Evolutionary artificial intelligence based peptide discoveries for effective Covid-19 therapeutics - Kabra, Ritika; Singh, Shailza (Biochimica et biophysica acta. Molecular basis of disease, 2021)
An epidemic caused by COVID-19 in China turned into pandemic within a short duration affecting countries worldwide. Researchers and companies around the world are working on all the possible strategies to develop a curative or preventive strategy for the same, which includes vaccine development, drug repurposing, plasma therapy, and drug discovery based on Artificial intelligence. Therapeutic approaches based on Computational biology and Machine-learning algorithms are specially considered, with a view that these could provide a fast and accurate outcome in the present scenario. As an effort towards developing possible therapeutics for COVID-19, we have used machine-learning algorithms for the generation of alignment kernels from diverse viral sequences of Covid-19 reported from India, China, Italy and USA. Using these diverse sequences we have identified the conserved motifs and subsequently a peptide library was designed against them. Of these, 4 peptides have shown strong binding affinity against the main protease of SARS-CoV-2 (Mpro) and also maintained their stability and specificity under physiological conditions as observed through MD Simulations. Our data suggest that these evolutionary peptides against COVID-19 if found effective may provide cross-protection against diverse Covid-19 variants. [Display omitted] •Machine-learning algorithm was employed to analyze diverse viral sequences of Covid-19.•Alignment kernels were generated which were subsequently used for motif prediction.•Optimized algorithm helped in designing library of evolutionary set of peptides.•MD Simulations revealed the stability and selectivity of the selected top 4 peptides with main protease Mpro.