CCTOP

CCTOP predicts transmembrane protein topology by combining outputs from ten topology prediction algorithms and integrating experimental topology data from PDBTM, TOPDB, and TOPDOM using hidden Markov models to improve accuracy and reliability.

Key Features:

• Integration of Multiple Prediction Methods: Combines predictions from ten topology prediction algorithms into a consensus result.
• Incorporation of Experimental Data: Integrates topology information from PDBTM, TOPDB, and TOPDOM within a hidden Markov model probabilistic framework.
• Signal Peptide Prediction and Protein Discrimination: Supports signal peptide prediction and discrimination between transmembrane and globular proteins.
• Customizable Constraints: Supports imposing custom constraints and selecting or deselecting specific prediction methods and mapped experimental data.
• Reliability Assessment: Reports a per-protein reliability score correlated with the accuracy of the predicted topology.
• Result Export: Provides prediction outputs exportable in XML format.

Scientific Applications:

• Structural Biology: Supports topology modeling for membrane proteins to inform structure–function studies.
• Membrane Protein Research: Aids investigation of membrane protein function, interactions, and mechanisms through accurate topology prediction.
• Bioinformatics Annotation: Supports functional annotation and topology-based analyses of membrane proteins.

Methodology:

Combines outputs from ten topology prediction algorithms into a consensus and integrates experimental topology data from PDBTM, TOPDB, and TOPDOM using a hidden Markov model–based probabilistic framework.