HMMTOP

HMMTOP predicts the localization of helical transmembrane segments and determines the overall topology of transmembrane proteins using Hidden Markov Models to support structural and functional analysis of membrane proteins.

Key Features:

• HMM-based algorithm: Uses Hidden Markov Models (HMMs) to model and predict transmembrane helices and overall protein topology.
• Transmembrane helix prediction: Predicts the presence and positions of helical transmembrane segments from amino acid sequences.
• Topology determination: Predicts orientation of protein regions as inside or outside the cell to define membrane topology.
• User-enhanced predictions (HMMTOP 2.0): Incorporates user-provided segment localization constraints to refine predictions and aid interpretation of experiments such as epitope insertion studies.

Scientific Applications:

• Protein structure analysis: Provides membrane-spanning region information to inform three-dimensional structural studies of membrane proteins.
• Functional annotation: Supports annotation of protein function by identifying transmembrane regions and membrane orientation.
• Experimental design and interpretation: Assists design and interpretation of experiments, including epitope insertion studies, by supplying expected topology and helix locations.

Methodology:

Applies Hidden Markov Models to predict transmembrane helices and topology and supports incorporation of user-specified segment localization constraints (HMMTOP 2.0).