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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Vydáno v:	Biochimica et biophysica acta. Molecular basis of disease Ročník 1867; číslo 1; s. 165978
Hlavní autoři:	Kabra, Ritika, Singh, Shailza
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Netherlands Elsevier B.V 01.01.2021
Témata:	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
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Abstract	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.
AbstractList	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. 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.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. 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. Unlabelled Image • 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. 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 ) 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.
ArticleNumber	165978
Author	Kabra, Ritika Singh, Shailza
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Snippet	An epidemic caused by COVID-19 in China turned into pandemic within a short duration affecting countries worldwide. Researchers and companies around the world...
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SubjectTerms	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
Title	Evolutionary artificial intelligence based peptide discoveries for effective Covid-19 therapeutics
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