Evaluation of SARS-CoV-2 main protease and inhibitor interactions using dihedral angle distributions and radial distribution function

Effectiveness of sesquiterpene derivatives from Cinnamomum genus in nicotine replacement therapy through blocking acetylcholine nicotinate: a computational analysis

Cigarette smoking poses various health risks, such as increasing the susceptibility to respiratory infections, contributing to osteoporosis, causing reproductive issues, delaying postoperative recovery, promoting ulcer formation and heightening the risk of diabetes. While many harmful effects of smoking are attributed to other cigarette components, it is nicotine's pharmacological effects that underlie tobacco addiction. Nicotine replacement therapy (NRT) aims to alleviate the urge to smoke and mitigate physiological and psychomotor withdrawal symptoms by delivering nicotine. This study explores the potential of sesquiterpene derivative compounds derived from the Cinnamomum genus using computational techniques. The research incorporates molecular docking analyses, Lipinski's rule of five filtration for drug-likeness, pharmacokinetic and toxicity predictions to assess safety profiles and molecular dynamics (MD) simulations to gauge interaction stability. The findings reveal that all sesquiterpene derivative compounds from the Cinnamomum genus can potentially inhibit nicotinic acetylcholine receptors (nAChRs), particularly nAChRÿ7. However, only abscisic acid exhibit active inhibition, along with suitable drug properties, pharmacokinetics and toxicity profiles. MD studies confirm the stability of interactions between abscisic acid with nAChRÿ7. Consequently, abscisic acid, as sesquiterpene derivatives from the Cinnamomum genus, holds substantial promise for further investigation as nAChRÿ7 inhibitors.Communicated by Ramaswamy H. Sarma.

SARS-CoV-2 main protease mutation analysis via a kinematic method

The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is the virus responsible for the COVID-19 pandemic. COVID-19 continues to cause millions of deaths globally in part due to immune-evading mutations. SARS-CoV-2 main protease (Mpro) is an important enzyme for viral replication and potentially an effective drug target. Mutations affect the dynamics of enzymes and thereby their activity and ability to bind ligands. Here, we use kinematic flexibility analysis (KFA) to identify how mutations and ligand binding changes the conformational flexibility of Mpro. KFA decomposes macromolecules into regions of different flexibility near-instantly from a static structure, allowing conformational dynamics analysis at scale. Altogether, we analyzed 47 mutation sites across 69 Mpro-ligand complexes resulting in more than 3300 different structures which includes 69 mutated structures with all 47 sites mutated simultaneously and 3243 single residue mutated structures. We found that mutations generally increased the conformational flexibility of the protein. Understanding the impact of mutations on the flexibility of Mpro is essential for identifying potential drug targets in the treatment of SARS-CoV-2. Further studies in this area can offer valuable insights into the mechanisms of molecular recognition.

Wordom update 2: A user-friendly program for the analysis of molecular structures and conformational ensembles

We present the second update of Wordom, a user-friendly and efficient program for manipulation and analysis of conformational ensembles from molecular simulations. The actual update expands some of the existing modules and adds 21 new modules to the update 1 published in 2011. The new adds can be divided into three sets that: 1) analyze atomic fluctuations and structural communication; 2) explore ion-channel conformational dynamics and ionic translocation; and 3) compute geometrical indices of structural deformation. Set 1 serves to compute correlations of motions, find geometrically stable domains, identify a dynamically invariant core, find changes in domain-domain separation and mutual orientation, perform wavelet analysis of large-scale simulations, process the output of principal component analysis of atomic fluctuations, perform functional mode analysis, infer regions of mechanical rigidity, analyze overall fluctuations, and perform the perturbation response scanning. Set 2 includes modules specific for ion channels, which serve to monitor the pore radius as well as water or ion fluxes, and measure functional collective motions like receptor twisting or tilting angles. Finally, set 3 includes tools to monitor structural deformations by computing angles, perimeter, area, volume, β-sheet curvature, radial distribution function, and center of mass. The ring perception module is also included, helpful to monitor supramolecular self-assemblies. This update places Wordom among the most suitable, complete, user-friendly, and efficient software for the analysis of biomolecular simulations. The source code of Wordom and the relative documentation are available under the GNU general public license at http://wordom.sf.net.

Comparison of Intermolecular Interactions of Irreversible and Reversible Inhibitors with Bruton’s Tyrosine Kinase via Molecular Dynamics Simulations

Bruton’s tyrosine kinase (BTK) is a key protein from the TEC family and is involved in B-cell lymphoma occurrence and development. Targeting BTK is therefore an effective strategy for B-cell lymphoma treatment. Since previous studies on BTK have been limited to structure-function analyses of static protein structures, the dynamics of conformational change of BTK upon inhibitor binding remain unclear. Here, molecular dynamics simulations were conducted to investigate the molecular mechanisms of association and dissociation of a reversible (ARQ531) and irreversible (ibrutinib) small-molecule inhibitor to/from BTK. The results indicated that the BTK kinase domain was found to be locked in an inactive state through local conformational changes in the DFG motif, and P-, A-, and gatekeeper loops. The binding of the inhibitors drove the outward rotation of the C-helix, resulting in the upfolded state of Trp395 and the formation of the salt bridge of Glu445-Arg544, which maintained the inactive conformation state. Met477 and Glu475 in the hinge region were found to be the key residues for inhibitor binding. These findings can be used to evaluate the inhibitory activity of the pharmacophore and applied to the design of effective BTK inhibitors. In addition, the drug resistance to the irreversible inhibitor Ibrutinib was mainly from the strong interaction of Cys481, which was evidenced by the mutational experiment, and further confirmed by the measurement of rupture force and rupture times from steered molecular dynamics simulation. Our results provide mechanistic insights into resistance against BTK-targeting drugs and the key interaction sites for the development of high-quality BTK inhibitors. The steered dynamics simulation also offers a means to rapidly assess the binding capacity of newly designed inhibitors.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.