期刊
BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE
卷 1867, 期 1, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.bbadis.2020.165978
关键词
Covid-19; Artificial intelligence; Evolutionary peptides; Computational biology; SARS-CoV-2
资金
- National Centre for Cell Science (NCCS)
Worldwide researchers and companies are exploring various strategies, including vaccine development, drug repurposing, and AI-based drug discovery, to combat COVID-19 pandemic. The use of computational biology and machine-learning algorithms is being considered for their potential to provide fast and accurate outcomes in this crisis. Furthermore, the development of evolutionary peptides through machine-learning algorithms shows promise in providing cross-protection against diverse Covid-19 variants.
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 (M-Pro) 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.
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