NDLI: HMM_RA: An Improved Method for Alpha-Helical Transmembrane Protein Topology Prediction

Content Provider	PubMed Central
Author	Hu, Jing Yan, Changhui
Copyright Year	2008
Abstract	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.
Ending Page	74
Starting Page	67
File Format	PDF
ISSN	11779322
e-ISSN	11779322
Journal	Bioinformatics and Biology Insights
Volume Number	2
Language	English
Publisher	Libertas Academica
Publisher Date	2008-01-31
Access Restriction	Open
Rights Holder	Libertas Academica
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Biochemistry Molecular Biology Computer Science Applications Computational Mathematics

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