DocumentCode :
893422
Title :
To Be or Not to Be: Predicting Soluble SecAs as Membrane Proteins
Author :
Hu, Hae-Jin ; Holley, Jeanetta ; He, Jieyue ; Harrison, Robert W. ; Yang, Hsiuchin ; Tai, Phang C. ; Pan, Yi
Author_Institution :
Molecular Basis of Disease Program, Georgia State Univ., Atlanta, GA
Volume :
6
Issue :
2
fYear :
2007
fDate :
6/1/2007 12:00:00 AM
Firstpage :
168
Lastpage :
179
Abstract :
SecA is an important component of protein translocation in bacteria, and exists in soluble and membrane-integrated forms. Most membrane prediction programs predict SecA as being a soluble protein, with the exception of TMpred and TopPred. However, the membrane associated predicted segments by TMpred and TopPred are inconsistent across bacterial species in spite of high sequence homology. In this paper we describe a new method for membrane protein prediction, PSSM_SVM, which provides consistent results for integral membrane domains of SecAs across bacterial species. This PSSM encoding scheme demonstrates the highest accuracy in terms of Q2 among the common prediction methods, and produces consistent results on blind test data. None of the previously described methods showed this kind of consistency when tested against the same blind test set. This scheme predicts traditional transmembrane segments and most of the soluble proteins accurately. The PSSM scheme applied to the membrane-associated protein SecA shows characteristic features. In the set of 223 known SecA sequences, the PSSM_SVM prediction scheme predicts eight to nine residue embedded membrane segments. This predicted region is part of a 12 residue helix from known X-ray crystal structures of SecAs. This information could be important for determining the structure of SecA proteins in the membrane which have different conformational properties from other transmembrane proteins, as well as other soluble proteins that may similarly integrate into lipid bi-layers.
Keywords :
biology computing; biomembranes; crystal structure; lipid bilayers; microorganisms; molecular biophysics; prediction theory; proteins; support vector machines; PSSM_SVM; TMpred; TopPred; X-ray crystal structures; bacteria; conformational properties; encoding; lipid bilayers; membrane Proteins; membrane prediction programs; protein translocation; soluble SecAs; Amino acids; Biomembranes; Computer science; Encoding; Helium; Lipidomics; Microorganisms; Prediction methods; Proteins; Testing; Embedded membrane segment; SecA protein; position specific scoring matrix; support vector machine; transmembrane segment; Adenosine Triphosphatases; Amino Acid Sequence; Artificial Intelligence; Bacterial Proteins; Cell Membrane; Computer Simulation; Membrane Transport Proteins; Models, Chemical; Models, Molecular; Molecular Sequence Data; Pattern Recognition, Automated; Sequence Analysis, Protein; Solubility;
fLanguage :
English
Journal_Title :
NanoBioscience, IEEE Transactions on
Publisher :
ieee
ISSN :
1536-1241
Type :
jour
DOI :
10.1109/TNB.2007.897486
Filename :
4220632
Link To Document :
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