SORTCERY-A High-Throughput Method to Affinity Rank Peptide Ligands.

Area of Science:

• Biochemistry
• Molecular Biology
• Computational Biology

Background:

• Understanding peptide and protein sequence relationships is crucial for predicting and manipulating protein-protein interactions.
• Experimentally generating data linking protein sequence to binding affinity is challenging, making such data rare.
• Existing methods lack the throughput to systematically rank large numbers of peptides by binding affinity.

Purpose of the Study:

• To introduce SORTCERY, a novel high-throughput method for ranking peptide affinities to protein targets.
• To demonstrate the utility of SORTCERY in elucidating sequence-function relationships in protein interactions.
• To provide a scalable approach for generating valuable binding data for computational modeling.

Main Methods:

• Utilizes yeast display for peptide library presentation.
• Employs fluorescence-activated cell sorting (FACS) to enrich for high-affinity binders.
• Integrates deep sequencing and computational analysis for high-resolution affinity ranking.

Main Results:

• Successfully ranked 1026 Bcl-2 homology 3 (BH3) peptides based on their affinities for the anti-apoptotic protein Bcl-xL.
• Achieved high-resolution ranking, with results showing striking agreement with experimentally measured affinities for individual peptides (Kd range: 0.1–60nM).
• Demonstrated the method's capability to generate quantitative binding data at scale.

Conclusions:

• SORTCERY is an effective high-throughput method for ranking peptide-protein binding affinities.
• The generated data significantly enhance understanding of sequence-function relationships.
• This approach supports the development of predictive and design models for novel protein interactions.