In silico approach for predicting toxicity of peptides and proteins.

<h4>Background</h4>Over the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have...

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Autores principales:	Sudheer Gupta, Pallavi Kapoor, Kumardeep Chaudhary, Ankur Gautam, Rahul Kumar, Open Source Drug Discovery Consortium, Gajendra P S Raghava
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7c12e6ae37b24f3ead92d7637e4ca0da
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spelling	oai:doaj.org-article:7c12e6ae37b24f3ead92d7637e4ca0da2021-11-18T08:55:22ZIn silico approach for predicting toxicity of peptides and proteins.1932-620310.1371/journal.pone.0073957https://doaj.org/article/7c12e6ae37b24f3ead92d7637e4ca0da2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24058508/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Over the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have emerged as promising therapeutic molecules against many diseases. However, one of the bottlenecks in peptide/protein-based therapy is their toxicity. Therefore, in the present study, we developed in silico models for predicting toxicity of peptides and proteins.<h4>Description</h4>We obtained toxic peptides having 35 or fewer residues from various databases for developing prediction models. Non-toxic or random peptides were obtained from SwissProt and TrEMBL. It was observed that certain residues like Cys, His, Asn, and Pro are abundant as well as preferred at various positions in toxic peptides. We developed models based on machine learning technique and quantitative matrix using various properties of peptides for predicting toxicity of peptides. The performance of dipeptide-based model in terms of accuracy was 94.50% with MCC 0.88. In addition, various motifs were extracted from the toxic peptides and this information was combined with dipeptide-based model for developing a hybrid model. In order to evaluate the over-optimization of the best model based on dipeptide composition, we evaluated its performance on independent datasets and achieved accuracy around 90%. Based on above study, a web server, ToxinPred has been developed, which would be helpful in predicting (i) toxicity or non-toxicity of peptides, (ii) minimum mutations in peptides for increasing or decreasing their toxicity, and (iii) toxic regions in proteins.<h4>Conclusion</h4>ToxinPred is a unique in silico method of its kind, which will be useful in predicting toxicity of peptides/proteins. In addition, it will be useful in designing least toxic peptides and discovering toxic regions in proteins. We hope that the development of ToxinPred will provide momentum to peptide/protein-based drug discovery (http://crdd.osdd.net/raghava/toxinpred/).Sudheer GuptaPallavi KapoorKumardeep ChaudharyAnkur GautamRahul KumarOpen Source Drug Discovery ConsortiumGajendra P S RaghavaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 9, p e73957 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sudheer Gupta Pallavi Kapoor Kumardeep Chaudhary Ankur Gautam Rahul Kumar Open Source Drug Discovery Consortium Gajendra P S Raghava In silico approach for predicting toxicity of peptides and proteins.
description	<h4>Background</h4>Over the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have emerged as promising therapeutic molecules against many diseases. However, one of the bottlenecks in peptide/protein-based therapy is their toxicity. Therefore, in the present study, we developed in silico models for predicting toxicity of peptides and proteins.<h4>Description</h4>We obtained toxic peptides having 35 or fewer residues from various databases for developing prediction models. Non-toxic or random peptides were obtained from SwissProt and TrEMBL. It was observed that certain residues like Cys, His, Asn, and Pro are abundant as well as preferred at various positions in toxic peptides. We developed models based on machine learning technique and quantitative matrix using various properties of peptides for predicting toxicity of peptides. The performance of dipeptide-based model in terms of accuracy was 94.50% with MCC 0.88. In addition, various motifs were extracted from the toxic peptides and this information was combined with dipeptide-based model for developing a hybrid model. In order to evaluate the over-optimization of the best model based on dipeptide composition, we evaluated its performance on independent datasets and achieved accuracy around 90%. Based on above study, a web server, ToxinPred has been developed, which would be helpful in predicting (i) toxicity or non-toxicity of peptides, (ii) minimum mutations in peptides for increasing or decreasing their toxicity, and (iii) toxic regions in proteins.<h4>Conclusion</h4>ToxinPred is a unique in silico method of its kind, which will be useful in predicting toxicity of peptides/proteins. In addition, it will be useful in designing least toxic peptides and discovering toxic regions in proteins. We hope that the development of ToxinPred will provide momentum to peptide/protein-based drug discovery (http://crdd.osdd.net/raghava/toxinpred/).
format	article
author	Sudheer Gupta Pallavi Kapoor Kumardeep Chaudhary Ankur Gautam Rahul Kumar Open Source Drug Discovery Consortium Gajendra P S Raghava
author_facet	Sudheer Gupta Pallavi Kapoor Kumardeep Chaudhary Ankur Gautam Rahul Kumar Open Source Drug Discovery Consortium Gajendra P S Raghava
author_sort	Sudheer Gupta
title	In silico approach for predicting toxicity of peptides and proteins.
title_short	In silico approach for predicting toxicity of peptides and proteins.
title_full	In silico approach for predicting toxicity of peptides and proteins.
title_fullStr	In silico approach for predicting toxicity of peptides and proteins.
title_full_unstemmed	In silico approach for predicting toxicity of peptides and proteins.
title_sort	in silico approach for predicting toxicity of peptides and proteins.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/7c12e6ae37b24f3ead92d7637e4ca0da
work_keys_str_mv	AT sudheergupta insilicoapproachforpredictingtoxicityofpeptidesandproteins AT pallavikapoor insilicoapproachforpredictingtoxicityofpeptidesandproteins AT kumardeepchaudhary insilicoapproachforpredictingtoxicityofpeptidesandproteins AT ankurgautam insilicoapproachforpredictingtoxicityofpeptidesandproteins AT rahulkumar insilicoapproachforpredictingtoxicityofpeptidesandproteins AT opensourcedrugdiscoveryconsortium insilicoapproachforpredictingtoxicityofpeptidesandproteins AT gajendrapsraghava insilicoapproachforpredictingtoxicityofpeptidesandproteins
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In silico approach for predicting toxicity of peptides and proteins.

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