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The evolution of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants around the globe has made the coronavirus disease 2019 (COVID-19) pandemic more worrisome, pressuring the health care system and resulting in an increased mortality rate. Recent studies recognized neuropilin-1 (NRP1) as a key facilitator in the invasion of the new SARS-CoV-2 into the host cell. Therefore, it is considered an imperative drug target for the treatment of COVID-19. Hence, a thorough analysis was needed to understand the impact and to guide new therapeutics development. In this study, we used structural and biomolecular simulation techniques to identify novel marine natural products which could block this receptor and stop the virus entry. We discovered that the binding affinity of CMNPD10175, CMNPD10017, CMNPD10114, CMNPD10115, CMNPD10020. CMNPD10018, CMNPD10153, CMNPD10149 CMNPD10464 and CMNPD10019 were substantial during the virtual screening (VS). We further explored these compounds by analyzing their absorption, distribution, metabolism, and excretion and toxicity (ADMET) properties and structural-dynamics features. Free energy calculations further established that all the compounds exhibit strong binding energy for NRP1. Consequently, we hypothesized that these compounds might be the best lead candidates for therapeutic interventions hindering virus binding to the host cell. This study provides a strong impetus to develop novel drugs against the SARS-CoV-2 by targeting NRP1.

Accurate and fast characterization of the subtype sequences of Avian influenza A virus (AIAV) hemagglutinin (HA) and neuraminidase (NA) depends on expanding diagnostic services and is embedded in molecular epidemiological studies. A new approach for classifying the AIAV sequences of the HA and NA genes into subtypes using DNA sequence data and physicochemical properties is proposed. This method simply requires unaligned, full-length, or partial sequences of HA or NA DNA as input. It allows for quick and highly accurate assignments of HA sequences to subtypes H1–H16 and NA sequences to subtypes N1–N9. For feature extraction, k-gram, discrete wavelet transformation, and multivariate mutual information were used, and different classifiers were trained for prediction. Four different classifiers, Naïve Bayes, Support Vector Machine (SVM), K nearest neighbor (KNN), and Decision Tree, were compared using our feature selection method. This comparison is based on the 30% dataset separated from the original dataset for testing purposes. Among the four classifiers, Decision Tree was the best, and Precision, Recall, F1 score, and Accuracy were 0.9514, 0.9535, 0.9524, and 0.9571, respectively. Decision Tree had considerable improvements over the other three classifiers using our method. Results show that the proposed feature selection method, when trained with a Decision Tree classifier, gives the best results for accurate prediction of the AIAV subtype.

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