MBPpred

MBPpred predicts membrane binding proteins by using profile Hidden Markov Models to detect membrane binding domains from amino acid sequences and classify their membrane association.

Key Features:

• Detection of Membrane Binding Proteins: MBPpred identifies proteins that contain one or more membrane binding domains (MBDs) which mediate non-covalent interactions with membrane lipids.
• Classification Based on Position Relative to the Membrane: MBPpred distinguishes peripheral membrane proteins from transmembrane proteins based on their positional relationship to the lipid bilayer.
• Utilization of Profile Hidden Markov Models: MBPpred employs profile Hidden Markov Models (profile HMMs) to capture sequence characteristics indicative of membrane binding properties.
• Application Across Eukaryotic Proteomes: The method has been applied to selected eukaryotic proteomes to examine the distribution and characteristics of MBPs across kingdoms and phyla.

Scientific Applications:

• Membrane biology: Provides predictions to study membrane-associated protein function and lipid interactions.
• Protein-protein interactions and signaling: Supports analysis of components of cellular signaling pathways and protein-protein interaction networks that involve membrane binding.
• Proteome annotation: Aids annotation of eukaryotic proteomes by identifying and classifying membrane binding proteins.
• Evolutionary and functional genomics: Enables comparative analysis of MBP distribution and characteristics across kingdoms and phyla.
• Disease mechanisms and therapeutic targeting: Offers insights into potential roles of MBPs in disease mechanisms and targets for therapeutic investigation.

Methodology:

MBPpred constructs profile HMMs representing sequence motifs of known membrane binding domains, scans protein sequences from eukaryotic organisms with these models, and classifies hits as peripheral or transmembrane by analyzing positional attributes relative to the lipid bilayer.