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CCTOP: a Consensus Constrained TOPology prediction web server.

László Dobson1, István Reményi1, Gábor E Tusnády2

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Nucleic Acids Research
|May 7, 2015
PubMed
Summary

The Consensus Constrained TOPology prediction (CCTOP) server accurately predicts transmembrane protein topology by integrating multiple methods and databases. It offers enhanced reliability assessment and visualization for biological research.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Transmembrane proteins play crucial roles in cellular functions.
  • Accurate prediction of transmembrane topology is essential for understanding protein function and structure.
  • Existing prediction tools have limitations in accuracy and integration of diverse data sources.

Purpose of the Study:

  • To develop and present the Consensus Constrained TOPology prediction (CCTOP) server.
  • To improve the accuracy and reliability of transmembrane topology prediction.
  • To integrate multiple prediction algorithms and experimental data for enhanced predictions.

Main Methods:

  • Utilizing 10 state-of-the-art transmembrane topology prediction methods.
  • Incorporating topology information from PDBTM, TOPDB, and TOPDOM databases.
  • Employing a hidden Markov model probabilistic framework.
  • Offering signal peptide prediction and transmembrane-globular protein discrimination.
  • Allowing user-defined constraints for prediction refinement.

Main Results:

  • CCTOP demonstrated superior performance compared to existing topology prediction approaches.
  • Reliability scores correlate with the accuracy of per-protein topology predictions.
  • Prediction results and experimental data are visualized and downloadable in XML format.
  • Programmable access via a client-side script is available.

Conclusions:

  • The CCTOP server provides a robust and accurate platform for transmembrane topology prediction.
  • Integration of diverse data and methods enhances prediction reliability.
  • CCTOP serves as a valuable resource for researchers studying transmembrane proteins.