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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Biophysical and physiological causes of coral reef microbialization

Coral reefs are declining globally as their primary producer communities shift from stony coral to fleshy macroalgae dominance. Previously, we have shown that the rise of fleshy macroalgae produces dissolved organic carbon (DOC) that lead to microbialization and coral death. Here we test the hypothesis that the biophysical cause of bacterial biomass accumulation is a relative decrease in electron acceptors relative to electron donors due to O2 loss from macroalgae. We show that 37 % of photosynthetic O2 produced by reef fleshy macroalgae is lost in the form of gas through ebullition from algae surfaces. O2 loss increases DOC:O2 ratios, decoupling the photosynthetically fixed carbon from oxidative potential for respiration. This biogeochemical environment drives heterotrophic microbes to increase cell-specific DOC consumption and cell sizes, accumulating biomass. In contrast, corals do not lose oxygen and support the growth of smaller and fewer bacteria. In situ biomass and metagenomic analyses of 87 reefs across the Pacific and Caribbean show that on algae-dominated reefs bacteria accumulate organic carbon through a Warburg-like increase in aerobic glycolytic metabolism. Because of its biophysical basis, microbialization is predicted to occur in other marine ecosystems shifting primary producer assemblages, such as planktonic communities in warming and acidifying conditions.

ecology↗

bioRxiv2018

A biophysical computational model for memory trace transfer from hippocampus to neocortex

The hippocampus plays important roles in memory formation and retrieval through sharp-wave-ripples. Recent studies have shown that certain neuron populations in the prefrontal cortex exhibit coordinated reactivations during awake ripple events. Also, the reactivation seems stronger during initial awake learning. These experimental findings suggest that the awake ripple is an important biomarker, through which the hippocampus interacts with the neocortex to assist the memory formation and retrieval. However, the computational mechanisms of this ripple based hippocampal-cortical coordination are still not clear. In this work, we build a biophysical model that includes both CA1 and layer V networks of the prefrontal cortex to investigate the possible mechanisms, by which the memory traces in the hippocampus can be transferred to prefrontal cortex. We first show that the local field potentials generated in the hippocampus and prefrontal cortex exhibit ripple range activities that are consistent with the recent experimental studies. Then, we find that the sequence information stored in the hippocampus can be successfully transferred to the prefrontal cortex recurrent networks through spike-timing dependent plasticity (STDP) and sequence replays. Further, we investigate the mechanisms of memory retrieval in the PFC network. Our findings suggest that the stored memory traces in the prefrontal cortex network can be retrieved through two different mechanisms, namely the cell-specific input and non-specific spontaneous background noise. Finally, we show that more SWRs and an optimal background noise level will both contribute to better sequence reactivations in the PFC network during memory retrieval. Our study presents a possible explanation for the memory trace transfer from the hippocampus to the neocortex through ripple coupling in awake states and reports two different mechanisms by which the stored memory traces can be successfully retrieved.\n\nAuthor SummaryThe hippocampal-cortical interactions have been found to be important for learning and memory. Recent experimental work reports interesting coordinations between the hippocampus and neocortex during ripple events in quiet awake states. Investigating this phenomena is critical for a deeper understanding of the mechanisms by which hippocampus contributes to the long-term memory formation in the neocortex. To this end, we build a biophysical computational model for both the hippocampal CA1 and prefrontal cortex networks. We demonstrate that under STDP rule and sequential inputs, the memory traces initially stored in the hippocampus can be transferred to the neocortex through the ripple events. The stored memory traces in the PFC network can be reactivated by two different mechanisms, namely the cell-specific inputs and non-specific background noises. Our study suggests that both the ripple event numbers and the level of background noises jointly determine the quality of memory retrieval during reactivation.

neuroscience↗

bioRxiv2019

Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons

Somatosensory thalamocortical (TC) neurons from the ventrobasal (VB) thalamus are central components in the flow of sensory information between the periphery and the cerebral cortex, and participate in the dynamic regulation of thalamocortical states including wakefulness and sleep. This property is reflected at the cellular level by the ability to generate action potentials in two distinct firing modes, called tonic firing and low-threshold bursting. Although the general properties of TC neurons are known, we still lack a detailed characterization of their morphological and electrical properties in the VB thalamus. The aim of this study was to build biophysically-detailed models of VB TC neurons explicitly constrained with experimental data from rats. We recorded the electrical activity of VB neurons (N = 49) and reconstructed morphologies in 3D (N = 50) by applying standardized protocols. After identifying distinct electrical types, we used a multi-objective optimization to fit single neuron electrical models (e-models), which yielded multiple solutions consistent with the experimental data. The models were tested for generalization using electrical stimuli and neuron morphologies not used during fitting. A local sensitivity analysis revealed that the e-models are robust to small parameter changes and that all the parameters were constrained by one or more features. The e-models, when tested in combination with different morphologies, showed that the electrical behavior is substantially preserved when changing dendritic structure and that the e-models were not overfit to a specific morphology. The models and their analysis show that automatic parameter search can be applied to capture complex firing behavior, such as co-existence of tonic firing and low-threshold bursting over a wide range of parameter sets and in combination with different neuron morphologies.\n\nAuthor summaryThalamocortical neurons are one of the main components of the thalamocortical system, which are implicated in key functions including sensory transmission and the transition between brain states. These functions are reflected at the cellular level by the ability to generate action potentials in two distinct modes, called burst and tonic firing. Biophysically-detailed computational modeling of these cells can provide a tool to understand the role of these neurons within thalamocortical circuitry. We started by collecting single cell experimental data by applying standardized experimental procedures in brain slices of the rat. Prior work has demonstrated that biological constraints can be integrated using multi-objective optimization to build biologically realistic models of neuron. Here, we employ similar techniques as those previously employed, but extend them to capture the multiple firing modes of thalamic neurons. We compared the model results with additional experimental data test their generalization and quantitatively reject those that deviated significantly from the experimental variability. These models can be readily integrated in a data-driven pipeline to reconstruct and simulate circuit activity in the thalamocortical system.

neuroscience↗

bioRxiv2019

Autonomous oscillations and phase-locking in a biophysically detailed model of the STN-GPe network

The aim of this study was to understand the relative role of autonomous oscillations and patterning by exogenous oscillatory inputs in the generation of pathological oscillatory activity within the subthalamic nucleus (STN) - external globus pallidus (GPe) network in Parkinsons disease. A biophysically detailed model that accounts for the integration of synaptic currents and their interaction with intrinsic membrane currents in dendritic structures within the STN and GPe was developed. The model was used to investigate the development of beta-band synchrony and bursting within the STN-GPe network by changing the balance of excitation and inhibition in both nuclei, and by adding exogenous oscillatory inputs with varying phase relationships through the hyperdirect cortico-subthalamic and indirect striato-pallidal pathways. The model showed an intrinsic susceptibility to beta-band oscillations that was manifest in weak autonomously generated oscillations within the STN-GPe network and in selective amplification of exogenous beta-band synaptic inputs near the networks endogenous oscillation frequency. The resonant oscillation frequency was determined by the net level of excitatory drive in the loop. Intrinsically generated oscillations were too weak to support a pacemaker role for the STN-GPe network, however, they were considerably amplified by sparse cortical beta inputs when their frequency range overlapped and were further amplified by striatal beta inputs that promoted anti-phase firing of the cortex and GPe, resulting in maximum transient inhibition of STN neurons. The model elucidates a mechanism of cortical patterning of the STN-GPe network through feedback inhibition whereby intrinsic susceptibility to beta-band oscillations can lead to phase locked spiking under parkinsonian conditions. These results point to resonance of endogenous oscillations with exogenous patterning of the STN-GPe network as a mechanism of pathological synchronization, and a role for the pallido-striatal feedback loop in amplifying beta oscillations.\n\nAuthor summaryExaggerated beta-frequency neuronal synchrony is observed throughout the basal ganglia in Parkinsons disease and is reduced with medication and during deep brain stimulation. The power of beta-band oscillations is increasingly used as a biomarker to guide antiparkinsonian therapies. Despite their importance as a clinical target, the mechanisms by which pathological beta-band oscillations are generated are not yet clearly understood. In vitro electrophysiological recordings support a theory of enhanced phase locking of the reciprocally connected subthalamo-pallidal network to beta-band cortical inputs but this has not yet been clearly demonstrated in a model. We present a new model of the subthalamo-pallidal network consisting of biophysically detailed cell models that captures the interaction between synaptic and intrinsic currents in dendritic structures. The model shows how phase locking of subthalamic and pallidal neurons and exaggerated bursting in subthalamic neurons can arise from the interaction of these currents when the balance of excitation and inhibition is changed and how phase locking is amplified under specific phase relationships between cortical and striatal beta inputs.

neuroscience↗

bioRxiv2019

Charge and hydrophobicity are key features in sequence trained machine learning models for predicting the biophysical properties of clinical-stage antibodies.

AO_SCPLOWBSTRACTC_SCPLOWImproved understanding of properties that mediate protein solubility and resistance to aggregation are important for developing biopharmaceuticals, and more generally in biotechnology and synthetic biology. Recent acquisition of large datasets for antibody biophysical properties enables the search for predictive models. In this report, machine learning methods are used to derive models for 12 biophysical properties. A physicochemical perspective is maintained in analysing the models, leading to the observation that models cluster largely according to charge (cross-interaction measurements) and hydrophobicity (self-interaction methods). These two properties also overlap in some cases, for example in a new interpretation of variation in hydrophobic interaction chromatography. Since the models are developed from differences of antibody variable loops, the next stage is to extend models to more diverse protein sets.\n\nAvailabilityThe web application for the sequence based algorithms are available on the protein-sol webserver, at https://protein-sol.manchester.ac.uk/abpred, with models and virtualisation software available at https://protein-sol.manchester.ac.uk/software.

bioinformatics↗

bioRxiv2019

A biophysical model of striatal microcircuits suggests theta-rhythmically interleaved gamma and beta oscillations mediate periodicity in motor control

Striatal oscillatory activity is associated with movement, reward, and decision-making, and observed in several interacting frequency bands. Local field potential recordings in rodent striatum show dopamine- and reward-dependent transitions between two states: a "spontaneous" state involving {beta} (~15-30 Hz) and low{gamma} (~40-60 Hz), and a state involving{theta} (~4-8 Hz) and high{gamma} (~60-100 Hz) in response to dopaminergic agonism and reward. The mechanisms underlying these rhythmic dynamics, their interactions, and their functional consequences are not well understood. In this paper, we propose a biophysical model of striatal microcircuits that comprehensively describes the generation and interaction of these rhythms, as well as their modulation by dopamine. Building on previous modeling and experimental work suggesting that striatal projection neurons (SPNs) are capable of generating {beta} oscillations, we show that networks of striatal fast-spiking interneurons (FSIs) are capable of generating{delta} /{theta} (ie, 2 to 6 Hz) and{gamma} rhythms. Under simulated low dopaminergic tone our model FSI network produces low{gamma} band oscillations, while under high dopaminergic tone the FSI network produces high{gamma} band activity nested within a{delta} /{theta} oscillation. SPN networks produce {beta} rhythms in both conditions, but under high dopaminergic tone, this {beta} oscillation is interrupted by{delta} /{theta}-periodic bursts of{gamma} -frequency FSI inhibition. Thus, in the high dopamine state, packets of FSI{gamma} and SPN {beta} alternate at a{delta} /{theta} timescale. In addition to a mechanistic explanation for previously observed rhythmic interactions and transitions, our model suggests a hypothesis as to how the relationship between dopamine and rhythmicity impacts motor function. We hypothesize that high dopamine-induced periodic FSI{gamma} -rhythmic inhibition enables switching between {beta}-rhythmic SPN cell assemblies representing the currently active motor program, and thus that dopamine facilitates movement in part by allowing for rapid, periodic shifts in motor program execution. Author summaryStriatal oscillatory activity is associated with movement, reward, and decision-making, and observed in several interacting frequency bands. The mechanisms underlying these rhythmic dynamics, their interactions, and their functional consequences are not well understood. In this paper, we propose a biophysical model of striatal microcircuits that comprehensively describes the generation and interaction of striatal rhythms, as well as their modulation by dopamine. Our model suggests a hypothesis as to how the relationship between dopamine and rhythmicity impacts the function of the motor system, enabling rapid, periodic shifts in motor program execution.

neuroscience↗

bioRxiv2019

Integrated biophysical characterization of fibrillar collagen-based hydrogels

This paper describes an experimental characterization scheme of the biophysical properties of reconstituted hydrogel matrices based on indentation testing, quantification of transport via microfluidics, and confocal reflectance microscopy analysis. While methods for characterizing hydrogels exist and are widely used, they often do not measure diffusive and convective transport concurrently, determine the relationship between microstructure and transport efficiency, and decouple matrix mechanics and transport properties. Our integrated approach enabled independent and quantitative measurements of the structural, mechanical, and transport properties of hydrogels in a single study. We used fibrillar type I collagen as the base matrix and investigated the effects of two different matrix modifications: 1) cross-linking with human recombinant tissue transglutaminase II (hrTGII) and 2) supplementation with the non-fibrillar matrix constituent hyaluronic acid (HA). hrTGII modified matrix structure and transport but not mechanical parameters. Furthermore, changes in matrix structure due to hrTGII were seen to be dependent on the concentration of collagen. In contrast, supplementation of HA at different collagen concentrations altered matrix microstructure and mechanical indentation behavior but not transport parameters. These experimental observations reveal the important relationship between ECM composition and biophysical properties. The integrated techniques are versatile, robust, and accessible; and as matrix-cell interactions are instrumental for many biological processes, the methods and findings described here should be broadly applicable for characterizing hydrogel materials used for 3-D tissue engineered culture models.

bioengineering↗

bioRxiv2025

A geometric criterion links HIV-1 capsid topography to its biophysical properties and function

Mathematical models of virus capsid structure are pillars of modern virology, aiding the understanding of viral mechanisms and the design of antiviral interventions. Traditionally, the HIV-1 capsid core geometry is represented as a fullerene lattice, akin to the icosahedral models of spherical viruses in Caspar-Klug theory. However, recent studies revealed that many viral capsids deviate from such idealised lattices, with important functional implication. Here we show that this is the case also for the conical HIV-1 core geometries, in which the hexamer and pentamer boundaries form a pseudo-tiling rather than a perfectly aligned fullerene network. We introduce a triangular geometric criterion that quantifies local deviations of an HIV-1 atomic model from its idealised fullerene backbone. Using this criterion, we present that this difference in geometric organisation between idealised (fullerene) and actual (data-derived) capsid model has implications for the capsids biophysical properties. We also discuss the use of the geometric criterion as a predictive tool regarding cofactor binding and implied geometric changes in the capsid surface coupled to the interfacial frustration response. Our results establish a quantitative framework linking capsid geometry, curvature, and biophysical function, offering new perspectives for assembly inhibitor design and lentiviral vector engineering.

bioinformatics↗

bioRxiv2025

Biophysically Grounded Deep Learning Improves Protein-Protein ΔΔG Prediction

Accurate {Delta}{Delta}G prediction requires integrating machine learning with biophysical insight. Existing approaches typically prioritize one while neglecting the other. We introduce an encoder-agnostic framework that injects interpretable biophysical priors into residue-level deep learning representations via cross-embedding attention. ProtBFF consistently improves performance under homology-based-clustering evaluation and enables general-purpose encoders to surpass state-of-the-art specialized models or larger models. Our results show that integrating simple, mechanistic priors into pretrained representations yields more trust-worthy predictors, offering a practical solution for broader protein engineering applications. Codegithub.com/Jfeldman34/ProtBFF

bioinformatics↗

bioRxiv2026

Spine-neck electrical bottlenecks tune temporal precision and inhibitory gating in cortical pyramidal neurons: A connectomics-based biophysical study

Dendritic spines are nano-scale compartments that host the majority of excitatory synapses on cortical pyramidal neurons (PNs); hundreds of spines/PN also receive an inhibitory synapse (dually-innervated spines, DiSs). Using analytic theory and detailed biophysical models of [~]2,000 densely reconstructed spines, we show that recurrent spine-neck narrowing substantially increases spine-neck resistance (Rneck), and that elevated Rneck accelerates spine-head voltage dynamics, shortening spinous postsynaptic potentials by up to [~]3-fold. Rneck improves the tracking of high-frequency synaptic inputs and strongly modulates Ca2+ signaling and the potency and temporal precision of inhibitory gating in DiSs. This work identifies Rneck as a dynamic "knob", directly linking spine ultrastructure to information processing and plasticity in cortical PNs and circuits, yielding testable experimental predictions. Our "biophysics of connectomics" paradigm naturally raises computational-oriented questions, including how inhibitory "gates" in dendritic spines expand context-dependent computations, implement single-cell and network-level routing, and enable selective encoding of precise temporal patterns.

neuroscience↗

bioRxiv2026

Integrated biophysical and spatial remodeling during insulin secretory granule maturation at the mitochondrial network

Effective insulin secretion and blood glucose homeostasis depend on the multistep maturation of insulin secretory granules (ISGs), a process that includes lumen acidification, enzymatic insulin processing, and biophysical remodeling of the granule. An under studied aspect of ISG maturation is the role of inter-organelle contacts in organelle remodeling. While a correlation between ISG-mitochondria contacts and ISG maturation has been observed, many questions remain on how this interaction may impact maturation (1-5). We sought to address this gap in knowledge by using multi-scale imaging approaches (fluorescent microscopy, soft X-ray tomography, and cryo-electron tomography) to examine how the biophysical properties and spatial organization of ISGs change around the mitochondrial network. Our data suggests that ISGs in proximity to mitochondria exhibit lower pH, higher biomolecular density, and smaller vesicle diameter. Time-resolved imaging using a SNAP tag labelling system also shows that as ISGs age, their proximity to the mitochondria network is increased between 3-6 hours after biosynthesis, suggesting that ISG-mitochondria association is dynamically spatiotemporally regulated in pancreatic {beta}-cells. These data suggest that mitochondrial proximity contributes to the maturation and remodeling of ISGs in pancreatic beta cells.

cell biology↗

bioRxiv2026

Whole organism 3D mapping reveals universal branching topology and biophysical optimization governs vascular and nervous system development

The vascular and nervous systems are transport networks essential for life, yet whether universal geometric and topological principles govern their formation remains unclear. The organism-wide molecular and biophysical coordination required to build and maintain these networks during embryonic development has inspired decades of theoretical work, offering predictions about their expected organization. However, the lack of complete three-dimensional (3D) data has limited validation to isolated structures, leaving whole-organism networks unexplored. Here, we developed a computational pipeline for whole-organism 3D imaging to reconstruct the complete vascular and nervous systems of rhesus macaque, mouse, and turtle embryos. Our analysis reveals that both networks share structural principles, including binary branching and scale-invariant bifurcation geometry, maintained across species and throughout development. Yet, from these shared rules emerge fundamentally different architectures. Vasculature exhibits fractal topology with a fractal dimension [~]3, forming space-filling trees that prioritize proximity to every cell in the body. Nervous system networks exhibit a fractal dimension [~]2, forming sheet-like arbors that prioritize electrical signal transmission. This architectural divergence originates from distinct biophysical constraints operating in bifurcations, where vascular junctions minimize energy expenditure while conserving fluid flow and nerve junctions maximize conduction velocity while conserving electrical current. These local optimization rules, iterated across generations, construct organism-wide networks governed by distinct physical constraints, revealing how evolution generated different solutions for fluid versus electrical transport.

developmental biology↗

bioRxiv2026

Subthreshold Asynchronous States and Computations in Biophysically Detailed Populations of Neurons

Spikes are metabolically costly to generate and transmit, and spiking rates should be kept to a minimum for efficient coding. How low can the spike rate go? We show that computations and asynchronous states based on excitatory/inhibitory balance can exist without firing any spikes through self-sustaining subthreshold voltage fluctuations in networks of biophysically detailed Hodgkin-Huxley neurons. This novel subthreshold asynchronous state, which we call subthreshold voltage chaos, can be controlled for useful computation and pattern generation, also without firing spikes. Further, we identify candidate ion channels, low-voltage-activated T-Type calcium channels that provide a biophysical mechanism for this type of subthreshold computation. Our work here provides computational evidence for the existence of efficient neural circuits that can compute exclusively with subthreshold voltage dynamics.

neuroscience↗

bioRxiv2026

Biophysical and enzymatic comparison of Bacillus safensis and Bacillus subtilis malate dehydrogenase (MDH) enzymes

Species of Bacillus bacteria including Bacillus safensis and Bacillus subtilis are finding increasing uses in biotechnology and bioremediation, thanks in part to their metabolic robustness. Malate dehydrogenase (MDH) is at the heart of central metabolism and thus a better understanding of Bacillus MDH proteins could aid in the optimization of these applications. MDH of Bacillus spp. belong to the lactate dehydrogenase (LDH)-like class of MDHs, otherwise known as the MDH3 class. Despite wide prevalence in nature among prokaryotes and archaea, this typically homotetrameric class is understudied compared to the MDH1 and MDH2 classes found in eukaryotes. We therefore recombinantly expressed and purified MDH proteins from two societally relevant Bacillus spp.-B. safensis and B. subtilis-and characterized them biophysically (via Size Exclusion Chromatography-Small Angle X-ray Scattering (SEC-SAXS) and Differential Scanning Fluorimetry (DSF)) and enzymatically (via spectroscopic activity assays). As expected based on their high sequence identity, the two MDH orthologs had similar properties in most regards, including a tetrameric structure and high susceptibility to substrate inhibition. However, we uncovered differences in conditional thermal stability, in addition to subtle differences in enzymatic activity that offer insight into the workings of LDH-like MDH. Summary statementMalate dehydrogenase (MDH) is a fundamental metabolic enzyme, from microbes to mammals, yet comparably little is known about microbial MDH, especially MDH of the tetrameric MDH3 class. We compare the biophysical and enzymatic properties of two such enzymes from the societally relevant bacterial species Bacillus subtilis and Bacillus safensis, offering useful insight with potential biotechnological implications.

biochemistry↗

bioRxiv2026

Holotomography reveals biophysical remodeling of mouse oocytes during post-ovulatory aging

Routine assessment of oocyte state in reproductive biology and clinical embryology still relies largely on two-dimensional transmitted-light morphology, leaving the underlying three-dimensional intracellular organization largely unmeasured. Here, we use holotomography to reconstruct volumetric refractive index (RI) distributions in unlabeled live mouse oocytes and combine this imaging with sparse-annotation, AI-assisted compartment segmentation and multidimensional feature extraction across geometry, RI statistics, dry-mass density, and intracellular texture. Applied to an in vitro model of post-ovulatory aging, holotomography-derived biophysical profiles separated fresh and aged oocytes more strongly than two-dimensional brightfield-derived features, and feature-family ablation indicated that non-morphological descriptors contributed substantially to this separation. At the population level, aged oocytes showed coordinated ooplasmic remodeling, including reduced volume, increased RI heterogeneity, and increased dry-mass density while total dry mass was broadly conserved. These findings demonstrate a label-free quantitative framework for detecting post-ovulatory aging-associated biophysical remodeling in intact mammalian oocytes. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=167 SRC="FIGDIR/small/733271v1_ufig1.gif" ALT="Figure 1"> View larger version (89K): org.highwire.dtl.DTLVardef@c7257aorg.highwire.dtl.DTLVardef@6d92f0org.highwire.dtl.DTLVardef@1c9f7eborg.highwire.dtl.DTLVardef@b36637_HPS_FORMAT_FIGEXP M_FIG C_FIG

Developmental Biology↗

bioRxiv2026

Biophysical properties of IgD determine thresholds for self-tolerance and selection into germinal centers

Immunoglobulin D (IgD) is among the most conserved antibody isotypes, found in virtually all jawed vertebrates1. Unlike other isotypes, IgD contains an unusually long hinge region of up to 160 amino acids that connects the constant and variable regions. Its expression pattern is also conserved; IgD is co-expressed with IgM on transitional and mature naive B cells. However, the function of IgD has remained enigmatic since its discovery in 19652,3. Here we present and test a biophysical model positing that IgD increases the entropic cost of bivalent antigen binding. Single-molecule measurements revealed that the antigen-binding arms of IgD are substantially more dynamic than those of IgM, suggesting that cell surface IgD would be energetically penalized in bivalent antigen binding. Consistent with the model and biophysical data, we find that the long hinge compromises antigen capture by IgD B cell receptors (BCRs) compared to IgM BCRs. To determine how the difference in antigen binding impacts immunity, we produced mice that express only IgM and IgD, exclusively IgM or IgD, or IgD with a truncated hinge region. The data indicate that the increased entropic cost of antigen binding imposed by the IgD hinge attenuates negative selection by self-antigen while increasing the affinity-based threshold for positive selection into the germinal center (GC). Together the results indicate that IgD functions physiologically to desensitize B cells to antigen, thereby expanding the B cell repertoire while optimizing affinity-based selection into the GC.

immunology↗

bioRxiv2026

A multi-scale structural and biophysical atlas of TCR-peptide-HLA recognition dynamics

Dynamic interactions between T cell receptor (TCR) and peptide-human leukocyte antigen (pHLA) complexes are central to peptide-specific immune recognition, influencing T cell activation and immune responses. While structural biology has provided valuable static structures of TCR-pHLA complexes, systematic datasets capturing their dynamic and interaction patterns remain limited. Here, we present DynaTPH, a curated structural dynamics dataset of human TCR-pHLA complexes. DynaTPH integrates TCR-pHLA structures, covering both HLA class I and class II complexes, and extends these static structural resources with standardized molecular dynamics simulations and derived biophysical properties. Through a multi-stage filtering procedure, we identified 256 representative complexes and performed standardized all-atom molecular dynamics simulations for each system, corresponding to a cumulative simulation time of 38.4 s. The dataset includes static structures, trajectories, corresponding frames, and derived physicochemical properties, including hydrogen bonds, intermolecular contacts, solvent accessibility, and backbone flexibility. By capturing the conformational flexibility and dynamic interaction patterns across diverse TCR-pHLA interfaces, DynaTPH extends static structural resources with multidimensional biophysical information. This dataset enables systematic investigation of TCR-pHLA recognition dynamics and supports applications in TCR engineering, vaccine design, and immune tolerance research and artificial intelligence-driven computational immunology.

molecular biology↗

bioRxiv2026

Connectome-based biophysical modeling of a figure-ground discrimination circuit

Figure-ground discrimination (FGD) via relative motion represents a highly conserved visual computation essential for breaking camouflage. Over forty years ago, the Reichardt-Poggio-Hausen (RPH) model proposed a circuit architecture to explain this phenomenon, relying on an inhibitory wide-field pool cell to aggregate global motion and subsequently inhibit elementary motion detectors. However, a biological implementation of the RPH model has remained elusive. Using Drosophila connectomics data and biophysically accurate modeling, we reveal a biological circuit implementing this canonical architecture. Strikingly, we find that while the dendrites of the elemental motion detectors, T4a, are tuned to motion, their axon terminals compute relative motion. This secondary computation is achieved via presynaptic shunting inhibition from the ventral Centrifugal Horizontal (vCH) cell, which acts as a global motion integrator that normalizes the signal carried by the T4 axon terminals during whole-field optic flow. We extend our model to include the motion-opponency and the binocular communication pathways and show that FGD depends on the T4a-vCH interactions. We further show that specific circuit adjustments are sufficient for our model to make predictions consistent with several physiological and behavioral experiments across different fly species. Ultimately, by uniting connectomic connectivity with morphologically accurate biophysics, our approach demonstrates how detailed, synapse-resolution models of the brain can yield fine-grained biological predictions.

neuroscience↗