PredTM

PredTM predicts transmembrane regions, with emphasis on β-barrel segments, from protein sequences to identify membrane-spanning regions for structural and genomic analyses.

Key Features:

• Algorithmic Foundation: Uses support vector machine (SVM) classifiers trained on sequence data from known transmembrane protein structures and analyzes amino acid adjacency frequency and position-specific amino acid preferences to predict transmembrane regions.
• Focus on β-Barrel Proteins: Incorporates amino acid pair frequency information from known β-barrel protein sequences to refine predictions of β-transmembrane segments.
• Position-Specific Data Integration: Integrates position-specific amino acid preference data into final predictions while explicitly not relying on evolutionary profile information.
• Performance Metrics: On a benchmark set of 35 β-transmembrane proteins, reports sensitivity 83.71%, precision 72.98%, and segment overlap score 82.19%, with higher precision and segment overlap compared to other state-of-the-art methods without compromising sensitivity.
• Application in Uncharacterized Proteins: Applied to predict β-barrel membrane regions in proteins lacking defined transmembrane annotations and in uncharacterized sequences across eight bacterial genomes.

Scientific Applications:

• Structural Biology: Supports elucidation of membrane protein architecture by predicting membrane-spanning regions relevant to structure and interaction studies.
• Drug Discovery: Informs studies of drug uptake mechanisms through prediction of transmembrane segments, as demonstrated in analyses of bilitranslocase.
• Genomic Research: Identifies potential membrane proteins from uncharacterized sequences, aiding expansion of known human influx carriers and other membrane protein repertoires.

Methodology:

Encodes protein segments using an amino acid adjacency matrix and amino acid pair frequencies, integrates position-specific amino acid preference data, and applies support vector machine (SVM) classifiers trained on sequence data from known transmembrane protein structures.