ACE2-Fc and DPP4-Fc decoy receptors against SARS-CoV-2 and MERS-CoV variants: a quick therapeutic option for current and future coronaviruses outbreaks.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and the Middle East respiratory syndrome coronavirus (MERS-CoV) are highly pathogenic human coronaviruses (CoVs). Anti-CoVs mAbs and vaccines may be effective, but the emergence of neutralization escape variants is inevitable. Angiotensin-converting enzyme 2 and dipeptidyl peptidase 4 enzyme are the getaway receptors for SARS-CoV-2 and MERS-CoV, respectively. Thus, we reformatted these receptors as Fc-fusion decoy receptors. Then, we tested them in parallel with anti-SARS-CoV (ab1-IgG) and anti-MERS-CoV (M336-IgG) mAbs against several variants using pseudovirus neutralization assay. The generated Fc-based decoy receptors exhibited a strong inhibitory effect against all pseudotyped CoVs. Results showed that although mAbs can be effective antiviral drugs, they might rapidly lose their efficacy against highly mutated viruses. We suggest that receptor traps can be engineered as Fc-fusion proteins for highly mutating viruses with known entry receptors, for a faster and effective therapeutic response even against virus harboring antibodies escape mutations.

Protein-Protein Interaction Network Extraction Using Text Mining Methods Adds Insight into Autism Spectrum Disorder.

Text mining methods are being developed to assimilate the volume of biomedical textual materials that are continually expanding. Understanding protein-protein interaction (PPI) deficits would assist in explaining the genesis of diseases. In this study, we designed an automated system to extract PPIs from the biomedical literature that uses a deep learning sentence classification model, a pretrained word embedding, and a BiLSTM recurrent neural network with additional layers, a conditional random field (CRF) named entity recognition (NER) model, and shortest-dependency path (SDP) model using the SpaCy library in Python. The automated system ensures that it targets sentences that contain PPIs and not just these proteins mentioned in the framework of disease discovery or other context. Our first model achieved 13% greater precision on the Aimed/BioInfr benchmark corpus than the previous state-of-the-art BiLSTM neural network models. The NER model presented in this study achieved 98% precision on the Aimed/BioInfr corpus over previous models. In order to facilitate the production of an accurate representation of the PPI network, the processes were developed to systematically map the protein interactions in the texts. Overall, evaluating our system through the use of 6027 abstracts pertaining to seven proteins associated with Autism Spectrum Disorder completed the manually curated PPI network for these proteins. When it comes to complicated diseases, these networks would assist in understanding how PPI deficits contribute to disease development while also emphasizing the influence of interactions on protein function and biological processes.