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.