Targeting Natural Plant Metabolites for Hunting SARS-CoV-2 Omicron BA.1 Variant Inhibitors: Extraction, Molecular Docking, Molecular Dynamics, and Physicochemical Properties Study.

(1) Background: SARS-CoV-2 Omicron BA.1 is the most common variation found in most countries and is responsible for 99% of cases in the United States. To overcome this challenge, there is an urgent need to discover effective inhibitors to prevent the emerging BA.1 variant. Natural products, particularly flavonoids, have had widespread success in reducing COVID-19 prevalence. (2) Methods: In the ongoing study, fifteen compounds were annotated from Echium angustifolium and peach (Prunus persica), which were computationally analyzed using various in silico techniques. Molecular docking calculations were performed for the identified phytochemicals to investigate their efficacy. Molecular dynamics (MD) simulations over 200 ns followed by molecular mechanics Poisson-Boltzmann surface area calculations (MM/PBSA) were performed to estimate the binding energy. Bioactivity was also calculated for the best components in terms of drug likeness and drug score. (3) Results: The data obtained from the molecular docking study demonstrated that five compounds exhibited remarkable potency, with docking scores greater than -9.0 kcal/mol. Among them, compounds 1, 2 and 4 showed higher stability within the active site of Omicron BA.1, with ΔGbinding values of -49.02, -48.07, and -67.47 KJ/mol, respectively. These findings imply that the discovered phytoconstituents are promising in the search for anti-Omicron BA.1 drugs and should be investigated in future in vitro and in vivo research.

Prevalence of Bacterial Pathogens among Symptomatic-SARS-CoV-2 PCR-Negative Patients.

The epidemiological and clinical aspects of coronavirus disease-2019 (COVID-19) have been subjected to several investigations, but little is known about symptomatic patients with negative SARS-CoV-2 PCR results. The current study investigated patients who presented to the hospital with respiratory symptoms (but negative SARS-CoV-2 RT-PCR results) to determine the prevalence of bacterial pathogens among these patients. A total of 1246 different samples were collected and 453 species of bacterial pathogens were identified by culture. Antibiotic susceptibility testing was performed via the Kirby Bauer disc diffusion test. Patients showed symptoms, such as fever (100%), cough (83%), tiredness (77%), loss of taste and smell (23%), rigors (93%), sweating (62%), and nausea (81%), but all tested negative for COVID-19 by PCR tests. Further examinations revealed additional and severe symptoms, such as sore throats (27%), body aches and pain (83%), diarrhea (11%), skin rashes (5%), eye irritation (21%), vomiting (42%), difficulty breathing (32%), and chest pain (67%). The sum of n = 1246 included the following: males, 289 were between 5 and 14 years, 183 (15-24 years), 157 (25-34 years), 113 (35-49 years), and 43 were 50+ years. Females: 138 were between 5 and 14 years, 93 (15-24 years), 72 (25-34 years), 89 (35-49 years), and 68 were 50+ years. The Gram-positive organisms isolated were Staphylococcus aureus (n = 111, 80.43%, MRSA 16.6%), E. faecalis (n = 20, 14.49%, VRE: 9.4%), and Streptococcus agalactiae (n = 7, 5.07%), while, Gram-negative organisms, such as E. coli (n = 135, 42.85%, CRE: 3.49%), K. pneumoniae (n = 93, 29.52%, CRE: 1.58%), P. aeruginosa (n = 43, 13.65%), C. freundii (n = 21, 6.66%), Serratia spp. (n = 8, 2.53%), and Proteus spp. (n = 15, 4.76%) were identified.

Target-Specific Machine Learning Scoring Function Improved Structure-Based Virtual Screening Performance for SARS-CoV-2 Drugs Development.

Leveraging machine learning has been shown to improve the accuracy of structure-based virtual screening. Furthermore, a tremendous amount of empirical data is publicly available, which further enhances the performance of the machine learning approach. In this proof-of-concept study, the 3CLpro enzyme of SARS-CoV-2 was used. Structure-based virtual screening relies heavily on scoring functions. It is widely accepted that target-specific scoring functions may perform more effectively than universal scoring functions in real-world drug research and development processes. It would be beneficial to drug discovery to develop a method that can effectively build target-specific scoring functions. In the current study, the bindingDB database was used to retrieve experimental data. Smina was utilized to generate protein-ligand complexes for the extraction of InteractionFingerPrint (IFP) and SimpleInteractionFingerPrint SIFP fingerprints via the open drug discovery tool (oddt). The present study found that randomforestClassifier and randomforestRegressor performed well when used with the above fingerprints along the Molecular ACCess System (MACCS), Extended Connectivity Fingerprint (ECFP4), and ECFP6. It was found that the area under the precision-recall curve was 0.80, which is considered a satisfactory level of accuracy. In addition, our enrichment factor analysis indicated that our trained scoring function ranked molecules correctly compared to smina's generic scoring function. Further molecular dynamics simulations indicated that the top-ranked molecules identified by our developed scoring function were highly stable in the active site, supporting the validity of our developed process. This research may provide a template for developing target-specific scoring functions against specific enzyme targets.