Prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms.

Recent analyses of human genome sequences have given rise to impressive advances in identifying non-synonymous single nucleotide polymorphisms (nsSNPs). By contrast, the annotation of nsSNPs and their links to diseases are progressing at a much slower pace. Many of the current approaches to analysin...

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Autores principales:	Tammy M K Cheng, Yu-En Lu, Michele Vendruscolo, Pietro Lio', Tom L Blundell
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2008
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c4457e324d0a456987d7b6e7200d27f1
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spelling	oai:doaj.org-article:c4457e324d0a456987d7b6e7200d27f12021-11-25T05:41:12ZPrediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms.1553-734X1553-735810.1371/journal.pcbi.1000135https://doaj.org/article/c4457e324d0a456987d7b6e7200d27f12008-07-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18654622/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Recent analyses of human genome sequences have given rise to impressive advances in identifying non-synonymous single nucleotide polymorphisms (nsSNPs). By contrast, the annotation of nsSNPs and their links to diseases are progressing at a much slower pace. Many of the current approaches to analysing disease-associated nsSNPs use primarily sequence and evolutionary information, while structural information is relatively less exploited. In order to explore the potential of such information, we developed a structure-based approach, Bongo (Bonds ON Graph), to predict structural effects of nsSNPs. Bongo considers protein structures as residue-residue interaction networks and applies graph theoretical measures to identify the residues that are critical for maintaining structural stability by assessing the consequences on the interaction network of single point mutations. Our results show that Bongo is able to identify mutations that cause both local and global structural effects, with a remarkably low false positive rate. Application of the Bongo method to the prediction of 506 disease-associated nsSNPs resulted in a performance (positive predictive value, PPV, 78.5%) similar to that of PolyPhen (PPV, 77.2%) and PANTHER (PPV, 72.2%). As the Bongo method is solely structure-based, our results indicate that the structural changes resulting from nsSNPs are closely associated to their pathological consequences.Tammy M K ChengYu-En LuMichele VendruscoloPietro Lio'Tom L BlundellPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 4, Iss 7, p e1000135 (2008)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Tammy M K Cheng Yu-En Lu Michele Vendruscolo Pietro Lio' Tom L Blundell Prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms.
description	Recent analyses of human genome sequences have given rise to impressive advances in identifying non-synonymous single nucleotide polymorphisms (nsSNPs). By contrast, the annotation of nsSNPs and their links to diseases are progressing at a much slower pace. Many of the current approaches to analysing disease-associated nsSNPs use primarily sequence and evolutionary information, while structural information is relatively less exploited. In order to explore the potential of such information, we developed a structure-based approach, Bongo (Bonds ON Graph), to predict structural effects of nsSNPs. Bongo considers protein structures as residue-residue interaction networks and applies graph theoretical measures to identify the residues that are critical for maintaining structural stability by assessing the consequences on the interaction network of single point mutations. Our results show that Bongo is able to identify mutations that cause both local and global structural effects, with a remarkably low false positive rate. Application of the Bongo method to the prediction of 506 disease-associated nsSNPs resulted in a performance (positive predictive value, PPV, 78.5%) similar to that of PolyPhen (PPV, 77.2%) and PANTHER (PPV, 72.2%). As the Bongo method is solely structure-based, our results indicate that the structural changes resulting from nsSNPs are closely associated to their pathological consequences.
format	article
author	Tammy M K Cheng Yu-En Lu Michele Vendruscolo Pietro Lio' Tom L Blundell
author_facet	Tammy M K Cheng Yu-En Lu Michele Vendruscolo Pietro Lio' Tom L Blundell
author_sort	Tammy M K Cheng
title	Prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms.
title_short	Prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms.
title_full	Prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms.
title_fullStr	Prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms.
title_full_unstemmed	Prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms.
title_sort	prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms.
publisher	Public Library of Science (PLoS)
publishDate	2008
url	https://doaj.org/article/c4457e324d0a456987d7b6e7200d27f1
work_keys_str_mv	AT tammymkcheng predictionbygraphtheoreticmeasuresofstructuraleffectsinproteinsarisingfromnonsynonymoussinglenucleotidepolymorphisms AT yuenlu predictionbygraphtheoreticmeasuresofstructuraleffectsinproteinsarisingfromnonsynonymoussinglenucleotidepolymorphisms AT michelevendruscolo predictionbygraphtheoreticmeasuresofstructuraleffectsinproteinsarisingfromnonsynonymoussinglenucleotidepolymorphisms AT pietrolio predictionbygraphtheoreticmeasuresofstructuraleffectsinproteinsarisingfromnonsynonymoussinglenucleotidepolymorphisms AT tomlblundell predictionbygraphtheoreticmeasuresofstructuraleffectsinproteinsarisingfromnonsynonymoussinglenucleotidepolymorphisms
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Prediction by graph theoretic measures of structural effects in proteins arising from non-synonymous single nucleotide polymorphisms.

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