DistanceP

DistanceP predicts distance constraints between amino acids from amino acid sequences to inform protein structure and residue spatial relationships.

Key Features:

• Sequence Separation and Distance Correlation: Investigates the relationship between sequence separation (measured in residues) and physical distance (in Angstroms), defining a distance threshold per separation to classify residue proximity.
• Motif Identification: Identifies characteristic sequence motifs (logos) associated with different sequence separations, with motifs showing a single central peak at small separations, additional peaks at residue positions for intermediate separations, and a smeared central peak at large separations.
• Statistical Analysis and Neural Network Design: Uses statistical analysis of correlations, particularly focusing on residues at motif centers, to inform neural network design that outperforms pair probability density function approaches, notably for sequence separations of 10–30 residues.
• Performance and Information Content: Reports that predictive accuracy increases with sequence separation and correlates this improvement with higher information content in the motifs.

Scientific Applications:

• Structural biology research: Provides distance constraints useful for interpreting protein spatial arrangements.
• Protein structure prediction and modeling: Supplies residue distance constraints that can be integrated into structure prediction and modeling workflows.
• Functional annotation: Supports functional annotation of proteins based on predicted structural features.
• Experimental design: Guides the design of experiments to validate predicted distance constraints.

Methodology:

Defines distance thresholds for various sequence separations and identifies characteristic sequence motifs (logos) via statistical motif analysis, then uses the resulting statistical correlations to inform neural network design and compare performance against pair probability density functions.