Artificial neural networks trained to detect viral and phage structural proteins.

Phages play critical roles in the survival and pathogenicity of their hosts, via lysogenic conversion factors, and in nutrient redistribution, via cell lysis. Analyses of phage- and viral-encoded genes in environmental samples provide insights into the physiological impact of viruses on microbial co...

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Autores principales:	Victor Seguritan, Nelson Alves, Michael Arnoult, Amy Raymond, Don Lorimer, Alex B Burgin, Peter Salamon, Anca M Segall
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/3fcd19c29c714c9c8afaeb6dc4345047
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spelling	oai:doaj.org-article:3fcd19c29c714c9c8afaeb6dc43450472021-11-18T05:51:03ZArtificial neural networks trained to detect viral and phage structural proteins.1553-734X1553-735810.1371/journal.pcbi.1002657https://doaj.org/article/3fcd19c29c714c9c8afaeb6dc43450472012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22927809/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Phages play critical roles in the survival and pathogenicity of their hosts, via lysogenic conversion factors, and in nutrient redistribution, via cell lysis. Analyses of phage- and viral-encoded genes in environmental samples provide insights into the physiological impact of viruses on microbial communities and human health. However, phage ORFs are extremely diverse of which over 70% of them are dissimilar to any genes with annotated functions in GenBank. Better identification of viruses would also aid in better detection and diagnosis of disease, in vaccine development, and generally in better understanding the physiological potential of any environment. In contrast to enzymes, viral structural protein function can be much more challenging to detect from sequence data because of low sequence conservation, few known conserved catalytic sites or sequence domains, and relatively limited experimental data. We have designed a method of predicting phage structural protein sequences that uses Artificial Neural Networks (ANNs). First, we trained ANNs to classify viral structural proteins using amino acid frequency; these correctly classify a large fraction of test cases with a high degree of specificity and sensitivity. Subsequently, we added estimates of protein isoelectric points as a feature to ANNs that classify specialized families of proteins, namely major capsid and tail proteins. As expected, these more specialized ANNs are more accurate than the structural ANNs. To experimentally validate the ANN predictions, several ORFs with no significant similarities to known sequences that are ANN-predicted structural proteins were examined by transmission electron microscopy. Some of these self-assembled into structures strongly resembling virion structures. Thus, our ANNs are new tools for identifying phage and potential prophage structural proteins that are difficult or impossible to detect by other bioinformatic analysis. The networks will be valuable when sequence is available but in vitro propagation of the phage may not be practical or possible.Victor SeguritanNelson AlvesMichael ArnoultAmy RaymondDon LorimerAlex B BurginPeter SalamonAnca M SegallPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 8, p e1002657 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Victor Seguritan Nelson Alves Michael Arnoult Amy Raymond Don Lorimer Alex B Burgin Peter Salamon Anca M Segall Artificial neural networks trained to detect viral and phage structural proteins.
description	Phages play critical roles in the survival and pathogenicity of their hosts, via lysogenic conversion factors, and in nutrient redistribution, via cell lysis. Analyses of phage- and viral-encoded genes in environmental samples provide insights into the physiological impact of viruses on microbial communities and human health. However, phage ORFs are extremely diverse of which over 70% of them are dissimilar to any genes with annotated functions in GenBank. Better identification of viruses would also aid in better detection and diagnosis of disease, in vaccine development, and generally in better understanding the physiological potential of any environment. In contrast to enzymes, viral structural protein function can be much more challenging to detect from sequence data because of low sequence conservation, few known conserved catalytic sites or sequence domains, and relatively limited experimental data. We have designed a method of predicting phage structural protein sequences that uses Artificial Neural Networks (ANNs). First, we trained ANNs to classify viral structural proteins using amino acid frequency; these correctly classify a large fraction of test cases with a high degree of specificity and sensitivity. Subsequently, we added estimates of protein isoelectric points as a feature to ANNs that classify specialized families of proteins, namely major capsid and tail proteins. As expected, these more specialized ANNs are more accurate than the structural ANNs. To experimentally validate the ANN predictions, several ORFs with no significant similarities to known sequences that are ANN-predicted structural proteins were examined by transmission electron microscopy. Some of these self-assembled into structures strongly resembling virion structures. Thus, our ANNs are new tools for identifying phage and potential prophage structural proteins that are difficult or impossible to detect by other bioinformatic analysis. The networks will be valuable when sequence is available but in vitro propagation of the phage may not be practical or possible.
format	article
author	Victor Seguritan Nelson Alves Michael Arnoult Amy Raymond Don Lorimer Alex B Burgin Peter Salamon Anca M Segall
author_facet	Victor Seguritan Nelson Alves Michael Arnoult Amy Raymond Don Lorimer Alex B Burgin Peter Salamon Anca M Segall
author_sort	Victor Seguritan
title	Artificial neural networks trained to detect viral and phage structural proteins.
title_short	Artificial neural networks trained to detect viral and phage structural proteins.
title_full	Artificial neural networks trained to detect viral and phage structural proteins.
title_fullStr	Artificial neural networks trained to detect viral and phage structural proteins.
title_full_unstemmed	Artificial neural networks trained to detect viral and phage structural proteins.
title_sort	artificial neural networks trained to detect viral and phage structural proteins.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/3fcd19c29c714c9c8afaeb6dc4345047
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Artificial neural networks trained to detect viral and phage structural proteins.

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