3DTF

3DTF computes position-specific weight matrices (PWMs) from crystallographic protein–DNA complex structures to characterize transcription factor DNA-binding specificity.

Key Features:

• Knowledge-based statistical potential: Uses a knowledge-based statistical potential derived from high-resolution crystallographic protein–DNA structures to represent binding preferences at specific nucleotide positions.
• PWM derivation from protein–DNA complexes: Derives position-specific weight matrices (PWMs) by analyzing protein–DNA contacts observed in 3D structures.
• Comprehensive structural database utilization: Aggregates information from hundreds of 3D structural entries and thousands of homologous proteins to generate PWMs applicable to diverse transcription factors.

Scientific Applications:

• Novel PWM derivation: Derives novel PWMs for transcription factors that lack known binding sites to support regulatory motif discovery.
• Structural biology insights: Maps interactions between specific amino acid residues and DNA bases to inform mechanisms of transcription factor–DNA recognition.
• Functional genomics: Predicts and analyzes transcription factor binding sites across genomes to aid identification of regulatory elements involved in gene expression.

Methodology:

Integrates crystallographic protein–DNA data with knowledge-based statistical potentials and statistical modeling to analyze spatial arrangements of protein–DNA interactions and construct PWMs that reflect nucleotide likelihoods at each binding-position.