Selection Of DNA-encoded Libraries Research Articles

Discovery Small-Molecule p300 Inhibitors Derived from a Newly Developed Indazolone-Focused DNA-Encoded Library.

The DNA-encoded library (DEL) is a robust tool for chemical biology and drug discovery. In this study, we developed a DNA-compatible light-promoted reaction that is highly efficient and plate-compatible for DEL construction based on the formation of the indazolone scaffold. Employing this high-efficiency approach, we constructed a DEL featuring an indazolone core, which enabled the identification of a novel series of ligands specifically targeting E1A-binding protein (p300) after DEL selection. Taken together, our findings underscore the feasibility of light-promoted reactions in DEL synthesis and unveil promising avenues for developing p300-targeting inhibitors.

Application of DNA‐Conjugated Aryl Diazonium Intermediates in DNA‐Encoded Libraries

AbstractAryl diazonium intermediates have emerged as highly versatile intermediates in modern organic synthesis and chemical biology, demonstrating remarkable reactivity under mild reaction conditions. Their significance has extended to DNA‐encoded libraries (DELs), where they serve as valuable alternatives to traditional aryl halide‐based cross‐coupling reactions. Aryl diazonium intermediates as N2‐annulation synthons can efficiently transform into benzotriazinone core on DNA besides C−C coupling. This concept paper illuminates recent advances in this field, emphasizing their applications and potential across various facets of DELs, off‐DNA chemistry, DELs selection, and DNA‐based probes. It is anticipated that these developments will propel the broader utilization of DNA‐conjugated aryl diazonium salt intermediates in DELs, protein labeling, hit discovery, and other interdisciplinary fields.

Innovative On-DNA Synthesis of Sulfides and Sulfoximines: Enriching the DEL Synthesis Toolbox.

DNA-encoded library (DEL) technologies enable the fast exploration of gigantic chemical space to identify ligands for the target protein of interest and have become a powerful hit finding tool for drug discovery projects. However, amenable DEL chemistry is restricted to a handful of reactions, limiting the creativity of drug hunters. Here, we describe a new on-DNA synthetic pathway to access sulfides and sulfoximines. These moieties, usually contemplated as challenging to achieve through alkylation and oxidation, can now be leveraged in routine DEL selection campaigns.

Discovery of a novel ROCK2 ATP competitive inhibitor by DNA-encoded library selection

Neurodegeneration disorders, such as Alzheimer's disease (AD), have garnered significant attention due to their impact on individuals and society as a whole. Understanding the mechanisms behind these disorders and developing effective therapy strategies is of utmost importance. One potential therapeutic target that has emerged is Rho-associated coiled-coil containing protein kinase 2 (ROCK2), as its accumulation and activity have been closely linked to memory loss. In this report, we present the findings of a recent discovery involving a new molecule that has the ability to competitively inhibit ROCK2 activity. This molecule was identified through the utilization of a DNA-encoded library (DEL) screening platform. Following selection against ROCK2, an off-DNA compound was synthesized and examined to ascertain its inhibitory properties, selectivity, mechanism of action, and binding mode analysis. From the screening, compound CH-2 has demonstrated an IC50 value of 28 nM against ROCK2, while exhibiting a 5-fold selectivity over ROCK1. Further analysis through molecular docking has provided insights into the specific binding modes of this compound. Our findings suggest that DEL selection offers a rapid method for identifying new inhibitors. Among these, the CH-2 compound shows promise as a potential ROCK2 inhibitor and warrants further investigation.

Discovery and Optimization of WDR5 Inhibitors via Cascade Deoxyribonucleic Acid-Encoded Library Selection Approach.

The DNA-encoded library (DEL) is a powerful hit generation tool for chemical biology and drug discovery; however, the optimization of DEL hits remained a daunting challenge for the medicinal chemistry community. In this study, hit compounds targeting the WIN binding domain of WDR5 were discovered by the initial three-cycle linear DEL selection, and their potency was further enhanced by a cascade DEL selection from the focused DEL designed based on the original first run DEL hits. As expected, these new compounds from the second run of focused DEL were more potent WDR5 inhibitors in the protein binding assay confirmed by the off-DNA synthesis. Interestingly, selected inhibitors exhibited good antiproliferative activity in two human acute leukemia cell lines. Taken together, this new cascade DEL selection strategy may have tremendous potential for finding high-affinity leads against WDR5 and provide opportunities to explore and optimize inhibitors for other targets.

Building Block-Based Binding Predictions for DNA-Encoded Libraries.

DNA-encoded libraries (DELs) provide the means to make and screen millions of diverse compounds against a target of interest in a single experiment. However, despite producing large volumes of binding data at a relatively low cost, the DEL selection process is susceptible to noise, necessitating computational follow-up to increase signal-to-noise ratios. In this work, we present a set of informatics tools to employ data from prior DEL screen(s) to gain information about which building blocks are most likely to be productive when designing new DELs for the same target. We demonstrate that similar building blocks have similar probabilities of forming compounds that bind. We then build a model from the inference that the combined behavior of individual building blocks is predictive of whether an overall compound binds. We illustrate our approach on a set of three-cycle OpenDEL libraries screened against soluble epoxide hydrolase (sEH) and report performance of more than an order of magnitude greater than random guessing on a holdout set, demonstrating that our model can serve as a baseline for comparison against other machine learning models on DEL data. Lastly, we provide a discussion on how we believe this informatics workflow could be applied to benefit researchers in their specific DEL campaigns.

Discovery, SAR Study of GST Inhibitors from a Novel Quinazolin-4(1H)-one Focused DNA-Encoded Library.

The DNA-encoded library (DEL) is a powerful hit-generation tool in drug discovery. This study describes a new DEL with a privileged scaffold quinazolin-4(3H)-one developed by a robust DNA-compatible multicomponent reaction and a series of novel glutathione S-transferase (GST) inhibitors that were identified through affinity-mediated DEL selection. A novel inhibitor 16 was subsequently verified with an inhibitory potency value of 1.55 ± 0.02 μM against SjGST and 2.02 ± 0.20 μM against hGSTM2. Further optimization was carried out via various structure-activity relationship studies. And especially, the co-crystal structure of the compound 16 with the SjGST was unveiled, which clearly demonstrated its binding mode was quite different from the known GSH-like compounds. This new type of probe is likely to play a different role compared with the GSH, which may provide new opportunities to discover more potent GST inhibitors.

DNA‐Encoded Libraries and Their Application to RNA

AbstractThe functional roles of structured RNAs in the regulation of biological processes, and hence RNA's potential as an effective therapeutic target, have only recently been appreciated. Robust and high‐throughput methods that identify potent RNA ligands are critical to the development of chemical probes and therapeutics. DNA‐encoded libraries (DEL) technology has emerged as a powerful tool for protein ligand discovery, and its ability to generate large, custom‐tailored, and novel chemical space offers unprecedented opportunities to discover the rules of RNA ligand design. In this review, we discuss the basic principles of DEL selection, current progress on the application of DEL to RNA targets, and the outlook of targeting RNA by DEL.

Machine-Learning-Based Data Analysis Method for Cell-Based Selection of DNA-Encoded Libraries.

DNA-encoded library (DEL) is a powerful ligand discovery technology that has been widely adopted in the pharmaceutical industry. DEL selections are typically performed with a purified protein target immobilized on a matrix or in solution phase. Recently, DELs have also been used to interrogate the targets in the complex biological environment, such as membrane proteins on live cells. However, due to the complex landscape of the cell surface, the selection inevitably involves significant nonspecific interactions, and the selection data are much noisier than the ones with purified proteins, making reliable hit identification highly challenging. Researchers have developed several approaches to denoise DEL datasets, but it remains unclear whether they are suitable for cell-based DEL selections. Here, we report the proof-of-principle of a new machine-learning (ML)-based approach to process cell-based DEL selection datasets by using a Maximum A Posteriori (MAP) estimation loss function, a probabilistic framework that can account for and quantify uncertainties of noisy data. We applied the approach to a DEL selection dataset, where a library of 7,721,415 compounds was selected against a purified carbonic anhydrase 2 (CA-2) and a cell line expressing the membrane protein carbonic anhydrase 12 (CA-12). The extended-connectivity fingerprint (ECFP)-based regression model using the MAP loss function was able to identify true binders and also reliable structure-activity relationship (SAR) from the noisy cell-based selection datasets. In addition, the regularized enrichment metric (known as MAP enrichment) could also be calculated directly without involving the specific machine-learning model, effectively suppressing low-confidence outliers and enhancing the signal-to-noise ratio. Future applications of this method will focus on de novo ligand discovery from cell-based DEL selections.

Machine Learning on DNA-Encoded Library Count Data Using an Uncertainty-Aware Probabilistic Loss Function.

DNA-encoded library (DEL) screening and quantitative structure-activity relationship (QSAR) modeling are two techniques used in drug discovery to find novel small molecules that bind a protein target. Applying QSAR modeling to DEL selection data can facilitate the selection of compounds for off-DNA synthesis and evaluation. Such a combined approach has been done recently by training binary classifiers to learn DEL enrichments of aggregated "disynthons" in order to accommodate the sparse and noisy nature of DEL data. However, a binary classification model cannot distinguish between different levels of enrichment, and information is potentially lost during disynthon aggregation. Here, we demonstrate a regression approach to learning DEL enrichments of individual molecules, using a custom negative-log-likelihood loss function that effectively denoises DEL data and introduces opportunities for visualization of learned structure-activity relationships. Our approach explicitly models the Poisson statistics of the sequencing process used in the DEL experimental workflow under a frequentist view. We illustrate this approach on a DEL dataset of 108,528 compounds screened against carbonic anhydrase (CAIX), and a dataset of 5,655,000 compounds screened against soluble epoxide hydrolase (sEH) and SIRT2. Due to the treatment of uncertainty in the data through the negative-log-likelihood loss used during training, the models can ignore low-confidence outliers. While our approach does not demonstrate a benefit for extrapolation to novel structures, we expect our denoising and visualization pipeline to be useful in identifying structure-activity trends and highly enriched pharmacophores in DEL data. Further, this approach to uncertainty-aware regression modeling is applicable to other sparse or noisy datasets where the nature of stochasticity is known or can be modeled; in particular, the Poisson enrichment ratio metric we use can apply to other settings that compare sequencing count data between two experimental conditions.

Discovery of SARS-CoV-2 main protease covalent inhibitors from a DNA-encoded library selection

Covalent inhibitors targeting the main protease (Mpro, or 3CLpro) of SARS-CoV-2 have shown promise in preclinical investigations. Herein, we report the discovery of two new series of molecules that irreversibly bind to SARS-CoV-2 Mpro. These acrylamide containing molecules were discovered using our covalent DNA-encoded library (DEL) screening platform. Following selection against SARS-CoV-2 Mpro, off-DNA compounds were synthesized and investigated to determine their inhibitory effects, the nature of their binding, and to generate preliminary structure-activity relationships. LC-MS analysis indicates a 1:1 (covalent) binding stoichiometry between our hit molecules and SARS-CoV-2 Mpro. Fluorescent staining assay for covalent binding in the presence of cell lysate suggests reasonable selectivity for SARS-CoV-2 Mpro. And lastly, inhibition of enzymatic activity was also observed against a panel of 3CLpro enzymes from different coronavirus strains, with IC50 values ranging from inactive to single digit micromolar. Our results indicate that DEL selection is a useful approach for identifying covalent inhibitors of cysteine proteases.

Target Protein Design and Preselection Analysis.

DNA-encoded library (DEL) technology utilizes affinity-based selection methods to screen chemical libraries. DEL technology possesses some unique features compared to other small molecule screening technologies, such as the use of DNA identification tags and use of target protein immobilization in the standard library screening process. Therefore, it is of great importance to ensure the target protein is properly designed for DEL selections, that the protein is of high quality, and that ligand binding sites are accessible under DEL selection conditions. Here we describe general considerations for target protein design and expression and experiments that are conducted before initiating selections to assessprotein quality and validate methods that will be used for the DEL selection.

Analysis of DNA-Encoded Library Screening Data: Selection of Molecules for Synthesis.

DNA-encoded library (DEL) screens are used to discover novel chemical matter capable of modulating the activity of pharmaceutically interesting protein targets. DEL selections are accomplished by immobilizing a target protein on a resin and capturing library molecules that bind to the target. The barcodes of the captured library molecules are then amplified and sequenced. This chapter outlines simple methods for visualizing the resulting screening data (using free open-source software), such that enriched molecules can be selected for synthesis and follow-up activity confirmation. Measures of enrichment and the concept of sub-libraries are also illustrated.

Understanding Data Noise and Uncertainty through Analysis of Replicate Samples in DNA-Encoded Library Selection.

By analyzing data sets of replicate DNA-Encoded Library (DEL) selections, an approach for estimating the noise level of the experiment has been developed. Using a logarithm transformation of the number of counts associated with each compound and a subset of compounds with the highest number of counts, it is possible to assess the quality of the data through normalizing the replicates and use this same data to estimate the noise in the experiment. The noise level is seen to be dependent on sequencing depth as well as specific selection conditions. The noise estimation is independent of any cutoff used to remove low frequency compounds from the data analysis. The removal of compounds with only 1-5 read counts greatly reduces some of the challenges encountered in DEL data analysis as it can reduce the data set by greater than 100-fold without impacting the interpretation of the results.

Recent Advances on the Selection Methods of DNA-Encoded Libraries.

DNA-encoded libraries (DEL) have come of age and become a major technology platform for ligand discovery in both academia and the pharmaceutical industry. Technological maturation in the past two decades and the recent explosive developments of DEL-compatible chemistries have greatly improved the chemical diversity of DELs and fueled its applications in drug discovery. A relatively less-covered aspect of DELs is the selection method. Typically, DEL selection is considered as a binding assay and the selection is conducted with purified protein targets immobilized on a matrix, and the binders are separated from the non-binding background via physical washes. However, the recent innovations in DEL selection methods have not only expanded the target scope of DELs, but also revealed the potential of the DEL technology as a powerful tool in exploring fundamental biology. In this Review, we first cover the "classic" DEL selection methods with purified proteins on solid phase, and then we discuss the strategies to realize DEL selections in solution phase. Finally, we focus on the emerging approaches for DELs to interrogate complex biological targets.

Triaging of DNA-Encoded Library Selection Results by High-Throughput Resynthesis of DNA-Conjugate and Affinity Selection Mass Spectrometry.

DNA encoded library (DEL) technology allows for rapid identification of novel small-molecule ligands and thus enables early-stage drug discovery. DEL technology is well-established, numerous cases of discovered hit molecules have been published, and the technology is widely employed throughout the pharmaceutical industry. Nonetheless, DEL selection results can be difficult to interpret, as library member enrichment may derive from not only desired products, but also DNA-conjugated byproducts and starting materials. Note that DELs are generally produced using split-and-pool combinatorial chemistry, and DNA-conjugated byproducts and starting materials cannot be removed from the library mixture. Herein, we describe a method for high-throughput parallel resynthesis of DNA-conjugated molecules such that byproducts, starting materials, and desired products are produced in a single pot, using the same chemical reactions and reagents as during library production. The low-complexity mixtures of DNA-conjugate are then assessed for protein binding by affinity selection mass spectrometry and the molecular weights of the binding ligands ascertained. This workflow is demonstrated to be a practical tool to triage and validate potential hits from DEL selection data.

Multimodal small-molecule screening for human prion protein binders

Prion disease is a rapidly progressive neurodegenerative disorder caused by misfolding and aggregation of the prion protein (PrP), and there are currently no therapeutic options. PrP ligands could theoretically antagonize prion formation by protecting the native protein from misfolding or by targeting it for degradation, but no validated small-molecule binders have been discovered to date. We deployed a variety of screening methods in an effort to discover binders of PrP, including 19F-observed and saturation transfer difference (STD) NMR spectroscopy, differential scanning fluorimetry (DSF), DNA-encoded library selection, and in silico screening. A single benzimidazole compound was confirmed in concentration-response, but affinity was very weak (Kd > 1 mm), and it could not be advanced further. The exceptionally low hit rate observed here suggests that PrP is a difficult target for small-molecule binders. Whereas orthogonal binder discovery methods could yield high-affinity compounds, non-small-molecule modalities may offer independent paths forward against prion disease.

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.

A simple method for determining compound affinity and chemical yield from DNA-encoded library selections

DNA-encoded libraries (DELs) can contain billions of unique chemical species; selecting against such large inputs, it is typical to find more candidate binders than is reasonable to pursue for follow-up synthesis and testing. Given this wealth of choices, common practice is to limit synthesis to only those compounds estimated to have the greatest chance of being high-affinity binders; of the many potential factors contributing to this estimation, the strength of the selection signal of a candidate binder is always important. We define here methods and equations which relate the theoretical selection signal of a compound to its affinity and chemical yield. Tests using known binders of BRD4 and ROCK2 support the theory backing these equations and suggest they should be of use for prospectively determining affinity and chemical yield from primary DEL selection data.

Exploring the Lower Limit of Individual DNA-Encoded Library Molecules in Selection.

DNA-encoded library (DEL) technology has been used as an ultra-high-throughput screening approach for hit identification of drug targets. This process is an affinity-based selection and requires incubation of DEL molecules with the target. Currently, in most reported cases, the input (i.e., the copy number) of individual DEL molecules varies from 105 to 107. With the ever-increasing DEL size and screening cost, lowering the input of DEL molecules while maintaining an appropriate signal-to-noise ratio in a selection is of paramount importance. In this article, we varied the input of DEL ranging from 103 to 105 in selections with two different protein targets to explore the lower limit of DEL molecule input. The results could facilitate the optimization of the DEL selection process and reduce costs related to library consumption.