In-Silico Tool for Predicting, Scanning, and Designing Defensins

Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a ne...

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Autores principales:	Dilraj Kaur, Sumeet Patiyal, Chakit Arora, Ritesh Singh, Gaurav Lodhi, Gajendra P. S. Raghava
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	innate immunity defensins AMPs computer aided machine learning Immunologic diseases. Allergy RC581-607
Acceso en línea:	https://doaj.org/article/f22d2233e6e44c9282b9de89c2e7142e
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spelling	oai:doaj.org-article:f22d2233e6e44c9282b9de89c2e7142e2021-11-22T06:26:17ZIn-Silico Tool for Predicting, Scanning, and Designing Defensins1664-322410.3389/fimmu.2021.780610https://doaj.org/article/f22d2233e6e44c9282b9de89c2e7142e2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fimmu.2021.780610/fullhttps://doaj.org/toc/1664-3224Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a need to develop a fast and accurate method for predicting defensins. In this study, a systematic attempt has been made to develop models for predicting defensins from available information on defensins. We created a dataset of defensins and non-defensins called the main dataset that contains 1,036 defensins and 1,035 AMPs (antimicrobial peptides, or non-defensins) to understand the difference between defensins and AMPs. Our analysis indicates that certain residues like Cys, Arg, and Tyr are more abundant in defensins in comparison to AMPs. We developed machine learning technique-based models on the main dataset using a wide range of peptide features. Our SVM (support vector machine)-based model discriminates defensins and AMPs with MCC of 0.88 and AUC of 0.98 on the validation set of the main dataset. In addition, we created an alternate dataset that consists of 1,036 defensins and 1,054 non-defensins obtained from Swiss-Prot. Models were also developed on the alternate dataset to predict defensins. Our SVM-based model achieved maximum MCC of 0.96 with AUC of 0.99 on the validation set of the alternate dataset. All models were trained, tested, and validated using standard protocols. Finally, we developed a web-based service “DefPred” to predict defensins, scan defensins in proteins, and design the best defensins from their analogs. The stand-alone software and web server of DefPred are available at https://webs.iiitd.edu.in/raghava/defpred.Dilraj KaurSumeet PatiyalChakit AroraRitesh SinghGaurav LodhiGajendra P. S. RaghavaFrontiers Media S.A.articleinnate immunitydefensinsAMPscomputer aidedmachine learningImmunologic diseases. AllergyRC581-607ENFrontiers in Immunology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	innate immunity defensins AMPs computer aided machine learning Immunologic diseases. Allergy RC581-607
spellingShingle	innate immunity defensins AMPs computer aided machine learning Immunologic diseases. Allergy RC581-607 Dilraj Kaur Sumeet Patiyal Chakit Arora Ritesh Singh Gaurav Lodhi Gajendra P. S. Raghava In-Silico Tool for Predicting, Scanning, and Designing Defensins
description	Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a need to develop a fast and accurate method for predicting defensins. In this study, a systematic attempt has been made to develop models for predicting defensins from available information on defensins. We created a dataset of defensins and non-defensins called the main dataset that contains 1,036 defensins and 1,035 AMPs (antimicrobial peptides, or non-defensins) to understand the difference between defensins and AMPs. Our analysis indicates that certain residues like Cys, Arg, and Tyr are more abundant in defensins in comparison to AMPs. We developed machine learning technique-based models on the main dataset using a wide range of peptide features. Our SVM (support vector machine)-based model discriminates defensins and AMPs with MCC of 0.88 and AUC of 0.98 on the validation set of the main dataset. In addition, we created an alternate dataset that consists of 1,036 defensins and 1,054 non-defensins obtained from Swiss-Prot. Models were also developed on the alternate dataset to predict defensins. Our SVM-based model achieved maximum MCC of 0.96 with AUC of 0.99 on the validation set of the alternate dataset. All models were trained, tested, and validated using standard protocols. Finally, we developed a web-based service “DefPred” to predict defensins, scan defensins in proteins, and design the best defensins from their analogs. The stand-alone software and web server of DefPred are available at https://webs.iiitd.edu.in/raghava/defpred.
format	article
author	Dilraj Kaur Sumeet Patiyal Chakit Arora Ritesh Singh Gaurav Lodhi Gajendra P. S. Raghava
author_facet	Dilraj Kaur Sumeet Patiyal Chakit Arora Ritesh Singh Gaurav Lodhi Gajendra P. S. Raghava
author_sort	Dilraj Kaur
title	In-Silico Tool for Predicting, Scanning, and Designing Defensins
title_short	In-Silico Tool for Predicting, Scanning, and Designing Defensins
title_full	In-Silico Tool for Predicting, Scanning, and Designing Defensins
title_fullStr	In-Silico Tool for Predicting, Scanning, and Designing Defensins
title_full_unstemmed	In-Silico Tool for Predicting, Scanning, and Designing Defensins
title_sort	in-silico tool for predicting, scanning, and designing defensins
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/f22d2233e6e44c9282b9de89c2e7142e
work_keys_str_mv	AT dilrajkaur insilicotoolforpredictingscanninganddesigningdefensins AT sumeetpatiyal insilicotoolforpredictingscanninganddesigningdefensins AT chakitarora insilicotoolforpredictingscanninganddesigningdefensins AT riteshsingh insilicotoolforpredictingscanninganddesigningdefensins AT gauravlodhi insilicotoolforpredictingscanninganddesigningdefensins AT gajendrapsraghava insilicotoolforpredictingscanninganddesigningdefensins
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In-Silico Tool for Predicting, Scanning, and Designing Defensins

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