In Silico Analysis of the Effects of Omicron Spike Amino Acid Changes on the Interactions with Human Proteins.

The SARS-CoV-2 variant Omicron is characterized, among others, by more than 30 amino acid changes occurring on the spike glycoprotein with respect to the original SARS-CoV-2 spike protein. We report a comprehensive analysis of the effects of the Omicron spike amino acid changes in the interaction with human antibodies, obtained by modeling them into selected publicly available resolved 3D structures of spike-antibody complexes and investigating the effects of these mutations at structural level. We predict that the interactions of Omicron spike with human antibodies can be either negatively or positively affected by amino acid changes, with a predicted total loss of interactions only in a few complexes. Moreover, our analysis applied also to the spike-ACE2 interaction predicts that these amino acid changes may increase Omicron transmissibility. Our approach can be used to better understand SARS-CoV-2 transmissibility, detectability, and epidemiology and represents a model to be adopted also in the case of other variants.

Structural Dissection of Viral Spike-Protein Binding of SARS-CoV-2 and SARS-CoV-1 to the Human Angiotensin-Converting Enzyme 2 (ACE2) as Cellular Receptor.

An outbreak by a new severe acute respiratory syndrome betacoronavirus (SARS-CoV-2) has spread CoronaVirus Disease 2019 (COVID-19) all over the world. Immediately, following studies have confirmed the human Angiotensin-Converting Enzyme 2 (ACE2) as a cellular receptor of viral Spike-Protein (Sp) that mediates the CoV-2 invasion into the pulmonary host cells. Here, we compared the molecular interactions of the viral Sp from previous SARS-CoV-1 of 2002 and SARS-CoV-2 with the host ACE2 protein by in silico analysis of the available experimental structures of Sp-ACE2 complexes. The K417 amino acid residue, located in the region of Sp Receptor-Binding Domain (RBD) of the new coronavirus SARS-CoV-2, showed to have a key role for the binding to the ACE2 N-terminal region. The R426 residue of SARS-CoV-1 Sp-RBD also plays a key role, although by interacting with the central region of the ACE2 sequence. Therefore, our study evidenced peculiarities in the interactions of the two Sp-ACE2 complexes. Our outcomes were consistent with previously reported mutagenesis studies on SARS-CoV-1 and support the idea that a new and different RBD was acquired by SARS-CoV-2. These results have interesting implications and suggest further investigations.

A review on drug repurposing applicable to COVID-19.

Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug-disease or drug-target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

An investigation into the molecular basis of cancer comorbidities in coronavirus infection.

Comorbidities in COVID-19 patients often worsen clinical conditions and may represent death predictors. Here, the expression of five genes, known to encode coronavirus receptors/interactors (ACE2, TMPRSS2, CLEC4M, DPP4 and TMPRSS11D), was investigated in normal and cancer tissues, and their molecular relationships with clinical comorbidities were investigated. Using expression data from GENT2 databases, we evaluated gene expression in all anatomical districts from 32 normal tissues in 3902 individuals. Functional relationships with body districts were analyzed by chilibot. We performed DisGeNet, genemania and DAVID analyses to identify human diseases associated with these genes. Transcriptomic expression levels were then analyzed in 31 cancer types and healthy controls from approximately 43 000 individuals, using GEPIA2 and GENT2 databases. By performing receiver operating characteristic analysis, the area under the curve (AUC) was used to discriminate healthy from cancer patients. Coronavirus receptors were found to be expressed in several body districts. Moreover, the five genes were found to associate with acute respiratory syndrome, diabetes, cardiovascular diseases and cancer (i.e. the most frequent COVID-19 comorbidities). Their expression levels were found to be significantly altered in cancer types, including colon, kidney, liver, testis, thyroid and skin cancers (P < 0.0001); AUC > 0.80 suggests that TMPRSS2, CLEC4M and DPP4 are relevant markers of kidney, liver, and thyroid cancer, respectively. The five coronavirus receptors are related to all main COVID-19 comorbidities and three show significantly different expression in cancer versus control tissues. Further investigation into their role may help in monitoring other comorbidities, as well as for follow-up of patients who have recovered from SARS-CoV-2 infection.