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Best α‐helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information
Author(s) -
Viklund Håkan,
Elofsson Arne
Publication year - 2004
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1110/ps.04625404
Subject(s) - hidden markov model , topology (electrical circuits) , computer science , membrane topology , markov chain , computational biology , sequence (biology) , markov model , membrane protein , artificial intelligence , pattern recognition (psychology) , data mining , algorithm , machine learning , biology , mathematics , genetics , membrane , combinatorics
Methods that predict the topology of helical membrane proteins are standard tools when analyzing any proteome. Therefore, it is important to improve the performance of such methods. Here we introduce a novel method, PRODIV‐TMHMM, which is a profile‐based hidden Markov model (HMM) that also incorporates the best features of earlier HMM methods. In our tests, PRODIV‐TMHMM outperforms earlier methods both when evaluated on “low‐resolution” topology data and on high‐resolution 3D structures. The results presented here indicate that the topology could be correctly predicted for approximately two‐thirds of all membrane proteins using PRODIV‐TMHMM. The importance of evolutionary information for topology prediction is emphasized by the fact that compared with using single sequences, the performance of PRODIV‐TMHMM (as well as two other methods) is increased by approximately 10 percentage units by the use of homologous sequences. On a more general level, we also show that HMM‐based (or similar) methods perform superiorly to methods that focus mainly on identification of the membrane regions.