High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor.

Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel noncovalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (Mpro) by employing a scalable high-throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this Mpro inhibitor with an inhibition constant (Ki) of 2.9 µM (95% CI 2.2, 4.0). Furthermore, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of Mpro forming stable hydrogen bond and hydrophobic interactions. We then used multiple µs-time scale molecular dynamics (MD) simulations and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by Mpro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits Mpro and offers a springboard for further therapeutic design.

Theory of the Interfacial Dzyaloshinskii-Moriya Interaction in Rashba Antiferromagnets.

In antiferromagnetic (AFM) thin films, broken inversion symmetry or coupling to adjacent heavy metals can induce Dzyaloshinskii-Moriya (DM) interactions. Knowledge of the DM parameters is essential for understanding and designing exotic spin structures, such as hedgehog Skyrmions and chiral Néel walls, which are attractive for use in novel information storage technologies. We introduce a framework for computing the DM interaction in two-dimensional Rashba antiferromagnets. Unlike in Rashba ferromagnets, the DM interaction is not suppressed even at low temperatures. The material parameters control both the strength and the sign of the interfacial DM interaction. Our results suggest a route toward controlling the DM interaction in AFM materials by means of doping and electric fields.

Light-Induced Anisotropic Skyrmion and Stripe Phases in a Rashba Ferromagnet.

An external off-resonant pumping is proposed as a tool to control the Dzyaloshinskii-Moriya interaction (DMI) in ferromagnetic layers with strong spin-orbit coupling. Combining theoretical analysis with numerical simulations for an s-d-like model, we demonstrate that linearly polarized off-resonant light may help stabilize novel noncollinear magnetic phases by inducing a strong anisotropy of the DMI. We also investigate how with the application of electromagnetic pumping one can control the stability, shape, and size of individual Skyrmions to make them suitable for potential applications.

Synthesis of new vasotocin analogues: effects on renal water and ion excretion in rats.

Vasopressin and nonmammalian hormone vasotocin are known to increase the water permeability of mammalian collecting ducts, frog skin and the urinary bladder. Neurohypophysial nonapeptides have also been shown to interfere with the regulation of renal ion transport. The subject of this study was a search for vasopressin and vasotocin analogues with selective effects on renal water, sodium and potassium excretion. During this study, we synthesised the following peptides: 13 vasotocin analogues modified at positions 4 (Thr or Arg), 7 (Gly or Leu) and 8 (D-Arg, Lys or Glu); 4 vasopressin analogues modified at positions 4 and 8; and 9 peptides shortened or extended at the C-terminal or with substitutions for Gly-NH2. Most of these peptides had mercaptopropionic acid (Mpa) instead of Cys in position 1. The effects of these nonapeptides on renal water, sodium and potassium transport were evaluated in in vivo experiments using Wistar rats. Some nonapeptides possessed antidiuretic, natriuretic and kaliuretic activities ([Mpa(1)]-arginine vasotocin, [Mpa(1), homoArg(8)]-vasotocin, [Mpa(1), Thr(4)]-arginine vasotocin and [Mpa(1), Arg(4)]-arginine vasopressin). Substitutions at positions 4 and 8 increased the selectivity of peptide actions. The antidiuretic [D-Arg(8)]-vasotocin analogues had no effects on sodium excretion. [Mpa(1), Arg(4)]-arginine vasotocin was antidiuretic and kaliuretic but not natriuretic. [Mpa(1), Glu(8)]-oxytocin had weak natriuretic activity without any effects on water and potassium transport. In accordance with the data obtained, synthesised vasotocin analogues could be good candidates for pharmaceuticals selectively regulating renal sodium and potassium transport, which is of clinical importance.

Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers.

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

NPIDB: a database of nucleic acids-protein interactions.

UNLABELLED: The database NPIDB (Nucleic Acids-Protein Interaction DataBase) contains information derived from structures of DNA-protein and RNA-protein complexes extracted from PDB (1834 complexes in July 2007). It is organized as a collection of files in PDB format and is equipped with a web-interface and a set of tools for extracting biologically meaningful characteristics of complexes. The content of the database is weekly updated. AVAILABILITY: http://monkey.belozersky.msu.ru/NPIDB/

Improvement of predicted fine and total particulate matter (PM) composition by applying several different chemical scenarios: a winter 2005 case study.

A new method using several different chemical scenarios is developed to predict chemical composition of fine (PM2.5) and total (PM10) aerosol. This method improves the accuracy of predicted PM concentrations. The Mesoscale Model version 5 (MM5) and a 3-dimensional Eulerian chemical model (CAMx4.2) are used to predict PM2.5 and PM10 concentrations using gridded input emissions (from the "Total" group) over a 48-72 h time period for Christchurch (New Zealand) for winter 2005. The aerosol concentrations are obtained for four different chemical compositions (chemical scenarios) of the input aerosol emissions. PM2.5 chemical compositions are based on previous Christchurch winter studies and from observations in other countries with similar winter pollution problems, and used in CAMx4.2 to model seven winter 2005 heavy pollution episodes. The error between observed and modelled PM2.5 concentrations is based on predictions of fine aerosol that are derived from linear regression with PM10. It is used to find the minimum difference between modelled and observed PM2.5 for an observation site located in the Christchurch residential area. Combination of the chemical scenarios with analysis of the minimum error is used to create a new complex chemical scenario. The new complex scenario is used to re-calculate all pollution episodes to obtain new values of PM with minimum error compared with observed aerosol concentrations. Mean Absolute Error of the calculated PM2.5 (for all pollution episodes) decreased from 21-24 microg m(-3) to 14-16 microg m(-3) compared with observations. The chemical composition of the modelled PM2.5 is also discussed.

Analysis of conserved hydrophobic cores in proteins and supramolecular complexes.

The conserved hydrophobic core is an important feature of a family of protein domains. We suggest a procedure for finding and the analysis of conserved hydrophobic cores. The procedure is based on using an original program called CluD (http://monkey.belozersky.msu.ru/CluD/cgi-bin/hftri.pl). Conserved hydrophobic cores of several families including homeodomains and interlock-containing domains are described. Hydrophobic clusters on some protein-DNA and protein-protein interfaces were also analyzed.

Selecting optimal monitoring site locations for peak ambient particulate material concentrations using the MM5-CAMx4 numerical modelling system.

Installation of temporary or long term monitoring sites is expensive, so it is important to rationally identify potential locations that will achieve the requirements of regional air quality management strategies. A simple, but effective, numerical approach to selecting ambient particulate matter (PM) monitoring site locations has therefore been developed using the MM5-CAMx4 air pollution dispersion modelling system. A new method, 'site efficiency,' was developed to assess the ability of any monitoring site to provide peak ambient air pollution concentrations that are representative of the urban area. 'Site efficiency' varies from 0 to 100%, with the latter representing the most representative site location for monitoring peak PM concentrations. Four heavy pollution episodes in Christchurch (New Zealand) during winter 2005, representing 4 different aerosol dispersion patterns, were used to develop and test this site assessment technique. Evaluation of the efficiency of monitoring sites was undertaken for night and morning aerosol peaks for 4 different particulate material (PM) spatial patterns. The results demonstrate that the existing long term monitoring site at Coles Place is quite well located, with a site efficiency value of 57.8%. A temporary ambient PM monitoring site (operating during winter 2006) showed a lower ability to capture night and morning peak aerosol concentrations. Evaluation of multiple site locations used during an extensive field campaign in Christchurch (New Zealand) in 2000 indicated that the maximum efficiency achieved by any site in the city would be 60-65%, while the efficiency of a virtual background site is calculated to be about 7%. This method of assessing the appropriateness of any potential monitoring site can be used to optimize monitoring site locations for any air pollution measurement programme.