Randomness in DNA Encoded Library Selection Data Can Be Modeled for More Reliable Enrichment Calculation

Kuai, Letian; O’Keeffe, Thomas; Arico-Muendel, Christopher C.

doi:10.1177/2472555218757718

Cited by 53 publications

(88 citation statements)

References 35 publications

(53 reference statements)

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“…Many experimental factors may influence screening performance. In addition to ligand affinity, the practical implementation of capture methodologies [51] and washing conditions [32] can have a profound impact on experimental results (e.g., enrichment factors and reproducibility) [53].…”

Section: Solution-phase Selectionsmentioning

confidence: 99%

DNA‐encoded chemical libraries – achievements and remaining challenges

et al. 2018

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Edited by Wilhelm JustDNA-encoded chemical libraries (DECLs) are collections of compounds, individually coupled to DNA tags serving as amplifiable identification barcodes. Since individual compounds can be identified by the associated DNA tag, they can be stored as a mixture, allowing the synthesis and screening of combinatorial libraries of unprecedented size, facilitated by the implementation of split-and-pool synthetic procedures or other experimental methodologies. In this review, we briefly present relevant concepts and technologies, which are required for the implementation and interpretation of screening procedures with DNA-encoded chemical libraries. Moreover, we illustrate some success stories, detailing how novel ligands were discovered from encoded libraries. Finally, we critically review what can realistically be achieved with the technology at the present time, highlighting challenges and opportunities for the future.

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Section: Solution-phase Selectionsmentioning

confidence: 99%

DNA‐encoded chemical libraries – achievements and remaining challenges

et al. 2018

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“…[82] With regards to larger libraries, improvements in informatic and sequencing technology to rapidly deconvolute hits will have an immediate impact. [82][83][84] Finally, a greater understanding of the dynamics of small molecule binding when attached to DNA could facilitate more effective selections. Ultimately the future success of the DEL-based screening paradigm will be reliant on a community-wide collaboration including both industry and academia to advance technology in informatics, sequencing, biochemistry, and synthetic organic chemistry.…”

Section: Discussionmentioning

confidence: 99%

DNA Encoded Libraries: A Visitor's Guide

Flood

Kingston

Vantourout

et al. 2020

Israel Journal of Chemistry

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In 1992, Brenner and Lerner hypothesized that individual chemical transformations could be encoded in DNA, allowing the rapid synthesis and screening of large collections of small molecules. Since their report, huge investments into the development of the DNA encoded library (DEL) technology have enabled the acceleration of the drug discovery process especially early phase discovery undertakings such as target validation and hit identification. As DEL lies at the nexus between chemistry and biology, there is an increasing need to expand the toolboxes of both organic transformations and biological methods. However, the myriad of techniques and reactions already reported can be difficult to digest for practitioners whose expertise resides outside the realm of DEL. This review therefore focuses on a stepwise presentation of DEL from the basic concepts to newest developments. The presentation includes the history, fundamentals, and successes of DEL, different methods for DEL synthesis and affinity selection, the conventional transformations, and finally the latest developments from a synthetic organic perspective.

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“…Satz 25 demonstrated that truncated products constitute a major confounder when analyzing binding assays based on raw read counts, and proposed a data aggregation approach to more robustly identify true patterns in selection outputs. Kuai et al 26 performed a large-scale replicate selection experiment, and demonstrated how DEL selection outputs -of an identical library -tend to be intrinsically noisy, especially at low read counts. The authors further suggested that random noise in these experiments can reliably be modelled using a Poisson distribution, and proposed a specific normalization approach to transform raw counts into an enrichment metric with associated confidence intervals.…”

Section: Introductionmentioning

confidence: 99%

Denoising DNA Encoded Library Screens with Sparse Learning

Kómár¹,

Kalinić²

2020

ACS Comb. Sci.

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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. File list (2) download file view on ChemRxiv deldenoiser_manuscript.pdf (2.78 MiB) download file view on ChemRxiv deldenoiser-SupportingInformation.pdf (10.59 MiB

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Randomness in DNA Encoded Library Selection Data Can Be Modeled for More Reliable Enrichment Calculation

Cited by 53 publications

References 35 publications

DNA‐encoded chemical libraries – achievements and remaining challenges

DNA‐encoded chemical libraries – achievements and remaining challenges

DNA Encoded Libraries: A Visitor's Guide

Denoising DNA Encoded Library Screens with Sparse Learning

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