Denoising DNA Encoded Library Screens with Sparse Learning.

ACS Combinatorial Science
Peter Komar, Marko Kalinic

Abstract

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 comm...Continue Reading

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Citations

Dec 5, 2020·Future Medicinal Chemistry·Daniel ConoleMichael J Waring
Mar 20, 2021·ACS Medicinal Chemistry Letters·Christopher A ReiherScott E Wolkenberg
Apr 24, 2021·Chembiochem : a European Journal of Chemical Biology·Yiran Huang, Xiaoyu Li
Aug 24, 2021·ACS Pharmacology & Translational Science·Adrián Gironda-MartínezDario Neri
Feb 6, 2022·Nature Chemistry·Yiran HuangXiaoyu Li

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