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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 721 records · Page 40Linked to original sources

bioRxiv2019

Quantitative three-dimensional nondestructive imaging of whole anaerobic ammonium-oxidizing bacteria

Anaerobic ammonium-oxidizing (anammox) bacteria play a key role in the global nitrogen cycle and the treatment of nitrogenous wastewater. These functions are closely related to the unique biophysical structure of anammox bacteria. However, the research on the biophysical ultrastructure of intact anammox bacteria is lacking. In this study, in-situ three-dimensional nondestructive ultrastructure imaging of whole anammox cell was performed using synchrotron soft X-ray nano-computed tomography and the total variation-based simultaneous algebraic reconstruction technique (TV-SART). Statistical and quantitative analyses of the ultrastructures of intact anammox bacteria were performed. The linear absorption coefficient values of the ultrastructures of anammox bacteria were calculated and the asymmetric structure of the anammox bacteria was quantified. On this basis, the shape adaptation of the anammox bacteria responses to Fe2+ were explored, and the underlying regulation mechanism of Fe2+ on anammox bacteria was explored. Furthermore, a promising method to study the biophysical properties of cells in different environments and engineering processes was proposed.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC=\"FIGDIR/small/709188v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (23K):\norg.highwire.dtl.DTLVardef@1994e5eorg.highwire.dtl.DTLVardef@1e80137org.highwire.dtl.DTLVardef@a7f418org.highwire.dtl.DTLVardef@8a77e5_HPS_FORMAT_FIGEXP M_FIG C_FIG Statement of SignificanceAnaerobic ammonium-oxidizing (anammox) bacteria play key role in global nitrogen cycle, and this physiological function depends on the unique morphology of anammox bacteria. In this study, synchrotron soft-X ray imaging technique coupled with simultaneous algebraic reconstruction technique with total variation (SART-TV) algorithm were performed to quantify the three-dimensional ultrastructure of the whole anammox bacteria for the first time. On this basis, the shape adaptation and mechanism of the anammox bacteria responses to Fe2+ were explored and a promising method for detecting the physiological properties of anammox bacteria was proposed.

cell biology↗

bioRxiv2019

Investigating the biochemical properties of Streptococcal polysaccharide capsules using atomic force microscopy

In common with many bacterial pathogens, Streptococcus pneumoniae has a polysaccharide capsule, which facilitates immune evasion and is a key virulence determinant. However, recent data has shown that the closely related Streptococcus mitis can also express polysaccharide capsules including those with an identical chemical structure to S. pneumoniae capsular serotypes. We have used atomic force microscopy (AFM) techniques to investigate the biophysical properties of S. mitis and S. pneumoniae strains expressing the same capsular serotypes that might relate to their differences in virulence potential. When comparing S. mitis and S. pneumoniae strains with identical capsule serotypes S. mitis strains were more susceptible to neutrophil killing and imaging using electron microscopy and AFM demonstrated significant morphological differences. Force-volume mapping using AFM showed distinct force-curve profiles for the centre and edge areas of encapsulated S. pneumoniae and S. mitis strains. This \"edge effect\" was not observed in the unencapsulated streptococcal strains and in an unencapsulated Staphylococcus aureus strain, and therefore was a direct representation of the mechanical properties of the bacterial capsule. When two strains of S. mitis and S. pneumoniae expressed an identical capsular serotype, they presented also similar biomechanical characteristics. This would infer a potential relationship between capsule biochemistry and nanomechanics, independent of the bacterial strains. Overall, AFM was an effective tool to explore the biophysical properties of bacterial capsules of living bacteria by reproducibly quantifying the elastic and adhesive properties of bacterial cell surfaces. Using AFM to investigate capsule differences over a wider range of strains and capsular serotypes of streptococci and correlate the data with phenotypic differences will elucidate how the biophysical properties of the capsule can influence its biological role during infection.

microbiology↗

bioRxiv2019

NeuroGPU, software for NEURON modeling in GPU-based hardware

The membrane potential of individual neurons depends on a large number of interacting biophysical processes operating on spatial-temporal scales spanning several orders of magnitude. The multi-scale nature of these processes dictates that accurate prediction of membrane potentials in specific neurons requires utilization of detailed simulations. Unfortunately, constraining parameters within biologically detailed neuron models can be difficult, leading to poor model fits. This obstacle can be overcome partially by numerical optimization or detailed exploration of parameter space. However, these processes, which currently rely on central processing unit (CPU) computation, often incur exponential increases in computing time for marginal improvements in model behavior. As a result, model quality is often compromised to accommodate compute resources. Here, we present a simulation environment, NeuroGPU, that takes advantage of the inherent parallelized structure of graphics processing unit (GPU) to accelerate neuronal simulation. NeuroGPU can simulate most of biologically detailed models 800x faster than traditional simulators when using multiple GPU cores, and even 10-200 times faster when implemented on relatively inexpensive GPU systems. We demonstrate the power of NeuoGPU through large-scale parameter exploration to reveal the response landscape of a neuron. Finally, we accelerate numerical optimization of biophysically detailed neuron models to achieve highly accurate fitting of models to simulation and experimental data. Thus, NeuroGPU enables the rapid simulation of multi-compartment, biophysically detailed neuron models on commonly used computing systems accessible by many scientists.

neuroscience↗

bioRxiv2019

A minimal synaptic model for direction selective neurons in Drosophila

Visual motion estimation is a canonical neural computation. In Drosophila, recent advances have identified anatomical and functional circuitry underlying direction-selective computations. Models with varying levels of abstraction have been proposed to explain specific experimental results, but have rarely been compared across experiments. Here we construct a minimal, biophysically inspired synaptic model for Drosophilas first-order direction-selective T4 cells using the wealth of available anatomical and physiological data. We show how this model relates mathematically to classical models of motion detection, including the Hassenstein-Reichardt correlator model. We used numerical simulation to test how well this synaptic model could reproduce measurements of T4 cells across many datasets and stimulus modalities. These comparisons include responses to sinusoid gratings, to apparent motion stimuli, to stochastic stimuli, and to natural scenes. Without fine-tuning this model, it sufficed to reproduce many, but not all, response properties of T4 cells. Since this model is flexible and based on straightforward biophysical properties, it provides an extensible framework for developing a mechanistic understanding of T4 neural response properties. Moreover, it can be used to assess the sufficiency of simple biophysical mechanisms to describe features of the direction-selective computation and identify where our understanding must be improved.

neuroscience↗

bioRxiv2025

Stiffness and Viscoelasticity of Adipose Tissue Decellularized Extracellular Matrix Hydrogels Influence Proliferation, Growth Pattern, Migration and Invasion of Breast Cancer Cells

Breast cancer remains the leading cause of cancer-related mortality among women. Cellular behavior is influenced by the physicochemical factors of the extracellular matrix (ECM) surrounding them. In order to model the breast ECM, adipose tissue decellularized extracellular matrix (dECM)-derived hydrogels are generated, which retain the full biochemical complexity of the source tissue, while also exhibiting stiffness and viscoelastic properties comparable to those of breast tissue. By recapitulating the characteristics of their native environment, validated through proteomic analysis and rheology, the poorly metastatic MCF-7 and the highly invasive MDA-MB-231 breast cancer cell lines exhibit their archetypal behaviors within the 3D hydrogels. Moreover, these cells display significantly distinct proliferation, invasion, and growth patterns in hydrogels with different stiffness and viscoelasticity, highlighting the importance of biophysical parameters in modulating cell phenotype. These results illustrate that user-friendly 3D biomaterial models based on adipose tissue dECM can effectively replicate crucial aspects of in vivo cellular behavior. Table of ContentsThis study presents 3D hydrogels derived from porcine adipose tissue dECM that mimic biophysical and biochemical properties of breast tissue. Two breast cancer cell lines mimicking luminal and triple-negative subtypes, exhibited distinct proliferation, migration, and invasion behaviors depending on hydrogel stiffness and viscoelasticity. The results highlight how biophysical cues shape cell phenotype in a user-friendly 3D model. O_FIG O_LINKSMALLFIG WIDTH=179 HEIGHT=200 SRC="FIGDIR/small/693682v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@d0216eorg.highwire.dtl.DTLVardef@42c45borg.highwire.dtl.DTLVardef@1693385org.highwire.dtl.DTLVardef@1b598e1_HPS_FORMAT_FIGEXP M_FIG Created with Biorender.com C_FIG

bioengineering↗

bioRxiv2025

PocketX: Preference Alignment for Protein Pockets Design through Group Relative Policy Optimization

Designing protein pockets that target specific ligands is crucial for drug discovery and enzyme engineering. Although deep generative models show promise in proposing high-quality pockets, they are usually trained purely to match the data distribution and therefore overlook key biophysical properties, such as binding affinity, expression, and solubility, that ultimately determine developability and success. We introduce PocketX, an online reinforcement learning framework that explicitly aligns a generative model with desired biophysical properties. The framework first trains a base model that co-designs pocket structures and sequences conditioned on a target ligand, and then fine-tunes this model with Group Relative Policy Optimization (GRPO) to reward the desired attributes. Because GRPO employs group-relative rewards, it produces lower-variance policy updates, resulting in more stable and efficient learning than competing alignment strategies. Evaluated on the CrossDocked2020 benchmark, PocketX surpasses existing methods in metrics such as binding energy and evolutionary plausibility. Ablation studies further show that GRPO outperforms alternative alignment strategies, including Direct Preference Optimization (DPO), confirming GRPOs effectiveness for biophysical property alignment.

bioinformatics↗

bioRxiv2026

Metaplastic neuronal state transition regulates species-specific interoceptive processing in Drosophila

Interoceptive processing, which involves the sensing and integration of internal physiological states, is fundamental to maintaining homeostasis. However, how species-specific interoceptive features arise from the underlying biophysical properties of neural circuit physiology remains unclear. We investigate the biophysical basis of species-specific interoception by examining protein-hunger dopamine neurons (DA-WED) in two Drosophila species with divergent dietary ecologies. We find that DA-WED neurons in D. melanogaster exhibit weak persistence of internal states, enabling flexible behavioral transitions during nutrient stress. In contrast, D. sechellia shows strong state persistence, locking neurons into a "preferred" configuration during protein deprivation. This divergence is supported by distinct intrinsic membrane properties, including protein deprivation-induced rebound spikes unique to D. sechellia. Analysis of synaptic dynamics and cardiomyocyte electrophysiology reveals species-specific physiological regulations coordinating central and peripheral systems. Behavioral assays confirm corresponding differences in protein consumption strategies, directly linking neural state geometry to ecologically relevant feeding behavior. Our findings establish metaplastic regulation of neural state transitions as a fundamental mechanism through which ecological specialization shapes interoceptive processing and brain-body coordination. Significance StatementWe identify physiological regulations of neural state transitions as a core mechanism underlying species-specific interoceptive processing. Through comparative electrophysiology in D. melanogaster and D. sechellia, we demonstrate that ecological specialization manifests through distinct intrinsic membrane properties of DA-WED neurons, fundamentally altering neural state space geometry during protein deprivation. Species-specific synaptic plasticity gates these transitions while coupling cardiac rhythms to central computation. Our findings reveal how evolution transforms nutrient sensing into divergent neural dynamics, establishing a mechanistic framework for understanding how ecological pressures sculpt the biophysical architecture of interoceptive circuits to coordinate adaptive brain-body interactions.

neuroscience↗

bioRxiv2026

BioGraphX-RNA: A Universal Physicochemical Graph Encoding for Interpretable RNA Subcellular Localization Prediction

RNA subcellular localization is a critical determinant of cellular function. However, current computational approaches often operate as "black boxes," overlooking the complex interplay among sequence, structure, and physicochemical interactions that govern RNA localization. Building upon the BioGraphX framework originally developed for proteins, we introduce BioGraphX-RNA, a universal physico-chemical graph-encoding framework that provides a structure-informed encoding by translating primary nucleotide sequences into multi-scale interaction graphs using explicit biophysical rules. When combined with frozen RiNALMo embeddings via an interpretable gated fusion layer, BioGraphX-RNA achieves competitive performance with DeepLocRNA and uniquely quantifies the relative contribution of sequence versus structure for each RNA. On human datasets, the gated fusion model attains macro-AUROC values of 0.7575 {+/-} 0.0054 (mRNA), 0.9228 {+/-} 0.0137 (miRNA), and 0.5600 {+/-} 0.0191 (lncRNA). For miRNA, the graph-only model alone reaches 0.9396 {+/-} 0.0045, out-performing both the RiNALMo language model and a RNAfold partition-function graph (0.9139 {+/-} 0.0138), validating the structure-informed proxy hypothesis. In a blind cross-species prediction task on mouse data, the model shows limited zero-shot transfer, indicating that biophysical graph features do not improve cross-species generalization. Gating analysis reveals RNA-type-specific modality reliance, with miRNA exhibiting a near-equilibrium balance between sequence and structure. SHAP-based interpretation suggests potential correlates such as patterned GC content for nuclear retention and structural accessibility for exosome targeting. These advances are achieved with only 2.05 million trainable parameters, aligning with Green AI principles. BioGraphX-RNA demonstrates that explicitly integrating biophysical constraints into graph-based encodings enables accurate and interpretable predictions for structured RNAs, advancing structure-aware RNA biology and laying a foundation for precision medicine.

bioinformatics↗

bioRxiv2026

Adversarial Sequence Mutations in AlphaFold andESMFold Reveal Nonphysical StructuralInvariance, Confidence Failures, and Concerns forProtein Design

AlphaFold has transformed structural biology and spawned an ecosystem of derivative tools for protein design, binding prediction, and drug discovery. However, whether AlphaFold has learned generalizable biophysical principles versus template-based pattern matching remains unclear--a distinction critical for applications beyond its training context. Here, we perform a systematic adversarial evaluation of AlphaFold 3 using point and deletion mutations across 200 proteins. Remarkably, predicted structures remain invariant to mutations of up to 40% of residues--including deliberately destabilizing substitutions--and to deletions of 10%. Notably, this invariance holds even for experimentally validated fold-switching proteins that are known to adopt alternative conformations in response to such mutations, despite the fact that these proteins are small and monomeric--precisely the category where AlphaFold is expected to perform best. Confidence metrics prove unreliable, as they select the most accurate structure at most 35% of the time and correlate with the structural quality of the best available training set template. This suggests that AlphaFolds uncertainty estimates reflect template availability more than biophysical reasoning. ESMFold exhibits greater, though still imperfect, mutational sensitivity, suggesting superior sequence-structure coupling. These findings indicate that AlphaFold may rely heavily on memorized templates rather than biophysical reasoning, with profound implications for the reliability of AlphaFold-based protein design, drug discovery, and modeling workflows.

bioinformatics↗

bioRxiv2026

Simplified model of intrinsically bursting neurons

Rhythmic neural activity underlies essential biological functions such as locomotion, breathing, and feeding. Computational models are widely used to study how such rhythms emerge from interactions between neuron-level and circuit-level dynamics. Intrinsically bursting neurons are key components of many central pattern generators (CPGs), yet existing models span a tradeoff between biological realism and practical usability. Biophysical models involve many parameters that are difficult to tune, whereas abstract models often integrate poorly into neural circuit simulations. We propose a simplified model of intrinsically bursting neurons derived from a reduced non-spiking biophysical formulation. The model integrates readily into neural circuits while enabling direct and independent control of bursting characteristics, including duration, amplitude, and shape. We show that the model reproduces single-unit biophysical responses to diverse stimuli as well as circuit-level activity patterns from crustacean and mammalian CPGs. This model provides a practical tool for studying rhythm generation in neural circuits.

neuroscience↗

bioRxiv2026

The prevalence of protein misfolding as a mechanism for hereditary deafness

Hearing loss is the most common sensory deficit impacting [~]5% of the worlds population. The Deafness Variation Database (DVD) is a public resource of deafness variants, containing over 380,000 missense variants across 224 genes, with 303,577 classified as a variant of uncertain significance (VUS). To address the challenge of evaluating each deafness associated VUS, we evaluate a family of probabilistic frameworks to quantify the strength of computational evidence based on ACMG/AMP recommendations. First, CADD and REVEL are compared using Bayesian models parameterized using either a ClinVar 2019 dataset or labeled DVD variants. The REVEL model built using the DVD dataset demonstrates the best accuracy, sensitivity, and specificity. Incorporation of (in)tolerance to missense variation based on sorting each gene into three bins (tolerant, average, intolerant) shows that intolerant DVD genes are consistent with a higher prior probability of being pathogenic (25.7%) than average (10.7%) or tolerant (8.7%) genes. Finally, the impact of protein folding stability was incorporated using a 2D likelihood, which surpassed the simpler models while also offering a biophysical rationale for the disease mechanism. The protein folding-informed Bayesian model results in 28,866 prioritized VUSs reaching a posterior probability of pathogenicity above 98% with a false positive rate of only 0.14%. Overall, 54,752 missense variants are predicted to cause protein folding destabilization of greater than 1.0 kcal/mol, while 18,706 of the 28,886 prioritized VUS (65%) surpass this threshold. From these VUSs, we identify twelve probands where the patients genetic diagnosis is upgraded to likely pathogenic/pathogenic. We highlight two variants that cause clear structural disruption, demonstrating the impact of biophysical characterization on variant evaluation. Author SummaryWe investigate the impacts of single amino acid changes on protein structure and folding in the context of hearing loss. Hearing loss is the most common impairment of the main senses affecting nearly 5% of the worlds population. About 45% of people with hearing loss receive a diagnosis after targeted genetic testing. Here, we integrate biophysical data that quantifies the effect of a change to protein sequence on protein folding in combination with genetic data to improve our ability to identify protein amino acid changes that are likely to impact hearing. Our work leads to 12 patients receiving an upgraded diagnosis with their variant disrupting protein stability. Although the method is applied to hearing loss, it can be used for interpreting protein sequence changes in other disease contexts.

genetics↗

bioRxiv2026

Effects of protein interface mutations on protein quality and affinity

Accurately modeling antibody-antigen interactions requires distinguishing intrinsic binding affinity ("protein-interaction") from protein biophysical properties ("protein-quality"), including folding, stability, and expression. However, high-throughput mutational measurements commonly used to train and benchmark computational models often conflate these effects, obscuring the true determinants of molecular recognition. Here, we present an experimental and analytical framework to disentangle protein-interaction effects from protein-quality effects in single-domain antibody (VHH)-antigen binding. Using a large-scale deep mutational scanning (DMS) dataset spanning four VHH-antigen complexes, with single and double mutations in both partners, we introduce control binders to quantify protein-quality changes independently of protein-interaction. This enables decomposition of experimentally measured affinity into protein-interaction and protein-quality components at scale. Leveraging the disentangled dataset, we evaluated state-of-the-art structure- and sequence-based models for protein-quality and protein-interaction prediction and show that their performance largely reflects protein-quality rather than protein-interaction effects. Our results highlight a major confounder in current datasets and suggest that accounting for protein-quality will be essential for training next-generation affinity-prediction models. Nomenclature Antibody related termsO_LIPrimary VHH: The VHH of a VHH-antigen complex for which the paratope and the epitope weremutated. C_LIO_LIControl VHH: A second VHH that binds to the same antigen as the primary VHH but has non-overlapping epitope positions and therefore does not bind to any of the mutated antigen positions. C_LI Affinity-related termsO_LIReal Affinity: "The strength of the interaction between two [...] molecules that bind reversibly (interact)" 1. In the context of antibody-antigen binding, it quantifies interactions between active proteins (which are expressed and correctly folded 2 and are therefore functionally and biologically active (see below). It is commonly quantified by the equilibrium dissociation constant, KD. C_LIO_LIObserved affinity ({degrees}KD): The interaction strength experimentally measured between two molecules. Unlike real affinity, this value is confounded by the biophysical properties of the individual binding partners, specifically their folding, stability, and expression levels. Consequently, the observed affinity often differs from the real/intrinsic affinity if a significant fraction of the protein population is inactive 3. NOTE: Unless otherwise specified, {degrees}KD is reported in - log10 space. For example, a {degrees}KD of -9 corresponds to 10-9M or 1nM. C_LIO_LIChange in observed affinity ({Delta}{degrees}KD): The shift in the observed affinity between two proteins upon mutation, reported as the log10-transformed fold change. A value of 1 reflects a 10-fold difference, a value of 2 a 100-fold difference, etc. This aggregate change resolves into two distinct biophysical components 2, 4: O_LIProtein-interaction change: The change in the intrinsic thermodynamic affinity between the two binding partners, each in its active state (i.e., the specific change in interface Gibbs free energy because both enthalpy and entropy are considered). C_LIO_LIProtein-quality change: The change in the fraction of the mutated protein population that is biologically active - meaning it is expressed, correctly folded, and stable 2, 5. O_LIFolding: The process that guides the polypeptide chain toward its native conformation, which is a prerequisite for forming a functional binding site. C_LIO_LIStability: The thermodynamic capacity to maintain the folded structure over time and under physiological conditions. Stability (decrease in Gibbs free energy from the unfolded to the folded state) ensures the binding interface remains intact and prevents competing processes such as aggregation 6. C_LIO_LIExpression: The steady-state abundance of the protein. This is largely dependent on proper folding and stability, as cellular quality control mechanisms degrade proteins that fail to fold or remain stable at functional concentrations. C_LI C_LI C_LIO_LIChange in relative affinity ({Delta}{Delta}{degrees}KD): the difference between the {Delta}{degrees}KD of the primary VHH compared to the control VHH for a given epitope mutation. C_LI Model-related termsO_LIESM-IF1 sc: Single-chain (sc) structure-conditioned inverse folding model (ESM-IF1), using the isolated monomer structure of the mutated protein: either the VHH or the antigen 7. C_LIO_LIESM-IF1 mc: Multi-chain (mc) structure-conditioned model (ESM-IF1), using the full complex structure (both antibody and antigen) 7. C_LIO_LIStability prediction score: Score that represents the predicted change in stability based on a single mutation, normally represented as {Delta}{Delta}G. C_LI

molecular biology↗

bioRxiv2026

AI-guided design and ex vivo validation of nanobodies targeting aggregation motifs of intrinsically disordered protein tau

Intrinsically disordered proteins (IDPs) represent major yet challenging therapeutic targets in neurodegenerative disease due to their conformational heterogeneity and aggregation-prone behavior. Tau protein is a prototypical IDP that forms pathological aggregates in Alzheimers disease and related tauopathies. Despite extensive clinical efforts, tau-directed monoclonal antibodies have demonstrated limited efficacy. Concurrently, single-domain antibodies (nanobodies) have been gaining importance because of their small size and membrane penetrating capabilities. New design paradigms are therefore required for nanobodies to enable precise targeting of disease-relevant conformations. Here, we describe a biophysical modelling and AI-guided nanobody discovery targeting the VQIVYK motif of tau, which constitutes the structural core of neurofibrillary tangles in Alzheimers Disease. Biophysical modelling-based target analysis identified low-energy conformational states of VQIVYK. These conformational insights were used to guide AI-driven nanobody design of CDR3 loops. Starting from a nanobody scaffold, we generated 145 candidate nanobodies through systematic backbone sampling and neural network-guided sequence design, followed by multi-dimensional computational prioritization. Two candidates demonstrated robust binding to synthetic full tau protein in ELISA binding assays, achieving binding indices of 148.9% and 140%, relative to reference controls. Notably, one candidate also exhibited strong reactivity in post-mortem Alzheimers disease human brain tissue, with a binding index of 236.1%, exceeding that of the positive control (222.9%). Structural analysis indicates that our nanobodies engineered CDR3 engages VQIVYK through optimized aromatic and hydrophobic interactions. Together, these findings establish a proof-of-concept for biophysics-guided, AI-guided nanobody engineering against IDPs and identifies them as a promising lead for tau-targeted single domain antibody development.

neuroscience↗

bioRxiv2026

Patient CACNA1A variant uncouples calcium channel conductance from synaptic output

Channelopathies are a class of neurodevelopmental disorders with often devastating consequences, and effective therapies depend on understanding how patient variants alter channel function. These effects are typically assessed by biophysical characterization in heterologous expression systems. Mutations in CACNA1A, which encodes the P/Q-type calcium channel CaV2.1, underlie a spectrum of neurological disorders in which symptoms are generally classified as loss-of-function (LoF) or gain-of-function (GoF). However, some patients present with overlapping phenotypes that defy this binary framework. Here we describe a CACNA1A variant for which heterologous assays fail to capture a key in vivo functional effect. We characterize a de novo variant of a highly conserved residue, D1634N, identified in a patient with a mixed clinical presentation that includes both LoF- and GoF-associated symptoms. Biophysical characterization in HEK293T cells supports a classic and severe LoF effect, including reduced current density and a right-shifted current-voltage relationship. In contrast, in vivo analysis of the corresponding endogenous variant in the C. elegans homolog reveals a paradoxical increase in spontaneous synaptic vesicle release, despite reduced channel expression. Molecular dynamics simulations predict that the mutation prolongs dwell time in a partially open state, potentially increasing calcium influx at rest. This model is supported by biophysical recordings of the human channel showing increased current at hyperpolarized potentials and by rescue of the C. elegans phenotype through genetic elevation of resting membrane potential. Together, these findings reconcile the patients clinical presentation by describing a complex, mixed-function variant, highlight the importance of cellular context in variant interpretation and therapeutic development, and establish C. elegans as a powerful in vivo platform for evaluating the functional consequences of pathogenic ion channel variants.

neuroscience↗

bioRxiv2026

Cell wall remodeling is required for crowding homeostasis during cell growth in S. cerevisiae

The cytoplasm is a densely crowded environment that plays essential roles in intracellular functions. Its biophysical properties can be markedly altered in response to environmental cues and changes in cellular state. Recent studies have shown that excessive growth leads to cytoplasmic dilution, contributing to cellular dysfunction. However, the molecular mechanisms regulating biophysical properties during excessive growth remain poorly understood. Here, we demonstrate that cell wall remodeling is crucial to maintain cytoplasmic crowding homeostasis during cell growth. Live-cell imaging and phosphoproteomic analysis showed that Mpk1, a component of Cell Wall Integrity (CWI) pathway, accumulates in the nucleus and its activity progressively increases as cells enlarge. Mpk1 is required to restrict cell volume expansion and maintain cytoplasmic crowding by adjusting cell wall thickness and mechanical resistance. Moreover, Mpk1 is essential for the survival of enlarged cells upon cell cycle re-entry. In addition to Mpk1, we identified multiple key components of the CWI pathway that are required to maintain physiological state of enlarged cells. Together, these findings establish the CWI signaling pathway as a key regulator of cell size homeostasis and cytoplasmic biophysical properties, providing a mechanistic link between cell wall remodeling and intracellular properties.

Cell Biology↗

bioRxiv2026

Determinants of Blood Group Antigen Expression and Prediction of Phenotypes by Machine Learning

Key PointsO_LIStructural and evolutionary features distinguish null and antigenic blood group variants across multiple systems. C_LIO_LIMachine learning can predict variant effects, enabling improved interpretation of novel mutations in transfusion medicine. C_LI Blood group antigens, defined by epitopes on the erythrocyte surface, are central to transfusion safety and maternal-fetal compatibility. While the genetic basis of many clinically relevant blood group antigens is well established, which structural and biophysical parameters determine whether a single-nucleotide variant gives rise to an antigenic phenotype remains unclear. Here, we integrate structural, biophysical, and evolutionary analyses to systematically evaluate features associated with single amino acid substitutions across 24 human protein-based blood group systems. We analyse 319 variants with curated phenotypic annotations alongside 481 control variants, identifying key determinants of null and antigenic phenotypes. Null variants are characterized by high evolutionary conservation, burial within the protein core, loss of hydrophobicity, increased polarity, and a propensity for arginine substitutions. Antigenic variants are also enriched in arginine; however, in contrast to null variants, they tend to occur at less conserved, more solvent-accessible, and structurally flexible sites. Supervised machine learning models trained on structural and biophysical descriptors were applied to distinguish (i) null and (ii) antigenic variants from controls, achieving balanced accuracies of 0.82 and 0.63, respectively. Feature importance analysis identified predicted pathogenicity, solvent accessibility, and evolutionary conservation as the most predictive determinants of null variants, whereas hydrophobicity, conservation, and flexibility dominated antigen prediction. This work establishes a framework linking molecular variation to blood group phenotypes and provides a foundation for predicting the impact of novel missense mutations in transfusion medicine and beyond.

bioinformatics↗

bioRxiv2026

From Hodgkin-Huxley to Pretrained Neural Inference AI

High-density probes record from thousands of neurons simultaneously, yet resolving single-neuron identity remains an illposed inverse problem. While detailed simulations precisely characterize the biophysical forward process, their utility for interpreting brain signal remains unclear. Here we show that biophysical simulations of population neuronal electrical signals serve as an effective bridge between theory and experiment. By pre-training artificial neural networks exclusively on large-scale synthetic data, we demonstrate robust zero-shot generalization across diverse brain regions, experimental paradigms and species, enabling the accurate inference of single-unit activities and cell-type properties without exposure to real data. Further-more, uncovering a substantial population of functionally competent but weakly active neurons systematically obscured by conventional heuristics, our framework resolves a long-standing discrepancy regarding ocular dominance in mouse primary visual cortex. These findings establish biophysical simulations as a reference standard, bridging the gap between theoretical understanding and experimental observation through data-driven inference.

neuroscience↗

bioRxiv2026

Ensembles of in silico structures enable T cell peptide-MHC binding prediction

Adaptive immunity relies on T-cell receptor (TCR) recognition of peptides presented by the major histocompatibility complex (pMHC). Accurate prediction of TCR:pMHC binding pairs from sequence data remains a longstanding challenge in computational immunology, limiting the development of precision immunotherapies like cancer vaccines and adoptive cell therapies. Here, we present enFoldX (ensemble of Folded compleXes), a structure-based approach leveraging biophysical characterization of AlphaFold3-generated ensembles to classify TCR:pMHC sequence pairs as cognate versus non-cognate. Unlike previous methods reliant on only sequence data or a single, static predicted structure, enFoldX extracts features from an entire generated ensemble with a custom focus on the biophysical binding interface. Our model distinguishes T cell reactivity between peptides differing by a single amino acid substitution, the resolution required for cancer neoantigens, and generalizes to unseen peptides, MHCs, and TCRs, a major objective for artificial intelligence (AI) in immunology. Our performance on these crucial tasks demonstrates that diverse, structural sampling of biophysical interactions over an ensemble is fundamental for accurate AI-driven binding predictions and offers lessons for efficient future data generation to improve models. Our findings therefore offer a scalable framework to accelerate therapeutic binder design, and we provide access to a publicly available code repository.

immunology↗