Evolutionary artificial intelligence based peptide discoveries for effective Covid-19 therapeutics

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, dru...

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Published in:Biochimica et biophysica acta. Molecular basis of disease Vol. 1867; no. 1; p. 165978
Main Authors: Kabra, Ritika, Singh, Shailza
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.01.2021
Subjects:
Antibodies, Viral - immunology
Antibodies, Viral - therapeutic use
Antiviral Agents - therapeutic use
Artificial Intelligence
Computational biology
Covid-19
COVID-19 - drug therapy
COVID-19 - pathology
COVID-19 - prevention & control
COVID-19 - virology
Evolutionary peptides
Humans
Molecular Dynamics Simulation
Peptide Library
Peptides - pharmacology
Peptides - therapeutic use
SARS-CoV-2
SARS-CoV-2 - immunology
SARS-CoV-2 - isolation & purification
SARS-CoV-2 - physiology
Viral Vaccines - immunology
Virus Replication - drug effects
RTC
Mpro
Covid-19
DEE
RMSF
MERS-CoV
3CLpro
RMSD
ACE2
SARS-CoV-2
Evolutionary peptides
RBD
MD
PLpro
Artificial intelligence
ML
Computational biology
ISSN:0925-4439, 1879-260X, 1879-260X
Online Access:Get full text
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Summary: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.
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ISSN:0925-4439
1879-260X
1879-260X
DOI:10.1016/j.bbadis.2020.165978