Potential neutralizing antibodies discovered for novel corona virus using machine learning

Abstract The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inh...

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Autores principales:	Rishikesh Magar, Prakarsh Yadav, Amir Barati Farimani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b4b7adeddee64f22aed3d515dc8eb84e
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spelling	oai:doaj.org-article:b4b7adeddee64f22aed3d515dc8eb84e2021-12-02T13:33:51ZPotential neutralizing antibodies discovered for novel corona virus using machine learning10.1038/s41598-021-84637-42045-2322https://doaj.org/article/b4b7adeddee64f22aed3d515dc8eb84e2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84637-4https://doaj.org/toc/2045-2322Abstract The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of SARS-CoV-2 will save the life of thousands. To predict neutralizing antibodies for SARS-CoV-2 in a high-throughput manner, in this paper, we use different machine learning (ML) model to predict the possible inhibitory synthetic antibodies for SARS-CoV-2. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, like XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine and Logistic Regression, we screened thousands of hypothetical antibody sequences and found nine stable antibodies that potentially inhibit SARS-CoV-2. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit SARS-CoV-2.Rishikesh MagarPrakarsh YadavAmir Barati FarimaniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rishikesh Magar Prakarsh Yadav Amir Barati Farimani Potential neutralizing antibodies discovered for novel corona virus using machine learning
description	Abstract The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of SARS-CoV-2 will save the life of thousands. To predict neutralizing antibodies for SARS-CoV-2 in a high-throughput manner, in this paper, we use different machine learning (ML) model to predict the possible inhibitory synthetic antibodies for SARS-CoV-2. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, like XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine and Logistic Regression, we screened thousands of hypothetical antibody sequences and found nine stable antibodies that potentially inhibit SARS-CoV-2. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit SARS-CoV-2.
format	article
author	Rishikesh Magar Prakarsh Yadav Amir Barati Farimani
author_facet	Rishikesh Magar Prakarsh Yadav Amir Barati Farimani
author_sort	Rishikesh Magar
title	Potential neutralizing antibodies discovered for novel corona virus using machine learning
title_short	Potential neutralizing antibodies discovered for novel corona virus using machine learning
title_full	Potential neutralizing antibodies discovered for novel corona virus using machine learning
title_fullStr	Potential neutralizing antibodies discovered for novel corona virus using machine learning
title_full_unstemmed	Potential neutralizing antibodies discovered for novel corona virus using machine learning
title_sort	potential neutralizing antibodies discovered for novel corona virus using machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b4b7adeddee64f22aed3d515dc8eb84e
work_keys_str_mv	AT rishikeshmagar potentialneutralizingantibodiesdiscoveredfornovelcoronavirususingmachinelearning AT prakarshyadav potentialneutralizingantibodiesdiscoveredfornovelcoronavirususingmachinelearning AT amirbaratifarimani potentialneutralizingantibodiesdiscoveredfornovelcoronavirususingmachinelearning
_version_	1718392815731343360

Potential neutralizing antibodies discovered for novel corona virus using machine learning

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