Pattern enrichment analysis for phage selection of stapled peptide ligands

Area of Science:

• Biochemistry
• Molecular Biology
• Drug Discovery

Background:

• Phage display is a primary method for discovering novel peptides that bind target proteins.
• Phage display techniques can suffer from compositional bias, limiting their effectiveness.
• Identifying specific amino acid patterns crucial for binding is challenging.

Purpose of the Study:

• To develop a novel method, 'pattern enrichment analysis,' to overcome limitations in phage display.
• To enhance the identification of key residues and peptide sequences responsible for target binding.
• To validate the utility of pattern enrichment analysis in combination with structured peptide libraries.

Main Methods:

• Devised a 'pattern enrichment analysis' comparing affinity-selected and non-selected phage libraries.
• Utilized next-generation sequencing to analyze sequence datasets and identify enriched three-residue patterns.
• Employed chemically stapled helical peptide libraries with a specific sequence pattern (X1C2X3X4X5X6X7X8C9X10).
• Validated the method by screening against the HDM2 protein and the SARS-CoV-2 spike receptor-binding domain (RBD).

Main Results:

• Pattern enrichment analysis successfully identified key hydrophobic residues (Phe, Tyr, Trp, Leu) for HDM2 binding.
• The method revealed enriched sequence patterns in peptides targeting the SARS-CoV-2 spike RBD.
• Spatial mapping of enriched patterns was achieved when combined with structured peptide libraries.
• The approach facilitated high-coverage comparison of sequence datasets, pinpointing critical binding residues.

Conclusions:

• Pattern enrichment analysis is an effective strategy to overcome phage display limitations like compositional bias.
• This method accurately identifies key residues and binding patterns for protein targets.
• The approach holds promise for advancing peptide-based drug discovery and understanding protein-protein interactions.