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Predicting transmembrane beta-barrels in proteomes.

Henry R Bigelow1, Donald S Petrey, Jinfeng Liu

  • 1CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, USA. bigelow@cubic.bioc.columbia.edu

Nucleic Acids Research
|May 14, 2004
PubMed
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Researchers developed a new computational method to predict beta-barrel membrane proteins (TMBs) from amino acid sequences. This advance identifies 164 novel TMBs in bacteria, aiding future experimental verification.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Genomics

Background:

  • Predicting transmembrane beta-barrel (TMB) proteins directly from sequence is challenging due to limited high-resolution structural data.
  • Existing methods often struggle with the sparse nature of experimental data for TMB proteins.

Purpose of the Study:

  • To develop and validate a novel profile-based hidden Markov model for accurate prediction and discrimination of TMBs from sequence.
  • To identify previously uncharacterized TMB proteins in sequenced bacterial proteomes.

Main Methods:

  • Designed a novel profile-based hidden Markov model incorporating explicit state modeling of transmembrane strands and a new definition of beta-hairpin motifs.
  • Employed a log-odds whole-protein discrimination score to distinguish TMBs from non-TMB proteins.

Related Experiment Videos

  • Applied the developed method to analyze proteomes of 72 Gram-negative bacteria.

    • Main Results:

    • Achieved 86% accuracy in four-state prediction (up-, down-strand, periplasmic-, outer-loop) of TMBs.
    • Demonstrated 100% accuracy in discriminating TMB from non-TMB proteins at 45% coverage.
    • Identified 164 high-confidence, previously uncharacterized TMB proteins in Gram-negative bacteria.

    Conclusions:

    • The novel hidden Markov model provides a highly accurate method for TMB prediction and discrimination.
    • The identification of 164 novel TMB candidates significantly expands the known repertoire of these proteins in bacteria.
    • The PROFtmb method and predictions are publicly available, facilitating further research and experimental validation.