finDr

finDr identifies and optimizes D-peptide ligands for protein targets using molecular docking and a customizable evolutionary algorithm to enable in silico discovery of protease-resistant D-peptides (dextrorotatory enantiomers of amino acids).

Key Features:

• Molecular docking: Virtual screening of a library of helical 12-mer peptides sourced from the RCSB Protein Data Bank (PDB) against specified protein targets.
• Evolutionary algorithm (EA): A customizable heuristic EA for de novo identification and optimization of D-peptide ligands.
• D-peptide focus: Targets D-peptides (dextrorotatory enantiomers) that are resistant to proteolytic degradation and thus have improved in vivo stability.
• Peptide library: Uses helical 12-mer peptide fragments extracted from structures in the RCSB PDB.
• Experimental validation: Proof-of-concept predictions were validated by in vitro binding assays against phenol soluble modulin alpha 3 (PSMα3).
• Alternative to mirror-image phage display: Enables identification of D-peptide binders without synthesizing protein targets in D-form, addressing challenges for large proteins.

Scientific Applications:

• D-peptide discovery: In silico identification and optimization of protease-resistant peptide ligands for therapeutic and biotechnological applications.
• Targeting bacterial toxins: Prediction and optimization of binders to phenol soluble modulin alpha 3 (PSMα3) from methicillin-resistant Staphylococcus aureus (MRSA).
• Screening when D-form synthesis is impractical: Provides a computational alternative to mirror-image phage display for large or difficult-to-synthesize protein targets.

Methodology:

Performs molecular docking-based virtual screening of helical 12-mer peptides from the RCSB PDB and applies a customizable evolutionary algorithm heuristic for de novo identification and optimization of D-peptide ligands.