitor for Mild Steel in an Acidic Medium

The corrosion behavior of mild steel in a 1 M aqueous sulfuric acid medium in the presence and absence of the drug Pantoprazole was investigated using potentiodynamic polarization and quantum chemical calculations as well as Monte Carlo and molecular dynamic simulations. The potentiodynamic experiments indicated that this molecule, as a result of its adsorption on a mild steel surface, functioned as a mixed inhibitor. The goal of the study was to use theoretical calculations to acquire a better understanding of how inhibition works. The adsorption behavior of the examined compounds on the Fe (1 1 0) surface was calculated using a Monte Carlo simulation. Furthermore, the molecules were studied using density functional theory (DFT), especially the PBE functional, to determine the relationship between the molecular structure and the corrosion inhibition behavior of the chemical under research. The adsorption energies of Pantoprazole (in its three different protonation states) iron were calculated more precisely using molecular mechanics with periodic boundary conditions (PBC). The predicted theoretical parameters were found to be in agreement with the experimental data, which was a considerable help in understanding the corrosion inhibition mechanism displayed by this chemical.

Autophagy and evasion of immune system by SARS-CoV-2. Structural features of the Non-structural protein 6 from Wild Type and Omicron viral strains interacting with a model lipid bilayer.

The viral cycle of SARS-CoV-2 is based on a complex interplay with the cellular machinery, which is mediated by specific proteins eluding or hijacking the cellular defense mechanisms. Among the complex pathways called by the viral infection autophagy is particularly crucial and is strongly influenced by the action of the non-structural protein 6 (Nsp6) interacting with the endoplasmic reticulum membrane. Importantly, differently from other non-structural proteins Nsp6 is mutated in the recently emerged Omicron variant, suggesting a possible different role of autophagy. In this contribution we explore, for the first time, the structural property of Nsp6 thanks to long-time scale molecular dynamic simulations and machine learning analysis, identifying the interaction patterns with the lipid membrane. We also show how the mutation brought by the Omicron variant may indeed modify some of the specific interactions, and more particularly help anchoring the viral protein to the lipid bilayer interface.