SLiMFinder

SLiMFinder identifies statistically over-represented short linear motifs (SLiMs) in protein sequences to discover de novo motifs relevant to protein–protein interactions and cellular regulation.

Key Features:

• De Novo Motif Discovery: Discovers novel short linear motifs (SLiMs) from input protein sequences without prior motif models.
• Statistical Analysis and Scoring: Identifies over-represented motifs using statistical methods and assigns a P-value to each motif to assess significance and reduce false positives.
• Evolutionary Consideration: Accounts for evolutionary relationships among proteins when evaluating motif over-representation.
• Contextual Control (Masking): Provides masking options to control contextual information considered during motif discovery.
• Comparative Analysis (CompariMotif): Compares discovered motifs against known SLiMs documented in the literature using CompariMotif.
• Result Alignments and Representations: Produces alignments of motif occurrences, alignments with homologous motifs, and schematic representations of top-ranked motifs.

Scientific Applications:

• Protein–Protein Interaction Analysis: Identification of SLiMs that mediate protein–protein interactions and contribute to interaction specificity.
• Signal Transduction and Cellular Regulation: Discovery of motifs involved in signal transduction pathways and regulatory mechanisms.
• Evolutionary and Functional Contextualization: Assessment of motif conservation and functional context to support interpretation of biological roles.

Methodology:

Identifies statistically over-represented motifs across input protein sequences using statistical analysis with P-value scoring; accounts for evolutionary relationships; applies masking options for contextual control; compares discovered motifs to known SLiMs via CompariMotif; and generates alignments of motif occurrences, alignments with homologous motifs, and schematic representations.