Biology subjects

Yin, Q.

Publications and source records attributed to Yin, Q..

At least 19 recordsLinked to original sources

bioRxiv2026

ProtCell: a foundation model for single-cell proteomics

Single-cell proteomics provides a direct functional readout of cellular states, but targeted assays measure limited and non-identical protein panels, fragmenting information across experiments. Here, we present ProtCell, a foundation model developed using approximately 380 million cells from 98 datasets for pretraining and evaluation. ProtCell combines protein-identity and continuous-abundance embeddings in a transformer encoder and learns cell- and protein-level representations through masked abundance modeling. ProtCell first connects fragmented profiles through transferable annotation and batch correction while preserving biological organization. It then extends limited profiles through abundance reconstruction and inference of an unmeasured cytotoxic protein program enriched in natural killer cells and memory CD8$^+$ T cells. The learned protein dependencies further guide the selection of compact panels that retain information for profile reconstruction and biological classification. ProtCell thus provides a framework for connecting fragmented proteomic profiles, extending information beyond measured panels and guiding more informative measurements.

bioinformatics↗

bioRxiv2026

RegDesign-Bench: Benchmarking computational methods for cell-type-specific regulatory DNA design

The de novo design of regulatory DNA sequences that drive cell-type-specific gene expression could enable precise gene control in synthetic biology, cell engineering and gene therapy. Various computational methods have been developed for this task, yet systematic comparisons that guide method selection for different design objectives remain limited. Here, we introduce RegDesign-Bench, a standardized framework for evaluating cell-type-specific cis-regulatory element design through a comprehensive set of computational analyses. Among the methods tested, strong predicted activity was not consistently accompanied by cell-type-selective accessibility or prominent cell-type-associated motif signals. Similarity to reference sequence distributions also did not ensure high predicted target-cell activity or specificity. These findings demonstrate that favourable performance on individual measurements does not necessarily extend across regulatory properties and library characteristics, revealing application-dependent strengths and limitations of current design methods. Overall, RegDesign-Bench offers a general-purpose computational framework for comparing and selecting methods to design regulatory DNA that drives cell-type-specific gene expression.

genomics↗

bioRxiv2026

The development of slow and fast theta oscillations in the human brain

Theta oscillations are pivotal for attention and memory, with developmental shifts in theta activity and connectivity tracking memory ability across the lifespan. Building on initial evidence that the theta band separates into slow and fast sub-bands across development within the medial temporal lobe (MTL) and the prefrontal cortex (PFC), this preregistered study (https://osf.io/gsru7) characterized the frequencies of slow (~1.5-4.5 Hz) and fast (~4.5-9.0 Hz) theta oscillations across the brain in a large developmental cohort. We analyzed intracranial EEG (iEEG) recordings from widespread brain regions in 101 children and adults (5.93-54.00 years, 63 males; n electrodes = 5691) using task-based (attention to to-be-remembered visual stimuli) and task-free (resting-state) data. We reveal distinct slow and fast theta oscillations in all regions and identify a spatial gradient in both frequency ranges, with slow theta oscillations speeding up and fast theta oscillations slowing down from posterior to anterior regions. The dissociation between slow and fast theta frequencies peaks during young adulthood, mirroring the developmental trajectory of attention and memory. In the hippocampus and PFC, attentional state (task-based, task-free) modulated age effects, and individual differences in task-based slow theta frequencies predicted individual differences in memory performance, linking the development of slow theta oscillations to the development of memory. Gray matter volume was not associated with age- or task-related differences in theta frequencies, suggesting that the development of theta oscillations is independent of regional brain structure. This study establishes developmental trajectories of slow and fast theta oscillations in localized brain regions and links the maturation of hippocampal and PFC slow theta oscillations to the development of attention and memory.

neuroscience↗

bioRxiv2026

scRep: A Latent-Space Self-Distilled Foundation Model for Single-Cell Representation Learning

Single-cell foundation models have shown strong potential for learning transferable representations from large-scale transcriptomic data. However, many existing approaches rely on reconstructing masked gene expression values, creating a potential mismatch between observation-space reconstruction and the goal of learning stable biological representations. This challenge is particularly relevant to single-cell RNA sequencing, where sparsity, incomplete gene detection, and technical variation can obscure the underlying biological state. Here, we introduce scRep, a compact latent-space self-distillation framework for single-cell representation learning. Rather than reconstructing raw expression values, scRep aligns differently perturbed views of the same cell through a momentum-updated teacher--student architecture, with self-distillation objectives at both the cell and gene levels. This representation-centered formulation encourages the model to capture biological information that remains stable across incomplete and perturbed transcriptomic observations. Using frozen representations without task-specific fine-tuning, scRep pretrained on approximately 2.8 million cells achieves the strongest overall performance across the evaluated frozen-representation benchmarks, demonstrating strong sample efficiency. A larger-scale scRep model pretrained on 30.72 million cells further demonstrates that the framework remains effective when scaled to a substantially larger and more diverse corpus. Beyond cell identity, scRep prioritizes established marker genes, recovers transcription factor--associated gene programs with cell-type-specific activity, and preserves continuous developmental structure that supports graph-based pseudotime inference. We further show that pretraining performance is closely associated with biological diversity: reducing redundant cells while improving cell-type coverage can match or exceed the performance of larger, less balanced training corpora. Together, these results establish latent-space self-distillation as an effective alternative to expression reconstruction for single-cell foundation modeling and suggest that efficient scaling depends not only on the number of cells, but also on the learning objective and the biological diversity of the pretraining corpus.

bioinformatics↗

bioRxiv2026

The mouse oocyte Balbiani body reassembles in growing follicles to continue organelle selection and upon dispersal influences nuclear position

The Balbiani body (Bb), a universal oocyte structure enriched in mitochondria and organelles, forms during zygotene but grows and behaves differently in several animal groups. The mouse Bb forms during pre-follicular oogenesis, but whether it persists during and after the primordial follicle state is unknown, unlike the long lived Bbs in the oocytes of lower vertebrates. Here we report that the mouse Bb, which has been postulated to facilitate mitochondrial selection and organelle quality control, re-assembles in growing mouse follicles. The re-assembled Bb is organized around a single microtubule-organizing center (MTOC), surrounded by mitochondria, Golgi apparatus, endoplasmic reticulum, and endosomes, like the original Bb. Thus, mouse oocytes contain a Bb whose action is paused by quiescence, but otherwise more closely resembles the primordial follicle Bb and the Bb in zebrafish and Xenopus oocytes. The mouse Bb eventually disperses as its MTOC relocates from the perinuclear region to the oocyte cortex, driven by opposing microtubule and actin forces that also reposition the nucleus to the oocyte center. We suggest that the Bb re-assembles and persists during oocyte growth until its function in mitochondrial and organelle quality control is complete.

developmental biology↗

bioRxiv2026

Reconstructing sequence-grammar trajectories enables interpretable and tunable cis- regulatory element design

Designing synthetic cis-regulatory elements (CREs) with cell-type-specific activity remains challenging, and optimization is usually treated as a black box, obscuring how regulatory grammar emerges and why design trajectories fail. Here, we present GO-CRE (Guided Optimization of Cis-Regulatory Elements), which combines efficient sequence generation, predictor-guided reinforcement learning, and trajectory-level interpretation. GO-CRE reconstructs iterative sequence changes in a shared sequence-grammar landscape and identifies coordinated update programs corresponding to search, commitment, and optimization. Productive trajectories in HepG2 and K562 progressively acquired cell-type-associated grammar, whereas SK-N-SH trajectories remained confined to local basins. In HepG2, trajectory analysis revealed a low-complexity polyG trap; introducing a polyG penalty redirected optimization toward HNF/FOXA-associated features. Final designs retained sequence diversity while converging on cell-type-associated motif patterns. Lentiviral MPRA validated cell-type-specific activity in K562 and HepG2 and showed higher average activity of HepG2 designs than endogenous CREs. Together, these findings establish sequence-grammar trajectory reconstruction as a basis for interpretable and tunable synthetic CRE design.

genomics↗

bioRxiv2026

The evolutionarily conserved APP-Spastin cooperation regulates endolysosomal homeostasis and apoptotic cell degradation

Efferocytosis, the recognition, engulfment, and degradation of apoptotic cells by phagocytes, is essential for tissue homeostasis and development, and its failure contributes to chronic inflammation and neurodegeneration. The amyloid precursor protein (APP), a central pathogenic factor in Alzheimers disease, retains physiological functions independent of amyloid production that remain poorly understood. Here, we identify a conserved, non-amyloidogenic role for APP in regulating apoptotic cell degradation via the endolysosomal pathway. Using Drosophila APPL as a model, structure-function analysis demonstrated that the intracellular internalization domain of APPL, but not its secreted ectodomain, is required for efficient apoptotic cell degradation. Immunoprecipitation coupled with mass spectrometry revealed a physical interaction between APPL and the microtubule severing ATPase Spastin, mediated by the microtubule-interacting and trafficking domain of Spastin. APPL interacts with Spastin on endosomal microtubules and modulates the dynamics of the Spastin-ESCRT-III complex, enabling Spastin to sever microtubules and promote endosomal tubule fission. Loss of APPL disrupts this process, causing aberrant endosomal tubulation and impaired lysosome biogenesis. Furthermore, it compromises the function of residual lysosomes, characterized by reduced acidity, diminished proteolytic activity, and increased lysosomal damage, which ultimately impairs the degradation of engulfed apoptotic cells. Critically, this phenotype is evolutionarily conserved in C. elegans and mice. Together, these findings establish a conserved APP-Spastin axis that regulates endolysosomal homeostasis and apoptotic cargo digestion. This reveals a critical non-amyloidogenic function of APP in maintaining tissue homeostasis through efficient efferocytosis, with broad implications for inflammatory and neurodegenerative disorders that warrant further investigation.

cell biology↗

bioRxiv2026

HoloCell: A Generative Foundation Model for Holistic Cellular Modeling

Single-cell multi-omics technologies have recently advanced to enable the profiling of epigenomic, transcriptomic, and proteomic layers within individual cells, offering new opportunities to characterize cellular states as integrated biological systems. However, developing a unified framework that can seamlessly integrate diverse omics modalities and remain robust to heterogeneous modality missingness remains challenging. Existing methods are often designed for specific modalities or modality pairs, relying on dataset-specific training or paired measurements. Here we present HoloCell, to our knowledge the first generative foundation model for joint representation learning and generative modeling across all three major single-cell omics modalities, i.e., epigenomics, transcriptomics, and proteomics. HoloCell contains over 860 million parameters and is pretrained on the Human-Multi-Omics-Corpus, which comprises approximately 468 million single-cell profiles across these three omics layers, corresponding to over 425 billion tokens. HoloCell introduces a a simple yet biologically motivated hierarchical tokenization strategy that encodes cis-regulatory elements, genes, and proteins as structured tokens within a shared modeling framework. We evaluated HoloCell across single-omics representation learning, paired multi-omics integration, unpaired multi-omics alignment, and cross-modal generation via iterative diffusion and remasking, demonstrating its superior performance and flexibility across diverse omics tasks. From a representation perspective, HoloCell provides a unified digital mapping of cellular states across multiple omics layers, capturing cell heterogeneity as an integrated system. From a generation perspective, its iterative diffusion and remasking frame-work permits flexible generation orders beyond fixed left-to-right causality, enabling in silico simulation of multi-omics information flow. Together, these capabilities position HoloCell as a versatile foundation model toward the emerging concept of a virtual cell, offering both systematic characterization and generative simulation of cellular systems within a unified framework.

bioinformatics↗

bioRxiv2026

xNNPCD identifies regulators of programmed cell death by integrating perturbation transcriptomes with cancer dependency profiles

Programmed cell death (PCD) encompasses multiple regulated processes whose dysregulation shapes cancer fitness, yet current computational studies largely use known PCD genes for prognosis rather than discovering regulators. We developed xNNPCD, an interpretable neural-network framework that links CRISPR-Cas9 perturbation signatures from CMap to gene dependency profiles from DepMap. The model constrains hidden neurons to five PCD pathways and iteratively refines a prior gene-pathway mask matrix derived from GO, KEGG, and Reactome using pathway-neuron ablation. This converts binary gene-pathway relationships into continuous-valued associations and improves dependency prediction over random forests, standard fully connected multi-layer perceptron, and its own non-iterative variant. The learned matrix recovers annotated death regulators and nominates candidate regulators, including RPL23A, HSPA5, SNRPA1, SLC6A2, and ASAH1; combined with dependency scores, it further separates pathway coupling from regulatory direction. Transferring the refined relationship matrix and learned weights to compound-induced perturbation data enables in silico drug screening, identifying BRD-K19103580 and decitabine as targeted therapeutic agents for apoptosis and ferroptosis, respectively. The pathway-resolved drug profiles can facilitate the rational design of combination therapies targeting complementary PCD pathways to overcome single-pathway resistance. Overall, xNNPCD offers a generalizable, interpretable approach for mapping the regulatory landscape and elucidating the molecular processes of PCD in cancer. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/724150v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@e74c0forg.highwire.dtl.DTLVardef@1326f4corg.highwire.dtl.DTLVardef@291e96org.highwire.dtl.DTLVardef@1970f10_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Elucidating genes sufficient for viral entry into cells through sequential genome-wide CRISPR activation screens

A preeminent goal of virology is to discover cellular genes that mediate virus entry. Genome-wide loss-of-function screens can illuminate single genes necessary for virus entry, but are stymied by genetic redundancy. Here we report a genome-wide CRISPR activation screening strategy to discover single genes that are sufficient for viral entry into normally-uninfectable cells. Sequential rounds of viral infection vastly enhanced screening sensitivity. This sequential screening strategy was generalizable to two unrelated viruses--Ebola and rabies viruses--and could broadly accelerate the discovery of viral entry factors.

microbiology↗

bioRxiv2025

TRAF6 coiled-coil domain mediates its trimerization

Tumor necrosis factor receptor-associated factor 6 (TRAF6) is an adaptor protein that plays a critical role in innate immune signaling. TRAF6 consists of an N-terminal RING domain, five zinc fingers, a coiled-coil domain, and a C-terminal TRAF domain. Although structural and oligomeric information for coiled-coiled domains in TRAF2-5 is known, information on TRAF6s coiled-coiled domain is relatively unknown. Here, we characterized the oligomeric state of the human TRAF6 domain (residues 288-348) using size-exclusion chromatography (SEC) and size-exclusion chromatography coupled multi-angle light scattering (SEC-MALS). Both His-tagged and tagless proteins displayed molecular masses consistent with a trimer. Coiled-coil computational tools identified a heptad repeat pattern that supported the trimeric findings. AlphaFold-Multimer modelling further showed a symmetric, trimeric coiled-coiled domain with a conserved hydrophobic core. These results provide experimental support for TRAF6 coiled-coiled domain trimerization and help establish a structural framework for understanding how TRAF6 assembles into higher-order structures.

biochemistry↗

bioRxiv2025

Distinct neurophysiological features and memory representations along the long axis of the developing medial temporal lobe

The medial temporal lobe (MTL) is crucial for episodic memory, whereby posterior MTL preferentially represents visuospatial information, and anterior MTL is involved in the representation of semantic or conceptual information. The neurophysiological underpinnings of content-preferential organization in the developing MTL are largely unknown. Here we utilized rare electrocorticography (ECoG) recordings from 23 pediatric epilepsy patients who completed a visual scene recognition memory task to systematically examine the neurophysiological underpinnings of memory formation along the MTL long axis. The timing of high-frequency activity (HFA, [~]70-150 Hz) differed between the posterior and anterior MTL, peaking after scene onset in the posterior MTL and around scene category response (indoor/outdoor scene categorization) in the anterior MTL. Further, in the posterior MTL, HFA was predictive of successful memory formation and positively linked to memory performance, highlighting the importance of posterior MTL HFA to memory formation. In contrast, theta frequency in the anterior MTL was linked to memory performance, and theta-HFA phase-amplitude coupling before scene category responses was predictive of successful memory formation, highlighting the importance of anterior MTL theta oscillations to memory formation. Our findings establish distinct neurophysiological features along the posterior-to-anterior axis of the developing MTL that differentially support the representation of perceptual and conceptual information during memory formation.

neuroscience↗

bioRxiv2025

A human arteriovenous differentiation roadmap reveals vein developmental mechanisms and vascular effects of viruses

Extracellular signals and cell-fate trajectories during vein development remain elusive, despite trailblazing insights into artery development. Here we exploit human pluripotent stem cell differentiation and mouse embryology to present a model that answers longstanding questions: vein endothelial cell (EC) differentiation unfolds in two steps driven by opposing extracellular signals. First, VEGF differentiates mesoderm into "primed" ECs, newly-defined progenitors that co-express certain arterial (SOX17) and venous (APLNR) markers. Second, primed ECs execute vein differentiation upon VEGF/ERK inhibition; however, upon VEGF activation they can instead form artery ECs. The arteriovenous plasticity of primed ECs was supported by intersectional lineage tracing. Future venous genes including NR2F2 harbor poised chromatin in primed ECs, but are only transcribed upon VEGF/ERK inhibition. SOXF transcription factors, including SOX17, confer primed ECs with vein differentiation competence. Collectively, this two-step vein differentiation model--entailing primed EC intermediates and VEGF/ERK inhibition to trigger vein differentiation--has implications for VEGF-modulating therapies.

developmental biology↗

bioRxiv2025

Meta-analysis of the uncultured gut microbiome across 11,115 global metagenomes reveals a new candidate biomarker of health

The human gut microbiome plays an important biological role in host health, yet over 60% of gut species remain uncultured and hence inaccessible to experimental manipulation. Here we analysed 11,115 human gut metagenomes from 39 countries, 13 noncommunicable diseases and healthy individuals to understand the clinical relevance of the uncultured microbiome worldwide. We identified 317 uncultured species linked to distinct health states, with most microbiome signatures being disease specific. Uncultured bacteria were more abundant in healthy controls, with the genus CAG-170 predicted as the strongest biomarker of health. The genetic diversity and abundance of CAG-170 negatively correlated with gut microbiome imbalance over time, and ecological modelling identified it as the top candidate keystone genus among healthy populations globally. Functional prediction analysis showed CAG-170 species have greater capabilities for vitamin B12 biosynthesis but lack key genes involved in arginine production, offering new biological insights into this elusive genus. Our findings shed light on the underexplored role of uncultured gut species in health and disease.

microbiology↗

bioRxiv2025

Discovery of chromatin-based determinants of azacytidine and decitabine anti-cancer activity

The DNA-incorporating nucleoside analogs azacytidine (AZA) and decitabine (DEC) have clinical efficacy in blood cancers, yet the precise mechanism by which these agents kill cancer cells has remained unresolved - specifically, whether their anti-tumor activity arises from conventional DNA damage or DNA hypomethylation via DNA methyltransferase 1 (DNMT1) inhibition. This incomplete mechanistic understanding has limited their broader therapeutic application, particularly in solid tumors, where early clinical trials showed limited efficacy. Here, through the assessment of drug sensitivity in over 600 human cancer models and comparison to a non-DNA-damaging DNMT1 inhibitor (GSK-3685032), we establish DNA hypomethylation, rather than DNA damage, as the primary killing mechanism of AZA and DEC across diverse cancer types. In further support of an epigenetic killing mechanism, CRISPR drug modifier screens identified a core set of chromatin regulators, most notably the histone deubiquitinase USP48, as AZA and DEC protective factors. We show that USP48 is recruited to newly hypomethylated CpG islands and deubiquitinates non-canonical histones, establishing USP48 as a key molecular link between the two components of epigenetic gene regulation: DNA methylation and chromatin modification. Furthermore, loss of USP48, which occurs naturally through biallelic deletions in human cancers, sensitized both hematologic and solid tumors to DNMT1 inhibition in vitro and in vivo. Our findings elucidate the epigenetic mechanism of action of AZA and DEC and identify a homeostatic link between DNA methylation and chromatin state, revealing new therapeutic opportunities for DNMT1 inhibitors in solid tumors.

cancer biology↗

bioRxiv2025

Prediction of cellular morphology change under perturbations with transcriptome-guided diffusion model

Investigating the cell morphology change after perturbations with high-throughput image-based profiling is of growing interest, considering its wide applications in phenotypic drug discovery, including MOA (Mechanism Of Action) prediction, compound bioactivity prediction, and drug repurposing. However, the vast space of chemical and genetic perturbations makes it infeasible to fully explore all the potential perturbations with image-profiling technologies. Consequently, developing a powerful in-silico method to simulate high-fidelity cell morphological response under perturbations can reduce the experiment costs and accelerate drug discovery. Motivated by this, we proposed MorphDiff, a transcriptome-guided latent diffusion model for accurately predicting the cell morphology response to perturbations. We applied MorphDiff to two large-scale datasets, including one drug perturbation and one genetic perturbation cell morphology dataset covering thousands of diverse perturbations. Extensive benchmarking and comparison with baseline methods show the remarkable accuracy and fidelity of MorphDiff in predicting cell morphological changes under unseen perturbations. Furthermore, we explored the utilities of MorphDiff in identifying and retrieving the MOAs of drugs, which is a crucial application in phenotypic drug discovery. With the designed pipeline for MOA retrieval, we demonstrated MorphDiffs capability to boost the retrieval of the drugs MOAs (Mechanism Of Actions) by generating realistic cell morphology profiles. The average MOA retrieval accuracy of MorphDiff-generated morphology is comparable with that of the ground truth cell morphology, and consistently outperforms the baseline method and gene expressionbased retrieval by 29.1% and 9.7% respectively. We also validated that complementary information provided by cell morphology generated by MorphDiff can help discover drugs with dissimilar structures but the same MOAs. In summary, with its strong capabilities in generating high-fidelity cell morphology on unseen perturbations, we envision MorphDiff as a powerful tool in phenotypic drug discovery by accelerating the phenotypic screening of vast perturbation space and improving MOA identification.

cell biology↗

bioRxiv2025

A neutralizing human antibody induces movement of the HCoV-229E receptor binding domain

HCoV-229E is an endemic Alphacoronavirus that typically causes common cold-like disease in most healthy adults, but can also cause severe respiratory disease in the very young and the elderly. Although the virus was discovered over sixty years ago and undergoes continuous antigenic drift, remarkably little is known about the humoral immune response to HCoV-229E infection. Here we report the isolation of two receptor binding domain-targeting neutralizing human antibodies raised in response to natural HCoV-229E infection. One of these, DH1533, potently neutralizes HCoV-229E, binds to spike with sub-nanomolar affinity and prevents the association between the RBD and the host cell receptor aminopeptidase N. Structural characterization of this antibody bound to HCoV-229E spike delineated a neutralization-sensitive epitope on the RBD and revealed that DH1533 induces conformational flexibility in neighboring RBDs, reminiscent of the "up-and-down" kinetics observed in the related Betacoronavirus spikes. These findings provide insight into the humoral immune response to HCoV-229E infection and will serve as a guide for the design of future therapeutic interventions.

immunology↗

bioRxiv2025

Divergent conformational mechanisms of JAK2V617F and TYK2V678F mutations in Thrombocythemia and related disorders: Molecular Dynamics insights into pathway activation and therapeutic targeting

Thrombocythemia and related disorders are primarily driven by persistent activation of the JAK-STAT signaling pathway, which is frequently associated with valine-to-phenylalanine (VF) point mutations in the JAK2 and TYK2 proteins. However, the precise mechanisms underlying the sustained downstream pathway activation induced by these mutations remain unclear. To investigate this, we employed molecular dynamics simulations and computational biophysical approaches to examine how VF mutations sustain JAK-STAT pathway activation. Our analyses, encompassing energetics, interaction forces, and surface electrostatic charge distributions, revealed that these mutations predominantly alter the spatial conformations and stability of the proteins by modulating inter-dimeric interaction forces and binding free energies. Specifically, the V617F mutation in JAK2 enhanced the stability of the JAK2 dimer and suppressed the activity of the JH2 pseudokinase domain. Similarly, the V678F mutation in TYK2 reducing the binding strength of the JH1 kinase domain and the binding free energy, increasing the binding affinity at the dimer interface. Both mutations ultimately resulted in heightened JH1 kinase domain activity and aberrant activation of downstream signaling pathways. Electrostatic analyses further demonstrated that JAK2V617F strengthened attractive forces within the dimer and TYK2V678F mutation mitigated repulsive forces. Decreased binding free energy confirmed enhanced dimer stability in JAK2V617F and TYK2V678F mutants, driven by distinct mechanisms: reinforced JH2 domain interactions in JAK2V617F and domain compaction with reoriented interfaces in TYK2V678F. These insights delineate the mutation-specific allosteric networks that underpin the pathogenesis of thrombocythemia. Furthermore, they establish a molecular foundation for designing isoform-selective inhibitors that target conformational vulnerabilities, thereby presenting novel therapeutic opportunities. Author SummaryO_LIThe VF mutations located in JAK2 and TYK2 stabilizes the JH2 pseudokinase domain and enhances dimer interfaces to disrupt autoinhibition in JAK2, while it induces structural reorganization of the dimer FERM/SH2 domains and JH1 kinase conformation to promote substrate accessibility in TYK2. C_LIO_LIWe developed a novel multiscale analyses framework, integrating conformational analysis, electrostatic analysis, hydrogen bond and salt bridges, and free energy decomposition, to collaboratively elucidate the effects of VF mutations on the protein multimers. C_LIO_LIThese findings highlights divergent allosteric networks underlying pathogenic signaling, offering a molecular mechanisms for designing isoform-selective inhibitors targeting conformational vulnerabilities in myeloproliferative disorders. C_LI

biophysics↗