PINTS

PINTS (Patterns IN Three-dimensional Structures) identifies recurring three-dimensional patterns of amino acids in protein structures to detect functionally significant local structural motifs that are spatially proximate but not necessarily contiguous in sequence.

Key Features:

• Local motif detection: Detects functionally significant local structural motifs formed by spatially proximate amino acids that are not contiguous in primary sequence.
• RMSD-based significance: Calculates the statistical significance of the root-mean-square deviation between atoms within local structural motifs.
• Statistical rigor: Employs a rigorous statistical approach to distinguish true functional or structural recurrences from random occurrences.
• Cross-fold recurrence identification: Identifies recurring motifs across different protein folds rather than focusing solely on global fold similarity.
• Motif examples: Detects recurring catalytic and functional motifs such as the serine-protease catalytic triad.

Scientific Applications:

• Functional annotation of unknown proteins: Provides functional clues for proteins of unknown function by identifying local structural motifs.
• Complement to fold comparison: Complements traditional fold comparison methods by revealing local spatial motifs that may be missed by global structure comparisons.
• Enzyme and active-site discovery: Aids discovery of enzyme catalytic motifs and other functional sites, exemplified by detection of the serine-protease catalytic triad.
• Structure–function hypothesis generation: Supports hypothesis generation about protein structure–function relationships from three-dimensional data.

Methodology:

Applies a rigorous statistical approach by calculating the statistical significance of the root-mean-square deviation between atoms within local structural motifs to discern true functional patterns from noise.