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HMM_RA: An Improved Method for Alpha-Helical Transmembrane Protein Topology Prediction
α-helical transmembrane (TM) proteins play important and diverse functional roles in cells. The ability to predict the topology of these proteins is important for identifying functional sites and inferring function of membrane proteins. This paper presents a Hidden Markov Model (referred to as HMM_R...
Autores principales: | , |
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Formato: | Texto |
Lenguaje: | English |
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Libertas Academica
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2735969/ https://www.ncbi.nlm.nih.gov/pubmed/19812766 |
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author | Hu, Jing Yan, Changhui |
author_facet | Hu, Jing Yan, Changhui |
author_sort | Hu, Jing |
collection | PubMed |
description | α-helical transmembrane (TM) proteins play important and diverse functional roles in cells. The ability to predict the topology of these proteins is important for identifying functional sites and inferring function of membrane proteins. This paper presents a Hidden Markov Model (referred to as HMM_RA) that can predict the topology of α-helical transmembrane proteins with improved performance. HMM_RA adopts the same structure as the HMMTOP method, which has five modules: inside loop, inside helix tail, membrane helix, outside helix tail and outside loop. Each module consists of one or multiple states. HMM_RA allows using reduced alphabets to encode protein sequences. Thus, each state of HMM_RA is associated with n emission probabilities, where n is the size of the reduced alphabet set. Direct comparisons using two standard data sets show that HMM_RA consistently outperforms HMMTOP and TMHMM in topology prediction. Specifically, on a high-quality data set of 83 proteins, HMM_RA outperforms HMMTOP by up to 7.6% in topology accuracy and 6.4% in α-helices location accuracy. On the same data set, HMM_RA outperforms TMHMM by up to 6.4% in topology accuracy and 2.9% in location accuracy. Comparison also shows that HMM_RA achieves comparable performance as Phobius, a recently published method. |
format | Text |
id | pubmed-2735969 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Libertas Academica |
record_format | MEDLINE/PubMed |
spelling | pubmed-27359692009-09-14 HMM_RA: An Improved Method for Alpha-Helical Transmembrane Protein Topology Prediction Hu, Jing Yan, Changhui Bioinform Biol Insights Original Research α-helical transmembrane (TM) proteins play important and diverse functional roles in cells. The ability to predict the topology of these proteins is important for identifying functional sites and inferring function of membrane proteins. This paper presents a Hidden Markov Model (referred to as HMM_RA) that can predict the topology of α-helical transmembrane proteins with improved performance. HMM_RA adopts the same structure as the HMMTOP method, which has five modules: inside loop, inside helix tail, membrane helix, outside helix tail and outside loop. Each module consists of one or multiple states. HMM_RA allows using reduced alphabets to encode protein sequences. Thus, each state of HMM_RA is associated with n emission probabilities, where n is the size of the reduced alphabet set. Direct comparisons using two standard data sets show that HMM_RA consistently outperforms HMMTOP and TMHMM in topology prediction. Specifically, on a high-quality data set of 83 proteins, HMM_RA outperforms HMMTOP by up to 7.6% in topology accuracy and 6.4% in α-helices location accuracy. On the same data set, HMM_RA outperforms TMHMM by up to 6.4% in topology accuracy and 2.9% in location accuracy. Comparison also shows that HMM_RA achieves comparable performance as Phobius, a recently published method. Libertas Academica 2008-01-31 /pmc/articles/PMC2735969/ /pubmed/19812766 Text en Copyright © 2008 The authors. http://creativecommons.org/licenses/by/3.0 This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Original Research Hu, Jing Yan, Changhui HMM_RA: An Improved Method for Alpha-Helical Transmembrane Protein Topology Prediction |
title | HMM_RA: An Improved Method for Alpha-Helical Transmembrane Protein Topology Prediction |
title_full | HMM_RA: An Improved Method for Alpha-Helical Transmembrane Protein Topology Prediction |
title_fullStr | HMM_RA: An Improved Method for Alpha-Helical Transmembrane Protein Topology Prediction |
title_full_unstemmed | HMM_RA: An Improved Method for Alpha-Helical Transmembrane Protein Topology Prediction |
title_short | HMM_RA: An Improved Method for Alpha-Helical Transmembrane Protein Topology Prediction |
title_sort | hmm_ra: an improved method for alpha-helical transmembrane protein topology prediction |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2735969/ https://www.ncbi.nlm.nih.gov/pubmed/19812766 |
work_keys_str_mv | AT hujing hmmraanimprovedmethodforalphahelicaltransmembraneproteintopologyprediction AT yanchanghui hmmraanimprovedmethodforalphahelicaltransmembraneproteintopologyprediction |