SLiMDisc

SLiMDisc identifies convergently evolved short linear motifs (SLiMs) in protein sequences to detect sequence features implicated in protein–protein interactions and other functional sites.

Key Features:

• Convergent evolution detection: Detects motifs that have evolved convergently across unrelated proteins to prioritize functionally significant SLiMs.
• Motif discovery with TEIRESIAS: Uses the TEIRESIAS algorithm to predict candidate motifs among sets of biologically related proteins.
• Normalization for ancestral relationships: Normalizes motif occurrences by accounting for evolutionary descent using BLAST local alignments.
• Scoring and ranking: Scores and ranks motifs using the product of normalized occurrence frequency and information content.
• Multiple Spanning Tree weighting: Applies a Multiple Spanning Tree weighting scheme to manage evolutionary relationships when assessing motif significance.
• Filtering and re-ranking: Enables filtering and re-ranking of motifs to focus on features with specified attributes.
• Comparison with known SLiMs: Compares discovered motifs against known SLiMs from the literature for validation.

Scientific Applications:

• Protein–protein interaction analysis: Identifying SLiMs involved in mediating protein–protein interactions.
• Sub-cellular localization inference: Inferring sub-cellular localization signals linked to short linear motifs.
• Functional motif discovery: Detecting convergently evolved motifs to reveal functional sites not explained by common ancestry.
• Support for molecular biology, bioinformatics, and systems biology: Providing motif-level evidence to support studies in these fields.

Methodology:

Motif prediction is performed with TEIRESIAS; occurrences are normalized using BLAST local alignments to correct for common ancestry; motifs are scored by the product of normalized occurrence frequency and information content and weighted using a Multiple Spanning Tree scheme.