Sever acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA (ssRNA) virus, responsible for severe acute respiratory disease (COVID-19). A large number of natural compounds are under trial for screening compounds, possessing potential inhibitory effect against the viral infection. Keeping in view the importance of marine compounds in antiviral activity, we investigated the potency of some marine natural products to target SARS-CoV-2 main protease (M pro) (PDB ID 6MO3). The crystallographic structure of M pro in an apo form was retrieved from Protein Data Bank and marine compounds from PubChem. These structures were prepared for docking and the complex with good docking score was subjected to molecular dynamic (MD) simulations for a period of 100 ns. To measure the stability, flexibility, and average distance between the target and compounds, root mean square deviations (RMSD), root mean square fluctuation (RMSF), and the distance matrix were calculated. Among five marine compounds, C-1 (PubChem CID 11170714) exhibited good activity, interacting with the active site and surrounding residues, forming many hydrogen and hydrophobic interactions. The C-1 also attained a stable dynamic behavior, and the average distance between compound and target remains constant. In conclusion, marine natural compounds may be used as a potential inhibitor against SARS-CoV-2 for better management of COVID-19.

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Gram-negative bacteria use various secretion systems to deliver their secreted effectors. Among them, type IV secretion system exists widely in a variety of bacterial species, and secretes type IV secreted effectors (T4SEs), which play vital roles in host-pathogen interactions. However, experimental approaches to identify T4SEs are time- and resource-consuming. In the present study, we aim to develop an in silico stacked ensemble method to predict whether a protein is an effector of type IV secretion system or not based on its sequence information. The protein sequences were encoded by the feature of position specific scoring matrix (PSSM)-composition by summing rows that correspond to the same amino acid residues in PSSM profiles. Based on the PSSM-composition features, we develop a stacked ensemble model PredT4SE-Stack to predict T4SEs, which utilized an ensemble of base-classifiers implemented by various machine learning algorithms, such as support vector machine, gradient boosting machine, and extremely randomized trees, to generate outputs for the meta-classifier in the classification system. Our results demonstrated that the framework of PredT4SE-Stack was a feasible and effective way to accurately identify T4SEs based on protein sequence information. The datasets and source code of PredT4SE-Stack are freely available at http://xbioinfo.sjtu.edu.cn/PredT4SE_Stack/index.php.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its new variants reported in different countries have posed a serious threat to human health and social fabrics worldwide. In addition, these new variants hindered the efforts of vaccines and other therapeutic developments. In this review article, we explained the emergence of new variants of SARS-CoV-2, their transmission risk, mortality rate, and, more importantly, the impact of each new variant on the efficacy of the developed vaccines reported in different literature and findings. The literature reported that with the emergence of new variants, the efficacy of different vaccines is declined, hospitalization is increased while the risk of reinfection also increased. The reports concluded that the emergence of a variant that entirely evades the immune response triggered by the vaccine is improbable. The emergence of new variants and reports of re-infections are creating a more distressing situation and therefore demands further investigation to formulate an effective therapeutic strategy.

One of the main problems with the joint use of multiple drugs is that it may cause adverse drug interactions and side effects that damage the body. Therefore, it is important to predict potential drug interactions. However, most of the available prediction methods can only predict whether two drugs interact or not, whereas few methods can predict interaction events between two drugs. Accurately predicting interaction events of two drugs is more useful for researchers to study the mechanism of the interaction of two drugs. In the present study, we propose a novel method, MDF-SA-DDI, which predicts drug–drug interaction (DDI) events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. MDF-SA-DDI is mainly composed of two parts: multi-source drug fusion and multi-source feature fusion. First, we combine two drugs in four different ways and input the combined drug feature representation into four different drug fusion networks (Siamese network, convolutional neural network and two auto-encoders) to obtain the latent feature vectors of the drug pairs, in which the two auto-encoders have the same structure, and their main difference is the number of neurons in the input layer of the two auto-encoders. Then, we use transformer blocks that include self-attention mechanism to perform latent feature fusion. We conducted experiments on three different tasks with two datasets. On the small dataset, the area under the precision–recall-curve (AUPR) and F1 scores of our method on task 1 reached 0.9737 and 0.8878, respectively, which were better than the state-of-the-art method. On the large dataset, the AUPR and F1 scores of our method on task 1 reached 0.9773 and 0.9117, respectively. In task 2 and task 3 of two datasets, our method also achieved the same or better performance as the state-of-the-art method. More importantly, the case studies on five DDI events are conducted and achieved satisfactory performance. The source codes and data are available at https://github.com/ShenggengLin/MDF-SA-DDI.