TMhhcp

TMhhcp predicts residue-residue contacts and interacting helix pairs in alpha-helix transmembrane proteins to support structural characterization of membrane proteins.


Key Features:

  • Residue-residue contact prediction: Predicts residue-residue contacts within alpha-helix transmembrane proteins.
  • Interacting helical pair identification: Identifies interacting transmembrane helix pairs.
  • Algorithm: Implements Random Forest (RF) models for prediction.
  • Evaluation metrics: Reports top L/5 prediction accuracies of 49.5% and 48.8% for two residue contact definitions.
  • Pair-level performance: Reports Matthew's correlation coefficients of 0.430 and 0.424 for the two residue contact definitions when identifying interacting helical pairs.
  • Benchmarking: Demonstrates superior prediction performance compared to TMHcon and MEMPACK.
  • Validation: Performance established using rigorous cross-validation tests.

Scientific Applications:

  • Membrane protein contact mapping: Provides contact maps for alpha-helix transmembrane proteins to inform structural models.
  • Helix-helix interaction analysis: Identifies interacting helix pairs to aid analysis of helix packing and interface residues.
  • Structural annotation: Supports structural characterization of integral membrane proteins that are underrepresented in the Protein Data Bank.
  • Functional inference: Contributes contact-derived insights relevant to membrane protein function and interactions.

Methodology:

Random Forest (RF) models were used and performance was evaluated by rigorous cross-validation using two residue contact definitions with top L/5 accuracies and Matthew's correlation coefficient metrics.

Topics

Details

Tool Type:
command-line tool
Operating Systems:
Linux, Windows, Mac
Programming Languages:
R
Added:
12/18/2017
Last Updated:
11/25/2024

Operations

Publications

Wang X, Chen Z, Wang C, Yan R, Zhang Z, Song J. Predicting Residue-Residue Contacts and Helix-Helix Interactions in Transmembrane Proteins Using an Integrative Feature-Based Random Forest Approach. PLoS ONE. 2011;6(10):e26767. doi:10.1371/journal.pone.0026767. PMID:22046350. PMCID:PMC3203928.

Documentation

Links