GROMACS is one of the most widely used HPC software packages using the Molecular Dynamics (MD) simulation technique. In this work, we quantify GROMACS parallel performance using different configurations, HPC systems, and FFT libraries (FFTW, Intel MKL FFT, and FFT PACK). We break down the cost of each GROMACS computational phase and identify non-scalable stages, such as MPI communication during the 3D FFT computation when using a large number of processes. We show that the Particle-Mesh Ewald phase and the 3D FFT calculation significantly impact the GROMACS performance. Finally, we discuss performance opportunities with a particular interest in developing GROMACS for the FFT calculations.

Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein-ligand complexes (on the order of 10(6) to 10(12)), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures.

Exploring the new therapeutic indications of known drugs for treating COVID-19, popularly known as drug repurposing, is emerging as a pragmatic approach especially owing to the mounting pressure to control the pandemic. Targeting multiple targets with a single drug by employing drug repurposing known as the polypharmacology approach may be an optimised strategy for the development of effective therapeutics. In this study, virtual screening has been carried out on seven popular SARS-CoV-2 targets (3CL(pro), PLpro, RdRp (NSP12), NSP13, NSP14, NSP15, and NSP16). A total of 4015 approved drugs were screened against these targets. Four drugs namely venetoclax, tirilazad, acetyldigitoxin, and ledipasvir have been selected based on the docking score, ability to interact with four or more targets and having a reasonably good number of interactions with key residues in the targets. The MD simulations and MM-PBSA studies showed reasonable stability of protein-drug complexes and sustainability of key interactions between the drugs with their respective targets throughout the course of MD simulations. The identified four drug molecules were also compared with the known drugs namely elbasvir and nafamostat. While the study has provided a detailed account of the chosen protein-drug complexes, it has explored the nature of seven important targets of SARS-CoV-2 by evaluating the protein-drug complexation process in great detail.

New variants of SARS-CoV-2 are being reported worldwide. The World Health Organization has reported Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2) and Omicron (B.1.1.529) as the variants of concern. There are speculations that the variants might evade the host immune responses induced by currently available vaccines and develop resistance to drugs under consideration. The first step of viral infection in COVID-19 occurs through the interaction of the spike protein's receptor-binding domain (RBD) with the peptidase domain of the human ACE-2 (hACE-2) receptor. This study aims to get a molecular-level understanding of the mechanism behind the increased infection rate in the alpha variant. We have computationally studied the spike protein interaction in both the wild-type and B.1.1.7 variant with the hACE-2 receptor using molecular dynamics and MM-GBSA based binding free energy calculations. The binding free energy difference shows that the mutant variant of the spike protein has increased binding affinity for the hACE-2 receptor (i.e. Delta G(N501Y,A570D) is in the range -7.2 to -7.6 kcal mol(-1)) and the results were validated using Density functional theory. We demonstrate that with the use of state-of-the-art computational approaches, we can, in advance, predict the virulent nature of variants of SARS-CoV-2 and alert the world healthcare system.

To assess a tumor biomarker like the cyclooxygenase-2 enzyme (COX-2), non-invasive imaging techniques are powerful tools. The (non-) linear optical properties of activatable fluorescent probes which are selectively bound to the biomarker can therefore be exploited. The here presented molecular modelling results based on multi-scale modelling techniques highlight the importance of the conformational versatility and of changes in the electronic interactions of such probes when they are embedded in water or in the COX-2 homodimer enzyme. The ANQIMC-6 probe, which combines the binding domain/scaffold of indomethacin (IMC) on COX-2 with the optical properties of acenaphtho[1,2-b]quinoxaline (ANQ), is found to be folded in the solvent and unfolded in the enzyme. A concerted movement of the probe and the protein is seen, while the rotational autocorrelation function exhibits also the intrinsic properties of the probe. Hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) calculations are used to simulate the one-photon and two-photon absorption spectra along with the first hyperpolarizability. The transition has a local character in vacuum, but changes to a charge transfer one in the presence of the microenvironment of the enzyme. This is also visible through a change of the shape of the absorption spectrum, while at the same time the simulated signals of second harmonic generation experiments are strongly enhanced. The results of this work prove that an environment sensitive probe with an anchoring group and an optical active part can be constructed for use in absorption spectroscopy, without the need to revert to fluorescence experiments.

The new variant of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), Omicron, has been quickly spreading in many countries worldwide. Compared to the original virus, Omicron is characterized by several mutations in its genomic region, including the spike protein's receptor-binding domain (RBD). We have computationally investigated the interaction between the RBD of both the wild type and Omicron variant of SARS-CoV-2 with the human angiotensin-converting enzyme 2 (hACE2) receptor using molecular dynamics and molecular mechanics-generalized Born surface area (MM-GBSA)-based binding free energy calculations. The mode of the interaction between Omicron's RBD with the hACE2 receptor is similar to the original SARS-CoV-2 RBD except for a few key differences. The binding free energy difference shows that the spike protein of Omicron has an increased affinity for the hACE2 receptor. The mutated residues in the RBD showed strong interactions with a few amino acid residues of hACE2. More specifically, strong electrostatic interactions (salt bridges) and hydrogen bonding were observed between R493 and R498 residues of the Omicron RBD with D30/E35 and D38 residues of the hACE2, respectively. Other mutated amino acids in the Omicron RBD, e.g., S496 and H505, also exhibited hydrogen bonding with the hACE2 receptor. A pi-stacking interaction was also observed between tyrosine residues (RBD-Tyr501: hACE2-Tyr41) in the complex, which contributes majorly to the binding free energies and suggests that this is one of the key interactions stabilizing the formation of the complex. The resulting structural insights into the RBD:hACE2 complex, the binding mode information within it, and residue-wise contributions to the free energy provide insight into the increased transmissibility of Omicron and pave the way to design and optimize novel antiviral agents.

Stereoselectivity is important in many pharmacological processes but its impact on drug membrane transport is scarcely understood. Recent studies showed strong stereoselective effects in the cellular uptake of fenoterol by the organic cation transporters OCT1 and OCT2. To provide possible molecular explanations, homology models were developed and the putative interactions between fenoterol enantiomers and key residues explored in silico through computational docking, molecular dynamics simulations, and binding free energy calculations as well as in vitro by site-directed mutagenesis and cellular uptake assays. Our results suggest that the observed 1.9-fold higher maximum transport velocity (v(max)) for (R,R)- over (S,S)-fenoterol in OCT1 is because the enantiomers bind to two distinct binding sites. Mutating PHE355 and ILE442, predicted to interact with (R,R)-fenoterol, reduced the v(max) ratio to 1.5 and 1.3, respectively, and to 1.2 in combination. Mutating THR272, predicted to interact with (S,S)-fenoterol, slightly increased stereoselectivity (vmax ratio of 2.2), while F244A resulted in a 35-fold increase in v(max) and a lower affinity (29-fold higher K-m) for (S,S)-fenoterol. Both enantiomers of salbutamol, for which almost no stereoselectivity was observed, were predicted to occupy the same binding pocket as (R,R)-fenoterol. Unlike for OCT1, both fenoterol enantiomers bind in the same region in OCT2 but in different conformations. Mutating THR246, predicted to interact with (S,S)-fenoterol in OCT2, led to an 11-fold decreased v(max). Altogether, our mutagenesis results correlate relatively well with our computational predictions and thereby provide an experimentally-corroborated hypothesis for the strong and contrasting enantiopreference in fenoterol uptake by OCT1 and OCT2.

SARS-CoV-2 pandemic has become a major threat to human healthcare and world economy. Due to the rapid spreading and deadly nature of infection, we are in a situation to develop quick therapeutics to combat SARS-CoV-2. In this study, we have adopted a multi-level scoring approach to identify multi-targeting potency of bioactive compounds in selected medicinal plants and compared its efficacy with two reference drugs, Nafamostat and Acalabrutinib which are under clinical trials to treat SARS-CoV-2. In particular, we employ molecular docking and implicit solvent free energy calculations (as implemented in the Molecular Mechanics -Generalized Born Surface Area approach) and QM fragmentation approach for validating the potency of bioactive compounds from the selected medicinal plants against four different viral targets and one human receptor (Angiotensin-converting enzyme 2 -ACE-2) which facilitates the SARS-CoV-2 entry into the cell. The protein targets considered for the study are viral 3CL main protease (3CLpro), papain-like protease (PLpro), RNA dependent RNA polymerase (RdRp), and viral spike protein-human hACE-2 complex (Spike:hACE2) including human protein target (hACE-2). Herein, there liable multi-level scoring approach was used to validate the mechanism behind the multi-targeting potency of selected phytochemicals from medicinal plants. The present study evidenced that the phytochemicals Chebulagic acid, Stigmosterol, Repandusinic acid and Geranin exhibited efficient inhibitory activity against PLpro while Chebulagic acid was highly active against 3CLpro. Chebulagic acid and Geranin also showed excellent target specific activity against RdRp. Luteolin, Quercetin, Chrysoeriol and Repandusinic acid inhibited the interaction of viral spike protein with human ACE-2 receptor. Moreover, Piperlonguminine and Piperine displayed significant inhibitory activity against human ACE-2 receptor. Therefore, the identified compounds namely Chebulagic acid, Geranin and Repandusinic acid can serve as potent multi-targeting phytomedicine for treating COVID-19.

With the promising advantages of the near-infrared region (NIR) emissive markers for serum albumin becoming very prominent recently, we devised CyG-NHS as the cyanine derived longest NIR-I emissive optical marker possessing albumin selective recognition ability in diverse biological milieu. Multiscale modeling involving molecular docking, molecular dynamics, and implicit solvent binding free energy calculations have been employed to gain insights into the unique binding ability of the developed probe at domain-I of albumin, in contrast to the good number of domain IIA or IIIA binding probes available in the literature reports. The binding free energy was found to be −31.8 kcal mol−1 majorly predominated by hydrophobic interactions. Besides, the conformational dynamics of CyG-NHS in an aqueous medium and the albumin microenvironment have been comprehensively studied and discussed. The potentiality of this optical platform to monitor the intracellular albumin levels in human hepatoma (HepG2) cells in different pathophysiological states has been demonstrated here. Also, the competency of the phenformin drug in restoring the albumin levels in chronic hyperinsulinemic and hypercholesterolemic in vitro models has been established through the visualization approach. Altogether, the findings of this study throw light on the significance of the development of a suitable optical marker for the visualization of critical bioevents related to albumin. 

The paradigm of advanced materials has grown exponentially over the last decade, with their new dimensions including digital design, dynamics, and functions. Materials modeling such as that of their properties and behavior in various environments using ab initio approaches, force-field methods and machine learning represents a key step in advanced research. Computational techniques and theoretical models pave the way for establishing the structure-property relationship for designing advanced materials with novel properties and improving their performances. Likewise, high accuracy and fewer computational resources of machine-learning approaches have been widely considered for materials design in the recent years. Furthermore, the information derived from materials studies needs to be properly stored and re-analyzed, making big data analysis an essential requirement for further investigations. The information thus generated has also led to the evolution of the genome of materials for the fostering of advanced materials. Thus, various theoretical and computational approaches provide useful predictions about materials properties and efficiency, ultimately leading to the substantial improvements for new-age devices.