Biology subjects

Qi, C.

Publications and source records attributed to Qi, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Astrocytic sEH-EET signaling regulates microglial lysosomal function and suppresses the STING-Type I interferon pathway in Alzheimer's disease

Background: The development of {beta}-amyloid (A{beta}) pathology in Alzheimer's disease (AD) is accompanied by profound changes in astrocytes and microglia and associated neuroinflammation. Soluble epoxide hydrolase (sEH), which degrades anti-inflammatory epoxyeicosatrienoic acids (EETs), is elevated in the AD brain, yet how EETs restrain inflammation remains poorly understood. Methods: We performed single-nucleus RNA sequencing (snRNA-seq) on brains from wild-type, sEH knockout, 5xFAD, and 5xFAD; sEH knockout mice, and used primary mouse microglia and human induced pluripotent stem cell (iPSC)-derived microglia to define the underlying mechanism. To test cell-type-specific contributions, we generated astrocyte- and endothelial-specific she knockout mice and crossed them with 5xFAD mice. Results: Genetic disruption of sEH in 5xFAD mice markedly reduced the abundance of a type I interferon (IFN)-responsive microglial subcluster and lowered phospho-STING (pSTING) levels. In primary microglia, supplementation with 11,12-EET attenuated STING activation and IFN target gene expression; blocking lysosomal acidification abolished this effect and caused pSTING to accumulate, identifying lysosomal degradation as a critical route for terminating STING signaling. 11,12-EET also enhanced microglial lysosomal acidification and A{beta} phagocytosis. Astrocytic, but not endothelial, disruption of sEH recapitulated the reductions in A{beta} pathology and microglial STING activation seen in global sEH-deficient mice, identifying astrocytes as the major cellular source of EETs regulating this pathway. Conclusion: We define an astrocyte-microglia axis in which astrocytic sEH limits EET availability and thereby sustains microglial STING signaling. By promoting lysosomal clearance of pSTING, EETs suppress type I IFN signaling and enhance microglial A{beta} clearance, highlighting sEH inhibition as a therapeutic strategy targeting both neuroinflammation and lysosomal dysfunction in AD.

neuroscience↗

bioRxiv2026

Cancer Cell Line Heterogeneity Imposes a Primary Bottleneck for Virtual Perturbation Screening at Scale

UniPert-G2CP (Li et al., Cell, 2026) bridges genetic and chemical screens from molecular representation to phenotype modeling across five cancer cell lines [1]. Here we extend this architecture to 162 cell lines, 32,039 compounds, and genome-wide output (12,328 genes), and find that the resulting prediction platform reveals a striking performance divide: normal and primary cell lines achieve substantially higher prediction fidelity (mean per-cell-line Pearson Correlation Coefficient (PCC) = 0.480, median 0.502) than cancer lines (0.296, median 0.324; Mann-Whitney p = 3.93e-11), identifying cancer cell line heterogeneity as a primary bottleneck for virtual perturbation screening at scale. To understand the sources of this divide, we analyzed per-cell-line performance, genome-wide directional accuracy, mechanism-clustering (SMD), and compound-protein interaction (CPI) enrichment. Directional accuracy on top-5% effect-size genes reaches 73.8% (genome-wide 60.8%), with pathway-dependent recovery: of four literature-supported perturbation-gene pairs queried across three cell lines, two were recapitulated (dexamethasone-TSC22D3/NFKBIA/FKBP5; bortezomib-BAG3/DNAJB1/HSPA1A), one was absent (CD36 depletion-PPARG/CEBPA in ASC), and one was partially recapitulated (metformin-SLC7A5 in HEPG2). Mechanism-clustering SMD of the learned embedding reached 1.636 (vs. original 1.85; 88.5% retention at 32x cell-line coverage), exceeding the ECFP4 fingerprint baseline (1.613), while self-consistency Mantel rho=0.852 confirmed the model retains compound mechanism structure internally. Overall held-out performance: genetic perturbation PCC=0.442 (978-gene subset); novel drug PCC=0.3047 (genome-wide). Analysis of CPI enrichment reveals that training-data overlap inflates apparent performance: 54.9% of Touchstone evaluation pairs overlap with our ChEMBL-derived training CPI pairs, reducing effective EF from 139 to 109 at top 0.5% yet remaining far above random (1.0). These findings establish the first large-scale characterization of cell-type-dependent generalization in perturbation-to-phenotype prediction. The observed performance stratification between normal and cancer lines generates testable hypotheses for why virtual cell models degrade on heterogeneous cancer contexts, and provides a diagnostic framework for identifying where and why such models fail--informing future architecture improvements targeting the CPI vocabulary gap, protein encoder design, and cell-type-aware training strategies.

bioinformatics↗

bioRxiv2026

CellPilot: an agentic framework that pilots small language models through autonomous single-cell annotation

Large language models can annotate cell types from marker gene lists, but they typically operate after preprocessing and clustering are complete, treating annotation as a terminal labeling step rather than controlling the analytical decisions that produce the evidence for cell identity. We present CellPilot, an agentic framework that guides a locally deployable small language model through the full single-cell analysis workflow, from raw count matrices to cluster-level annotation. CellPilot combines standard single-cell analysis tools with structured workflow control and observation-guided reasoning, allowing the model to plan analyses, execute tools, inspect intermediate results and revise decisions within a traceable session. On GTEx, structured workflow orchestration raised the same 8B model from 0.39 in a prompt-only setting to 0.89, closing most of the gap to GPT-4o (0.92) within the same framework; the framework gain was substantially larger for the smaller backbone across datasets (+0.35 versus +0.19). Across GTEx, Tabula Sapiens, and Mouse Cell Atlas, CellPilot achieves cluster-level annotation accuracies of 0.891, 0.750, and 0.773, outperforming representative reference-based, marker-based, and LLM-based methods. CellPilot confidence scores were associated with annotation correctness and supported post hoc filtering, while complete execution traces were retained for each analysis. These results suggest that structured workflow orchestration can be a critical determinant of performance in multi-step single-cell analysis, enabling locally deployable small language models to approach larger proprietary models while preserving transparency and practical usability.

bioinformatics↗

bioRxiv2026

A bulk cell heterozygous knock-in strategy for targeted protein degradation

Targeted protein degradation using conditional degron tag (CDT) technology is a powerful method for rapidly degrading a protein of interest (POI) upon the addition of a degrader drug. A prerequisite for the temporally controlled degradation of an endogenous POI is the generation of homozygous knock-in cells with the degron tag integrated at either the N- or C-terminus of their gene loci. However, obtaining those homozygous knock-in cells often requires selecting many single-cell clones, as human cells typically exhibit low homology-directed repair (HDR) activities. Additionally, tagging a degron to an endogenous protein may inadvertently reduce protein expression, potentially affecting protein function even before the drug is administered. Here, we develop a method for generating degron-tagged knock-in cells that allows us to skip the laborious single-cell cloning. This method arose from our observation that most knock-in cells carry the degron tag only in one allele (heterozygous), while the other allele typically harbors a frameshift insertion/deletion. This observation allowed us to bypass the need for single-cell cloning. We validated our method by knocking in degron tags at the N-terminus of cytoplasmic dynein1 subunits or Adaptor Protein 2 (AP2) subunit. Our experiments confirmed the rapid degradation of these proteins and their functional inhibition in bulk cell populations. Additionally, to mitigate the reduced expression often associated with the degron tagging, we established a method to control expression levels by inserting a mini-promoter immediately upstream of the knock-in cassette. Our method simplifies the workflow for degron tag knock-ins and enhances the versatility of these valuable technologies.

cell biology↗

bioRxiv2026

Single-cell lineage tracing maps clonal and transcriptional dynamics in melanoma metastasis

Melanoma metastasis is driven by extensive intratumoral heterogeneity and phenotypic plasticity, yet how clonal identity relates to transcriptional programs during metastasis remains unclear. Here, we applied MeRLin, a single-cell lineage tracing platform, to dissect the clonal and transcriptional heterogeneity of metastatic melanoma in a patient-derived spontaneous metastasis model. Clonal analyses revealed hierarchical structures during tumor progression, with a subset of lineages from primary tumors consistently enriched across metastatic sites, supporting a model of polyclonal seeding followed by selective expansion of pre-existing highly metastatic subpopulations. Single-cell transcriptomic profiling identified two major metastatic subpopulations of distinct transcriptional programs, characterized by neural crest stem cell-like and lipid metabolism signatures. Both programs were enriched for invasion-associated genes and maintained across organs through distinct regulatory networks. Spatial mapping by barcode RNA-FISH linked these transcriptional states to their tissue context and showed that OLFML3 expression partially co-localized with a dominant subpopulation at the tumor-liver interface, marking the invasive fronts of metastatic growth. Together, these findings establish a framework in which clonal identity, transcriptional state, and spatial organization jointly shape metastatic melanoma progression.

cancer biology↗

bioRxiv2026

When Multimodal Fusion Fails: Contrastive Alignment as a Necessary Stabilizer for TCR--Peptide Binding Prediction

Multimodal learning is commonly assumed to improve predictive performance, yet in biological applications auxiliary modalities are often imperfect and can degrade learning if fused naively. We investigate this problem in TCR-peptide binding prediction, where sequence embeddings from pretrained protein language models are strong and transferable, but structure-derived residue graphs are built from predicted folds and heuristic discretization. In this setting, structural views can be noisy, inconsistent, and difficult to optimize jointly with sequence features. We introduce TRACE, a lightweight multimodal framework that encodes each entity (TCR and peptide) with parallel sequence and graph towers, then applies CLIP-style intra-entity contrastive alignment before interaction modeling. The alignment objective regularizes representation geometry by encouraging modality consistency for the same biological entity, thereby preventing unstable graph signals from dominating fusion. Across protocol-aware TCHard RN evaluations, naive sequence+graph fusion frequently underperforms a sequence-only baseline and can collapse toward near-random behavior. In contrast, TRACE consistently restores and improves performance. Controlled noise and supervision sweeps show that these gains persist under increasing graph corruption and positive-label scarcity, indicating that alignment is especially important when training conditions are hard. Our results challenge the assumption that adding modalities is inherently beneficial. Instead, they highlight a central principle for robust multimodal bioinformatics: performance depends not only on what modalities are used, but on how their interaction is constrained during optimization. TRACE provides a simple and general recipe for leveraging imperfect structural information without sacrificing stability.

bioinformatics↗

bioRxiv2026

CoLa-VAE: Cell-Cell Communication-aware Variational Autoencoder with Dynamic Graph Laplacian Constraints

Single-cell RNA sequencing provides a powerful view of cellular heterogeneity, but its sparsity and dropout noise remain major obstacles for recovering biologically meaningful gene expression programs and for downstream analyses that depend on reliable expression measurements. Ligand-receptor-based cell-cell communication inference is such analysis, missing ligand or receptor expression can cause substantial false negatives in sparse single-cell data. Here, we present CoLa-VAE, a cell-cell communication-aware variational autoencoder that jointly learns latent representations and denoised expression profiles by incorporating ligand-receptor-derived communication topology through dynamic graph Laplacian regularization. Rather than treating denoising as a secondary output of representation learning, CoLa-VAE uses denoised expression to iteratively refine communication estimates and uses the resulting communication structure to guide both latent organization and expression reconstruction. In addition to improving latent space organization and producing robust denoised expression matrices, CoLa-VAE-denoised matrices also improved downstream biological analyses, including the detection of robust differential cell-cell communication programs, mitigation of batch-associated variation and enhanced spatial transcriptomic deconvolution when spatially constrained communication structure was incorporated. Together, these results establish CoLa-VAE as a communication-guided denoising and representation learning framework that recovers biologically meaningful expression signals from sparse single-cell and spatial transcriptomic data, enabling more sensitive and reliable downstream analysis.

bioinformatics↗

bioRxiv2026

The Pick fold in tau filaments from human MAPT mutants

Mutations in MAPT, the tau gene, give rise to forms of frontotemporal dementia and parkinsonism linked to chromosome 17 (FTDP-17T), with abundant filamentous tau inclusions in brain cells. Some mutations that encode missense and deletion variants can give rise to a clinical picture of Picks disease and filaments made of three-repeat tau. Here we report the electron cryo-microscopy (cryo-EM) structures of tau filaments from individuals with MAPT mutations D252V, G272V, S320F and {Delta}G389-I392. The two-layered Pick fold was present in the individuals with mutations D252V and {Delta}G389-I392. By contrast, mutations G272V and S320F gave rise to a more open variant of the Pick fold, with residues 272-341 rotated by 20-25{degrees} with respect to the rest of the structure. These findings show that missense mutations within the filament core can modify the Pick fold, generating closely related structural variants. In addition, we were able to reconstitute the Pick fold and some of its variants using seeded assembly with recombinant 0N3R tau carrying 12 serine or threonine to aspartate substitutions (PAD12) and missense mutations D252V, G272V or S320F. This work provides a foundation for the development of structure-based diagnostic and therapeutic approaches.

biophysics↗

bioRxiv2026

Molecular basis of enfumafungin inhibition and resistance in Candida glabrata β-1,3-Glucan synthase FKS1

{beta}-1,3-Glucan synthase FKS1 is essential for fungal cell wall biogenesis and serves as a validated target of widely prescribed antifungal drugs. However, the molecular mechanism of pathogenic FKS1, its modes of inhibition, and the associated resistance mechanisms remain elusive, hindering the antifungal development. Here, we focus on Candida glabrata FKS1, a clinically relevant target frequently linked to antifungal resistance. We present cryo-EM structures of C. glabrata FKS1 in multiple detergent environments and in complex with the triterpenoid antifungal enfumafungin. Integrated functional studies revealed a key catalytic residue within the conserved ED motif and uncovered structural adaptations to different membrane-mimetic environments, rationalizing FKS1s sensitivity to its surrounding membrane context. Enfumafungin binds at the extracellular membrane leaflet of cgFKS1 transmembrane domain, engaging a convex surface separated from the cytosolic active site, consistent with its role as a non-competitive inhibitor. Further analyses identified key enfumafungin-binding residues from TM5-TM6 and elucidated the molecular basis of drug resistance in C. glabrata. Collectively, these findings establish a mechanistic framework for pathogenic FKS1 function and inhibition and provide a molecular basis for the rational design of next-generation antifungal therapeutics.

microbiology↗

bioRxiv2026

The genetic repertoire of the deep sea: from sequence to structure and function

The deep sea as the largest and maybe most hostile environment on Earth is still underexplored especially regarding its genetic repertoire. Yet, previous work has revealed significant habitat-specific deep-sea biodiversity. Here, we present an integrated deep-sea genetic dataset comprising 502 million nonredundant genes from 2,138 samples and 2.4 million predicted structures, and used it to link specific protein structures with genetic variants associated with life in the deep sea and to assess their biotechnology potential. Combining global sequence analysis with biophysical and biochemical measurements revealed unprecedented sequence diversity, yet substantial structural conservation of proteins. Especially proteins involved in replication, recombination, and repair were identified to be under rapid evolution and with specialized properties. Among these, a structurally divergent helicase exhibited advantages in controlling nanopore sequencing speed. Thus, our work positions the deep sea as a unique evolutionary engine that generates and hosts genetic diversity and bridges genetic knowledge with biotechnology.

bioinformatics↗

bioRxiv2025

Astrocyte cell-surface proteomics identified CD44 as an OPN/SPP1 receptor regulating lipid metabolism and glial crosstalk in Alzheimer s disease

Abstract/SummaryThe development of {beta}-amyloid (A{beta}) pathology in Alzheimers disease (AD) is accompanied by profound changes in astrocytes and microglia. How these responses are orchestrated by cell surface proteins, key mediators of cell-cell communication, remain unclear. Using in situ astrocyte cell-surface proteome profiling in 5xFAD mice, we identified a set of dysregulated surface proteins induced by A{beta} pathology, including CD44. CD44 was selectively upregulated in plaque-adjacent astrocytes and interacted with osteopontin (OPN), encoded by the disease-associated microglia gene Spp1, to promote lipid accumulation, and this effect is {gamma}-secretase dependent. Astrocytic CD44 in turn regulated Spp1 expression and microglial activity. Conditional deletion of Cd44 in adult astrocytes of 5xFAD mice attenuated glial reactivity, reduced A{beta} pathology, and improved cognition. These findings define a plaque-proximal OPN-CD44 axis that controls astrocyte lipid metabolism and glial activity, positioning CD44 as a surface-accessible therapeutic target in AD.

neuroscience↗

bioRxiv2025

Modeling TCR-pMHC Binding with Dual Encoders and Cross-Attention Fusion

Accurately modeling the binding between T-cell receptors (TCRs) and peptide-MHC (pMHC) complexes is essential for guiding immunotherapy development and personalized vaccine design. However, the vast diversity of TCR repertoires and the scarcity of experimentally validated interactions make generalization to unseen epitopes challenging. This paper proposes TIDE, a cross-attention-driven dual-encoder framework that leverages large protein and molecular language models to learn discriminative representations of TCRs and peptides. In TIDE, TCR sequences are encoded using Evolutionary Scale Modeling (ESM), while peptides are transformed into SMILES strings and processed by MolFormer to capture chemical and structural properties. Multi-layer cross-attention then refines and integrates these embeddings, highlighting interaction-relevant patterns without requiring explicit structural alignment. Evaluated on the TCHard benchmark under both zero-shot and few-shot settings, TIDE achieves superior predictive accuracy and robustness compared to state-of-the-art baselines such as ChemBERTa, TITAN, and NetTCR. These results demonstrate that combining pretrained language models with cross-attention fusion offers a powerful approach for TCR-pMHC binding prediction and paves the way for more reliable computational immunology applications.

bioinformatics↗

bioRxiv2025

A deep learning and co-conservation framework enable discovery of non-canonical Cas proteins

CRISPR-Cas systems are central to prokaryotic adaptive immunity, widely harnessed for biotechnology. Yet, their vast and uncharacterized diversity, especially non-canonical variants, impedes full exploitation. Here we present BioPrinCRISPR, a class-agnostic computational framework leveraging gene co-conservation, protein domain co-occurrence, and embedding similarity to identify and characterize CRISPR-Cas systems across prokaryotic genomes. Applying BioPrinCRISPR to over one million bacterial genomes, we uncovered extensive canonical and uncharacterized systems, revealing a rich landscape of atypical Cas proteins and novel domain architectures. Notably, we identified recurrent fusion proteins with unique enzymatic combinations, suggesting roles in regulatory control or nucleic acid remodeling. Experimental validation of two divergent Cas13an-like effectors demonstrated RNA knockdown capacity in human cells, confirming our frameworks predictive power. These findings expand the functional repertoire of CRISPR-associated proteins and highlight unexplored modes of microbial immunity. BioPrinCRISPR thus stands as a powerful tool for comprehensively mapping CRISPR-Cas diversity, offering new insights into prokaryotic defense and facilitating discovery of novel candidates for next-generation genome engineering. An accompanying interactive web platform was also developed to facilitate data exploration.

synthetic biology↗

bioRxiv2025

Clonal dynamics shaped by diverse drug-tolerant persister states in melanoma resistance

Most advanced melanomas initially respond to targeted therapy but eventually relapse. Rather than acquiring new mutations, resistance is driven by drug-tolerant persister cells that enter a reversible drug-refractory state. We developed MeRLin, a high-resolution lineage tracing platform integrating cellular barcoding, single-cell transcriptomics, RNA fluorescence in situ hybridization (FISH), and computational analyses to track clonal and transcriptional dynamics in patient-derived melanoma models during prolonged therapy. Clonal dynamics revealed that persister subpopulations first responded to treatment but persisted and expanded during minimal residual disease, ultimately leading to tumor recurrence. Pre-treatment melanoma populations diversified into four conserved persister states characterized by stress-like, lipid metabolism, PI3K signaling, and extracellular matrix remodeling programs associated with adaptive resistance. Spatial transcriptomics showed the organization of these adaptive programs and a complex signaling network of autocrine and paracrine interactions among persister subpopulations. Barcoded RNA-FISH enabled spatial mapping of clonal identity and gene expression, revealing in situ co-localization of a dominant resistant clone with SLC2A1 expression. MeRLin provides a robust framework for dissecting cancer heterogeneity and identifying vulnerabilities in persister populations.

cancer biology↗

bioRxiv2025

Competing Programs Shape Cortical Sensorimotor-Association Axis Development

The neocortex is organized along a dominant sensorimotor-to-association (S-A) axis, anchored by modality-specific primary sensorimotor areas at one end and transmodal association areas that form distributed networks supporting abstract cognition at the other. The developmental mechanisms shaping this axis remain elusive. Here, we present converging multispecies evidence supporting the Multinodal Induction-Exclusion in Network Development (MIND) model, in which S-A patterning is governed by competing processes of induction and exclusion, driven by opposing transcriptomically-defined identity programs emerging from different nodes. Key molecular and connectional features of association cortices arise through pericentral programs, originating around fronto-temporal poles and partially regulated by retinoic acid. They progress inward toward central territories of the naive neocortex along fronto-temporally polarized trajectories. Central programs are induced through interactions between topographically separated first-order sensorimotor thalamocortical inputs and the neocortex, promoting the formation of primary areas while excluding pericentral programs. Influenced by SATB2 and ZBTB18, these evolutionarily conserved programs compete for the same territory and create spatial compartmentalization of axon guidance, cell-cell adhesion, retinoic acid signaling, synaptogenesis, Wnt signaling, and autism risk genes. Notably, PLXNC1 and SEMA7A exhibit anti-correlated expression and repulsive functions in shaping cortico-cortical connectivity along the S-A axis. These processes of induction and exclusion establish an S-A equilibrium and topography in which primary sensorimotor areas emerge as focal islands within the broader ocean of distributed associative networks. The MIND model provides a unifying framework for understanding experimental, evolutionary, and clinical phenomena, revealing induction and exclusion as antagonistic complementary principles shaping the S-A axis and processing hierarchies.

neuroscience↗

bioRxiv2025

Adaptive Evolution of Gene Regulatory Networks in Mammalian Neocortical Neurons

Mammals have evolved a plethora of adaptations that have enabled them to thrive in diverse environments. Among the most significant is the emergence of a more complex brain, exemplified by the dramatic transformation of the dorsal cortex from a single layer of excitatory projection neurons (ExNs) in ancestors to a multilayered cerebral neocortex enriched with diverse intratelencephalic (IT) and extratelencephalic (ET) ExN subtypes. These ExNs established specialized projection systems, such as the corticospinal tract and corpus callosum, enhancing brain connectivity and functionality. However, the evolutionary mechanisms underlying these mammalian-specific adaptations remain elusive. By comparing the landscape of gene expression and cis-regulatory elements (CREs) in mouse ExN subtypes and by cross-species examination of mammalian and non-mammalian CREs, we identified mammalian-specific CREs and expression patterns. The mammalian-specific CREs include a subset bound by ZBTB18 that are associated with genes defining IT and ET subtypes and connectivity. Both ZBTB18 and these target genes have previously been implicated in intellectual disability and autism. Deletion of Zbtb18 in mouse ExNs dysregulated target gene expression, reduced molecular diversity, diminished corticospinal and callosal projections, and increased intrahemispheric cortico-cortical association projections to the prefrontal cortex, resembling features of non-mammalian dorsal pallium. Interestingly, ZBTB18 binding motifs are highly enriched in callosally projecting IT-biased CREs, where they show higher conservation specifically in mammals. This study uncovers critical components and mammalian-specific evolutionary adaptations within a regulatory node essential for neocortical ExN identity and connectivity, with implications for neurodevelopmental and neuropsychiatric disorders.

neuroscience↗

bioRxiv2024

Radiation damage behavior of soft matter in ultrafast cryo-electron microscopy (cryo-UEM)

Whether time-modulated pulsed-electron imaging in ultrafast electron microscopy (UEM) can mitigate the electron radiation damage that occurs to samples, is still controversial. The effectiveness of such mitigation effect and relevant potential application in cryo-EM remain to be explored. Herein, we built an ultrafast cryo-EM (cryo-UEM) device based on an ultrafast laser system. Using such equipment and the saturated aliphatic hydrocarbon compounds (C44H90), the fading curves of diffraction intensity and corresponding critical electron doses (Ne) of the samples were carefully measured under different imaging modes, temperatures, imaging dose rates and pulsed repetition rates. Our experimental results show that, the fading curves and Ne values of the C44H90 crystals are uncorrelated with the imaging electron dose rates and do not show dependence on the dose-rate effect. As the temperature decreased, the Ne values of the sample increased, indicating the cryoprotective effect on radiation damage to the samples. Surprisingly, at a constant temperature, the fading curves and Ne values of the sample in multi-electrons-packet and near-single-electron-packet pulsed modes are all approximately the same as those in conventional continuous electron-beam mode, even when the results are obtained at different pulsed repetition rates. These results show that the time-modulated pulsed electron beam does not seem to mitigate the electron radiation damage that occurs on samples. Our findings offer new insights and experimental basis for the radiation damage behavior of samples under electron beams, and provide guidance and inspiration for elucidating the fundamental principles of radiation damage.

biophysics↗

bioRxiv2024

Mechanistic Studies of Small Molecule Ligands Selective to RNA Single G Bulges

Small-molecule RNA binders have emerged as an important pharmacological modality. A profound understanding of the ligand selectivity, binding mode, and influential factors governing ligand engagement with RNA targets is the foundation for rational ligand design. Here, we report a novel class of coumarin derivatives exhibiting selective binding affinity towards single G RNA bulges. Harnessing the computational power of all-atom Gaussian accelerated Molecular Dynamics (GaMD) simulations, we unveiled a rare minor groove binding mode of the ligand with a key interaction between the coumarin moiety and the G bulge. This predicted binding mode is consistent with results obtained from structure-activity-relationship (SAR) studies and transverse relaxation measurements by NMR spectroscopy. We further generated 444 molecular descriptors from 69 coumarin derivatives and identified key contributors to the binding events, such as charge state and planarity, by lasso (least absolute shrinkage and selection operator) regression. Strikingly, small structure perturbations on these key contributors, such as the addition of a methyl group that disrupts the planarity of the ligand resulted in > 100-fold reduction in the binding affinity. Our work deepened the understanding of RNA-small molecule interactions and integrated a new generalizable platform for the rational design of selective small-molecule RNA binders.

biochemistry↗