Cargando…

Bi-Directional SIFT Predicts a Subset of Activating Mutations

Advancements in sequencing technologies have empowered recent efforts to identify polymorphisms and mutations on a global scale. The large number of variations and mutations found in these projects requires high-throughput tools to identify those that are most likely to have an impact on function. N...

Descripción completa

Detalles Bibliográficos
Autores principales: Lee, William, Zhang, Yan, Mukhyala, Kiran, Lazarus, Robert A., Zhang, Zemin
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2788704/
https://www.ncbi.nlm.nih.gov/pubmed/20011534
http://dx.doi.org/10.1371/journal.pone.0008311
_version_ 1782175009884602368
author Lee, William
Zhang, Yan
Mukhyala, Kiran
Lazarus, Robert A.
Zhang, Zemin
author_facet Lee, William
Zhang, Yan
Mukhyala, Kiran
Lazarus, Robert A.
Zhang, Zemin
author_sort Lee, William
collection PubMed
description Advancements in sequencing technologies have empowered recent efforts to identify polymorphisms and mutations on a global scale. The large number of variations and mutations found in these projects requires high-throughput tools to identify those that are most likely to have an impact on function. Numerous computational tools exist for predicting which mutations are likely to be functional, but none that specifically attempt to identify mutations that result in hyperactivation or gain-of-function. Here we present a modified version of the SIFT (Sorting Intolerant from Tolerant) algorithm that utilizes protein sequence alignments with homologous sequences to identify functional mutations based on evolutionary fitness. We show that this bi-directional SIFT (B-SIFT) is capable of identifying experimentally verified activating mutants from multiple datasets. B-SIFT analysis of large-scale cancer genotyping data identified potential activating mutations, some of which we have provided detailed structural evidence to support. B-SIFT could prove to be a valuable tool for efforts in protein engineering as well as in identification of functional mutations in cancer.
format Text
id pubmed-2788704
institution National Center for Biotechnology Information
language English
publishDate 2009
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-27887042009-12-14 Bi-Directional SIFT Predicts a Subset of Activating Mutations Lee, William Zhang, Yan Mukhyala, Kiran Lazarus, Robert A. Zhang, Zemin PLoS One Research Article Advancements in sequencing technologies have empowered recent efforts to identify polymorphisms and mutations on a global scale. The large number of variations and mutations found in these projects requires high-throughput tools to identify those that are most likely to have an impact on function. Numerous computational tools exist for predicting which mutations are likely to be functional, but none that specifically attempt to identify mutations that result in hyperactivation or gain-of-function. Here we present a modified version of the SIFT (Sorting Intolerant from Tolerant) algorithm that utilizes protein sequence alignments with homologous sequences to identify functional mutations based on evolutionary fitness. We show that this bi-directional SIFT (B-SIFT) is capable of identifying experimentally verified activating mutants from multiple datasets. B-SIFT analysis of large-scale cancer genotyping data identified potential activating mutations, some of which we have provided detailed structural evidence to support. B-SIFT could prove to be a valuable tool for efforts in protein engineering as well as in identification of functional mutations in cancer. Public Library of Science 2009-12-14 /pmc/articles/PMC2788704/ /pubmed/20011534 http://dx.doi.org/10.1371/journal.pone.0008311 Text en Lee et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Lee, William
Zhang, Yan
Mukhyala, Kiran
Lazarus, Robert A.
Zhang, Zemin
Bi-Directional SIFT Predicts a Subset of Activating Mutations
title Bi-Directional SIFT Predicts a Subset of Activating Mutations
title_full Bi-Directional SIFT Predicts a Subset of Activating Mutations
title_fullStr Bi-Directional SIFT Predicts a Subset of Activating Mutations
title_full_unstemmed Bi-Directional SIFT Predicts a Subset of Activating Mutations
title_short Bi-Directional SIFT Predicts a Subset of Activating Mutations
title_sort bi-directional sift predicts a subset of activating mutations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2788704/
https://www.ncbi.nlm.nih.gov/pubmed/20011534
http://dx.doi.org/10.1371/journal.pone.0008311
work_keys_str_mv AT leewilliam bidirectionalsiftpredictsasubsetofactivatingmutations
AT zhangyan bidirectionalsiftpredictsasubsetofactivatingmutations
AT mukhyalakiran bidirectionalsiftpredictsasubsetofactivatingmutations
AT lazarusroberta bidirectionalsiftpredictsasubsetofactivatingmutations
AT zhangzemin bidirectionalsiftpredictsasubsetofactivatingmutations