U Hang Chan , Fengling Li , Frances M. Bashore , Scott Houliston , Catherine Vu , Irene Chau , Alison D. Axtman , Levon Halabelian

Alzheimer's & Dementia

DOI: 10.1002/alz70859_096394

Abstract

Background To diversify Alzheimer’s Disease (AD) drug targets, a bioinformatics core is established to provide an unbiased ranking of AD risk‐associated genes by integrating multiple lines of genetic and multi‐omic evidence. From which, several RNA helicases, including RIG‐I‐like receptor 3 (LGP2), melanoma differentiation‐associated protein 5 (MDA5) and Dead Box 1 (DDX1) have been identified as high priority targets differentially expressed in AD brains. All three helicases play a role in the innate immune response pathway against viral RNA. Given the previous link between viral infection and AD pathology, this prompted the development of small molecule chemical probe against these targets to further elucidate their roles in AD. Method Purified proteins were used for ATPase assay development and compound screening. The ATPase assay was performed in the presence of annealed 24mer RNA, double‐stranded RNA (dsRNA) with a 25‐nt 3ʹ overhang, or single‐stranded DNA (ssDNA). We employed DNA‐encoded chemical library (DEL) and computational methods for small molecule hit discovery. Hit confirmation was carried out by ATPase assay, Surface Plasmon Resonance (SPR), Differential Scanning Fluorimetry (DSF) and 19Fluorine‐ Nuclear Magnetic Resonance (19F‐NMR). Hit expansion was carried out for the most promising hits to increase potency and selectivity. Result We describe the development and optimization of a bioluminescence assay to kinetically characterize the activity of three human RNA helicases involved in innate immune response pathway, including MDA5, LGP2, and DDX1. Through DEL‐ML screening, we identified a selective hit for MDA5, and characterized its activity by ATPase assay with IC50 of 8 µM, and orthogonally confirmed by F‐NMR. Ongoing studies aim to elucidate the ligand binding site using X‐ray crystallography. Conclusion We present a robust high‐throughput in vitro assay designed for small molecule screening in a 384‐well format, enabling hit optimization and facilitating the discovery of inhibitors for MDA5, LGP2, and DDX1. Through DEL‐ML screen, we identified a selective MDA5 inhibitor that can be used to further interrogate its role in AD pathogenesis, and serve as a chemical starting point for future drug discovery efforts. This ligand represents first‐in‐class small molecule inhibitor for MDA5, a target that has been underexplored in the context of its role in AD.

Benjamin C. Whitehurst , Niall A. Anderson , Argyrides Argyrou , Peter Astles , Bernard Barlaam , Elaine B. Cadogan , Luca Carlino , Gavin W. Collie , Alex Edwards , Linda Kitching , Yaqin Li , Alexander G. Milbradt , Jenni Nikkilä , Sarah Northall , Sara Pahlén , Saleha Patel , Wendy Savory , Markus Schade , Jonathan A. Spencer , Darren Stead , Christopher J. Stubbs , Aquan Wang , Wenxin Wang

ACS Medicinal Chemistry Letters

DOI: 10.1021/acsmedchemlett.5c00651

Abstract

DNPH1 is a hydrolase enzyme that degrades the noncanonical nucleotide 5-hydroxymethyl-2′-deoxyuridine 5′-monophosphate (hmdUMP), thus acting as a nucleotide pool sanitizer by preventing its aberrant incorporation into DNA. Recent studies have shown that loss of DNPH1 enhances the sensitivity of homologous recombination repair-deficient cancer cells to PARP inhibitors, highlighting its potential as an attractive therapeutic target. Herein we report the design and prosecution of an integrated hit finding strategy combining high-throughput screening, DNA-encoded library screening, and fragment-based lead generation which enabled the discovery of the first non-nucleotide ligands for DNPH1. We compare four hit compounds which differ markedly in their chemical structures, physicochemical properties, and binding modes and summarize parallel hit-to-lead workup efforts. We also provide discussion of the merits of an integrated approach for hit discovery when applied to challenging novel targets such as DNPH1.

Summary

DNPH1 is a nucleotide-pool sanitizing hydrolase whose deletion selectively sensitizes homologous-recombination-deficient tumors to PARP inhibitors. To enable small-molecule validation of this synthetic-lethal target, AstraZeneca executed a fully integrated hit-finding program that combined high-throughput screening (HTS, 1.8 M compounds), DNA-encoded library (DEL, 7.1 billion compounds) affinity selection and fragment-based lead generation (FBLG). The campaign delivered four structurally distinct, non-nucleotide chemotypes—thiadiazine, imidazole, triazole and tetrahydro-isoquinoline (THIQ)—that were biophysically validated (IC₅₀ 2–24 µM; SPR Kd 2–9 µM) and structurally characterized by X-ray crystallography. Subsequent parallel optimization showed that only the thiadiazine series could be advanced to low-nM, cell-permeable inhibitors (e.g. compound 10: IC₅₀ 0.5 nM, cellular TE IC₅₀ 61 nM) and to potent PROTAC degraders (e.g. 11: DC₅₀ 28 nM). DEL-derived triazole ligands also furnished early PROTACs (e.g. 13) that achieved >90 % DNPH1 degradation before the more drug-like quinazoline/quinoline series became available. Imidazole and THIQ cores could not be driven below ~1 µM potency, illustrating the necessity of an acidic anchor for high-affinity binding and the penalty of stabilizing a folded bioactive conformation.

Highlights

• First non-nucleotide ligands for DNPH1 discovered through a tri-platform approach (HTS + DEL + FBLG).
• Four validated chemotypes reveal divergent binding modes within a flexible, dimeric catalytic site.
• Thiadiazine → quinazoline core hop overcame permeability hurdles, yielding nM cell-active inhibitors and efficient PROTACs.
• DEL screen accelerated biology by enabling direct-to-biology PROTAC synthesis before lead-optimization completion.
• Structural and SAR data demonstrate that strong engagement of the phosphate-binding pocket (charged H-bond) is critical for sub-µM potency.
• Integrated screening maximized chemical coverage and mitigated single-technology failure (FBLG produced no confirmed hits).

Conclusion

By concurrently deploying HTS, DEL and FBLG, the team rapidly generated a diversified hit collection against the previously ligand-naïve target DNPH1. Crystal structures illuminated both opportunities and limitations: loop plasticity and the requirement for polar anchoring complicated optimization of neutral scaffolds, whereas acid-bearing thiadiazines were successfully morphed into quinazoline/quinoline analogues with single-digit nM enzymatic potency, robust cellular activity and efficient target degradation. A DEL-derived triazole further enabled early PROTAC proof-of-concept, underscoring the strategic value of exploiting the DNA-conjugation vector. Overall, the work delivers chemical tools that confirm DNPH1 as a druggable node in DNA-damage response pathways and exemplifies how an integrated discovery engine can de-risk and accelerate prosecution of challenging, novel targets within industrial timelines.

Yuichi Onda ,  Yurika Ochi ,  Toshihiro Araki ,  Miho Kageoka ,  Shuzo Takeda ,  Kazunori Yamada ,  Takehiko Ueda ,  Ken Ohno ,  Minoru Tanaka ,  Daiki Sakai ,  Miki Hasegawa ,  Yoshihito Tanaka 

bioRxiv - Synthetic Biology

DOI: 10.1101/2025.11.26.690606

Abstract

Targeted protein degradation (TPD), including proteolysis targeting chimeras (PROTACs) and molecular glue degraders (MGDs), is a promising therapeutic approach. However, systematic discovery of such small molecules remains a major challenge. Here, we present PhenoDEL, a novel phenotypic DNA-encoded library (DEL) screening platform that integrates one-bead one-compound DEL (OBOC-DEL) with the Beacon® optofluidic system for high-throughput, single-cell analysis. By co-culturing individual OBOC-DEL beads and engineered reporter cells in nanoliter-scale chambers, PhenoDEL enables direct observation of compound-induced protein degradation at single-cell resolution. We demonstrate this approach by identifying compounds that induce degradation of FKBP12F36V-EGFP fusion proteins in PC-3 cells. The workflow allows precise linkage between compound identity and cellular phenotype via DNA barcoding and next-generation sequencing. PhenoDEL overcomes limitations of conventional screening methods, offering high sensitivity, spatial control, and scalability. This platform holds significant potential for mechanism-driven drug discovery, including identification of novel PROTACs and MGDs.

Summary

This preprint introduces PhenoDEL, a novel phenotypic screening platform that integrates One-Bead One-Compound DNA-Encoded Library (OBOC-DEL) technology with the Beacon® optofluidic system for high-throughput, single-cell analysis of targeted protein degradation (TPD). The platform enables direct observation of compound-induced protein degradation by co-culturing individual OBOC-DEL beads with engineered reporter cells in nanoliter-scale chambers (NanoPens). Upon UV-A irradiation, compounds are photoreleased from beads and diffuse to the cell, causing degradation of a FKBP12F36V-EGFP fusion protein. Beads associated with cells showing EGFP fluorescence loss are recovered, and their DNA barcodes are sequenced to identify active compounds. The authors optimized compound release kinetics (200 ms UV exposure), retention conditions (halting CO₂ flow increases concentration 5-fold), and imaging protocols. In a proof-of-concept screen, PhenoDEL successfully identified PROTAC molecules with >99% cell viability, demonstrating its capacity for mechanism-driven discovery of protein degraders including PROTACs and molecular glues.

Highlights

• Single-cell resolution screening: PhenoDEL achieves 1:1 pairing of individual OBOC-DEL beads and cells in 0.75 nL NanoPen chambers, enabling direct linkage between compound identity and cellular phenotype via DNA barcoding.
• Real-time quality control: The platform excludes dead or damaged cells by monitoring EGFP fluorescence before and after compound release, reducing false positives and improving data reliability compared to pooled screening methods.
• Optimized compound delivery: UV-A irradiation (390 nm, 200 ms) cleaves photolabile linkers to achieve biologically relevant concentrations (10-90 µM) while stopping CO₂ flow enhances compound retention within chambers.
• High throughput: Up to four OptoSelect® chips (3,500 nanopens/chip) can run simultaneously, enabling >10,000 samples per run; scalable to 80,000 compounds using 20k-nanopen chips.
• Validated proof-of-concept: Engineered PC-3 cells expressing FKBP12F36V-EGFP showed robust degradation of the fusion protein within 6 hours of PROTAC FKBP Degrader-3 exposure, with minimal cytotoxicity and stable EGFP baseline (>5,000 fluorescence units).
• Versatile applicability: The system is compatible with various cell types (suspension, adherent, primary cells, organoids) and reporter systems, positioning it for personalized medicine and comprehensive functional genomics.

Conclusion

PhenoDEL represents a significant advancement in DNA-encoded library screening by overcoming limitations of conventional affinity-based and droplet-based methods. The integration of OBOC-DEL with Beacon's optofluidic technology enables high-resolution, activity-based screening at the single-cell level, providing spatial control, real-time phenotypic tracking, and direct genotype-phenotype correlation. The platform's ability to precisely modulate compound release, maintain cell viability, and automatically filter out low-quality data points establishes a robust framework for discovering novel PROTACs, molecular glue degraders, and other proximity-inducing molecules. With demonstrated scalability and compatibility across diverse biological models, PhenoDEL holds substantial potential for next-generation drug discovery, particularly in targeting previously undruggable proteins through event-driven pharmacology.

Li Zhou , Yong Ju , Zhijuan Cao , Sheng Cai , Jiayuan Su , Jianzhong Lu 

Journal of Pharmaceutical Analysis

DOI: 10.1016/j.jpha.2025.101498

Highlight

• Through screening 31 DNA-encoded chemical libraries, totaling 4.4 billion molecules, we identified a novel class of selective CDK2 inhibitors.

• The drug-likeness of C172 at the cellular level was evaluated, such as in vitro enzymatic and cellular assays, mechanistic studies on protein degradation, ADME characterization, single-dose pharmacokinetics in rats and metabolite identification.

Yiwei Zhang, Yuqiu Lan, Rufeng Fan, Lei Feng, Guoliang Wang, Xinyuan Wu, Lulu Wen, Zhiqiang Duan, Yueyue Xia, Xudong Wang, Lingrui Zhang, Lu Zhou, Minjia Tan, Cangsong Liao, Xiaojie Lu 

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c14634

Abstract

DNA-encoded libraries (DELs) have emerged as an effective and efficient selection strategy for lead compound discovery in academia and industry over the past few decades. Despite recent advancements in this field, DEL remains limited by sensitive DNA-based constructs, particularly with low selection success rates resulting from the random selection of targets. Here, we describe a chemoenzymatic on-DNA reaction for DEL syntheses and develop a chemoproteomic-guided DEL selection platform. This platform, termed FF tags-biocatDEL, integrates DEL technology, chemoenzymatic synthesis, and fully functionalized (FF) chemical tags to match DELs with selection targets, even with limited information about ligandable hotspots. Using two diazirine-based FF indole probes, we comprehensively surveyed binding partners in cells and identified phosphoglycerate dehydrogenase (PHGDH) as a potential target for DEL selection. DEL01 and DEL02 were designed, synthesized, and selected against PHGDH, leading to the identification of a novel enzyme-active compound with an IC₅₀ value of 2.5 μM. Our strategy, utilizing FF tags-biocatDEL, establishes a generalizable workflow for rapid target hunting and ligand discovery. It provides an effective method for precisely matching DELs with potential targets, demonstrating its significant potential as a complementary approach to drug discovery based on DELs.

Summary

This study presents a novel FF tags-biocatDEL platform that integrates chemoenzymatic synthesis, chemoproteomics, and DNA-encoded library (DEL) technology to overcome the low success rates of traditional DEL selection. The researchers developed a DNA-compatible decarboxylative aldol reaction using the PLP-dependent enzyme ApUstD to generate indole scaffolds bearing amine and carboxyl functional groups. Through chemoproteomic profiling with diazirine-based fully functionalized (FF) indole probes, they identified phosphoglycerate dehydrogenase (PHGDH) as a high-priority target from 2,208 enriched proteins. Two focused DELs were constructed: DEL01 (281,158 members via 2-cycle synthesis) and DEL02 (1.35 million members derived from a lactone fragment). Affinity selection against PHGDH yielded L5, a novel indole-based inhibitor with an IC₅₀ of 2.5 μM that acts via an allosteric mechanism. This strategy demonstrates that chemoproteomic guidance significantly enhances DEL selection efficiency and expands the chemical space for challenging targets.

Highlights

• Innovative Chemoenzymatic Reaction: The first application of ApUstD on DNA substrates, achieving quantitative conversion (up to 100%) under mild aqueous conditions to generate complex indole scaffolds with γ-hydroxy-α-amino acid structures.
• Chemoproteomic-Guided Target Identification: Diazirine-based FF indole probes enabled unbiased profiling of 2,208 ligandable proteins, with PHGDH emerging as a clinically relevant target (ranked 111th) for cancer and neurodegenerative diseases.
• Potent Allosteric Inhibitor Discovery: L5, derived from a lactone byproduct (L3) scaffold, showed sub-micromolar potency (IC₅₀ = 2.5 μM) and allosteric inhibition independent of NAD⁺ concentration, representing a new chemotype for PHGDH.
• Scaffold Optimization via DEL Iteration: Initial hits (L1, L2) showed modest activity, but leveraging a side-product scaffold (L3) to build DEL02 (1.35M compounds) enabled a 20-fold activity improvement over the parent fragment.
• Technical Milestones: Successfully synthesized large-scale DELs using biocatalysis, validated target engagement via photo-crosslinking and thermal shift assays, and established a generalizable workflow combining fragment-based DELs with proteome-wide targeting data.

Conclusion

The FF tags-biocatDEL platform successfully bridges biocatalysis, chemoproteomics, and DEL technology to create a highly efficient, target-directed drug discovery workflow. By using chemoproteomic data to rationally select PHGDH and focused DELs to optimize a biocatalytically derived indole scaffold, the team discovered L5, a novel, compact PHGDH inhibitor with promising activity. This approach significantly outperforms random target selection and expands the accessible chemical space for traditionally challenging enzymes. While the platform currently leverages biocatalysis primarily for scaffold generation, future expansion to multiple DEL synthesis steps could further enhance diversity. Additionally, the affinity-based selection may identify non-functional binders that could be repurposed as PROTACs or other modalities. Overall, this strategy offers a robust complement to conventional DEL methods and holds substantial promise for accelerating lead discovery against emerging therapeutic targets.

Wenyi Zhang, Yuxing Wang, Rui Zhan, Runtong Qian, Qi Hu, Jing Huang

bioRxiv - Biophysics

DOI: 10.1101/2025.06.12.659183

Abstract

DNA-encoded libraries (DELs) facilitate high-throughput screening of trillions of molecules against protein targets through split-pool synthesis and DNA tagging. Despite their potential, only a few DEL-derived compounds have advanced to clinical trials or reached the market. A better understanding of the defining characteristics of target proteins, particularly those with binding pockets suitable for DEL screening, is critical to improving success rates. However, existing approaches remain limited in assessing pocket flexibility and functional similarity. Here, we present ErePOC, a pocket representation model based on contrastive learning with ESM-2 embeddings to address these challenges. ErePOC captures both structural and functional features of binding pockets, enabling identification of shared characteristics among DEL targets. By integrating analyses of low-dimensional physicochemical properties and high-dimensional ErePOC embeddings, we provide a comprehensive view of DEL target space. With 98% precision in downstream classification tasks, ErePOC demonstrates high performance in pocket representation, which is then applied to predict human proteins suitable for DEL screening, with enrichment uncovered across 18 functional categories. This work establishes a new framework for enhancing DEL-based drug discovery through more effective target selection and pocket similarity analysis.

Summary

This study introduces ErePOC, a novel pocket representation model that employs contrastive learning with ESM-2 embeddings to decode the defining characteristics of protein binding pockets amenable to DNA-encoded library (DEL) screening. Despite DEL technology's capacity to screen trillions of compounds, clinical translation remains limited due to poor understanding of target druggability. The researchers analyzed 128 successful DEL targets and compared them to 326,416 general ligand pockets (BioLiP2) and 340 FDA-approved drug pockets, revealing that DEL pockets are uniquely larger (28.1 vs 16.1 residues), more hydrophobic, and enriched in specific amino acids (Met, Tyr, Trp, Phe, Leu). ErePOC was trained to map pockets to a 256-dimensional latent space aligned with ligand chemical similarity, achieving 98% precision in functional classification. Applied to 23,391 AlphaFold2-predicted human proteins, the model identified 2,739 DEL-compatible targets with pockets showing >0.8 cosine similarity to known DEL pockets. Enrichment analysis revealed 18 functional categories, particularly oxidoreductases, transferases, and multifunctional enzymes. In silico docking of 2.8 million virtual DEL compounds against 14 selected targets confirmed that ErePOC-enriched proteins exhibit significantly better predicted binding affinities than neutral controls. This work establishes a computational framework for rational DEL target selection beyond traditional structural similarity metrics.

Highlights

• Distinct DEL Pocket Signature: DEL-binding pockets are 1.3× larger (3,301 ų volume), more hydrophobic (50.7% hydrophobic interactions vs 32.5% in natural pockets), and enriched in flexible aromatic/hydrophobic residues (Met, Tyr, Trp, Phe) compared to regular ligand and FDA-approved drug pockets.
• ErePOC Model Innovation: A contrastive learning framework that aligns pocket representations with ligand Morgan fingerprints via KL divergence loss, generating function-aware embeddings that capture physicochemical and evolutionary features beyond 3D geometry, robust to pocket flexibility.
• Robust Zero-Shot Performance: Achieves superior classification of 7 ligand-binding pocket types (~43,000 pockets) with 98.5–98.9% accuracy; maintains strong performance even for pocket classes excluded from training, demonstrating powerful generalization.
• Large-Scale Human Proteome Screening: Identified 2,739 unique human proteins with DEL-compatible pockets from AlphaFold2 structures, with significant enrichment in transferases (17.9%), hydrolases (11.6%), and oxidoreductases (9.4%), plus novel classes like RNA-binding proteins and chromatin regulators.
• Experimental Validation via Docking: In silico screening of 2.8M DEL-like molecules against ErePOC-selected targets showed statistically significant better binding affinity (mean Z-score –2.18 vs –1.07) and higher enrichment for DEL-enriched vs DEL-neutral protein families.
• Case Study Insights: The regulatory protein MAT2B exhibits higher DEL compatibility (cosine similarity 0.93, docking –8.8 kcal/mol) than its catalytic paralog MAT2A (0.66, –5.3 kcal/mol), demonstrating ErePOC's ability to resolve subtle family-level differences in druggability.

Conclusion

ErePOC provides a transformative approach to DEL target selection by learning high-dimensional, function-aware representations of binding pockets that transcend traditional structural alignment limitations. The model successfully deciphers a unique DEL pocket pattern—characterized by larger size, enhanced hydrophobicity, and specific amino acid biases—and leverages this to predict over 2,700 human proteins likely amenable to DEL screening across 18 enriched functional categories. By capturing physicochemical relationships rather than relying solely on geometric similarity, ErePOC addresses the critical challenge of pocket flexibility and low structural overlap among functionally related sites. The significant enrichment of oxidoreductases, transferases, and multifunctional enzymes validates known DEL success stories while expanding the targetable space to include chromatin regulators and RNA-binding proteins. In silico validation confirms that ErePOC-selected targets bind DEL-like molecules more favorably, supporting its practical utility. This framework not only enhances DEL efficiency but also offers broad applicability for virtual screening, molecule generation, and protein design, particularly when integrated with advanced structure prediction tools like AlphaFold3.