Denoising DNA Encoded Library Screens with Sparse Learning

DNA-encoded libraries (DELs) are large, pooled collections of compounds in which every library member is attached to a stretch of DNA encoding its complete synthetic history. DEL-based hit discovery involves affinity selection of the library against a protein of interest, whereby compounds retained by the target are subsequently identified by next-generation sequencing of the corresponding DNA tags. When analyzing the resulting data, one typically assumes that sequencing output (i.e., read counts) is proportional to the binding affinity of a given compound, thus enabling hit prioritization and elucidation of any underlying structure-activity relationships (SAR). This assumption, though, tends to be severely confounded by a number of factors, including variable reaction yields, presence of incomplete products masquerading as their intended counterparts, and sequencing noise. In practice, these confounders are often ignored, potentially contributing to low hit validation rates, and universally leading to loss of valuable information. To address this issue, we have developed a method for comprehensively denoising DEL selection outputs. Our method, dubbed "deldenoiser", is based on sparse learning and leverages inputs that are commonly available within a DEL generation and screening workflow. Using simulated and publicly available DEL affinity selection data, we show that "deldenoiser" is not only able to recover and rank true binders much more robustly than read count-based approaches but also that it yields scores, which accurately capture the underlying SAR. The proposed method can, thus, be of significant utility in hit prioritization following DEL screens.

Development of a Selection Method for Discovering Irreversible (Covalent) Binders from a DNA-Encoded Library

DNA-encoded libraries (DELs) have been broadly applied to identify chemical probes for target validation and lead discovery. To date, the main application of the DEL platform has been the identification of reversible ligands using multiple rounds of affinity selection. Irreversible (covalent) inhibition offers a unique mechanism of action for drug discovery research. In this study, we report a developing method of identifying irreversible (covalent) ligands from DELs. The new method was validated by using 3C protease (3CP) and on-DNA irreversible tool compounds (rupintrivir derivatives) spiked into a library at the same concentration as individual members of that library. After affinity selections against 3CP, the irreversible tool compounds were specifically enriched compared with the library members. In addition, we compared two immobilization methods and concluded that microscale columns packed with the appropriate affinity resin gave higher tool compound recovery than magnetic beads.

Mapping protein-protein interactions with phage-displayed combinatorial peptide libraries and alanine scanning

One avenue for inferring the function of a protein is to learn what proteins it may bind to in the cell. Among the various methodologies, one way for doing so is to affinity select peptide ligands from a phage-displayed combinatorial peptide library and then to examine if the proteins that carry such peptide sequences interact with the target protein in the cell. With the protocols described in this chapter, a laboratory with skills in microbiology, molecular biology, and protein biochemistry can readily identify peptides in the library that bind selectively, and with micromolar affinity, to a given target protein on the time scale of 2 months. To illustrate this approach, we use a library of bacteriophage M13 particles, which display 12-mer combinatorial peptides, to affinity select different peptide ligands for two different targets, the SH3 domain of the human Lyn protein tyrosine kinase and a segment of the yeast serine/threonine protein kinase Cbk1. The binding properties of the selected peptide ligands are then dissected by sequence alignment, Kunkel mutagenesis, and alanine scanning. Finally, the peptide ligands can be used to predict cellular interacting proteins and serve as the starting point for drug discovery.