MemBrain

MemBrain predicts transmembrane helices and N-terminal signal peptides in alpha-helical membrane proteins to improve topology annotation and accurate TMH end detection when high-resolution structural data are unavailable.

Key Features:

• Transmembrane Helix Prediction: Identifies the number and locations of transmembrane helices (TMHs) within protein sequences and improves on methods based solely on amino acid hydrophobicity scales or purely statistical approaches.
• Enhanced TMH End Prediction: Predicts TMH boundaries with improved accuracy to refine protein topology and functional interpretation.
• Detection of Unusual-Length TMHs: Detects short TMHs shorter than 15 residues that are often missed by conventional predictors.
• N-terminal Signal Peptide Detection: Identifies N-terminal signal peptides in protein sequences.

Scientific Applications:

• Functional and Structural Characterization: Assists functional and structural characterization of alpha-helical membrane proteins by providing TMH locations and boundary information.
• Molecular Biology and Biochemistry: Supports molecular biology and biochemistry studies that require accurate membrane protein topology for experimental design and interpretation.
• Pharmacology and Drug Design: Informs pharmacology and drug design efforts by improving membrane protein topology data relevant to target structure and ligand interaction hypotheses.

Methodology:

Employs machine-learning approaches including sequence representation by multiple sequence alignment matrix, an optimized evidence-theoretic K-nearest neighbor prediction algorithm, fusion of multiple prediction window sizes, and classification by dynamic threshold.