Biology subjects

Lin, J.

Publications and source records attributed to Lin, J..

At least 127 records · Page 7Linked to original sources

bioRxiv2025

Sequence based prediction of cell type specific microRNA binding and mRNA degradation for therapeutic discovery

MicroRNAs and RNA binding proteins are crucial elements of post-transcriptional gene regulation, which governs the fate of mRNA molecules in the cell. However, the landscape of these regulatory interactions, particularly across different mammalian cell types, remains underexplored. We describe REPRESS, a deep learning model that predicts cell-type-specific microRNA binding and mRNA degradation directly from RNA sequence. REPRESS was trained on AGO2-CLIP, miR-eCLIP and Degradome-Seq data profiling millions of microRNA binding and mRNA degradation sites across multiple cell types in human and mouse. It reveals biology that other state-of-the-art methods did not, such as identifying repressive non-canonical miRNA target sites and decoding the regulatory effects of sequence context and miRNA binding site multiplicity. REPRESS outperforms other advanced methods and neural architectures on a comprehensive suite of seven orthogonal tasks, including identifying genetic variants that affect microRNA binding, predicting out-of-distribution data from massively parallel reporter assays, and predicting canonical and non-canonical miRNA mediated repression. To demonstrate the general utility of REPRESS, we show that it provides insights into novel biology and the design of RNA therapeutics. Code is available at : https://github.com/deepgenomics/repress

genomics↗

bioRxiv2025

PW-GAN: Pseudo-Warping Field Guided GAN for Unsupervised Denoising of Fetal Brain MRI Images

Fetal brain magnetic resonance imaging is of great importance for prenatal neural disorder diagnosis. To improve signal-to-noise ratio and coverage, fetal brain MRI often uses thick-slice scanning to reduce motion artifacts and ensure image quality for single slices. Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D slices has shown promise in imaging of moving subjects like fetal MRI. However, raw scans acquired with varying slice thicknesses present substantial disparities in quality when retrospectively reconstructed into isotropic high-resolution volumes (e.g. 0.8 mm slice thickness). In particular, thick-slice acquisitions (e.g. 5-6 mm) tend to yield suboptimal reconstruction results compared to thin-slice scans (e.g. 2-3 mm), often exhibiting residual noise, interpolation-induced artifacts, and inter-slice structural discontinuities. This challenge has highlighted the need to further reduce noise caused by reconstruction errors when the reconstructed volumes are used for downstream tasks. With the success of neural networks in computer vision, deep learning methods based on architectures such as convolutional neural networks (CNNs) and generative adversarial networks (GANs) have achieved outstanding performance in single-image denoising. Nevertheless, CNN-based methods still heavily rely on large-scale datasets comprising noisy-clean image pairs. Although unsupervised GAN variants such as CycleGAN have been developed to mitigate this dependency, the inherent variability of GAN-generated outputs often results in tiny anatomical distortions, significantly limiting the applicability of GAN-based methods. While several approaches have introduced regularizations to address this issue, they largely focus on denoising individual slices, overlooking inter-slice structural inconsistencies that arise from treating slices independently. In this work, we propose Pseudo-Warping Field Guided Unsupervised Generative Adversarial Network (PW-GAN), which formulates post-reconstruction optimization as an unpaired style transfer problem between low-quality and high-quality MRI domains. Moreover, by incorporating a pseudo-deformation field module based on optical flow estimation, our method significantly enhances inter-slice continuity in the reconstructed volumes while effectively suppressing residual noise and interpolation artifacts introduced during the reconstruction process. Evaluations on both simulated and in vivo data demonstrate that our method outperforms existing unsupervised models and achieves performance on par with several state-of-the-art supervised methods.

neuroscience↗

bioRxiv2025

PhysDock: A Physics-Guided All-Atom Diffusion Model for Protein-Ligand Complex Prediction

Accurate prediction of protein-ligand complexes remains a central challenge in structural biology. Traditional methods are computationally inefficient and prone to local minima, whereas deep learning approaches struggle to capture structural flexibility and physical plausibility. We introduce PhysDock, a physics-guided diffusion model that uniquely integrates (i) all-atom diffusion to model ligand flexibility and protein precision-flexibility (i.e., subtle conformational adjustments); (ii) physical priors as diffusion conditioning, alongside two-phase physics guidance during the denoising diffusion to ensure physical plausibility. PhysDock demonstrates state-of-the-art performance in redocking benchmarks and excels in the more challenging cross-docking assessments. For practical utility, PhysDock (i) resolves cannabinoid receptor selectivity across diverse molecules, achieving accuracy comparable to experiments; (ii) distinguishes most drug candidates from weak binders in virtual screening of NTRK3 kinase, while uncovering novel candidates with structural insights. PhysDock serves as a versatile tool for protein-ligand complex prediction, with substantial potential to accelerate structure-based drug discovery.

bioinformatics↗

bioRxiv2025

Neural dynamics encoding risky choices during deliberation reveal separate choice subspaces

Human decision-making involves the coordinated activity of multiple brain areas, acting in concert, to enable humans to make choices. Most decisions are carried out under conditions of uncertainty, where the desired outcome may not be achieved if the wrong decision is made. In these cases, humans deliberate before making a choice. The neural dynamics underlying deliberation are unknown and intracranial recordings in clinical settings present a unique opportunity to record high temporal resolution electrophysiological data from many (hundreds) brain locations during behavior. Combined with dynamic systems modeling, these allow identification of latent brain states that describe the neural dynamics during decision-making, providing insight into these neural dynamics and computations. Results show that the neural dynamics underlying risky decision, but not decisions without risk, converge to separate subspaces depending on the subjects preferred choice and that the degree of overlap between these subspaces declines as choice approaches, suggesting a network level representation of evidence accumulation. These results bridge the gap between regression analyses and data driven models of latent states and suggest that during risky decisions, deliberation and evidence accumulation toward a final decision are represented by the same neural dynamics, providing novel insights into the neural computations underlying human choice. HighlightsO_LIHighly accurate decoding can be accomplished with dynamical systems modeling that revealed distinct attractor-like subspaces, one for each of the two options (gamble or safe bet). C_LIO_LIEarly during deliberation, single trial neural trajectories show rapid transitioning between these subspaces, but as the time of choice selection approached, the single trial trajectories converged towards one of the subspaces, whose identity was consistent with the subjects choice. C_LIO_LIThese dynamics are specific to risky decisions as we found classification accuracy dropped to chance for trials where the gamble option was guaranteed to be successful (100% win probability) or unsuccessful (0% win probability). C_LIO_LIThese results suggest that deliberation and evidence accumulation toward a final decision in the presence of any risk can be represented by the same neural dynamics, providing novel insights into the neural computations underlying human choice. C_LI

neuroscience↗

bioRxiv2025

Computer Simulations and Analyses of Coupling Among Reproductive Barriers in Late-Stage Sympatric Speciation

The mechanisms driving sympatric speciation remain an unresolved challenge in evolutionary biology. In nature, closely related "good" species are observed to possess multiple different barriers in their genomes that collectively generate strong and irreversible reproductive isolation (RI). Theorists hypothesize that early-stage mechanisms responsible for establishing an initial reproductive barrier differ from those driving the coupling of barriers in later stages of sympatric speciation. In a prior study, we developed a two-allele mathematical model of mating-bias traits to demonstrate how initial premating RI can arise in a sympatric population under disruptive ecological selection. Here, we extend this model to investigate how different pre- and post-mating barriers can couple with such an initial barrier and with one another during late-stage sympatric speciation to establish strong and irreversible RI. We developed computer applications to examine the properties of various barrier mechanisms and the conditions required for their invasion and coupling. Early-stage, adaptive premating barriers, driven by maladaptive hybrid loss, are effective in establishing initial RI and coupling with other barriers but are easily reversed if disruptive ecological selection weakens. Late-stage barriers, by contrast, often rely on earlier barriers to create an environment of reduced gene flow to facilitate their invasion and coupling. Mutations reducing hybrid viability are underdominant in inter-niche matings and can only invade by hitchhiking with barriers conferring a fitness advantage. Chromosomal inversions, an adaptive late-stage mechanism, can combine different barrier properties into a supergene and gain a net fitness advantage to invade and couple with other barriers to create strong and less reversible RI. Late-stage nonadaptive Bateson-Dobzhansky-Muller (BDM) barriers evolve more slowly but confer the strongest and least reversible RI. Our findings reveal a positive feedback loop in which early-stage barriers facilitate the establishment of late-stage barriers, while late-stage barriers strengthen and secure early-stage barriers. This positive reinforcement progressively strengthens overall RI until it becomes irreversible. By examining the properties and invasion dynamics of various barrier mechanisms, this study complements our previous study to propose a comprehensive process of sympatric speciation that explains how barriers emerge and couple to complete the speciation process.

evolutionary biology↗

bioRxiv2025

Systemic trafficking of mRNA lipid nanoparticle vaccine following intramuscular injection generates potent tissue-specific T cell response

The mRNA lipid nanoparticles (LNPs) represent a new generation of vaccine carriers designed to elicit potent immune responses against infectious diseases and cancer. Despite the clinical success and rapid advancements in mRNA LNP technologies, the trafficking patterns of LNPs after intramuscular (i.m.) administration and the subsequent tissue-specific immunological effects have not been systematically characterized. Here, we report that trafficking of mRNA LNPs to different organs following i.m. injection is crucial for the induction of tissue-specific immunity beyond systemic immune response, particularly in tissue-resident CD8+ T cell generation, which is important for localized defense. By fine-tuning the composition of mRNA LNPs, trafficking patterns to systemic organs can be modulated, which can alter the resulting tissue-specific immune response. Formulations with a greater ability to enter the bloodstream can preferentially localize and transfect cells in specific organs like the liver, elicit stronger tissue-specific CD8+ T cell immune responses, and achieve enhanced efficacy in a liver tumor model. These findings highlight the potential to tailor mRNA LNP compositions to modulate trafficking following i.m. injection, thereby providing novel strategies for designing tissue-specific vaccines. Such strategies are particularly valuable for organ-specific diseases like cancer and infectious diseases, where tissue targeting and long-lasting immunity are essential for therapeutic success.

bioengineering↗

bioRxiv2025

Deciphering the Sequence Basis and Application of Transcriptional Initiation Regulation in Plant Genomes Through Deep Learning

Transcription initiation is a critical regulatory step in plant gene expression, yet its sequence determinants remain largely elusive. Here we introduce GenoRetriever, an interpretable deep learning model that deciphers the sequence basis of transcriptional initiation regulation across plant genomes. Trained on STRIPE-seq data from 16 soybean tissues and six other crop species, GenoRetriever identifies 27 core sequence motifs that govern transcription start site (TSS) selection and usage. The model predicts TSS locations and usage levels with high accuracy, as validated by in silico motif insertions, saturation mutagenesis, and CRISPR-Cas9 promoter editing. It further reveals that 31.85% of natural variation between wild and domesticated soybean drives shifts in promoter motif usage during domestication, and uncovers lineage-specific motif effects between monocots and dicots. This interpretable model and its user-friendly web server for promoter analysis and design make GenoRetriever both a methodological innovation and practical tool for plant functional genomics and crop improvement.

plant biology↗

bioRxiv2025

Allosteric modulation of dimeric GPR3 by ligands in the dimerization interface

Class C G-protein coupled receptors (GPCRs) are widely recognized to function as a dimer, whereas the existence, molecular mechanisms and functional importance of dimerization in class A GPCRs remain poorly explored. GPR3, GPR6 and GPR12 are closely related to cannabinoid receptors, and are associated with neurogenerative diseases such as Parkinsons and Alzheimers diseases. Here, we determined cryo-EM structures of GPR3-Gs complex in the active state and GPR3 alone in the inactive state. We observe that GPR3 forms symmetric dimers in both states, with the dimerization interface primarily involving TM5 and TM6. Upon activation, GPR3 undergoes an outward movement of TM6 and an inward movement of TM7, alongside stabilization of helix 8, which is disordered in the inactive state. Functionally, GPR3 dimerization attenuates Gs signaling. Furthermore, we identify AF64394 as a negative allosteric modulator that targets the GPR3 dimerization interface and allosterically shifts the -helical structure of ICL2 into a random coil structure, thereby impairing Gs coupling. Our findings provide structural and mechanistic insights into class A GPCR dimerization, offering potential therapeutic strategies targeting the dimerization interface of these receptors.

biochemistry↗

bioRxiv2025

Leveraging foundation models to dissect the genetic basis of cluster compactness and yield in grapevine

Grape cluster compactness is a key trait that influence fruit quality, yield, and disease susceptibility. Understanding the genetic basis of this trait is essential for optimizing vineyard management and improving grapevine cultivars. In this study, we performed quantitative trait locus (QTL) mapping to identify genomic regions associated with cluster architecture and yield components in a bi-parental population derived from Vitis vinifera cv. Riesling x Cabernet Sauvignon. A total of 138 full-sibling progeny were evaluated over two growing seasons at Oakville, Napa Valley, California. Traditional yield-related traits were measured, including cluster number, total cluster weight, and average cluster weight. Additionally, an image-based phenotyping pipeline leveraging the foundation model Segment Anything Model (SAM) was employed to segment individual berries, measure their size and shape, and compute cluster compactness with minimal manual intervention. Trait correlations revealed that compact clusters tended to have a higher berry count but smaller berry size, highlighting the role of compactness in modulating cluster structure. Heritability estimates varied across traits, with berry dimensions and compactness displaying moderate to high heritability, indicating strong genetic control. Two parental linkage maps were constructed using a pseudo-test cross strategy. QTL mapping identified multiple loci associated with cluster architecture and yield components, with several stable QTLs detected across both years. Notably, a QTL for cluster compactness was found in both seasons on chromosome 1 in Cabernet Sauvignon. Other stable QTLs were associated with berry size (chromosomes 6 and 17) and berry count (chromosome 5 in Cabernet Sauvignon and chromosome 7 in Riesling). Additional QTLs were detected in a single year, reflecting the influence of environmental variation. Our findings provide valuable insights into the application of foundation models requiring no prior training and minimal intervention for high-quality segmentation and enhance our understanding of the genetic architecture of cluster compactness and yield traits. The genomic regions identified in this study offer promising targets for breeding programs aimed at improving grape quality and disease resistance.

genetics↗

bioRxiv2025

THA_1941 from Thermosipho africanus: A Thermostable β-1,3-Glucan Phosphorylase for Efficient β-1,3-Glucan Synthesis

{beta}-1,3-Glucan phosphorylases capable of utilizing glucose as a priming substrate are key biocatalysts for the synthesis of functional {beta}-1,3-glucan. In this study, we identified THA_1941 from Thermosipho africanus (Ta{beta}GP) as a GH161 {beta}-1,3-glucan phosphorylase exhibiting robust synthetic activity towards glucose, as confirmed by 13C nuclear magnetic resonance, liquid chromatography-mass spectrometry, and sequence and structural analyses. Ta{beta}GP displayed exceptional thermostability, retaining 93% of its activity at 60 {degrees}C for 180 h, and showed broad pH tolerance ranging from pH 5.0 to 10.0, surpassing the performance of previously reported homologs. In addition, Ta{beta}GP exhibited broad substrate flexibility, accepting both - and {beta}-linked disaccharides, and demonstrated strong resistance to metal ions and lignocellulose-derived inhibitors. In the presence of 150 mM glucose 1-phosphate as the donor substrate, Ta{beta}GP synthesized {beta}-1,3-glucan with a tunable average degree of polymerization (10-32), depending on the concentration of glucose used as the primer. The combination of thermostability, inhibitor resistance, and substrate versatility makes Ta{beta}GP a promising biocatalyst for the economically viable and environmentally sustainable synthesis of {beta}-1,3-glucan from non-food biomass sources.

biochemistry↗

bioRxiv2025

The Genetic Basis of Microbiome Recruitment in Grapevine and its Association with Fermentative and Pathogenic Taxa

O_LIWhile grapevine is an exceptional perennial model for studying host-microbiome interactions, the host genomes role in microbiome assembly is often masked by environmental factors. This research provides a first insight into the genetic mechanisms shaping berry-associated microbial communities. C_LIO_LIUsing QTL mapping in a newly established population of 140 F1-progeny grapevine genotypes in a complete random block design, we were able to control abiotic effects and investigate how the host genome influences grape berry-associated bacterial and fungal communities. C_LIO_LIWe identify significant associations between various microorganisms and the grape genome, including pathogenic fungi such as Botrytis spp. and fermentative yeasts such as Saccharomyces cerevisiae. Many of these taxa map to the same genetic loci associated with plant immune responses, suggesting that specific genetic loci broadly influence microbial community assembly in fruits. C_LIO_LIOur findings demonstrate that grapevine genetics significantly shape the microbiome, even under varying environmental conditions; moreover, that broad, rather than known symbiont-specific mechanisms control microbial colonization of fruit, revealing an emergent "domino" effect with implications for plant-fungal-bacterial interactions. We provide a framework for understanding genotype-microbiome interactions in perennial plants, enabling future targeted experiments to establish causal relationships in microbiome recruitment and offer a potential avenue for breeding programs advancing sustainable viticulture. C_LI

microbiology↗

bioRxiv2025

AI-Driven Efficient De Novo design of GLP-1RAs with Extended Half-Life and Enhanced Efficacy

Peptide drug has revolutionized modern therapeutics, offering novel treatment avenues for various diseases. However, low efficacy, time consumption, and high cost hinder peptide drug design and discovery. We present an efficientin approach that integrates deep learning-based protein design with efficient functional screening, enabling the rapid design of biotechnologically important peptides with improved the stability and efficacy. We designed 10,000 de novo GLP-1 receptor agonists (GLP-1RAs), of which 60 met the stability, efficacy, and diversity criteria in the vitral functional screening. In vitro validation revealed a 62% success rate, while in vivo experiment demonstrated that two designed GLP-1RAs exhibited significantly extended half-lives, approximately three times longer than Semaglutide. In diabetic and obesity mouse models, the most competitive candidate showed superior therapeutic efficacy over Semaglutide. Our AI driven peptide design pipline integrats protein design, functional screening, and experiment validation, reducing the number of iterations required to find novel peptide candidates. The entire process, from design to screening, can be completed in a single cycle within two weeks.

bioengineering↗

bioRxiv2025

Optimal distributions of receptors on arbitrarily shaped cell surfaces

Efficient absorption of signaling molecules and carbohydrates through receptors on cell surfaces is crucial for various biological processes. While ubiquitous patterns of receptor distributions, including polar localization in rod-shaped cells, have been widely observed experimentally, their underlying evolutionary advantage is unclear. In this work, we study how spatial distributions of receptors on cell surfaces affect the total flux entering the cell. We innovate a method by which one can calculate the fluxes through all receptors using linear equations, which applies to arbitrarily shaped cells. Our theories recover previous results for spherical cells and further show that the flux through each receptor is spatially dependent in non-spherical cells. In particular, the fluxes are the highest near the poles in rod-shaped cells and the highest near the invagination in defective spherical cells. Surprisingly, we prove that the optimal receptor distribution on an arbitrarily shaped cell maximizing the total flux is precisely the charge density distribution on an ideal conductor of the same shape, which agrees with numerical simulations. Our work unveils the evolutionary origin of receptor localizations.

biophysics↗

bioRxiv2025

The denitrosylase SCoR2 controls cardioprotective metabolic reprogramming

Acute myocardial infarction (MI) is a leading cause of morbidity and mortality, and therapeutic options remain limited. Endogenously generated nitric oxide (NO) is highly cardioprotective, but protection is not replicated by nitroso-vasodilators (e.g., nitrates, nitroprusside) used in clinical practice, highlighting specificity in NO-based signaling and untapped therapeutic potential. Signaling by NO is mediated largely by S-nitrosylation, entailing specific enzymes that form and degrade S-nitrosothiols in proteins (SNO-proteins), termed nitrosylases and denitrosylases, respectively. SNO-CoA Reductase 2 (SCoR2; product of the Akr1a1 gene) is a recently discovered protein denitrosylase. Genetic variants in SCoR2 have been associated with cardiovascular disease, but its function is unknown. Here we show that mice lacking SCoR2/Akr1a1 exhibit robust protection in an animal model of MI. SCoR2 regulates ketolytic energy availability, antioxidant levels and polyol homeostasis via S-nitrosylation of key metabolic effectors. Human cardiomyopathy shows reduced SCoR2 expression and an S-nitrosylation signature of metabolic reprogramming, mirroring SCoR2-/- mice. Deletion of SCoR2 thus coordinately reprograms multiple metabolic pathways--ketone body utilization, glycolysis, pentose phosphate shunt and polyol metabolism--to limit infarct size, establishing SCoR2 as a novel regulator in the injured myocardium and a potential drug target. Impact statementMice lacking the denitrosylase enzyme SCoR2 demonstrate robust cardioprotection resulting from coordinate reprogramming of multiple metabolic pathways. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=196 SRC="FIGDIR/small/642752v2_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@72bfbborg.highwire.dtl.DTLVardef@189cbc4org.highwire.dtl.DTLVardef@1a0cc51org.highwire.dtl.DTLVardef@1c61004_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2025

Optimal scaling between mRNA and protein maximizes cell fitness

The connection between protein and mRNA abundance represents a central question in gene expression. While high correlations between mRNA and protein levels are widely observed across different organisms, the evolutionary forces driving these correlations remain elusive. Here, by balancing the cost and benefit of gene expression, we identify the optimal scalings between mRNA and protein levels maximizing cell fitness, where the scaling exponent is positively related to the toxicity effects of protein overexpression. Notably, we predict a lower bound for the exponent, which is 0.5. For the model organism Saccharomyces cerevisiae, our model predictions, which incorporate biologically relevant parameters, align well with experimental data. By analyzing genome-wide data across organisms, we find that all organisms exhibit a scaling exponent above the predicted lower bound. These universal phenomena, together with our cost-benefit trade-off model, rationalize the dominance of transcriptional regulation. Our work demonstrates how evolutionary driving forces govern the principles of gene expression regulation.

systems biology↗

bioRxiv2025

Hydroxychloroquine-functionalized Ionizable Lipids Mitigate Inflammatory Responses in mRNA Therapeutics

Lipid nanoparticle (LNP)-based mRNA therapeutics, highlighted by the success of SARS-CoV-2 vaccines, face challenges due to inflammation caused by ionizable lipids. These ionizable lipids can activate the immune system, particularly when co-delivered with nucleic acids, leading to undesirable inflammatory responses. We introduce a novel class of anti-inflammatory ionizable lipids functionalized with hydroxychloroquine (HCQ), which suppresses both lipid-induced and nucleic acid-induced immune activation. These HCQ-functionalized LNPs (HL LNPs) exhibit reduced proinflammatory responses while maintaining efficient mRNA delivery. Structural and physicochemical analyses revealed that HCQ-functionalization results in a distinct particle structure with significantly improved stability. The efficacy of HL LNPs was demonstrated across various therapeutic contexts, including a prophylactic vaccination model against varicella-zoster virus (VZV) and CRISPR-Cas9 gene editing targeting PCSK9. Notably, HL LNPs showed robust mRNA expression after repeated administration, addressing concerns of inflammation and ensuring sustained therapeutic effects. These findings highlight the potential of HCQ-functionalized LNPs in expanding the safe use of mRNA therapeutics, particularly for applications requiring repeated dosing and in scenarios where inflammation-induced side effects must be minimized.

bioengineering↗

bioRxiv2025

Structure-Activity-Relationship Exploration and Animal Validation of Novel Assembly Modulator Small Molecule Chemical Series with Pan-Cancer Selective Cytotoxicity

PAV-620, and PAV-805 are chemical analogs from a protein assembly modulating small molecule lead series that displays broad anti-cancer activity. These compounds are active against all cancers in the NCI-60 cancer cell line screen, which is representative of diverse blood, brain, breast, colon, lung, ovarian, prostate, renal, and skin cancers. Safety in mice is observed up to doses of 10 mg/kg daily and nontoxicity to healthy human peripheral blood mononuclear cells at doses up to 20uM. The compounds are as efficacious as Paclitaxel in reducing tumor growth and metastasis in the aggressive 4T1 mouse allograft model for triple negative breast cancer. The mechanism of action of PAV-620 and PAV-805 in primary carcinoma cells under conditions inducing programmed cell death was found to be distinct from the cytotoxic mechanisms of Staurosporine, Paclitaxel, and Etoposide. PAV-805 was shown to target a small subset of protein disulfide isomerase (PDI) in a dynamic multi-protein complex enriched in the cancer hallmark proteins involved in reprogramming energy metabolism. These data suggest a new approach to cancer therapeutics, with selectivity arising not from targeting PDI itself, an abundant cellular protein, but from targeting a disease-associated assembly state of PDI-containing complexes as a therapeutically exploitable dimension of cancer biology.

biochemistry↗

bioRxiv2025

Open-Source DNA-Encoded Library Package for Design, Decoding, and Analysis: DELi

DNA-encoded library (DEL) technology has become a powerful tool in modern drug discovery. Fully harnessing its potential requires the use of extensive computational methodologies, which are often available only through proprietary software. This restricts accessibility for small teams lacking robust informatics support, hindering the growth of the technology. Here, we present DELi, an open-source DEL informatics platform designed for library design, NGS decoding and calling, and enrichment analysis. DELi supports a simple and easy to understand configuration setup to present a straightforward user interface. To showcase its capabilities, we used DELi to design an in-house custom, benzimidazole-based DEL (UNC DEL006), and performed proof-of-concept selection experiments against Bromodomain-containing Protein 4 (BRD4). The DELi decoding and analysis modules identified top-performing compounds, leading to the off-DNA synthesis of UNC11951, which was confirmed as a nanomolar BRD4 binder via isothermal titration calorimetry (ITC) and differential scanning fluorimetry (DSF). These results demonstrate DELi as an effective tool for DEL design and analysis. Furthermore, its open-source nature will promote ongoing development and contributions from the DEL community to expand its applications and capabilities, making DEL technology more widely accessible. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/640184v3_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1d4ca88org.highwire.dtl.DTLVardef@13cb4feorg.highwire.dtl.DTLVardef@8ea7ecorg.highwire.dtl.DTLVardef@1b294fc_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗