title: To be or not to be: predicting soluble SecAs as membrane proteins.
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 Top-Pred. 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.
authors: Molecular Basis of Disease Program, Georgia State University, Atlanta, GA 30303, USA. haejin.hu@gmail.com
authors: Hae-Jin
authors: HJ
authors: Hu
authors: Jeanetta
authors: J
authors: Holley
authors: Jieyue
authors: J
authors: He
authors: Robert W
authors: RW
authors: Harrison
authors: Hsiuchin
authors: H
authors: Yang
authors: Phang C
authors: PC
authors: Tai
authors: Yi
authors: Y
authors: Pan
mesh_terms: D000251:Adenosine Triphosphatases / Q000737:chemistry* / Q000378:metabolism; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000737:chemistry* / Q000378:metabolism; D002462:Cell Membrane / Q000737:chemistry*; D003198:Computer Simulation; D026901:Membrane Transport Proteins / Q000737:chemistry* / Q000378:metabolism; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated; D000069816:SEC Translocation Channels; D000081416:SecA Proteins; D020539:Sequence Analysis, Protein / Q000379:methods*; D012995:Solubility
chemical_list: D001426:Bacterial Proteins; D026901:Membrane Transport Proteins; D000069816:SEC Translocation Channels; D000251:Adenosine Triphosphatases; D000081416:SecA Proteins