DILIMOT

DILIMOT identifies short, over-represented linear peptide motifs (3-8 amino acids) in sets of protein sequences to reveal motif-mediated interactions involved in signaling, trafficking, and targeting.

Key Features:

• Motif Identification: Detects linear motifs of 3-8 amino acids, often located in intrinsically disordered regions, and recognizes known sequence patterns such as SH3-binding PxxP.
• Data-Driven Approach: Leverages genome-scale interaction studies and emphasizes motif over-representation in non-homologous sequences to rediscover known motifs and predict novel ones.
• Functional Prediction: Analyzes sets of proteins sharing a common functional property (e.g., a common interaction partner or localization) using statistical methods to rank motifs by over-representation and conservation across homologues in related species.
• Filtering Mechanism: Applies sequence filtering to exclude regions unlikely to contain linear motifs prior to motif discovery.

Scientific Applications:

• Protein interaction network analysis: Identifies molecular details encoded by linear motifs to map and interpret protein interaction networks.
• Elucidation of motif-mediated processes: Links short peptide patterns to roles in signaling, trafficking, and targeting to clarify mechanism-specific interactions.
• Discovery of novel motifs: Predicts novel linear motifs and aids rediscovery of known ones, exemplified by DxxDxxxD for protein phosphatase 1 binding and VxxxRxYS for Translin binding.

Methodology:

Performs statistical analysis of motif over-representation in non-homologous sequences, assesses conservation across homologues in related species, and filters input sequences to remove regions unlikely to contain linear motifs.