HMMpTM

HMMpTM predicts transmembrane protein topology and concurrently identifies kinase-specific phosphorylation sites and N- and O-linked glycosylation sites to integrate post-translational modification information with membrane topology analysis.

Key Features:

• Topology Prediction: Uses topogenic signals, including the distribution of positively charged residues in extramembrane loops and N-terminal signals, to predict orientation and arrangement of transmembrane segments.
• Phosphorylation Site Prediction: Identifies kinase-specific phosphorylation sites along protein sequences to provide site-specific regulatory information.
• Glycosylation Site Prediction: Predicts N-linked and O-linked glycosylation sites relevant to folding, stability, and compartment-specific protein modification.
• Integration of PTM Data: Incorporates phosphorylation and glycosylation predictions into topology analysis to provide combined structural and modification context.
• Improved Performance: Combines PTM prediction with topology modeling to improve prediction accuracy relative to methods that omit these modifications.

Scientific Applications:

• Structural Biology: Supports interpretation of membrane protein structure by linking transmembrane topology with location of PTMs.
• Functional Genomics: Provides predictions of PTMs and topology to inform studies of protein regulation and signaling pathways.
• Drug Discovery: Informs target characterization by revealing membrane topology and modification sites relevant to ligand binding and pharmacology.

Methodology:

Employs a Hidden Markov Model framework to analyze protein sequences, capturing amino acid sequential properties and propensities for structural features and modifications, and is trained on datasets of transmembrane proteins with annotated post-translational modifications to predict these characteristics in novel sequences.