Universal Baseline for in vitro Selection of Genetically Encoded Libraries

Area of Science:

• Biotechnology and Molecular Biology
• Drug Discovery and Development
• Bioinformatics and Computational Biology

Background:

• Genetically encoded (GE) libraries are crucial for identifying high-affinity ligands for molecular targets via in vitro selection and next-generation sequencing (NGS).
• Reproducibility in GE-molecular discoveries is challenging due to the lack of a systematic evaluation framework.
• Existing in vitro discovery systems rarely evaluate reproducibility against a defined experimental baseline.

Purpose of the Study:

• To introduce a systematic framework for evaluating reproducibility in GE-molecular discoveries.
• To establish a universal baseline response for normalizing and standardizing in vitro selection data.
• To enable rigorous normalization, confidence assessment, and cross-target comparison of molecular discovery outcomes.

Main Methods:

• Introduction of the 'baseline response' concept, achieved by spiking a random DNA-barcoded population into selection libraries.
• Calibration of the baseline concept using Bioconductor EdgeR differential enrichment (DE) analysis of NGS data from phage-displayed selections.
• Application of baseline-stratified DE-analysis to 66 parallel selections across 22 extracellular targets, including HER1-3, EpCAM, CAIX, PD-L1, and integrin receptors.

Main Results:

• Automated DE-analysis of hundreds of NGS files yielded validated hits and synthetic macrocyclic ligands with mid-nanomolar affinity.
• Demonstrated baseline-calibrated NGS data for optimizing peptide macrocycles, yielding potent single-digit nanomolar ligands for PD-L1.
• Mixing discovery campaigns (e.g., chitin-enriched peptides as baseline for NS3a* selection) proved effective for baseline establishment.

Conclusions:

• The baseline response provides a reproducible method to partition active and inactive members in in vitro selection.
• Baseline-based analyses of NGS data offer a scalable framework for reproducible hit discovery and standardized analysis across diverse GE selection campaigns.
• This framework introduces practical standards for reproducibility and statistical benchmarking across genetically encoded display platforms.