Simulated Screens of DNA Encoded Libraries: The Potential Influence of Chemical Synthesis Fidelity on Interpretation of Structure-Activity Relationships

Flexibility-tuning of dual-display DNA-encoded chemical libraries facilitates cyclic peptide ligand discovery

Cyclic peptides constitute an important drug modality since they offer significant advantages over small molecules and macromolecules. However, access to diverse chemical sets of cyclic peptides is difficult on a large library scale. DNA-encoded Chemical Libraries (DELs) provide a suitable tool to obtain large chemical diversity, but cyclic DELs made by standard DEL implementation cannot efficiently explore their conformational diversity. On the other hand, dual-display Encoded Self-Assembling Chemical (ESAC) Libraries can be used for modulating macrocycle flexibility since the two displayed peptides can be connected in an incremental fashion. In this work, we construct a 56 million dual-display ESAC library using a two-step cyclization strategy. We show that varying the level of conformational restraint is essential for the discovery of specific ligands for the three protein targets thrombin, human alkaline phosphatase and streptavidin.

Protein-templated ligand discovery via the selection of DNA-encoded dynamic libraries

DNA-encoded chemical libraries (DELs) have become a powerful technology platform in drug discovery. Dual-pharmacophore DELs display two sets of small molecules at the termini of DNA duplexes, thereby enabling the identification of synergistic binders against biological targets, and have been successfully applied in fragment-based ligand discovery and affinity maturation of known ligands. However, dual-pharmacophore DELs identify separate binders that require subsequent linking to obtain the full ligands, which is often challenging. Here we report a protein-templated DEL selection approach that can identify full ligand/inhibitor structures from DNA-encoded dynamic libraries (DEDLs) without the need for subsequent fragment linking. Our approach is based on dynamic DNA hybridization and target-templated in situ ligand synthesis, and it incorporates and encodes the linker structures in the library, along with the building blocks, to be sampled by the target protein. To demonstrate the performance of this method, 4.35-million- and 3.00-million-member DEDLs with different library architectures were prepared, and hit selection was achieved against four therapeutically relevant target proteins.

Evolution of chemistry and selection technology for DNA-encoded library

DNA-encoded chemical library (DEL) links the power of amplifiable genetics and the non-self-replicating chemical phenotypes, generating a diverse chemical world. In analogy with the biological world, the DEL world can evolve by using a chemical central dogma, wherein DNA replicates using the PCR reactions to amplify the genetic codes, DNA sequencing transcripts the genetic information, and DNA-compatible synthesis translates into chemical phenotypes. Importantly, DNA-compatible synthesis is the key to expanding the DEL chemical space. Besides, the evolution-driven selection system pushes the chemicals to evolve under the selective pressure, i.e., desired selection strategies. In this perspective, we summarized recent advances in expanding DEL synthetic toolbox and panning strategies, which will shed light on the drug discovery harnessing in vitro evolution of chemicals via DEL.

Dose-Response Activity-Based DNA-Encoded Library Screening

Dose-response, or "conforming" behavior, increases confidence in a screening hit's authenticity. Here, we demonstrate dose-response solid-phase DNA-encoded library (DEL) screening. Compound dose in microfluidic droplets is modulated via the UV intensity of photocleavage from DEL beads. A 55,296-member DEL was screened at different UV intensities against model enzyme drug targets factor Xa (FXa) and autotaxin (ATX). Both screens yielded photochemical dose-dependent hit rates (FXa hit rates of 0.08/0.05% at 100/30% UV exposure; ATX hit rates of 0.24/0.08% at 100/20% UV exposure). FXa hits contained structures reflective of FXa inhibitors and four hits inhibited FXa (IC50 = 4.2 ± 0.1, 7.4 ± 0.3, 9.0 ± 0.3, and 19 ± 2 μM.) The top ATX hits (two dihydrobenzamidazolones and a tetrahydroisoquinoline) were validated as inhibitors (IC50 = 7 ± 2, 13 ± 2, and 1 ± 0.3 μM). Photochemical dose-response DEL screening data prioritized hits for synthesis, the rate-limiting step in DEL lead identification.

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.

Deep Learning Approach for the Discovery of Tumor-Targeting Small Organic Ligands from DNA-Encoded Chemical Libraries

DNA-Encoded Chemical Libraries (DELs) have emerged as efficient and cost-effective ligand discovery tools, which enable the generation of protein-ligand interaction data of unprecedented size. In this article, we present an approach that combines DEL screening and instance-level deep learning modeling to identify tumor-targeting ligands against carbonic anhydrase IX (CAIX), a clinically validated marker of hypoxia and clear cell renal cell carcinoma. We present a new ligand identification and hit-to-lead strategy driven by machine learning models trained on DELs, which expand the scope of DEL-derived chemical motifs. CAIX-screening datasets obtained from three different DELs were used to train machine learning models for generating novel hits, dissimilar to elements present in the original DELs. Out of the 152 novel potential hits that were identified with our approach and screened in an in vitro enzymatic inhibition assay, 70% displayed submicromolar activities (IC₅₀ < 1 μM). To generate lead compounds that are functionalized with anticancer payloads, analogues of top hits were prioritized for synthesis based on the predicted CAIX affinity and synthetic feasibility. Three lead candidates showed accumulation on the surface of CAIX-expressing tumor cells in cellular binding assays. The best compound displayed an in vitro K_D of 5.7 nM and selectively targeted tumors in mice bearing human renal cell carcinoma lesions. Our results demonstrate the synergy between DEL and machine learning for the identification of novel hits and for the successful translation of lead candidates for in vivo targeting applications.

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.

Translating the Genome into Drugs

The Human Genome Project ultimately aimed to translate DNA sequence into drugs. With the draft in hand, the Molecular Libraries Program set out to prosecute all genome-encoded proteins for drug discovery with automated high-throughput screening (HTS). This ambitious vision remains unfulfilled, even while innovations in sequencing technology have fully democratized access to genome-scale sequencing. Why? While the central dogma of biology allows us to chart the entirety of cellular metabolism through sequencing, there is no direct coding for chemistry. The rules of base pairing that relate DNA gene to RNA transcript and amino acid sequence do not exist for relating small-molecule structure with macromolecular binding partners and subsequently cellular function. Obtaining such relationships genome-wide is unapproachable via state-of-the-art HTS, akin to attempting genome-wide association studies using turn-of-the-millennium Sanger DNA sequencing.Our laboratory has been engaged in a multipronged technology development campaign to revolutionize molecular screening through miniaturization in pursuit of genome-scale drug discovery capabilities. The compound library was ripe for miniaturization: it clearly needed to become a consumable. We employed DNA-encoded library (DEL) synthesis principles in the development of solid-phase DELs prepared on microscopic beads, each harboring 100 fmol of a single library member and a DNA tag whose sequence describes the structure of the library member. Loading these DEL beads into 100 pL microfluidic droplets followed by online photocleavage, incubation, fluorescence-activated droplet sorting, and DNA sequencing of the sorted DEL beads reveals the chemical structures of bioactive compounds. This scalable library synthesis and screening platform has proven useful in several proof-of-concept projects involving current clinical targets.Moving forward, we face the problem of druggability and proteome-scale assay development. Developing biochemical or cellular assays for all genome-encoded targets is not scalable and likely impossible as most proteins have ill-defined or unknown activity and may not function outside of their native contexts. These are the dark undruggable expanses, and charting them will require advanced synthesis and analytical technologies that can generalize probe discovery, irrespective of mature protein function, to fulfill the Genome Project's vision of proteome-wide control of cellular pharmacology.

Combining pharmacophore models derived from DNA-encoded chemical libraries with structure-based exploration to predict Tankyrase 1 inhibitors

DNA-encoded chemical libraries (DECLs) interrogate the interactions of a target of interest with vast numbers of molecules. DECLs hence provide abundant information about the chemical ligand space for therapeutic targets, and there is considerable interest in methods for exploiting DECL screening data to predict novel ligands. Here we introduce one such approach and demonstrate its feasibility using the cancer-related poly-(ADP-ribose)transferase tankyrase 1 (TNKS1) as a model target. First, DECL affinity selections resulted in structurally diverse TNKS1 inhibitors with high potency including compound 2 with an IC50 value of 0.8 nM. Additionally, TNKS1 hits from four DECLs were translated into pharmacophore models, which were exploited in combination with docking-based screening to identify TNKS1 ligand candidates in databases of commercially available compounds. This computational strategy afforded TNKS1 inhibitors that are outside the chemical space covered by the DECLs and yielded the drug-like lead compound 12 with an IC50 value of 22 nM. The study further provided insights in the reliability of screening data and the effect of library design on hit compounds. In particular, the study revealed that while in general DECL screening data are in good agreement with off-DNA ligand binding, unpredictable interactions of the DNA-attachment linker with the target protein contribute to the noise in the affinity selection data.

Combining DELs and machine learning for toxicology prediction

DNA-encoded libraries (DELs) allow starting chemical matter to be identified in drug discovery. The volume of experimental data generated also makes DELs an attractive resource for machine learning (ML). ML allows modeling complex relationships between compounds and numerical endpoints, such as the binding to a target measured by DELs. DELs could also empower other areas of drug discovery. Here, we propose that DELs and ML could be combined to model binding to off-targets, enabling better predictive toxicology. With enough data, ML models can make accurate predictions across a vast chemical space, and they can be reused and expanded across projects. Although there are limitations, more general toxicology models could be applied earlier during drug discovery, illuminating safety liabilities at a lower cost.

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.

Photochemical C-H arylation of heteroarenes for DNA-encoded library synthesis

DNA-encoded library (DEL) technology has emerged as a time- and cost-efficient technique for the identification of therapeutic candidates in the pharmaceutical industry. Although several reaction classes have been successfully validated in DEL environments, there remains a paucity of DNA-compatible reactions that harness building blocks (BBs) from readily available substructures bearing multifunctional handles for further library diversification under mild, dilute, and aqueous conditions. In this study, the direct C-H carbofunctionalization of medicinally-relevant heteroarenes can be accomplished via the photoreduction of DNA-conjugated (hetero)aryl halides to deliver reactive aryl radical intermediates in a regulated fashion within minutes of blue light illumination. A broad array of electron-rich and electron-poor heteroarene scaffolds undergo transformation in the presence of sensitive functional groups.

Multiplexed Small-Molecule-Ligand Binding Assays by Affinity Labeling and DNA Sequence Analysis

Small-molecule binding assays to target proteins are a core component of drug discovery and development. While a number of assay formats are available, significant drawbacks still remain in cost, sensitivity, and throughput. To improve assays by capitalizing on the power of DNA sequence analysis, we have developed an assay method that combines DNA encoding with split-and-pool sample handling. The approach involves affinity labeling of DNA-linked ligands to a protein target. Critically, the labeling event assesses ligand binding and enables subsequent pooling of several samples. Application of a purifying selection on the pool for protein-labeled DNAs allows detection of ligand binding by quantification of DNA barcodes. We demonstrate the approach in both ligand displacement and direct binding formats and demonstrate its utility in determination of relative ligand affinity, profiling ligand specificity, and high-throughput small-molecule screening.

DNA Conjugates as Tool Compounds for DEL Selections

DEL selections are designed to discover novel small molecule compounds attached to DNA that bind to a target protein. A known small molecule or peptide ligand that binds to the target protein can be conjugated to DNA to mimic compounds contained in DEL libraries. The conjugate can be used as a ligand in preselection binding assays to validate a target protein and optimize selection methods and serve as a positive control in selection experiments. In this chapter, the design, synthesis, and use of DNA conjugate tool compounds are discussed. As an example, I describe the design of a DNA conjugate of a ligand to the CCR5 receptor and its use to optimize selection conditions and as a spike-in positive control in a DEL selection experiment. These methods are broadly applicable to both soluble protein targets and to targets that are displayed on the cell surface and to various types of compounds that can be conjugated to DNA.

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.

DNA-Encoded Chemistry: Drug Discovery from a Few Good Reactions

Click chemistry, proposed nearly 20 years ago, promised access to novel chemical space by empowering combinatorial library synthesis with a "few good reactions". These click reactions fulfilled key criteria (broad scope, quantitative yield, abundant starting material, mild reaction conditions, and high chemoselectivity), keeping the focus on molecules that would be easy to make, yet structurally diverse. This philosophy bears a striking resemblance to DNA-encoded library (DEL) technology, the now-dominant combinatorial chemistry paradigm. This review highlights the similarities between click and DEL reaction design and deployment in combinatorial library settings, providing a framework for the design of new DEL synthesis technologies to enable next-generation drug discovery.

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.

Toward the assembly and characterization of an encoded library hit confirmation platform: Bead-Assisted Ligand Isolation Mass Spectrometry (BALI-MS)

The ability to predict chemical structure from DNA sequence has to date been a necessary cornerstone of DNA-encoded library technology. DNA-encoded libraries (DELs) are typically screened by immobilized affinity selection and enriched library members are identified by counting the number of times an individual compound's sequence is observed in the resultant dataset. Those with high signal reads (DEL hits) are subsequently followed up through off-DNA synthesis of the predicted small molecule structures. However, hits followed-up in this manner often fail to translate to confirmed ligands. To address this low conversion rate of DEL hits to off-DNA ligands, we have developed an approach that eliminates the reliance on chemical structure prediction from DNA sequence. Here we describe our method of combining non-combinatorial resynthesis on-DNA following library procedures as a rapid means to assess the probable molecules attached to the DNA barcode. Furthermore, we apply our Bead-Assisted Ligand Isolation Mass Spectrometry (BALI-MS) technique to identify the true binders found within the mixtures of on-DNA synthesis products. Finally, we describe a Normalized Enrichment (NE) metric that allows for the quantitative assessment of affinity selection in these studies. We exemplify how this combined approach enables the identification of putative hit matter against a clinically relevant therapeutic target bisphosphoglycerate mutase, BPGM.

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 DNA-encoded library for the identification of natural product binders that modulate poly (ADP-ribose) polymerase 1, a validated anti-cancer target

Natural products have been an invaluable source of drug discovery, but their targets remain largely unknown. Natural products enriched DNA-encoded chemical libraries (nDELs) empower the researchers to rapidly and economically screen numerous natural products against various protein targets, and therefore promote the elucidation of the molecular mechanisms. In this work, we used poly (ADP-ribose) polymerase 1 (PARP1), as an example to explore the usage of nDEL for the functional natural products selection. We used late-stage modification approach to label three positive binders with unique DNA barcodes, whose dissociation constants range from sub-micromolar to micromolar. The selection criterion was set up according to the enrichment of these controls. Five natural products selected by this criterion directly bind to PARP1 in SPR, among which luteolin exhibits the highest inhibitory activity against PARP1. Moreover, luteolin selectively induces accumulation of DNA double-strand breaks and G2/M phase arrest in BRCA-deficient cells. All the findings from these investigations on luteolin support that PARP1 inhibition is one of the mechanisms for its anti-cancer activity.

The Impact of Variable Selection Coverage on Detection of Ligands from a DNA-Encoded Library Screen

DNA-encoded library (DEL) technology has become a prominent screening platform in drug discovery owing to the capacity to screen billions or trillions of compounds in a single experiment. Although numerous successes with DEL technology have been reported, we are unaware of a rigorous examination of the many different variables that can influence a screen’s success. Herein, we explore the impact of variable sample sequencing depth on the detection of tool compounds with known affinities toward a given target while simultaneously probing the effect of initial compound input. Our sequencing data confirm reports that high-affinity compounds can be discovered directly from a DEL screen, but we demonstrate that a mismatch between selection output and sequencing quantity can obscure useful ligands. Our results highlight the importance of selection coverage in grasping the entire picture of a DEL screen where the signal of a weak or underrepresented ligand may be suppressed by the inherent noise of a selection. These potential missed ligands may be critical to the success or failure of a drug discovery program.

Off-DNA DNA-Encoded Library Affinity Screening

DNA-encoded library (DEL) technology is emerging as a key element of the small molecule discovery toolbox. Conventional DEL screens (i.e., on-DNA screening) interrogate large combinatorial libraries via affinity selection of DNA-tagged library members that are ligands of a purified and immobilized protein target. In these selections, the DNA tags can materially and undesirably influence target binding and, therefore, the experiment outcome. Here, we use a solid-phase DEL and droplet-based microfluidic screening to separate the DEL member from its DNA tag (i.e., off-DNA screening), for subsequent in-droplet laser-induced fluorescence polarization (FP) detection of target binding, obviating DNA tag interference. Using the receptor tyrosine kinase (RTK) discoidin domain receptor 1 (DDR1) as a proof-of-concept target in a droplet-scale competition-binding assay, we screened a 67 100-member solid-phase DEL of drug-like small molecules for competitive ligands of DDR1 and identified several known RTK inhibitor pharmacophores, including azaindole- and quinazolinone-containing monomers. Off-DNA DEL affinity screening with FP detection is potentially amenable to a wide array of target classes, including nucleic acid binding proteins, proteins that are difficult to overexpress and purify, or targets with no known activity assay.

A Solution Phase Platform to Characterize Chemical Reaction Compatibility with DNA-Encoded Chemical Library Synthesis

DNA-encoded chemical library (DECL) synthesis must occur in aqueous media under conditions that preserve the integrity of the DNA encoding tag. While the identification of "DNA-compatible" reaction conditions is critical for the development of DECL designs that explore previously inaccessible chemical space, reports measuring such compatibility have been largely restricted to methods that do not faithfully capture the impact of reaction conditions on DNA fidelity in solution phase. Here we report a comprehensive methodology that uses soluble DNA substrates that exactly recapitulate DNA's exposure to the chemically reactive species of DECL synthesis. This approach includes the assessment of chemical fidelity (reaction yield and purity), encoding fidelity (ligation efficiency), and readability (DNA compatibility), revealing the fate of the DNA tag during DECL chemistry from a single platform.

DNA Barcoding a Complete Matrix of Stereoisomeric Small Molecules

It is challenging to incorporate stereochemical diversity and topographic complexity into DNA-encoded libraries (DELs) because DEL syntheses cannot fully exploit the capabilities of modern synthetic organic chemistry. Here, we describe the design, construction, and validation of DOS-DEL-1, a library of 107 616 DNA-barcoded chiral 2,3-disubsituted azetidines and pyrrolidines. We used stereospecific C-H arylation chemistry to furnish complex scaffolds primed for DEL synthesis, and we developed an improved on-DNA Suzuki reaction to maximize library quality. We then studied both the structural diversity of the library and the physicochemical properties of individual compounds using Tanimoto multifusion similarity analysis, among other techniques. These analyses revealed not only that most DOS-DEL-1 members have "drug-like" properties, but also that the library more closely resembles compound collections derived from diversity synthesis than those from other sources (e.g., commercial vendors). Finally, we performed validation screens against horseradish peroxidase and carbonic anhydrase IX, and we developed a novel, Poisson-based statistical framework to analyze the results. A set of assay positives were successfully translated into potent carbonic anhydrase inhibitors (IC₅₀ = 20.1-68.7 nM), which confirmed the success of the synthesis and screening procedures. These results establish a strategy to synthesize DELs with scaffold-based stereochemical diversity and complexity that does not require the development of novel DNA-compatible chemistry.

Functionality-Independent DNA Encoding of Complex Natural Products

DNA encoded chemical libraries (DELs) link the powers of genetics and chemical synthesis via combinatorial optimization. Through combinatorial chemistry, DELs can grow to the unprecedented size of billions to trillions. To take full advantage of the DEL approach, linking the power of genetics directly to chemical structures would offer even greater diversity in a finite chemical world. Natural products have evolved an incredible structural diversity along with their biological evolution. Herein, we used traditional Chinese medicines (TCMs) as examples in a late-stage modification toolbox approach to annotate these complex organic compounds with amplifiable DNA barcodes, which could be easily incorporated into a DEL. The method of end-products labeling also generates a cluster of isomers with a single DNA tag at different sites. These isomers provide an additional spatial diversity for multiple accessible pockets of targeted proteins. Notably, a novel PARP1 inhibitor from TCM has been identified from the natural products enriched DEL (nDEL).

Activity-Based DNA-Encoded Library Screening

Robotic high-throughput compound screening (HTS) and, increasingly, DNA-encoded library (DEL) screening are driving bioactive chemical matter discovery in the postgenomic era. HTS enables activity-based investigation of highly complex targets using static compound libraries. Conversely, DEL grants efficient access to novel chemical diversity, although screening is limited to affinity-based selections. Here, we describe an integrated droplet-based microfluidic circuit that directly screens solid-phase DELs for activity. An example screen of a 67 100-member library for inhibitors of the phosphodiesterase autotaxin yielded 35 high-priority structures for nanomole-scale synthesis and validation (20 active), guiding candidate selection for synthesis at scale (5/5 compounds with IC₅₀ values of 4-10 μM). We further compared activity-based hits with those of an analogous affinity-based DEL selection. This miniaturized screening platform paves the way toward applying DELs to more complex targets (signaling pathways, cellular response) and represents a distributable approach to small molecule discovery.

Quantitative Comparison of Enrichment from DNA-Encoded Chemical Library Selections

DNA-encoded chemical libraries (DELs) provide a high-throughput and cost-effective route for screening billions of unique molecules for binding affinity for diverse protein targets. Identifying candidate compounds from these libraries involves affinity selection, DNA sequencing, and measuring enrichment in a sample pool of DNA barcodes. Successful detection of potent binders is affected by many factors, including selection parameters, chemical yields, library amplification, sequencing depth, sequencing errors, library sizes, and the chosen enrichment metric. To date, there has not been a clear consensus about how enrichment from DEL selections should be measured or reported. We propose a normalized z-score enrichment metric using a binomial distribution model that satisfies important criteria that are relevant for analysis of DEL selection data. The introduced metric is robust with respect to library diversity and sampling and allows for quantitative comparisons of enrichment of n-synthons from parallel DEL selections. These features enable a comparative enrichment analysis strategy that can provide valuable information about hit compounds in early stage drug discovery.

Second generation DNA-encoded dynamic combinatorial chemical libraries

A novel DNA-encoded chemical library architecture can mimic the mechanisms of immunity to evolve binders through recombination, dynamics and adaption.

A DNA-Encoded Library of Chemical Compounds Based on Common Scaffolding Structures Reveals the Impact of Ligand Geometry on Protein Recognition

A DNA-encoded chemical library (DECL) with 1.2 million compounds was synthesized by combinatorial reaction of seven central scaffolds with two sets of 343×492 building blocks. Library screening by affinity capture revealed that for some target proteins, the chemical nature of building blocks dominated the selection results, whereas for other proteins, the central scaffold also crucially contributed to ligand affinity. Molecules based on a 3,5-bis(aminomethyl)benzoic acid core structure were found to bind human serum albumin with a Kd value of 6 nm, while compounds with the same substituents on an equidistant but flexible l-lysine scaffold showed 140-fold lower affinity. A 18 nm tankyrase-1 binder featured l-lysine as linking moiety, while molecules based on d-Lysine or (2S,4S)-amino-l-proline showed no detectable binding to the target. This work suggests that central scaffolds which predispose the orientation of chemical building blocks toward the protein target may enhance the screening productivity of encoded libraries.

DNA-encoded chemical libraries - achievements and remaining challenges

DNA-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.

Robustness of In Vitro Selection Assays of DNA-Encoded Peptidomimetic Ligands to CBX7 and CBX8

The identification of protein ligands from a DNA-encoded library is commonly conducted by an affinity selection assay. These assays are often not validated for robustness, raising questions about selections that fail to identify ligands and the utility of enrichment values for ranking ligand potencies. Here, we report a method for optimizing and utilizing affinity selection assays to identify potent and selective peptidic ligands to the highly related chromodomains of CBX proteins. To optimize affinity selection parameters, statistical analyses (Z' factors) were used to define the ability of selection assay conditions to identify and differentiate ligands of varying affinity. A DNA-encoded positional scanning library of peptidomimetics was constructed around a trimethyllysine-containing parent peptide, and parallel selections against the chromodomains from CBX8 and CBX7 were conducted over three protein concentrations. Relative potencies of off-DNA hit molecules were determined through a fluorescence polarization assay and were consistent with enrichments observed by DNA sequencing of the affinity selection assays. In addition, novel peptide-based ligands were discovered with increased potency and selectivity to the chromodomain of CBX8. The results indicate low DNA tag bias and show that affinity-based in vitro selection assays are sufficiently robust for both ligand discovery and determination of quantitative structure-activity relationships.

Second-generation DNA-templated macrocycle libraries for the discovery of bioactive small molecules

DNA-encoded libraries have emerged as a widely used resource for the discovery of bioactive small molecules, and offer substantial advantages compared with conventional small-molecule libraries. Here, we have developed and streamlined multiple fundamental aspects of DNA-encoded and DNA-templated library synthesis methodology, including computational identification and experimental validation of a 20 × 20 × 20 × 80 set of orthogonal codons, chemical and computational tools for enhancing the structural diversity and drug-likeness of library members, a highly efficient polymerase-mediated template library assembly strategy, and library isolation and purification methods. We have integrated these improved methods to produce a second-generation DNA-templated library of 256,000 small-molecule macrocycles with improved drug-like physical properties. In vitro selection of this library for insulin-degrading enzyme affinity resulted in novel insulin-degrading enzyme inhibitors, including one of unusual potency and novel macrocycle stereochemistry (IC₅₀ = 40 nM). Collectively, these developments enable DNA-templated small-molecule libraries to serve as more powerful, accessible, streamlined and cost-effective tools for bioactive small-molecule discovery.

Versatile protein recognition by the encoded display of multiple chemical elements on a constant macrocyclic scaffold

In nature, specific antibodies can be generated as a result of an adaptive selection and expansion of lymphocytes with suitable protein binding properties. We attempted to mimic antibody-antigen recognition by displaying multiple chemical diversity elements on a defined macrocyclic scaffold. Encoding of the displayed combinations was achieved using distinctive DNA tags, resulting in a library size of 35,393,112. Specific binders could be isolated against a variety of proteins, including carbonic anhydrase IX, horseradish peroxidase, tankyrase 1, human serum albumin, alpha-1 acid glycoprotein, calmodulin, prostate-specific antigen and tumour necrosis factor. Similar to antibodies, the encoded display of multiple chemical elements on a constant scaffold enabled practical applications, such as fluorescence microscopy procedures or the selective in vivo delivery of payloads to tumours. Furthermore, the versatile structure of the scaffold facilitated the generation of protein-specific chemical probes, as illustrated by photo-crosslinking.

tagFinder: A Novel Tag Analysis Methodology That Enables Detection of Molecules from DNA-Encoded Chemical Libraries

Available tools to analyze sequencing data coming from DNA-encoded chemical libraries (DELs) are often limited to in-house methods, which usually rely on strictly looking for the particular DEL structure used. Current methods do not take into account technological errors, such as library codification and sequencing errors, when detecting the sequences. The vast amount of data produced by next-generation sequencing of DEL screens is usually enough to extract the minimum information needed for compound identification. Here, we report a methodology to deconvolute encoding oligonucleotides, thus optimizing the sequencing power regardless of the library size, design complexity, or sequencing technology chosen. tagFinder is a highly flexible tool for fast tag detection and thorough DEL results characterization, which requires minimal hardware resources, scales linearly, and does not introduce any analytical error. The methodology can even deal with sequencing errors and PCR duplicates on single- or double-stranded DNA, enhancing the analytical detection and quantification of molecules and the informativeness of the entire process. Source code is available at https://github.com/jamigo/tagFinder .

Poisson Statistics of Combinatorial Library Sampling Predict False Discovery Rates of Screening

Microfluidic droplet-based screening of DNA-encoded one-bead-one-compound combinatorial libraries is a miniaturized, potentially widely distributable approach to small molecule discovery. In these screens, a microfluidic circuit distributes library beads into droplets of activity assay reagent, photochemically cleaves the compound from the bead, then incubates and sorts the droplets based on assay result for subsequent DNA sequencing-based hit compound structure elucidation. Pilot experimental studies revealed that Poisson statistics describe nearly all aspects of such screens, prompting the development of simulations to understand system behavior. Monte Carlo screening simulation data showed that increasing mean library sampling (ε), mean droplet occupancy, or library hit rate all increase the false discovery rate (FDR). Compounds identified as hits on k > 1 beads (the replicate k class) were much more likely to be authentic hits than singletons (k = 1), in agreement with previous findings. Here, we explain this observation by deriving an equation for authenticity, which reduces to the product of a library sampling bias term (exponential in k) and a sampling saturation term (exponential in ε) setting a threshold that the k-dependent bias must overcome. The equation thus quantitatively describes why each hit structure’s FDR is based on its k class, and further predicts the feasibility of intentionally populating droplets with multiple library beads, assaying the micromixtures for function, and identifying the active members by statistical deconvolution.

Analysis of Current DNA Encoded Library Screening Data Indicates Higher False Negative Rates for Numerically Larger Libraries

To optimize future DNA-encoded library design, we have attempted to quantify the library size at which the signal becomes undetectable. To accomplish this we (i) have calculated that percent yields of individual library members following a screen range from 0.002 to 1%, (ii) extrapolated that ∼1 million copies per library member are required at the outset of a screen, and (iii) from this extrapolation predict that false negative rates will begin to outweigh the benefit of increased diversity at library sizes >10⁸. The above analysis is based upon a large internal data set comprising multiple screens, targets, and libraries; we also augmented our internal data with all currently available literature data. In theory, high false negative rates may be overcome by employing larger amounts of library; however, we argue that using more than currently reported amounts of library (≫10 nmoles) is impractical. The above conclusions may be generally applicable to other DNA encoded library platforms, particularly those platforms that do not allow for library amplification.

High-throughput Identification of DNA-Encoded IgG Ligands that Distinguish Active and Latent Mycobacterium tuberculosis Infections

The circulating antibody repertoire encodes a patient's health status and pathogen exposure history, but identifying antibodies with diagnostic potential usually requires knowledge of the antigen(s). We previously circumvented this problem by screening libraries of bead-displayed small molecules against case and control serum samples to discover "epitope surrogates" (ligands of IgGs enriched in the case sample). Here, we describe an improved version of this technology that employs DNA-encoded libraries and high-throughput FACS-based screening to discover epitope surrogates that differentiate noninfectious/latent (LTB) patients from infectious/active TB (ATB) patients, which is imperative for proper treatment selection and antibiotic stewardship. Normal control/LTB (10 patients each, NCL) and ATB (10 patients) serum pools were screened against a library (5 × 10⁶ beads, 448 000 unique compounds) using fluorescent antihuman IgG to label hit compound beads for FACS. Deep sequencing decoded all hit structures and each hit's occurrence frequencies. ATB hits were pruned of NCL hits and prioritized for resynthesis based on occurrence and homology. Several structurally homologous families were identified and 16/21 resynthesized representative hits validated as selective ligands of ATB serum IgGs (p < 0.005). The native secreted TB protein Ag85B (though not the E. coli recombinant form) competed with one of the validated ligands for binding to antibodies, suggesting that it mimics a native Ag85B epitope. The use of DNA-encoded libraries and FACS-based screening in epitope surrogate discovery reveals thousands of potential hit structures. Distilling this list down to several consensus chemical structures yielded a diagnostic panel for ATB composed of thermally stable and economically produced small molecule ligands in place of protein antigens.

Recent advances on the encoding and selection methods of DNA-encoded chemical library

DNA-encoded chemical library (DEL) has emerged as a powerful and versatile tool for ligand discovery in chemical biology research and in drug discovery. Encoding and selection methods are two of the most important technological aspects of DEL that can dictate the performance and utilities of DELs. In this digest, we have summarized recent advances on the encoding and selection strategies of DEL and also discussed the latest developments on DNA-encoded dynamic library, a new frontier in DEL research.

DNA-encoded chemical libraries: foundations and applications in lead discovery

DNA-encoded chemical libraries have emerged as a powerful tool for hit identification in the pharmaceutical industry and in academia. Similar to biological display techniques (such as phage display technology), DNA-encoded chemical libraries contain a link between the displayed chemical building block and an amplifiable genetic barcode on DNA. Using routine procedures, libraries containing millions to billions of compounds can be easily produced within a few weeks. The resulting compound libraries are screened in a single test tube against proteins of pharmaceutical interest and hits can be identified by PCR amplification of DNA barcodes and subsequent high-throughput sequencing.