PyPLIF

PyPLIF analyzes protein-ligand interactions using interaction fingerprinting (IFP) to convert three-dimensional binding patterns into one-dimensional bitstrings that inform structure-based virtual screening (SBVS).

Key Features:

• Interaction Fingerprinting (IFP): Translates three-dimensional protein-ligand interactions into one-dimensional bitstrings that encode interaction profiles.
• 3D-to-1D Bitstring Encoding: Represents detailed interaction patterns as bitstrings for concise comparison.
• IFP Comparison: Compares IFPs of docked poses against IFPs of known reference ligands to assess interaction similarity.
• Scoring for SBVS: Generates similarity scores from IFP comparisons to inform hit prioritization in structure-based virtual screening (SBVS).
• Complementary to Docking Scores: Provides interaction-specific metrics that capture specificity and complexity beyond docking scores' approximate affinity estimates.
• Python Implementation: Implemented in Python for computational analysis of protein-ligand interactions.

Scientific Applications:

• Structure-based Virtual Screening (SBVS): Enhancing hit prioritization by incorporating interaction fingerprint similarity into screening workflows.
• Docked Pose Evaluation: Validating and comparing predicted ligand poses by matching their IFPs to reference ligand interaction profiles.
• Estrogen α Receptor (ERα) Antagonist Identification: Applied to identify antagonists for the estrogen α receptor (ERα) in targeted ligand discovery.

Methodology:

Translates 3D protein-ligand interactions into 1D bitstrings via interaction fingerprinting and compares IFPs of docked poses to those of known reference ligands to produce similarity scores.