Novel Inhibitors of 2′-O-Methyltransferase of the SARS-CoV-2 Coronavirus

Structure-Based Virtual Screening for Methyltransferase Inhibitors of SARS-CoV-2 nsp14 and nsp16

The ongoing COVID-19 pandemic still threatens human health around the world. The methyltransferases (MTases) of SARS-CoV-2, specifically nsp14 and nsp16, play crucial roles in the methylation of the N7 and 2'-O positions of viral RNA, making them promising targets for the development of antiviral drugs. In this work, we performed structure-based virtual screening for nsp14 and nsp16 using the screening workflow (HTVS, SP, XP) of Schrödinger 2019 software, and we carried out biochemical assays and molecular dynamics simulation for the identification of potential MTase inhibitors. For nsp14, we screened 239,000 molecules, leading to the identification of three hits A1-A3 showing N7-MTase inhibition rates greater than 60% under a concentration of 50 µM. For the SAM binding and nsp10-16 interface sites of nsp16, the screening of 210,000 and 237,000 molecules, respectively, from ZINC15 led to the discovery of three hit compounds B1-B3 exhibiting more than 45% of 2'-O-MTase inhibition under 50 µM. These six compounds with moderate MTase inhibitory activities could be used as novel candidates for the further development of anti-SARS-CoV-2 drugs.

A Comprehensive Study to Unleash the Putative Inhibitors of Serotype2 of Dengue Virus: Insights from an In Silico Structure-Based Drug Discovery

Dengue fever is a mosquito-borne disease that claims the lives of millions of people around the world. A number of factors like disease's non-specific symptoms, increased viral mutation, growing antiviral drug resistance due to reduced susceptibility, unavailability of an effective vaccine for dengue, weak immunity against the virus, and many more are involved. Dengue belongs to the Flaviviridae family of viruses. The two species of the vector transmitting dengue are Aedes aegypti and Aedes albopictus, with the former one being dominant. Serotypes 2 of dengue fever are spread to the human body and cause severe illness. Recently, dengue has imposed an aggressive effect synergistically with the COVID-19 pandemic. As a result, we concentrated our efforts on finding a potential therapeutic. For this, we chose natural compounds to fight dengue fever, which is currently regarded as successful among many drug therapies. Following this, we started the in silico experiment with 922 plant extracts as lead compounds to fight serotype 2. In this study, we used SwissADME for analyzing ligand drug-likeness, pkCSM for designing an ADMET profile, Autodock vina 4.2 and Swissdock tools for molecular docking, and finally Desmond for molecular dynamics simulation. Ultimately 45 were found effective against the 2'O methyltransferase protein of serotype 2. CHEMBL376820 was found as possible therapeutic candidates for inhibiting methyltransferase protein in this thorough analysis. Nevertheless, more in vitro and in vivo research are required to substantiate their potential therapeutic efficacy.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

Coronavirus 2'-O-methyltransferase: A promising therapeutic target

Coronaviruses (CoVs) have been the source of multiple epidemics and a global pandemic since the start of century, and there is an urgent need to understand CoV biology and develop better therapeutics. Here, we review the role of NSP16 in CoV replication, specifically its importance to 2'-O-methylation and CoV RNA capping. We describe the attenuation phenotypes of NSP16-mutant CoVs, the roles of MDA5 and IFITs in sensing and antagonizing viral RNA lacking 2'O methylation, and the dependence on 2'-O-methylation in other virus families. We also detail the growing body of research into targeting 2'-O-methylation for therapeutics or as a platform for live attenuated vaccines. Beyond its role in RNA capping, NSP16 may have yet uncharacterized importance to CoV replication, highlighting the need for continued studies into NSP16 functions. Understanding the full contribution of NSP16 to the replicative fitness of CoVs will better inform the development of treatments against future CoV outbreaks.

High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior

Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness. Here, we present the architectural aspects of highthroughput screening for SARS-CoV-2 inhibitors, especially three generations of virtual screening methodologies with structural dynamics: ligand-based screening, receptor-based screening, and machine learning (ML)-based scoring functions (SFs). By outlining the benefits and drawbacks, we hope that researchers will be motivated to adopt these methods in the development of novel anti- SARS-CoV-2 agents.

Computational Analysis of SAM Analogs as Methyltransferase Inhibitors of nsp16/nsp10 Complex from SARS-CoV-2

Methyltransferases (MTases) enzymes, responsible for RNA capping into severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are emerging important targets for the design of new anti-SARS-CoV-2 agents. Here, analogs of S-adenosylmethionine (SAM), obtained from the bioisosteric substitution of the sulfonium and amino acid groups, were evaluated by rigorous computational modeling techniques such as molecular dynamics (MD) simulations followed by relative binding free analysis against nsp16/nsp10 complex from SARS-CoV-2. The most potent inhibitor (2a) shows the lowest binding free energy (-58.75 Kcal/mol) and more potency than Sinefungin (SFG) (-39.8 Kcal/mol), a pan-MTase inhibitor, which agrees with experimental observations. Besides, our results suggest that the total binding free energy of each evaluated SAM analog is driven by van der Waals interactions which can explain their poor cell permeability, as observed in experimental essays. Overall, we provide a structural and energetic analysis for the inhibition of the nsp16/nsp10 complex involving the evaluated SAM analogs as potential inhibitors.

Identification and characterization of alternative sites and molecular probes for SARS-CoV-2 target proteins

Three protein targets from SARS-CoV-2, the viral pathogen that causes COVID-19, are studied: the main protease, the 2′-O-RNA methyltransferase, and the nucleocapsid (N) protein. For the main protease, the nucleophilicity of the catalytic cysteine C145 is enabled by coupling to three histidine residues, H163 and H164 and catalytic dyad partner H41. These electrostatic couplings enable significant population of the deprotonated state of C145. For the RNA methyltransferase, the catalytic lysine K6968 that serves as a Brønsted base has significant population of its deprotonated state via strong coupling with K6844 and Y6845. For the main protease, Partial Order Optimum Likelihood (POOL) predicts two clusters of biochemically active residues; one includes the catalytic H41 and C145 and neighboring residues. The other surrounds a second pocket adjacent to the catalytic site and includes S1 residues F140, L141, H163, E166, and H172 and also S2 residue D187. This secondary recognition site could serve as an alternative target for the design of molecular probes. From in silico screening of library compounds, ligands with predicted affinity for the secondary site are reported. For the NSP16-NSP10 complex that comprises the RNA methyltransferase, three different sites are predicted. One is the catalytic core at the conserved K-D-K-E motif that includes catalytic residues D6928, K6968, and E7001 plus K6844. The second site surrounds the catalytic core and consists of Y6845, C6849, I6866, H6867, F6868, V6894, D6895, D6897, I6926, S6927, Y6930, and K6935. The third is located at the heterodimer interface. Ligands predicted to have high affinity for the first or second sites are reported. Three sites are also predicted for the nucleocapsid protein. This work uncovers key interactions that contribute to the function of the three viral proteins and also suggests alternative sites for ligand design.