DLocalMotif

DLocalMotif identifies local motifs in protein sequences aligned to a specified sequence landmark by integrating positional information and negative data to improve the specificity and biological relevance of discovered motifs.

Key Features:

• Discriminative Scoring Functions: Employs three scoring functions—Motif Spatial Confinement (MSC), Motif Over-Representation (MOR), and Motif Relative Entropy (MRE)—to quantify spatial confinement, over-representation, and sequence entropy of candidate motifs.
• Use of Negative Data: Incorporates negative data sets of sequences known to lack the local motif to increase specificity and distinguish true biological signals from noise.
• Positional Information Integration: Leverages positional information relative to a biologically defined anchor or landmark to detect motifs confined to specific sequence intervals, including motifs that are weakly over-represented.

Scientific Applications:

• Peroxisomal Targeting Signals: Identified several novel motifs located immediately upstream of the dominant peroxisomal targeting signal-1.
• Proline-Tyrosine Nuclear Localization Signals: Revealed multiple motifs overlapping C2H2 zinc finger domains associated with proline-tyrosine nuclear localization signals.
• Classical Nuclear Localization and Endoplasmic Reticulum Retention Signals: Recovered biologically relevant sequence properties in analyses of classical nuclear localization signals and endoplasmic reticulum retention signals.

Methodology:

Integrates positional information relative to a sequence landmark with negative data sets and applies three discriminative scoring functions—Motif Spatial Confinement (MSC), Motif Over-Representation (MOR), and Motif Relative Entropy (MRE)—to identify spatially confined local motifs.