Biology subjects

Wang, L.

Publications and source records attributed to Wang, L..

At least 271 records · Page 15Linked to original sources

bioRxiv2026

AMPBAN: A Deep Learning Framework Integrating Protein Sequence and Structural Features for Antimicrobial Peptide Prediction

The escalating crisis of antimicrobial resistance poses a devastating and immediate threat to human life. Antimicrobial peptides (AMPs) are a promising antibiotic substitute to combat antimicrobial resistance. Compared with the traditional wet-lab screening approaches, computational models have largely improved the efficiency of predicting antimicrobial peptide. However, most computational models overlook or underutilize the evolution and structural information of peptides, which is crucial for understanding the peptide functions. Here, we proposed a sophisticated deep learning model to predict AMPs, Antimicrobial Peptide Bilinear Attention Network (AMPBAN), which incorporates peptide evolution features from ESM3 protein language model, structure features from ESMFold predicted with equivariant graph neural network (EGNN), and the joint information from sequence and structure learned via Bilinear Attention Network. AMPBAN consistently demonstrated superior accuracy and generalization compared to nine state-of-the-art AMP prediction models across multiple independent benchmarks. Furthermore, an ablation study confirms that our multimodal fusion strategy significantly refines the integration of sequence and structural signals, yielding superior predictive balance over single-modality models. This framework provides a robust tool for the accelerated discovery of novel AMPs and the advancement of next-generation antimicrobial drug development. The datasets, source code and models are available at https://github.com/baiwenhuim/ampban.

bioinformatics↗

bioRxiv2026

Iron Deficiency Drives Sarcopenia in the Elderly: HIF-1α-Mediated Fibro-Adipogenic Progenitor Differentiation Induces Fat Infiltration and Impairs Muscle Function

Abnormal iron metabolism is closely linked to sarcopenia; however, the specific iron metabolism features of fat-infiltrating sarcopenia remain poorly understood. Proteomic sequencing revealed that in skeletal muscle with severe fat infiltration, the expression of iron utilization-related proteins was significantly downregulated, whereas that of iron uptake and storage proteins was markedly upregulated, thereby presenting an abnormal iron deficiency (ID) phenotype. Nevertheless, the mechanism by which ID drives intramuscular fat infiltration has not been fully elucidated. Using clinical samples, aged murine ID models, and in vitro cell assays, this study is the first to demonstrate that ID stabilizes hypoxia-inducible factor-1 (HIF-1), promoting aberrant adipogenic differentiation of fibro-adipogenic progenitors (FAPs), disrupting the homeostatic balance between satellite cells (SCs) and FAPs, exacerbating skeletal muscle fat infiltration, and impairing muscle repair capacity. Notably, treatment with the HIF-1 inhibitor PX478 reversed these pathological alterations and improved muscle function. Collectively, our findings identify the ID-HIF-1-FAPs axis as a key driver of intramuscular fat infiltration, offering a novel therapeutic target for the clinical management of fat-infiltrating sarcopenia.

cell biology↗

bioRxiv2026

Covalent Leader Peptide Probes Enable Proteome-Level Mapping of RiPP Enzymes beyond Biosynthetic Gene Clusters

Genome-based analyses have enabled widespread discovery and biosynthetic annotation of ribosomally synthesized and post-translationally modified peptides (RiPPs) pathways, yet often fail to capture all participating enzymes, particularly those encoded outside canonical biosynthetic gene clusters. In this study, leveraging the central role of leader peptides (LPs) in RiPP enzyme-substrate recognition, we developed a substrate-guided chemical-proteomic strategy using covalent LP probes to directly identify LP-binding enzymes from native proteomes. Using the LctA-LctM and PatE-LynD systems, we demonstrate that LP probes carrying a photocrosslinker engage enzymes at their authentic LP-recognition sites while preserving full catalytic competence. Application of this strategy to a model strain Streptomyces sparsogenes uncovered lanthipeptide synthetases encoded outside canonical biosynthetic gene clusters and revealed graded cross-cluster activities among multiple RiPP pathways. This work establishes a proof-of-concept model platform for proteome-level discovery of LP-binding RiPP enzymes and highlights cross-cluster enzymatic crosstalk as a latent mechanism for structural diversification.

biochemistry↗

bioRxiv2026

Dynamic hinge-motion of PfRIPR revealed by malaria invasion inhibitory antibodies

The PfPCRCR complex is essential for invasion of human erythrocytes by the deadliest malaria parasite, Plasmodium falciparum. Antibodies against each subunit of PfPCRCR prevent erythrocyte invasion and the PfRH5 component is currently the most advanced blood-stage malaria vaccine. Central within PfPCRCR is PfRIPR. This complex molecule contains a core and a flexible tail and allows PfPCRCR to bridge the parasite and erythrocyte during invasion. In this study, we generated a small panel of human monoclonal antibodies against PfRIPR. We structurally characterised four PfRIPR tail-binding antibodies in complex with PfRIPR fragments. We show that growth-inhibitory antibody RP.012 induces a kink in the PfRIPR tail while non-inhibitory antibodies do not. Furthermore, we show that these four antibodies modulate each other, either through antagonism or by acting synergistically. These studies have implications for the design of PfRIPR-based vaccine immunogens and indicate that the tail of PfRIPR undergoes essential conformational changes during erythrocyte invasion.

immunology↗

bioRxiv2026

UniWave: A Waveform-Based Encoding Framework for Nucleic Acid Feature Extraction

Nucleic acid sequence analysis constitutes a core research area in biomedical and health informatics, playing a critical role in infectious disease surveillance, epigenetic regulation, and genomic biomarker discovery. However, most existing sequence encoding methods rely on discrete representations, which are inadequate for capturing the intrinsic continuous structural properties of biological sequences. Inspired by waveform representations in modern physics, we propose UniWave, a novel encoding framework that converts discrete nucleotide sequences into biologically informative one-dimensional continuous waveforms through base mapping, windowed sinc interpolation, and wavelet-based downsampling. To enable more efficient spatiotemporal modeling, we design a learnable positional encoding module, WavePosition, which incorporates positional information to project the one-dimensional waveform into a two-dimensional continuous representation. In addition, we develop a lightweight dual-attention network, InceptionTime-ATT, to facilitate efficient multi-scale extraction of biological features. Experiments conducted on multiple representative genomic benchmark tasks demonstrate that UniWave consistently and significantly outperforms conventional encoding methods in viral genotype classification, cross-species enhancer identification, epigenetic modification site detection, and bacterial genome classification. Moreover, UniWave maintains excellent robustness under perturbation and noise stress tests, while its low-dimensional continuous representation further contributes to reduced model complexity. In summary, UniWave establishes a novel paradigm for biomedical nucleic acid sequenceencoding that is interpretable, noise-resilient, and computationally efficient, offering new avenues for uncovering disease-associated genomic patterns and advancing data-driven biomedical research.

bioinformatics↗

bioRxiv2026

Multiomics reveals epigenetic control of fibroblast activity after myocardial infarction and a key role for RUNX transcription factors

BackgroundAfter myocardial infarction (MI), cardiac fibroblasts proliferate and undergo a sequential differentiation process. They first transition into cardiac myofibroblasts, a transient and highly contractile state, and ultimately into matrifibrocytes, a more stable state that partially resembles chondrocytes. These dynamic transitions are essential for infarct healing and scar formation. While insufficient fibroblast activation can compromise infarct integrity, excessive activation promotes pathological fibrosis that impairs cardiac function. Despite its clinical importance, the transcriptional and epigenetic regulation of these transitions remain poorly understood. Elucidating underlying mechanisms is critical for developing strategies to fine-tune fibroblast activity during cardiac repair. MethodsWe performed bulk RNAseq, ATACseq, CUT&Tag, CUT&RUN, EMseq, and Hi-C on cardiac fibroblasts from uninjured and post-MI mouse hearts. In parallel, we conducted single-nucleus multiomic (snRNAseq and snATACseq) profiling across multiple time points after MI. Subsequent integrated analysis explored epigenetic mechanisms regulating cardiac fibroblast gene expression and activity. Using an improved computational strategy, we constructed gene regulatory networks to identify key transcription factors and biological processes regulated by these transcription factors. To assess the role of Runx1 specifically, we used tamoxifen-inducible, fibroblast-specific Runx1 knockout mice to evaluate transcriptional, epigenetic, and functional outcomes with the same genomic tools and additional complementary assays. ResultsCardiac fibroblasts undergo extensive chromatin remodeling after MI, which is highly correlated with changes in transcriptomic profiles. In contrast, the role of DNA methylation is relatively minor. Gene regulatory network analysis identified Runx1 as a central regulator of cardiac fibroblast proliferation and matrifibrocyte differentiation. In vitro and in vivo validation confirmed Runx1 as a key modulator of transcriptional and epigenetic changes in cardiac fibroblasts. Runx1 KO reduced cardiac fibroblast proliferation, disrupted the myofibroblast-to-matrifibrocyte transition, and affected macrophage cytokine expression through altered cardiac fibroblast-macrophage communication. Fibroblast-specific Runx1 knockout mice showed improved post-MI survival and reduced cardiac dilatation, especially in males. Simultaneous Runx2 deletion further enhanced the effects of Runx1 knockout. ConclusionsCardiac fibroblast activation and differentiation after MI are regulated by dynamic epigenetic changes. Runx1 plays a pivotal role in modulating cardiac fibroblast activities, and its deletion improves cardiac repair by mitigating maladaptive fibroblast responses. By illuminating the centrality of Runx1 in post-MI repair, this study identifies an actionable pathway for therapeutically steering fibroblast responses.

genomics↗

bioRxiv2026

Exercise-Induced Epoxy-Eicosanoids Promote Cardiomyocyte Proliferation and Post-Infarction Repair via NR4A1

BACKGROUNDMyocardial infarction (MI) causes irreversible cardiomyocyte loss that exceeds the adult hearts regenerative capacity, leading to adverse remodeling and heart failure. While exercise provides cardiovascular protection, the mechanisms underlying exercise-induced cardiac repair remain unclear. METHODSPlasma levels of free fatty acids and polyunsaturated fatty acid metabolites were quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). RNA sequencing was performed to identify differentially expressed genes and enriched pathways. NR4A1 was knocked down in mice via tail vein injection of AAV9 carrying shRNA targeting NR4A1 (AAV9-shNR4A1). The role of 17,18-EEQ was evaluated by continuous subcutaneous infusion via osmotic minipump; the involvement of the cAMP-PKA-CREB axis was assessed using specific pharmacological inhibitor. Molecular analyses included ELISA, immunofluorescence staining, QPCR, and western blotting. Additionally, clinical samples were collected from 36 endurance-trained athletes and 30 sedentary individuals (age 18-22, male) for comparative analysis. RESULTSHere we demonstrate that preventive aerobic training promotes post-MI cardiomyocyte proliferation and confers cardioprotection by reducing fibrosis area and improving cardiac function. Targeted lipidomics revealed that exercise elevates circulating epoxyeicosatetraenoic acids (EEQs), particularly 17,18-EEQ; osmotic pump delivery of 17,18-EEQ mimicked the protective effects of exercise, reducing infarct burden and enhancing proliferation markers (Ki67, pH3, Aurora B). Transcriptomic analysis identified NR4A1 as the most prominent up-regulated mRNA under exercise induction; AAV9-mediated NR4A1 knockdown abolished exercise-mediated cardioprotection and proliferative benefits, establishing NR4A1 as essential for exercise-driven repair. Phosphoproteomic profiling revealed that 17,18-EEQ activates the PKA-CREB pathway to induce NR4A1 expression; pharmacological inhibition of adenylyl cyclase, PKA, or CREB phosphorylation reversed EEQ-mediated cardioprotection and proliferation, confirming pathway necessity. Dietary supplementation with eicosapentaenoic acid (EPA), the metabolic precursor of 17,18-EEQ, amplified exercise-induced cardioprotection by enhancing EEQ biosynthesis. CONCLUSIONOur study identifies 17,18-EEQ as an exercise-derived exerkine that promotes cardiomyocyte proliferation and post-MI repair through a PKA-CREB-NR4A1 signaling axis, advancing our understanding of exercise cardioprotection and myocardial regeneration mechanisms while providing novel therapeutic targets for cardiac intervention. What IS New?O_LIAerobic exercise enhances post-myocardial infarction cardiac repair by upregulating the cardio-protective lipid mediator 17,18-EEQ and its downstream effector NR4A1. C_LIO_LI17,18-EEQ promotes cardiomyocyte proliferation and cardiac repair through activation of the cAMP-PKA-NR4A1 signaling axis. C_LIO_LICombining aerobic exercise with omega-3 fatty acid supplementation synergistically improves cardiac remodeling and functional recovery after ischemic injury. C_LI What Are the Clinical Implications?O_LITargeting endogenous cardiomyocyte proliferation represents a promising therapeutic strategy for cardiac regeneration after ischemic injury. C_LIO_LIDietary supplementation with omega-3 fatty acids may be a clinically feasible approach to amplify exercise-induced cardioprotection and improve outcomes after myocardial infarction. C_LI

physiology↗

bioRxiv2026

MicroRNA spatial profiling for assessing drug efficacy in BRCA1-related triple-negative breast tumors

BRCA1/2-mutated breast cancers exhibit homologous recombination deficiency (HRD) and are initially sensitive to poly(ADP-ribose) polymerase (PARP) inhibitors, but 40-70% of patients develop resistance, creating a need for predictive biomarkers that capture treatment-associated spatial heterogeneity. Using the K14-Cre Brca1f/fTrp53f/fmodel with tumors that acquired PARP inhibitor resistance, we evaluated PARP inhibitor combinations with either PI3K inhibition or Poly(I:C) in vivo. To determine how treatment altered tumor spatial microRNA (miRNA) profiles, we applied our hydrogel-based, nanoliter well array in situ miRNA assay to quantify and spatially profile miRNAs on FFPE sections from tumors treated for 10 days and developed spatial miRNA analysis frameworks integrating latent Dirichlet allocation (LDA) and principal component analysis (PCA). We also incorporated immune architecture using Structural Similarity Index Measure (SSIM) maps to assess co-localization of immune infiltration and miRNA topics. Both combinations improved antitumor activity compared to PARP inhibition alone. The resulting spatial miRNA topics stratified early tumors according to subsequent PARP inhibitor sensitivity or resistance and distinguished their treatment regimens, while SSIM analysis revealed co-localization of immune infiltration and miRNA topics. This integrative spatial miRNA assay and analysis identified a let-7a-dominant topic associated with PARP inhibitor resistance, indicating that spatial miRNA profiling may inform therapeutic stratification in BRCA1/2-related breast cancers.

biochemistry↗

bioRxiv2026

Structural insights into the recognition and catalysis of tRNA by human NSUN2

The human RNA m5C methyltransferase NSUN2 catalyzes site-specific cytosine methylation across diverse RNA substrates and thereby regulates a wide range of biological and physiological processes. However, the molecular basis by which NSUN2 achieves broad substrate recognition while maintaining catalytic specificity has remained unclear. Here we determine structures of human NSUN2 in both substrate-free and substrate-bound states using X-ray crystallography and cryo-electron microscopy. Structures of NSUN2 in complex with multiple tRNA substrates reveal a structure-first, sequence-tolerant strategy in which NSUN2 actively remodels tRNA architecture, exposing the buried target cytosine and positioning it within the catalytic pocket for methyl transfer. This recognition strategy enables NSUN2 to accommodate diverse tRNA substrates through a largely conserved interaction interface. Guided by these structural insights, we identify a small-molecule inhibitor that suppresses NSUN2 activity and cancer cell proliferation. Together, our findings define the molecular principles underlying NSUN2-mediated RNA m5C modification and provide a structural foundation for targeting NSUN2 in cancers.

molecular biology↗

bioRxiv2026

Emergence of Biological Structural Discovery in General-Purpose Language Models

Large language models (LLMs) are evolving into engines for scientific discovery, yet the assumption that biological understanding requires domain-specific pre-training remains largely unchallenged. Here we report that general-purpose LLMs possess an emergent capability for biological structural discovery. Under strict, shortcut-controlled evaluation, a small-scale GPT-2 (124M) fine-tuned solely on English paraphrase discrimination detects protein homology zero-shot at ROC-AUC 0.79 on a shortcut-controlled benchmark. Controls establish that the ability is conferred by pre-training, not architecture: a randomly initialized GPT-2 is at chance (0.52). To exclude the possibility that public checkpoints were contaminated with biological data, we train our own GPT-2 from scratch on an English-only web corpus; it reproduces the transfer (0.76), proving the effect arises from linguistic pre-training alone. Network-based interpretability reveals a deep structural isomorphism: the discriminative signal localizes to deep layers (0.97 at layer 9), and attention analysis surfaces modality-agnostic "difference" operators. Scaling to massive instruction-tuned models further improves performance, including in the remote-homology "twilight zone", which we report as an exploratory upper bound because those models training corpora are undisclosed. We formalize these tasks through the BioPAWS benchmark. Our controlled results--obtained entirely on models with known training data--establish that abstract logical structures distilled from human language constitute a genuine, if bounded, cognitive prior for decoding the syntax of biology.

bioinformatics↗

bioRxiv2026

Discovery of potent oligopeptides for various metabolic diseases using deep learning

Artificial intelligence (AI)-based methods are increasingly critical in peptide drug discovery, but their applications are limited in a narrow scope such as antimicrobial peptides. For indications where peptide therapy inherently excels, such as metabolism, endocrinology, and tissue regeneration, AI-based pipeline for therapeutic peptide discovery is long awaited but still unmet. Here, we propose a deep learning-based pipeline, Deepeptide, capable of identifying therapeutic oligopeptides for various metabolism-related indications. Leveraging the intrinsical relationship of disease indication -- biological processes -- molecular functions, Deepeptide discovers oligopeptides with indication-ameliorating-related molecular functions as lead candidates for indication of interest. Deepeptide was applied in five representative indications of metabolism, endocrinology, and tissue regeneration: angiogenesis, lipid metabolism, osteogenesis, glucose metabolism, and anti-angiogenesis. Overall, 62% of the identified oligopeptide candidates demonstrated significant bioactivity in vitro, with most of them showing comparable potency to the first-line drugs. Notably, the heptapeptide AP7 exhibited angiogenic potency comparable to VEGF in excisional wound splinting mouse model by promoting cell migration rather than proliferation, and hexapeptide TP6 showed significant dual-efficacy against hyperlipidemia and obesity in high-fat diet mice by inhibiting lipid synthesis and regulating gut microbiota. These findings highlight the potential and generalizability of Deepeptide in therapeutic oligopeptide discovery for metabolic diseases.

bioinformatics↗

bioRxiv2026

Progenitor Diversity and Architecture of the Human Ganglionic Eminences Shaping the Basal Ganglia

The embryonic medial and lateral ganglionic eminences (MGE, LGE) are the principal sources of most neurons and glia for the basal ganglia. In primates, the MGE has a distinctive cytoarchitecture characterized by doublecortin enriched cellular nests (DENs), yet the architectonic organization underlying DEN formation, the molecular heterogeneity of ganglionic eminence progenitors and their lineage relationships, remain poorly understood. Here, using paired single-nucleus transcriptomics and chromatin accessibility profiling of the three GEs, we identify distinct progenitor populations, delineate their gene regulatory networks, and reconstruct their lineage trajectories. Live imaging reveals a unipolar outer radial glia-like population (GE-oRG) that undergoes mitotic somal translocation. Spatial transcriptomics identifies a distinct CRABP1+/ANGPT2+ domain within the MGE. Integrated spatial and electron microscopy demonstrates a periphery-to-center gradient of differentiation in the MGE. Leveraging DEN-forming MGE organoids derived from PCDH19 knockout human pluripotent stem cell lines, we identify the protocadherin, PCDH19, as a key regulator of DEN formation.

neuroscience↗

bioRxiv2025

Structural, biophysical, and virological mechanistic characterization of HIV-1 capsid-targeting antivirals

Due to its significant role in virus replication, the HIV capsid is an attractive antiviral target. This is validated by the recent clinical approval of lenacapavir for both treatment and pre-exposure prophylaxis (PrEP). PF74 is a well-characterized capsid-targeting antiviral that was discontinued in further study due to potency and metabolic issues. We hypothesized that making chemical modifications at certain sites of PF74 could result in capsid-targeting antivirals with improved potency and bioavailability. Our cumulative studies show that making changes at the R1 and R3 positions of PF74 results in compounds with increased antiviral potency, increased stability of wild-type HIV capsid hexamers and virions, tighter binding to wild-type HIV capsid hexamer compared to PF74, and different interactions at the "FG" binding site of capsid compared to PF74. These data provide insights into the design of future capsid-targeting antivirals relevant for clinical use.

biochemistry↗

bioRxiv2025

Template-based RNA structure prediction advanced through a blind code competition

Automatically predicting RNA 3D structure from sequence remains an unsolved challenge in biology and biotechnology. Here, we describe a Kaggle code competition engaging over 1700 teams and 43 previously unreleased structures to tackle this challenge. The top three submitted algorithms achieved scores within statistical error of the winners of the recent CASP16 competition. Unexpectedly, the top Kaggle strategy involved a pipeline for discovering 3D templates, without the use of deep learning. We integrated this template-modeling pipeline and other Kaggle strategies to develop a single model RNAPro that retrospectively outperformed individual Kaggle models on the same test set. These results suggest a growing importance of template-based modeling in RNA structure prediction.

biophysics↗

bioRxiv2025

STEAP4+ neutrophils: a promising circulating biomarker for lung metastasis

As the leading contributor to cancer-related mortality, metastasis often evades detection until advanced stages, underscoring the critical need for early predictive biomarkers. Neutrophils are naturally circulating sentinels that have been implicated in pioneering pre-metastatic niche formation in distant organs. Nevertheless, their heterogeneity within evolving metastatic microenvironments remains poorly characterized. Here, through dynamically delineating neutrophil subsets via single-cell transcriptomics, we identified a conserved feature of lung metastasis in breast cancer and other cancer models. Among the core genes, a cell surface marker, STEAP4, was selected and validated to indicate lung metastases at both RNA and protein levels. In both humans and mice, these STEAP4+ neutrophils were detectable not only in lung metastatases but also in the circulation. Clinically, the abundance of circulating STEAP4+ neutrophils robustly discriminates patients with lung metastasis from those with localized primary tumor. Our findings uncover STEAP4+ neutrophils as a promising biomarker and lay the groundwork for a non-invasive, blood-based diagnostic strategy for metastatic disease.

cancer biology↗

bioRxiv2025

LISA: A Comprehensive R Package for Lentiviral Integration Site Analysis in Gene Therapy Safety Assessment

BackgroundGene therapy using integrating viral vectors has shown remarkable clinical success, but the random integration of vectors into the host genome poses potential safety concerns, particularly insertional mutagenesis. Common Integration Sites (CIS) near oncogenes significantly increase the risk of clonal expansion and malignant transformation. Current tools for integration site (IS) analysis lack comprehensive functionality for safety assessment, particularly in identifying risk-associated genomic regions and tracking clonal dynamics over time. ResultsWe developed LISA (Longitudinal Integration Site Analysis), a comprehensive R package specifically designed for gene therapy safety evaluation. LISA incorporates a novel approach that trains integration site profiles using cumulative curves derived from normal cell infusion samples and normal patient samples, with subsequent statistical testing to distinguish these normal samples from those with exceptional clonal expansion, thereby enabling effective identification of dominant clone formation. For longitudinal studies, LISA monitors fluctuations in clonal dynamics via Richness and Evenness metrics, and integrates this cumulative curve-based statistical testing approach into its early warning system to detect potentially dominant clones. Additionally, LISA features an optimized CIS detection algorithm with O (n log n) time complexity, achieved through chromosome-position encoding and a seed-site strategy, which facilitates efficient analysis of large-scale clinical datasets. The package also provides systematic functional annotations, including annotations for Safe Harbor regions, enhancer/promoter elements, and proximity to three curated gene sets: adverse event-associated genes (AE genes, n=36), cancer-related genes (n=980), and immune-related genes (n=2,485). Furthermore, it encompasses six categories of interactive visualizations and has been validated using real clinical data from gene therapy trials.

bioinformatics↗

bioRxiv2025

Spacing effect improves generalization in biological and artificial systems

Generalization is a fundamental criterion for evaluating learning effectiveness, a domain where biological intelligence excels yet artificial intelligence continues to face challenges. In biological learning and memory, the well-documented spacing effect shows that appropriately spaced intervals between learning trials can significantly improve behavioral performance. While multiple theories have been proposed to explain its underlying mechanisms, one compelling hypothesis is that spaced training promotes integration of input and innate variations, thereby enhancing generalization to novel but related scenarios. Here we examine this hypothesis by introducing a bio-inspired spacing effect into artificial neural networks, integrating input and innate variations across spaced intervals at the neuronal, synaptic, and network levels. These spaced ensemble strategies yield significant performance gains across various benchmark datasets and network architectures. Biological experiments on Drosophila further validate the complementary effect of appropriate variations and spaced intervals in improving generalization, which together reveal a convergent computational principle shared by biological learning and machine learning.

neuroscience↗

bioRxiv2025

Fractionation-Free Protein Corona Quantification Through Synchrotron-Based Small-Angle X-ray Scattering

When nanoparticles (NPs) enter biological environments, they are rapidly coated by biomolecules, forming the protein corona (PC) that defines their biological identity and dictates how NPs are recognized, distributed, and processed by living systems. Capturing the authentic features of the PC demands experimental conditions that preserve its native state, which are difficult to achieve once NPs are removed from their biological milieu. Despite significant progress, current PC quantification methods still rely on separating the NP-PC complex from its native environment, which compromises the coronas integrity and prevents accurate evaluation of its physicochemical properties. Here, we introduce a fractionation-free approach based on synchrotron small-angle X-ray scattering (SAXS) to quantitatively determine the amount of protein adsorbed onto silica NPs under native conditions. By modeling the scattering contribution of free versus bound proteins, we directly extracted the adsorbed mass in both single-protein (BSA) and complex proteomic (human serum) systems. The resulting adsorption isotherms enabled the determination of thermodynamic parameters such as binding constants and cooperativity, distinguishing between monolayer and multilayer adsorption regimes. Together, these findings establish SAXS as a robust, non-invasive, and quantitative technique for probing the protein corona in situ, without perturbing the native equilibrium. This methodology paves the way for the development of new in situ analytical frameworks across diverse nanomaterial and proteomic systems, advancing SAXS toward quantitative characterization of the protein corona. Table of Contents Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/695217v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@eea1f1org.highwire.dtl.DTLVardef@c9dcdforg.highwire.dtl.DTLVardef@c3dff6org.highwire.dtl.DTLVardef@1a49813_HPS_FORMAT_FIGEXP M_FIG C_FIG High-throughput synchrotron SAXS enables in situ, quantitative tracking of protein corona formation on nanoparticles.

biophysics↗