Discovering potent inhibitors against the Mpro of the SARS-CoV-2. A medicinal chemistry approach.

The global pandemic caused by a single-stranded RNA (ssRNA) virus known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still at its peak, with new cases being reported daily. Although the vaccines have been administered on a massive scale, the frequent mutations in the viral gene and resilience of the future strains could be more problematic. Therefore, new compounds are always needed to be available for therapeutic approaches. We carried out the present study to discover potential drug compounds against the SARS-CoV-2 main protease (Mpro). A total of 16,000 drug-like small molecules from the ChemBridge database were virtually screened to obtain the top hits. As a result, 1032 hits were selected based on their docking scores. Next, these structures were prepared for molecular docking, and each small molecule was docked into the active site of the Mpro. Only compounds with solid interactions with the active site residues and the highest docking score were subjected to molecular dynamics (MD) simulation. The post-simulation analyses were carried out using the in-built GROMACS tools to gauge the stability, flexibility, and compactness. Principal component analysis (PCA) and hydrogen bonding were also calculated to observe trends and affinity of the drugs towards the target. Among the five top compounds, C1, C3, and C6 revealed strong interaction with the target's active site and remained highly stable throughout the simulation. We believe the predicted compounds in this study could be potential inhibitors in the natural system and can be utilized in designing therapeutic strategies against the SARS-CoV-2.

CoronaPep: An Anti-Coronavirus Peptide Generation Tool.

The novel coronavirus (COVID-19) infections have adopted the shape of a global pandemic now, demanding an urgent vaccine design. The current work reports contriving an anti-coronavirus peptide scanner tool to discern anti-coronavirus targets in the embodiment of peptides. The proffered CoronaPep tool features the fast fingerprinting of the anti-coronavirus target serving supreme prominence in the current bioinformatics research. The anti-coronavirus target protein sequences reported from the current outbreak are scanned against the anti-coronavirus target data-sets via CORONAPEP which provides precision-based anti-coronavirus peptides. This tool is specifically for the coronavirus data, which can predict peptides from the whole genome, or a gene or protein's list. Besides it is relatively fast, accurate, userfriendly and can generate maximum output from the limited information. The availability of tools like CORONAPEP will immeasurably perquisite researchers in the discipline of oncology and structure-based drug design.

Structures of SARS-CoV-2 RNA-Binding Proteins and Therapeutic Targets.

BACKGROUND: The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) epidemic has resulted in thousands of infections and deaths worldwide. Several therapies are currently undergoing clinical trials for the treatment of SARS-CoV-2 infection. However, the development of new drugs and the repositioning of existing drugs can only be achieved after the identification of potential therapeutic targets within structures, as this strategy provides the most precise solution for developing treatments for sudden epidemic infectious diseases. SUMMARY: In the current investigation, crystal and cryo-electron microscopy structures encoded by the SARS-CoV-2 genome were systematically examined for the identification of potential drug targets. These structures include nonstructural proteins (Nsp-9; Nsp-12; and Nsp-15), nucleocapsid (N) proteins, and the main protease (Mpro). Key Message: The structural information reveals the presence of many potential alternative therapeutic targets, primarily involved in interaction between N protein and Nsp3, forming replication-transcription complexes (RTCs) which might be a potential drug target for effective control of current SARS-CoV-2 pandemic. RTCs consist of 16 nonstructural proteins (Nsp1-16) that play the most essential role in the synthesis of viral RNA. Targeting the physical linkage between the envelope and single-stranded positive RNA, a process facilitated by matrix proteins may provide a good alternative strategy. Our current study provides useful information for the development of new lead compounds against SARS-CoV-2 infections.

AI-driven drug discovery: A boon against COVID-19?

The COVID-19 is an issue of international concern and threat to public health and there is an urgent need of drug/vaccine design. There is no vaccine or specific drug yet made as of July 23, 2020, for the coronavirus disease (COVID-19). Thus, the patients currently can only be treated symptomatically. A quick identification of the drugs for COVID-19 may act as a potential therapeutic medication which has been used earlier in patients to answer the present pandemic condition before it could get more worse. According to our view, an artificial intelligence (AI) based tool that may predict drugs/peptides directly from the sequences of infected patients and thereby, they might have better affinity with the target and contribute towards vaccine design against COVID-19. Researchers across the world proposed several vaccines/drugs for COVID-19 utilizing AI based approaches, however, testing of these proposed vaccines/drugs will be needed to verify the safety and feasibility for combating COVID-19.

Comparative study on air quality status in Indian and Chinese cities before and during the COVID-19 lockdown period.

Amidst COVID-19 pandemic, extreme steps have been taken by countries globally. Lockdown enforcement has emerged as one of the mitigating measures to reduce the community spread of the virus. With a reduction in major anthropogenic activities, a visible improvement in air quality has been recorded in urban centres. Hazardous air quality in countries like India and China leads to high mortality rates from cardiovascular diseases. The present article deals with 6 megacities in India and 6 cities in Hubei province, China, where strict lockdown measures were imposed. The real-time concentration of PM2.5 and NO2 were recorded at different monitoring stations in the cities for 3 months, i.e. January, February, and March for China and February, March, and April for India. The concentration data is converted into AQI according to US EPA parameters and the monthly and weekly averages are calculated for all the cities. Cities in China and India after 1 week of lockdown recorded an average drop in AQIPM2.5 and AQINO2 of 11.32% and 48.61% and 20.21% and 59.26%, respectively. The results indicate that the drop in AQINO2 was instantaneous as compared with the gradual drop in AQIPM2.5. The lockdown in China and India led to a final drop in AQIPM2.5 of 45.25% and 64.65% and in AQINO2 of 37.42% and 65.80%, respectively. This study will assist the policymakers in devising a pathway to curb down air pollutant concentration in various urban cities by utilising the benchmark levels of air pollution.