SEARCH · bioRxiv Science

Results for “Biophysics”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,297 records · Page 72Linked to original sources

bioRxiv2026

Human TBC1 domain-containing kinase is a class I multidomain pseudokinase

TBCK-related encephalopathy (TBCKE) is a neurodevelopmental disorder associated with biallelic mutations in TBCK. Despite the increasing number of reported cases worldwide, the biochemical and biophysical properties of TBCK remain unclear, hindering molecular understanding of its role in disease. Here, we present the successful expression, purification, and biochemical characterization of full-length human TBCK produced in Spodoptera frugiperda cells. Biochemical and biophysical analyses reveal that the catalytically inactive pseudokinase domain of TBCK lacks nucleotide binding, consistent with the absence of the canonical VAIK, HRD, and DFG motifs required for catalysis. These findings support that TBCK is a class I pseudokinase and provide a foundation for future structural and functional studies to elucidate its biological role.

biochemistry↗

bioRxiv2026

Thermodynamic rigidity of harmonic brain states relates to general mental ability in juvenile myoclonic epilepsy

Cognitive difficulties are increasingly recognized in juvenile myoclonic epilepsy (JME), but scalable biomarkers linking resting-state brain dynamics to general mental ability remain limited. Here, we combined topological data analysis, graph signal processing, machine learning, inverse Langevin modeling, and biophysical simulations to test whether EEG-derived network dynamics capture individual differences in general mental ability in JME. We studied 54 patients with JME and 45 healthy controls using resting-state high-density EEG and the raw estimated full-scale score derived from the Wechsler Abbreviated Scale of Intelligence (WASI), used here as an index of general mental ability. Subject-specific low-alpha activity was reconstructed with generalized eigendecomposition, and graph-derived features were extracted from the projection of topological and alpha-power signals onto the functional connectome, providing a graph-harmonic description of large-scale brain-state dynamics. In controls, dynamic EEG-derived features significantly predicted general mental ability, whereas the same framework failed in JME. Because prediction in controls was driven mainly by dynamic measures of smoothness (Dirichlet energy), we next examined the temporal organization of alpha-power smoothness using an inverse Langevin framework. Within the patient group, greater thermodynamic rigidity--that is, stronger confinement of fluctuations around preferred network states--was associated with lower general mental ability. Relative to controls, patients also showed lower thermodynamic noise, indicating a reduced tendency to explore alternative network regimes. Biophysical simulations suggested that reduced dendritic arborization can generate rigidity directly, whereas pharmacological stabilization of hyperexcitable circuits can shift the system toward a more rigid, lower-noise regime. Together, these findings suggest that cognition in JME is linked not only to altered resting-state network dynamics but also to stronger confinement of network-state fluctuations, with both intrinsic circuit abnormalities and treatment-related stabilization representing plausible routes to this rigid phenotype.

neuroscience↗

bioRxiv2026

Distinct cell-type contributions and network topography of theta-nested gamma oscillations in the medial entorhinal cortex

Theta-nested gamma oscillations in the medial entorhinal cortex (mEC) are essential for spatial coding and memory, but the underlying cellular mechanisms remain unclear. We combined optogenetics, whole-cell electrophysiology, intracellular voltage imaging, and local field potential (LFP) recordings in acute slices from CaMKII-ChR2 mice to investigate how excitation and inhibition shape theta-gamma coupling in layer II/III mEC. During theta-frequency stimulation, fast-spiking interneurons received strong gamma-frequency excitation and fired rhythmic bursts, whereas stellate and pyramidal neurons fired more sparsely and were dominated by gamma-frequency inhibition. This sparse firing could support the selective firing of grid cells. Excitatory post-synaptic currents in interneurons preceded inhibitory currents and LFP gamma by [~]3 ms, supporting a pyramidal-interneuron network gamma (PING) mechanism. Pyramidal neurons fired on the descending phase of the gamma cycle, whereas stellate cells and fast-spiking interneurons fired before and after the trough, respectively. Intracellular voltage imaging revealed network gamma synchronization among excitatory neurons at a population level, with topographic clustering of subthreshold membrane potentials, but not spike timing, while individual neurons often skipped gamma cycles. These findings identify the dominant role of reciprocal E-I interactions in generating theta-nested gamma oscillations and highlight distinct cell-type contributions to the temporal dynamics of the mEC. Further, a biophysically realistic computational model predicted gamma cycle skipping in stellate cells and burst firing in fast-spiking interneurons during PING. Our experimental and computational results provide mechanistic insight into how the intrinsic properties of mEC cell types generate oscillatory activity in a manner that could support grid cell function and spatial computation. Significance StatementTheta-nested gamma oscillations in the medial entorhinal cortex (mEC) are essential for spatial navigation and memory, yet the underlying circuit mechanisms remain incompletely understood. Using optogenetics, voltage imaging, electrophysiology, and biophysically realistic computational modeling, we show that reciprocal interactions between excitatory neurons and fast-spiking interneurons generate robust gamma oscillations through a pyramidal-interneuron network gamma (PING) mechanism. We reveal cell-type specific differences in gamma phase-locking and demonstrate that principal neurons synchronize at the population-level across large laminar distances (up to 800 {micro}m). Further, the voltage activity, but not spike timing, of principal neurons exhibited topographic clustering. These findings clarify how local circuits in mEC generate temporal dynamics critical for grid cell coding and further constrain computational models of spatial representation.

neuroscience↗

bioRxiv2026

Divergent CRD-Dependent Mechanisms Govern RAS Isoform-Selective Recruitment of CRAF and ARAF

RAF kinases interpret signals from the three major RAS isoforms to initiate MAPK pathway activation, yet the molecular logic that governs isoform-specific RAS recruitment and the early events that relieve RAF autoinhibition are not yet fully understood. In particular, how the modular N-terminal regulatory architecture of CRAF and ARAF, anchored by the multifunctional cysteine-rich domain (CRD), discriminates among HRAS, KRAS, and NRAS has remained a central unresolved question. Here, we combine quantitative biophysical measurements with structural and dynamic analyses to define how RAS isoform identity and CRD engagement shape the earliest steps of RAF activation. These studies reveal unexpectedly divergent modes of RAS recognition between CRAF and ARAF and expose previously unappreciated functions of the CRD in modulating RAS affinity and intramolecular regulatory contacts. We further identify a direct link between RAS binding and destabilization of RAF autoinhibition, providing mechanistic insight into how RAS initiates the transition from an inactive monomer to an activation-competent assembly. Finally, we show that emerging KRAS inhibitors variably perturb KRAS-CRAF interactions, offering insight into how these therapeutics influence early RAS-RAF signaling events. Together, this work uncovers distinct biophysical principles that govern RAS-RAF selectivity and reveals a regulatory role for the CRD that reframes our understanding of RAF activation and its dysregulation in RAS-driven cancers. SignificanceProteins in the RAS-RAF signaling pathway control cell growth and are frequently mutated in cancer. Despite their importance, how different RAS proteins selectively recruit RAF kinases has remained incompletely understood. This study reveals that the cysteine-rich regulatory region of RAF plays a central role in distinguishing RAS isoforms and controlling RAF activation. These insights clarify early steps in MAPK signaling and may guide the development of improved therapies targeting RAS-driven cancers.

biochemistry↗

bioRxiv2026

DeepBioGS: a hybrid framework for integrating crop growth modelling with genomic prediction through neural networks

Ensuring global food security under rapid climate change demands accelerated genetic gain and breeding strategies that address complex Genotype-by-Environment (GxE) interactions. Traditional genomic selection models often fail to account for novel or extreme climates.Furthermore, integrating mechanistic crop growth models (CGMs) using traditional Bayesian frameworks to solve this issue presents severe computational bottlenecks. Here, we introduce DeepBioGS, a novel hybrid framework that integrates genomic selection with biophysical growth modelling via a fully differentiable deep learning architecture. DeepBioGS utilises a parameter-prediction multi-layer perceptron to map high-dimensional genomic markers to latent, highly heritable physiological traits (Genotype-Specific Parameters; GSP). These parameters mechanistically predict crop phenology across diverse environments. Using two multi-environment wheat datasets comprising over 6,000 genotypes, DeepBioGS extracted latent traits with near-perfect SNP-based heritability values (0.95-1.00). Crucially, the framework demonstrated superior or comparable predictive accuracy (up to r2 = 0.77) against standard genomic best linear unbiased prediction (GBLUP) and traditional Bayesian CGM-WGP models. Its architecture drastically improved computational scalability by enabling standard backpropagation, effectively bypassing the stochastic sampling limitations of approximate Bayesian methods. Most importantly for climate adaptation, DeepBioGS allowed accurate forecasting of genotype performance in entirely unobserved environmental conditions. By merging the representational power of deep learning with the structural constraints of biophysics, DeepBioGS provides a highly scalable, interpretable tool to navigate GxE interactions, enabling the assessment of cultivars under future climate scenarios, thus optimising crop breeding for a changing global environment.

plant biology↗

bioRxiv2026

Gating effects of a Cav2.3 calcium channel mutation linked to developmental and epileptic encephalopathy

Developmental and epileptic encephalopathies (DEEs) are a group of neurological disorders primarily affecting young children and are characterized by severe seizures. DEEs are challenging to manage, with some patients experiencing severe side effects or not responding to frontline therapies. This is partly because of the many underlying mechanisms involved in DEE pathology and the relatively limited mechanism-specific action of current treatments. The CACNA1E gene, which encodes the voltage-gated calcium channel Cav2.3 (R-type), has recently been associated with DEEs. More than fifteen different mutations in CACNA1E have been identified in patients with DEEs; however, the mechanisms by which these mutations affect channel function and, thus, their relationship to DEEs, remain largely unknown. Previous research has begun to characterize the functional effects of R-type channel mutations on channel biophysics, but only a handful of mutations have been studied functionally to date. Here, we transiently expressed Cav2.3 channels and used whole-cell patch-clamp to examine the biophysics of one specific disease-associated R-type channel mutant in which leucine 228 is substituted with a proline (L228P). Compared to wild-type, the L228P mutant did not alter peak current density, inactivation kinetics, or recovery from inactivation, but showed a significant shift towards hyperpolarized voltages in both voltage-dependent activation and steady-state inactivation. This resulted in a broader window current shifted towards more hyperpolarized potentials, which predicts increased channel availability and activity at subthreshold voltages relative to wild-type channels. Our results contribute to the ongoing characterization of R-type mutants, with the long-term goal of informing mechanism-specific therapies for DEEs.

neuroscience↗

bioRxiv2026

Multimodal physical evidence uncovers interpretable gene regulatory networks for perturbation prediction

Gene regulatory networks govern cell fate transitions through dynamic causal mechanisms1. Since exhaustively mapping this vast perturbation space experimentally is prohibitive2, scalable computational models are essential. Yet, current frameworks fall short because they infer statistical co-expression rather than physical mechanisms, remain blind to non-canonical regulators lacking classical DNA-binding motifs, and fail to generalize across unseen perturbation factors or cell lines. Here we show that a multimodal biophysical framework, VitaGRN, overcomes these barriers by constructing a biophysical regulatory scaffold from multimodal evidence and propagating interactions to capture non-canonical regulators. By leveraging structurally aligned protein embeddings, VitaGRN predicts zero-shot perturbation responses and uncovers non-canonical translational control programs. Notably, VitaGRN demonstrates robust generalization across unseen factors, cell lines, and developmental transitions. Ultimately, VitaGRN generates a confidence-calibrated virtual perturbation atlas spanning over a thousand factors. This resource reframes gene regulatory networks from static correlation graphs into dynamically generalizable and mechanistically trans-parent models, streamlining wet-lab candidate prioritization.

bioinformatics↗

bioRxiv2026

Histologically Informed Multiscale Modeling of the Neuronal Elements Activated by TMS

BackgroundThe primary neural site(s) at which action potentials are initiated by transcranial magnetic stimulation (TMS) remain poorly understood. Multiscale computational models provide biophysically based hypotheses, but model accuracy is constrained by limited histological knowledge of the microscopic organization of neural tissue. Recent high-resolution electron microscopy, in particular the petavoxel H01 dataset, provides novel, detailed axon morphologies and myelination patterns within the human cortex and superficial white matter. ObjectiveTo compare systematically multiple candidates for the neural elements activated by TMS using computer simulations informed by an extensive body of histological measurements, including neuron models directly reconstructed from the H01 dataset. MethodsWe developed a novel modeling pipeline to extract individual morphologically realistic multi-compartment models with exact myelination from serial section electron microscopic segmentations. To assess candidate excitation sites, we simulated the extracted neuron models together with parameterized models of a "ball-and-two-sticks", bifurcation, termination, and bend under uniform electric fields. In addition, smooth and sharply bending myelinated axons were embedded in a realistic human head model to evaluate activation thresholds under anatomically realistic electric field distributions. ResultsAxon terminations were only excitable by TMS when they were fully myelinated, which the histology suggested is unlikely. Partial myelination, even when separated by only 10 {micro}m from the terminal, increased activation thresholds by more than 100%. Reconstructed H01 neurons exhibited correspondingly high activation thresholds at axon terminals due to a lack of myelination. Further, most other candidate structures exhibited low thresholds only for histologically unrealistic parameter choices. In contrast, myelinated axonal bends of fibers transitioning from cortex to superficial white matter consistently showed low activation thresholds for both uniform electric fields and in realistic head model simulations. These thresholds fell within physiologically realistic ranges and, for larger diameter fibers, approached experimentally measured motor thresholds. ConclusionThese results identify myelinated axons bending from cortex into superficial white matter as possible neural targets for transcranial magnetic stimulation, and demonstrate the relevance of detailed histological and biophysical information to support robust modeling results.

neuroscience↗

bioRxiv2026

Neuronal Activity-Dependent Electroosmosis and Its Potential Role in Interstitial Fluid Flow in the Glymphatic System

The mechanisms driving interstitial fluid flow through brain parenchyma remain unresolved, limiting our understanding of how fluid transport contributes to glymphatic waste clearance and broader aspects of brain metabolism and neuronal activity. Existing theories based on diffusion or pressure gradients fail to explain sustained, directional flow through the tortuous extracellular space (ECS), particularly under normal physiologic conditions. Here we propose that electroosmosis, fluid motion driven by endogenous electric fields acting on charged brain tissue, provides a biophysically consistent mechanism for intraparenchymal interstitial fluid transport while generating pressure distributions favorable for periarterial influx and perivenous efflux. Using computational modeling informed by anatomical reconstructions of ECS microstructure and local field potential (LFP) recordings, we show that electroosmotic flow generates physiologically realistic velocities and reproduces brain state dependent differences in glymphatic transport, including the enhanced glymphatic flow observed during sleep compared to wakefulness. A physics-informed reduced-order model (ROM) further demonstrates that these microstructure-resolved results upscale consistently to tissue-level transport. Moreover, electroosmotic flow can induce directional pressure gradients across perivascular interfaces, facilitating both influx and efflux. This mechanism provides a unifying framework linking neuronal activity, parenchymal flow, and compartmental pressure regulation. In contrast, pressure gradients substantially larger than physiological estimates generated much smaller velocities and failed to account for the observed transport rates. These findings address a major gap in glymphatic physiology and suggest that modulation of electric field properties, via endogenous activity or external neuromodulation, could serve as a therapeutic strategy to enhance solute clearance in neurological disorders. Significance StatementA major challenge in brain physiology is the lack of a unifying physical model that explains how fluid moves through the narrow and tortuous extracellular space; a process essential for nutrient distribution and waste clearance. Existing frameworks cannot account for sustained, directional transport under normal physiological conditions. We show that electroosmosis (fluid motion generated when endogenous neuronal electric fields act on charged cellular surfaces) provides a biophysically consistent mechanism for this transport. Using realistic extracellular microstructures and a validated tissue scale reduced order model, our model reproduces experimentally observed brain state dependent transport. Establishing electroosmosis as a possible contributor to interstitial fluid movement offers a new conceptual basis for linking neuronal activity to fluid circulation and for guiding strategies to enhance brain clearance.

bioengineering↗

bioRxiv2026

Predicting optimal growth temperatures of bacteria using learned structural information from a single protein

Temperature is a fundamental determinant of bacterial physiology and ecology. Optimal growth temperature (OGT) is highly variable across species, contributing to differences in where and when species are most likely to thrive. Although the OGTs for most bacteria remain unknown, the increasing availability of genomes from uncultivated and cultivated taxa has made it advantageous to build genomic, cultivation-independent models to infer OGT. However, pre-existing genomic models often lack the generalizability and mechanistic grounding required for robust inferences of OGT. We propose a novel framework for predicting bacterial OGT which uses learned protein structural signatures of thermal adaptation. We hypothesize that biophysical tradeoffs which dictate enzymatic functions across variable temperatures provide a more robust empirical basis for OGT prediction than broad genomic features. Our OGT-predicting model, ROSEATE, is based on a single gene, adenylate kinase (ADK), that encodes for a ubiquitous enzyme essential for energy homeostasis. ROSEATE uses high-dimensional latent space encoding via MSA Transformer, a protein language model which embeds ADKs in a manner which preserves biophysical information about embedded proteins. We show that the accuracy of the ROSEATE model is on par with other genome-based models, has a high degree of phylogenetic generalizability, and the ESM embeddings effectively capture key temperature-adaptive enzyme characteristics derived from AlphaFold structures. Because ROSEATE is based on analyses of a single ubiquitous protein, it can be used with metagenomic data to infer the community-level variation in bacterial OGTs. We demonstrate this feature of ROSEATE by reconstructing ADK sequences from over 500 environmental and host-associated metagenomes, successfully distinguishing community-wide thermal preferences across diverse habitats, from polar oceans to mammalian guts. By transitioning from genomic proxies to informationally dense protein structural features, this work provides an efficient, interpretable tool for predicting bacterial OGTs across taxa and whole communities. Author SummaryThe temperature preferences of bacteria are key to determining where species are most likely to grow and how bacterial communities may respond to changes in temperature regimes. Unfortunately, the optimal growth temperatures of most bacteria, including a broad diversity of bacteria found in many host-associated and environmental systems, currently remain unknown as many bacterial species cannot be grown or studied in a laboratory. While we now have genomic data for many bacteria, using these data to infer optimal temperatures for bacterial growth has remained a persistent challenge. We developed and validated a novel approach to predict bacterial temperature preferences. When heated, proteins often unfold, becoming nonfunctional. To adapt to warmer environments, organisms evolve more stable proteins which resist denaturing at high temperatures. Instead of analyzing a bacterias entire genome, our approach uses a protein language model to quantify stability-enhancing changes in a single protein found across all bacteria. We found that this single-protein approach can be used to effectively predict the optimal growth temperatures of individual bacterial species and even whole bacterial communities. By changing how we use genomic information to predict temperature preferences, our framework provides a scalable blueprint for predicting other important bacterial traits from protein structure information.

bioinformatics↗

bioRxiv2026

High Throughput Characterization of Eukaryotic 2A-Like Peptides Identifies Novel Leucine-Associated Reduction in Protein Abundance

Virally-derived ribosomal skipping 2A peptides are a popular tool for protein co-expression. Despite their use in over 9,000 publications, the biochemical and biophysical properties underlying the skipping mechanism remain largely unexplored. We identified 4,218 2A-like peptides originating from non-viral organisms. We developed and utilized the Trifluorescent Reporter fluorescent tool for high-throughput multiplexable analysis of ribosomal skipping, and tested 3,271 2A-like peptide sequences. We identified peptides that skipped, failed to skip, and skipped but failed to restart translation, in addition to peptides that induced a reduction in protein abundance. Peptides that skipped and induced reductions in protein abundance largely originated from eukaryotes. A poly-leucine stretch in an alpha-helix N-terminal to the conserved GDxExNPGP motif drove both skipping and the reduction in protein abundance. Analysis of the native eukaryotic protein contexts revealed that reduction may be harnessed as an expression regulator. The high-throughput approach used in this work greatly expands the functional knowledge of what biophysical and biochemical characteristics lead to ribosomal skipping, including an apparent latent eukaryotic leucine stall-helix motif. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/732966v2_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@d6dc26org.highwire.dtl.DTLVardef@f7475org.highwire.dtl.DTLVardef@a6cb1aorg.highwire.dtl.DTLVardef@603f46_HPS_FORMAT_FIGEXP M_FIG C_FIG

Systems Biology↗

bioRxiv2026

System Identification and Control for Optogenetics in Mammalian Nucleocytoplasmic Transport

Optogenetics enables experiments in out-of-equilibrium conditions to clarify biological mechanisms and quantify biophysical parameters. However, modelling and control techniques to study mammalian cell biology under optogenetic perturbation remain underutilised. Here, we benchmark these methods within mammalian cells by steering nucleocytoplasmic transport via the optogenetic LEXY protein in outcome-driven microscopy. First, we employ system identification to obtain models that predict transport dynamics by minimising the prediction error. We quantify this prediction accuracy for one biophysical model and two black-box models. Second, we evaluate closed-loop control efficacy by steering transport along a predefined trajectory using model-free Proportional Integral (PI) control, model-based Linear Quadratic Regulation (LQR) and Model Predictive Control (MPC). Both the predictive models and the applied control techniques demonstrate robust performance against cell-to-cell variation. This biological variation is quantified by the parameter distributions obtained from model identification with single-cell trajectories. While we show that model-free techniques such as PI and gain-scheduled PI achieve steering without explict model knowledge, predictive architectures offer better performance under this cell-to-cell variation and time-varying setpoints. Moreover, black-box predictive accuracy suggests that this model-based control is possible, even when explicit mechanistic understanding is missing. Ultimately, we demonstrate that predictive modelling and optogenetics enable quantitative characterisation and precise manipulation of mammalian cells, while offering practical guidelines for the implementation of these techniques. SIGNIFICANCEOptogenetics provides the unique ability to steer cellular processes with light. Variable and localised light inputs allow us to extract more data and guide living cells to specific states. Despite this potential, application of predictive modelling and control techniques with optogenetics remain underutilised in mammalian cell biology. Here, we benchmark these techniques within mammalian cells to steer nucleocytoplasmic transport. Benchmarking these techniques demonstrates their direct applicability to mammalian systems, enabling the extraction of new biological insight in outcome-driven microscopy.

bioengineering↗

bioRxiv2026

Negative Autoregulation Promotes the Evolution of Strong Environmental Switching

Living systems rely on gene regulatory circuits to respond to environmental change. Such circuits often act as molecular switches: OFF in the absence of a cue, and ON in its presence. We do not know how a circuits regulatory architecture affect its ability to evolve such responsiveness. In nature, a very frequent and simple regulatory architecture involves a transcriptional regulator that negatively regulates its own expression. To study how such autoregulation affects adaptive evolution, we engineered E. coli circuits in which a target gene is regulated by the repressor TetR with (A-) or without (A0) negative autoregulation of TetR. We evolved TetR in both architectures toward responsiveness to a novel environment, embodied by a novel inducer of TetR. Early during their evolution, TetR circuits with negative autoregulation evolved stronger environmental switching. A combination of high throughput DNA sequencing, protein engineering, and biophysical modeling showed why. Only A-circuits favored TetR alleles that interact strongly with both the inducer and DNA. Such alleles combine strong repression caused by strong DNA binding with strong derepression caused by strong inducer binding, the defining property of a strong environmental response. Our biophysical model shows that negative autoregulation helps to create this regulatory regime. As a result, only A- circuits favor alleles that create strong molecular switches. Altogether, our work shows that even the simplest form of gene regulation can change the topography of a fitness landscape, and enable new modes of evolutionary change.

evolutionary biology↗

bioRxiv2026

Calcium triggers Cryptococcus neoformans aggregation by forming coordination bonds with capsular glucuronoxylomannan

The cryptococcal polysaccharide capsule is a unique eukaryotic virulence factor that is a target for the immune system and the development of therapeutic antibodies. Our understanding of capsular architecture is limited to a few studies suggesting that metal dications play a role. In this work we explore a mechanism of cryptococcal aggregation that depends on calcium phosphate precipitation. We describe the chemical and biophysical properties of calcium interaction with the predominant cryptococcal polysaccharide, glucuronoxylomannan (GXM). We show that cell aggregation is a pH-dependent and occurs in a calcium dose-dependent manner. Furthermore, this cellular aggregation phenomenon as well as interpolymer capsular polysaccharide interactions are unique to calcium dications and do not occur with other mono- or dications as shown by size exclusion chromatography and circular dichroism. Diffusion ordered spectroscopy nuclear magnetic resonance and ab-initio calculations support complexation of calcium with glucuronic acid (GlcA). The ab-initio calculations also suggest that calcium ions can complex up to four GlcA monomers. Not only does calcium act as a scaffold for the cryptococcal capsule, interacting with up to four glucuronic acid residues of GXM, but calcium phosphate treatment of cells reduces the anti-phagocytic properties of the capsule, promoting ingestion by macrophages and altering antibody interactions with the capsule. This work advances our understanding of the cryptococcal capsule, its biophysical properties, by providing a model for the critical role of calcium interactions with capsular polymers of Cryptococcus neoformans including important impacts at the host-cell interface.

biochemistry↗

bioRxiv2026

Genomic regulation of chemo-mechanical stability in plant-derived extracellular vesicles: a multiscale model of composite reinforcement

Plant-derived extracellular vesicles (PDEVs) have emerged as superior candidates for oral drug delivery, exhibiting a gastrointestinal survivability that significantly exceeds that of mammalian exosomes or synthetic liposomes. However, the biophysical rules governing how plant genomic regulation translates into this exceptional mechanical resilience remain unknown. Here, we present a predictive multiscale model of plant-derived extracellular vesicles, linking a parameterised genetic state space to emergent mesoscale mechanics via supra-molecular coarse-grained molecular dynamics (SCG-MD). We demonstrate that the upregulation of sterol methyltransferases (SMT) during the plants theoretical Defence state drives the formation of a phase-separated composite architecture, where rigid domains occupying approximately 36% of the membrane surface area effectively arrest crack propagation. This state achieves a critical rupture tension of 367.0 {+/-} 0.7 mN m-1 corresponding to a 39% increase over the wild-type Ripening state. Crucially, we find that chemical composition alone is insufficient for this reinforcement; vesicles with actively sorted lipid domains (Seeded topology) outperform randomised mixtures (Spontaneous topology) by 23% at identical concentrations. Furthermore, while fluid vesicles stiffen reactively under gastric acid shock (pH 2.5) due to the steric jamming of thermodynamically neutralised headgroups, the Defence state exhibits mechanical homeostasis. These findings suggest that PDEVs function as genetically tunable composite materials, offering a design blueprint for next-generation bio-inspired drug delivery vectors. Ultimately, these theoretical indices provide a predictive biophysical framework awaiting empirical confirmation via in vitro nanomechanical assays.

plant biology↗

bioRxiv2026

The nucleolus is a mechanosensitive condensate that adapts ribosome biogenesis to mechanical forces

Intracellular compartmentalization is fundamental to cellular organization, yet mechanobiology has been largely understood through membrane-delimited structures and associated signaling pathways. Whether mechanical forces directly regulate biomolecular condensates, which organize many core cellular functions, remains largely unknown. This question is particularly relevant for the nucleolus, a prominent nuclear condensate that coordinates ribosome biogenesis and is known to remodel in response to diverse biochemical perturbations, placing it at the interface between cellular state and biosynthetic control. Here, we show that mechanical compression remodels nucleolar organization and reduces (ribosomal DNA) rDNA transcription, and identify nucleolin as a key mediator of this adaptive response. Compression induces rapid and reversible redistribution of nucleolin from the nucleolus to the nucleoplasm, accompanied by reduced occupancy at rDNA promoter regions and changes in rDNA transcription and precursor rRNA processing. The nucleolar response occurs independently of classical post-translational regulation of nucleolin and instead depends on the rate of nuclear deformation, with nucleolar organization and function scaling with nuclear volume loss, supporting a mechanism of biophysical regulation. Together, our findings establish the nucleolus as a mechanosensitive condensate and reveal dual regulation of ribosome biogenesis by mechanical compression, through rapid nucleolin-based biophysical adaptation followed by slower epigenetic remodeling.

cell biology↗

bioRxiv2026

Curiosity shapes brain-like architectures and functions

How does complex cognition emerge from simpler underlying processes? We show that two components are sufficient: infant-like curiosity and brain-like biophysical constraints jointly drive the emergence of complex neuronal architectures and cognitive abilities. We first tested curiosity-driven exploration in 275 8-to 15-month-old infants. We then implemented these mechanisms in artificial recurrent neural networks, letting them actively sample 20 cognitive tasks during training, while forcing the networks to operate under brain-like biophysical constraints. These two components were sufficient for a range of biological and cognitive phenomena to emerge: The resulting networks captured human synaptic development, the adult brains architecture, and displayed compositional generalisation, a hallmark of human cognition. Curiosity, long viewed as a downstream consequence of complex brains, is also a driver of their complexity.

neuroscience↗

bioRxiv2026

Scop3P in 2026: an expanded proteomics-informed resource contextualizing phosphorylation sites through sequence, structure, mutation, and experimental provenance

Protein phosphorylation is a central regulatory mechanism controlling protein activity, interactions, and cellular signalling, and its dysregulation is implicated in numerous diseases. Advances in mass spectrometry-based phosphoproteomics have led to a rapid expansion in the number of reported phosphorylation sites; however, interpretation of these data remains challenging due to fragmented evidence, limited structural context, and the lack of uniform experimental provenance across resources. Interpretation is further complicated by the fact that the biological meaning of reported phosphosites can vary substantially across tissues, cell lines, perturbations, and disease settings. Here, we present a major update of Scop3P, a proteomics-informed knowledgebase that contextualizes human phosphorylation sites within integrated sequence, structural, biophysical, evolutionary, and mutational frameworks. The current release incorporates uniformly reprocessed human phosphoproteomics data from 116 PRIDE datasets alongside curated UniProt annotations, retaining peptide-spectrum matches, site localization confidence, and direct links to primary mass spectrometry evidence via Universal Spectrum Identifiers. This integration yields 152,350 unique serine, threonine, and tyrosine phosphorylation sites across 16,533 human proteins, supported by full experimental provenance. Beyond site identification, Scop3P provides residue-level contextual annotations derived from experimentally determined protein structures and proteome-wide AlphaFold models, enabling near-complete structural coverage of phosphorylation sites. Structural context is further complemented by residue-level biophysical, evolutionary, and mutational annotations, supporting integrated assessment of phosphorylation in functional and disease-related settings. The current release also introduces residue interaction network representations derived from AlphaFold-predicted structures, capturing spatial connectivity and local interaction environments of phosphorylation and mutation sites. A redesigned web interface enables interactive exploration through coordinated 1D, 2D, 2.5D, and 3D visualizations, peptide-level coverage views, and direct access to original spectra via PRIDE. By bridging experimental phosphoproteomics with structural, functional, and disease-related context, Scop3P provides a scalable and provenance-aware resource for phosphosite interpretation, hypothesis generation, and data-driven modelling of phosphorylation-dependent regulation.

bioinformatics↗