JOURNAL ARTICLE

CW-PRED: Prediction of C-terminal surface anchoring sorting signals in bacteria and Archaea.

  • Published In: Journal of Bioinformatics & Computational Biology, 2024, v. 22, n. 4. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Chatziargyri, Aikaterini G.; Stasi, Evangelia A.; Tsirigos, Konstantinos I.; Litou, Zoi I.; Iconomidou, Vassiliki A.; Bagos, Pantelis G. 3 of 3

Abstract

Sorting signals are crucial for the anchoring of proteins to the cell surface in archaea and bacteria. These proteins often feature distinct motifs at their C-terminus, cleaved by sortase or sortase-like enzymes. Gram-positive bacteria exhibit the LPXTGX consensus motif, cleaved by sortases, while Gram-negative bacteria employ exosortases recognizing motifs like PEP. Archaea utilize exosortase homologs known as archaeosortases for signal anchoring. Traditionally identification of such C-terminal sorting signals was performed with profile Hidden Markov Models (pHMMs). The Cell-Wall PREDiction (CW-PRED) method introduced for the first time a custom-made class HMM for proteins in Gram-positive bacteria that contain a cell wall sorting signal which begins with an LPXTG motif, followed by a hydrophobic domain and a tail of positively charged residues. Here we present a new and updated version of CW-PRED for predicting C-terminal sorting signals in Archaea, Gram-positive, and Gram-negative bacteria. We used a large training set and several model enhancements that improve motif identification in order to achieve better discrimination between C-terminal signals and other proteins. Cross-validation demonstrates CW-PRED's superiority in sensitivity and specificity compared to other methods. Application of the method in reference proteomes reveals a large number of potential surface proteins not previously identified. The method is available for academic use at http://195.251.108.230/apps.compgen.org/CW-PRED/ and as standalone software. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Bioinformatics & Computational Biology. 2024/08, Vol. 22, Issue 4, p1
  • Document Type:Article
  • Subject Area:Botany
  • Publication Date:2024
  • ISSN:0219-7200
  • DOI:10.1142/S0219720024500215
  • Accession Number:179689793
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