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Preprint: explore 300 source-linked works published from 2026 to 2026, with original documents and citations.

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Includes records with this source-supplied label or an explicit phrase match in their metadata. Matches indicate a mention, not proof that a paper uses a method or tests a material. Source versions are consolidated by DOI.

Sources: biorxiv. Collection updated 2026-09-15. Counts describe this index, not the complete source archives.

bioRxiv2026

Mind the gap between functional groups and surface of magnetic nanoparticles for highly specific magnetic-based protein assays in biological medium

Magnetic readout-based assays are compatible with unprocessed biological samples as unbound background molecules do not interfere with magnetic signal. Yet, a true challenge is their poor specificity and susceptibility of magnetic nanoparticles (MNPs) to clusters in complex biological media, hampering their true advancement. Here, we demonstrate that the spatial organization of functional groups at the external periphery of custom magnetic nanoparticles by harnessing ultra-dense double-stranded DNA results in an efficient antibody conjugation with good accessibility toward antigen. By labeling our MNPs with anti-S protein neutralizing IgG antibody, we showcase the detection of S1 subunit of SARS-CoV-2 Spike protein in a wash-free fashion in less than five minutes in nM regime using magnetic particle spectrometer. By mixing our IgG-labelled MNPs with DMEM cell culture (10-20% FBS serum), we sense the S1 proteins in a one-pot fashion with high specificity. Our results show that by having the ultra-dense dsDNA shell on MNPs, the entropic cost of an irreversible protein binding to particle surface is high, thus allowing the formation of dynamic protein corona on the DNA shell that can be replaced with S1 protein with high affinity. When the azide moieties are placed at the close proximity of MNPs by using non-functional dsDNA, antibody conjugation becomes inefficient, to a level not sufficient for S1 protein detection. Our study highlights the importance of spatial organization of functional moieties on the nanoscale on magnetic nanoparticles for highly specific assays in biologically complex media.

biochemistry↗
bioRxiv2026

The Gordian Knot Enhances Ubiquitin Binding in UCH-L1

UCH-L1 is a monomeric deubiquitinating enzyme whose native structure embeds a shallow $5_2$ knot located near the N-terminus, placing the knotted topology in direct proximity to both the substrate-binding pocket and the catalytic site. While our previous work established that N-terminal integrity is critical for catalytic activity, the energetic cost of unknotting and its structural consequences remained unquantified. Here, we combine steered molecular dynamics with an umbrella sampling scheme to generate topologically modified variants of UCH-L1 and, for the first time, reconstruct the free-energy profile of UCH-L1 unknotting. The potential of mean force reveals a steep energetic barrier to knot disruption, consistent with knotting being a late, rate-limiting folding step that is effectively locked in once the native structure is established. Long unbiased MD simulations of fully unknotted variants in both apo and holo states show that knot removal increases local flexibility at the N-terminus without inducing significant global structural destabilization. Binding energy calculations indicate that the unknotted variant binds to ubiquitin less tightly than the wild-type ($\sim$-62~vs~$\sim$-76~kcal/mol), suggesting that topological integrity contributes to substrate affinity. Together, these results show that the $5_2$ knot in UCH-L1 is not a passive structural feature but a functional element that fine-tunes folding kinetics and contributes to substrate binding efficiency.

biophysics↗
bioRxiv2026

Microsecond molecular dynamics of SOD1 variants suggest a structural basis for divergent ALS clinical outcomes

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterised by progressive motor neuron degeneration. Mutations in the SOD1 gene represent the second most common genetic cause of ALS (ALS), and distinct SOD1 missense variants present with markedly different clinical profiles. A4V leads to an aggressive form of the disease (median survival [~]1y), H46R confers a mild, slowly progressive course and I113T exhibits an intermediate phenotype. The molecular basis by which these mutations produce divergent clinical outcomes remains poorly understood. We performed extensive classical molecular dynamics simulations of wild-type SOD1 and the three ALS-associated variants in the apo monomeric state to attempt to investigate the mechanisms behind such phenotypic differences. Structural stability, global compactness, and conformational flexibility, as well as analysis of collective motions between residues and estimation of free energy, were assessed. The H46R, A4V, and I113T variants exhibited distinct dynamic behaviours, highlighting differences in structural stability, local flexibility, and intramolecular interactions. These findings suggest that specific structural regions may contribute differently to protein dysfunction and could represent key elements for understanding the relationship between molecular dynamic properties and the differing clinical severity associated with these variants. Most strikingly, H46R exhibited exceptional structural stability across every analytical level, the lowest global deviation, most attenuated local flexibility, strongest internal dynamic coordination, and the deepest, most confined free energy basins of any system examined. This convergent multi-layered evidence of structural restraint provides a compelling mechanistic basis for the mild and slowly progressive clinical course of H46R ALS, suggesting that enhanced conformational rigidity, rather than bulk destabilisation, is the defining biophysical feature of this variant, and that its pathogenic mechanism operates through a route fundamentally decoupled from the aggregation-driven toxicity that characterises the more aggressive SOD1-ALS mutations.

genomics↗
bioRxiv2026

Evolutionary origins of protein novelty across an entire yeast subphylum

Novel protein-coding sequences fuel molecular and cellular evolutionary innovations and frequently contribute to species-defining characteristics. They can originate either de novo from previously noncoding sequences or through extreme divergence of already coding ones. How frequently each mechanism occurs and how they shape the structural and functional potential of the resulting proteins remains unclear. Here, we conducted a broad computational investigation of genetic and protein novelty throughout the entire subphylum of Saccharomycotina yeasts. We detected more than 5,000 robust de novo genes across 332 species and compared them to more than 6,000 novel genes resulting from extreme sequence divergence, revealing two quantitatively similar but qualitatively distinct modes of evolution of novelty. A remarkable 40% of de novo proteins are predicted to localize to mitochondria compared to only 20% of divergent, with the latter also being substantially longer and more disordered. A detailed analysis of conservatively predicted tertiary structures of novel proteins shows that ''invention'' of new folds occurs more frequently through de novo emergence. We also illustrate cases of evolutionary ''re-invention'' of existing protein folds from noncoding sequences. Our work deepens our understanding of the origins and importance of novel proteins, opening new directions for further structural and functional characterization.

genomics↗
bioRxiv2026

Quantifying sprint force-velocity elasticity: implications for individualized training decisions

This study aimed to (1) develop an elasticity framework for the sprint force-velocity (F-V) relationship and (2) examine how maximal force (F_{0}), maximal velocity (v_{0}), and sprint distance modulate the four derived elasticity metrics, and (3) explore these elasticity metrics' interrelation. After modelling the F-V relationship differential equation, four elasticity metrics were defined as force elasticity (F_{e}), the elasticity of sprint time to F_{0}; velocity elasticity (v_{e}), the elasticity of sprint time to v_{0}; the force-velocity elasticity norm {(\mathrm{F}-\mathrm{V}}_{\mathrm{EN}}=\sqrt{F_{e}^{2}+v_{e}^{2}}), capturing the combined sprint time sensitivity to proportional changes in F_{0} and v_{0}; and the force-velocity elasticity ratio {(\mathrm{F}-\mathrm{V}}_{\mathrm{ER}}=F_{e}{\div v}_{e}), indicating which variable dominates the sprint time response. Model simulations showed that F_{e} decreased with rising F_{0} and increased with rising v_{0}, while v_{e} showed the opposite pattern. With increasing sprint distance, F_{e} decreased and v_{e} increased. Given its negligible effect on sprint time, ignoring air resistance yields a conservation law (2F_{e}+v_{e}\equiv 1), indicating that a gain in one elasticity metric necessarily diminishes the other in a fixed proportion. This framework also identifies a valley distance (d_{valley}) at {\mathrm{F}-\mathrm{V}}_{\mathrm{ER}}=2, where {\mathrm{F}-\mathrm{V}}_{\mathrm{EN}} is minimized (\sqrt{0.2}) and sprint time is least responsive to changes in F-V relationship variables. Empirical data confirmed that the two theoretical laws still hold approximately when air resistance is considered. By linking changes in F_{0} and v_{0} to sprint time across different distances, the elasticity framework provides a quantitative basis for estimating the theoretical sprint time response to documented changes in F-V relationship variables.

biophysics↗
bioRxiv2026

TigerAI: An AI-powered genetic evidence platform to support clinical development

Genetic evidence is a major determinant of clinical success in drug development, yet its aggregation has long relied on laborious human curation. Large language models (LLMs) have the potential to rapidly synthesize knowledge across biomedical resources, providing a route to scalable AI-driven genetic evidence generation. Here we develop a novel domain-grounded instruction framework to systematically evaluate GPT-5 for producing genetic evidence relevant to clinical trial success. Using 13,022 target-indication pairs from a comprehensive drug development database, we benchmark LLM-derived evidence against a recent exhaustive human expert-curated study. We find that GPT-5 yields genetic evidence that is at least as informative as expert curation for inferring clinical success, while substantially expanding coverage relative to traditional curation resources. Building on these results, we introduce TigerAI (https://tigerai.bio/), a dual-purpose platform for AI-powered genetic evidence that (i) benchmarks emerging state-of-the-art LLMs and (ii) provides an accessible service for querying reliable AI-generated genetic evidence. These contributions outline a practical, domain-grounded pathway for integrating AI-powered genetic evidence into drug development pipelines and for realizing the potential of LLMs to inform clinical success.

genetics↗
bioRxiv2026

Multiscale modelling of drug-host-pathogen interaction: quantifying drug and immune contributions to treatment response

Background and Objective: Predicting treatment outcomes in infectious diseases requires accounting for the interplay between drug effects, pathogen dynamics, and host immunity. Integrating pharmacological and immunological approaches into a single simulation environment remains a fundamental challenge in both theory and practice. We aimed to develop and validate a multiscale in silico framework coupling these processes, and to quantify their respective contributions to bacterial clearance. Methods: We present the Drug-Host-Pathogen Interaction (DHPI) framework, combining three independent mechanistic components: a physiologically based pharmacokinetic model of drug disposition, a pharmacokinetic-pharmacodynamic model of drug-induced bacterial killing, and a stochastic agent-based model of the immune response. Continuous concentration profiles are time-averaged onto the agent-based time grid, assigned to bacterial phenotypic states, and converted into per-agent killing probabilities, so that drug-mediated and immune-mediated death events are recorded separately at each step. The framework was applied to simulate symptomatic pulmonary tuberculosis. Phenotype-specific drug-efficacy parameters were inferred using Approximate Bayesian Computation from historical clinical data on eight weeks of 600 mg rifampicin monotherapy, and validated against independent early bactericidal activity data over a disjoint time window. Results: The calibrated framework reproduced the observed decline in bacterial load, and matched reported early bactericidal activity over the first week. In a virtual cohort of symptomatic patients, drug-mediated killing accounted for 81-88% and immune-mediated killing for 12-19% of total bacterial elimination over the 60-day treatment course, while the dormant, granuloma-contained fraction rose from 0.20-0.29 in the first week to 0.85-0.89 at treatment completion. Over a follow-up of up to 50 years, patients reaching clinical cure had accumulated more memory lymphocytes during treatment than those progressing to clinical failure or death; moreover, the final outcome depended on the immune changes occurring during therapy rather than on the initial disease stage. Conclusions: The results show that the DHPI framework can reproduce treatment dynamics observed in patients and enable the analysis of how therapy reshapes host immune responses and subsequent disease trajectories. By explicitly representing drug-host-pathogen interactions, it provides a mechanistic basis for in silico treatment simulations and for the study of long-term immune consequences of antimicrobial therapy.

systems biology↗
bioRxiv2026

Dissecting the TMEM132A-EGFR Dependency to Unlock Translational Therapeutic Opportunities for Pan-Solid Tumor

Solid tumors remain refractory to conventional treatments, yet cell surface proteins, by virtue of their extracellular accessibility and critical roles in tumor signaling, represent an attractive class of targets for precision-targeted therapy. Here, we report that TMEM132A is an essential and previously unrecognized pan-cancer target. TMEM132A interacts directly with EGFR and stabilizes its expression, thereby tethering EGFR at the plasma membrane and sustaining constitutive activation of lipid synthesis. Mechanistically, the TMEM132A-EGFR axis promotes lipogenesis by facilitating SREBP nuclear translocation, which in turn upregulates ACLY and ACSS2 expression to drive acetyl-CoA production and downstream lipid biosynthesis, ultimately disrupting lipid droplet homeostasis. To therapeutically target this axis, we developed a nanobody, LFNanoT132A#3, which effectively blocks the TMEM132A-EGFR interaction, abrogates downstream signaling activation, and potently inhibits proliferation across multiple solid tumor types. Notably, LFNanoT132A also exerts robust antitumor activity against H1975 xenografts, a model resistant to first- and second- generation EGFR inhibitors, underscoring its potential to overcome conventional drug resistance. Our findings establish TMEM132A#3 as a critical node in membrane-tethered oncogenic signaling and metabolic rewiring, and position LFNanoT132A#3 as a promising therapeutic candidate for precision cancer therapy.

cancer biology↗
bioRxiv2026

miR-34/449 miRNAs regulate choroid plexus ciliogenesis to control cerebrospinal fluid production

A developmental increase in cerebrospinal fluid (CSF) production during development is essential for neuronal growth and ventricular expansion. A key regulator of CSF production is the specialized sensory multicilia of the choroid plexus (ChP), which mediate non-canonical Sonic hedgehog (Shh) signaling to suppress water channel and ion transporter expression, thereby limiting CSF production. ChP multicilia progressively shortens during development, attenuating Shh signaling and promoting CSF production. Here, we identify miR-34/449 miRNAs as essential regulators of ChP multiciliogenesis. Whereas mutations in canonical ciliogenesis genes elevate CSF production and contribute to hydrocephaly, deletion of miR-34/449 reduces CSF volume and causes microcephaly. Loss of miR-34/449 miRNAs causes excessive basal body amplification, defective basal body docking, and failure of developmental multiciliary shortening. Consequently, miR-34/449-deficient ChP cilia remain abnormally long and fail to attenuate Shh signaling, resulting in sustained repression of water channel and ion transporter expression and reduced CSF production. Mechanistically, miR-34/449 miRNAs directly target Gmnc, a master transcriptional regulator of multiciliogenesis, to restrain basal body amplification and promote basal body docking. Together, our findings identify miR-34/449 miRNAs as critical regulators of ChP multiciliogenesis and establish the developmental remodeling of ChP multicilia as a mechanism to couple Shh signaling dynamics to developmental control of CSF production.

developmental biology↗
bioRxiv2026

A Metabolic Labeling Strategy for Tracking Protein Synthesis in Complex Biological Systems

Protein synthesis supports most biological processes. In the brain in particular, protein synthesis plays a critical role in physiological and pathological states. Here, we describe Tellurophene-Alkyne Cycloaddition-mediated Amino acid Tagging (TeACAT), a versatile strategy for fast, facile, and flexible tagging of newly synthesized proteins in mice. TeACAT is based on metabolic incorporation of the non-canonical amino acid TePhe into proteins by the endogenous protein synthesis machinery. Due to their high similarity, TePhe can efficiently replace canonical Phe without dietary or genetic manipulation. The subsequent bio-orthogonal reaction of TePhe with either fluorescent dyes or affinity handles enables both visualization and affinity enrichment of proteins synthesized during TePhe exposure. TeACAT is compatible with immunofluorescence for cell-type specific visualization of protein synthesis with subcellular resolution and can be used in conjunction with routine proteomics to identify and quantify newly synthesized proteins. Robust incorporation into the mouse proteome was observed on the scale of hours to days, allowing the interrogation of various biological processes. In summary, TeACAT enables the visualization and quantification of protein synthesis with minimal perturbation for biological discoveries.

molecular biology↗
bioRxiv2026

Local mechanical heterogeneity drives epidermal cell delamination

Delamination within stratified epithelia like the skin epidermis describes the detachment and upward motion of cells originating from the basal layer. Despite its fundamental importance for tissue development, homeostatic regeneration and repair, the mechanisms that drive delamination remain a longstanding open question. Upward motion follows cell shape changes, which are inherently driven by physical forces, but their role is elusive. Here, we investigate delamination in stratifying keratinocytes by combining imaging, force measurements and theoretical modeling. We identify a local change in force balance between differentiating cells and their environment as the key step initiating delamination. Within a homogeneous cell layer with apically polarized contractility, differentiation leads to actomyosin remodeling, redistributing cellular force exertion to the basal side. Such mechanical heterogeneity then results in differentiating cells experiencing and inward basal and outward apical forces that manifest in the formation of a +1 force defect and promote shape changes culminating in upward motion. Simultaneously, delaminating cells actively pull on their underlying neighbors, generating convergent tissue flows which close the basal layer below. Together, we propose a general physical description of delamination initiation, which may act across various multilayered epithelia.

biophysics↗
bioRxiv2026

Structure-inspired design of Nsp8-based protein inhibitors to suppress SARS-CoV-2 replication

SARS-CoV-2 relies on a conserved RNA-dependent RNA polymerase (RdRp) complex composed of nsp12 and its cofactors nsp7 and nsp8 to replicate its RNA genome. Whereas most antiviral strategies target viral enzymes or surface proteins directly, an alternative approach is to disrupt the assembly or function of an essential viral molecular machine using a defective component derived from the pathogen itself. Here, guided by structural analyses of the nsp12-nsp7-nsp8 replication complex, we designed truncated nsp8 proteins that retain the ability to associate with nsp12 but are defective in engaging RNA. Using a purified nsp12-nsp7-nsp8 system capable of RNA primer extension, we show that selected truncated nsp8 variants inhibit polymerase activity when introduced into an otherwise functional complex. These results are consistent with a competitive mechanism in which the defective nsp8 variants associate with nsp12 and interfere with incorporation or function of wild-type nsp8, thereby compromising formation of a productive replication complex. To further explore this strategy, we used structure-guided in silico analysis of the nsp8-nsp12 interface to identify interaction hotspots and screened corresponding single-amino-acid substitutions. Several variants exhibited enhanced inhibitory activity in the reconstituted polymerase assay. Together, these findings establish a proof-of-concept strategy in which a structurally engineered, pathogen-derived protein can act as a dominant-negative inhibitor of an essential viral replication machinery. This approach provides a framework for developing protein- or peptide-based inhibitors that target conserved protein-protein interactions within viral replication complexes.

biochemistry↗
bioRxiv2026

Stochastic Biophysics of Cellular Radiosensitivity: From Molecular Noise and Repair Kinetics to Evolutionary Demographics

Radiation-induced DNA double-strand breaks (DSBs) drive cellular mortality, mutagenesis, and severe evolutionary bottlenecks. While classical phenomenological models, such as the Linear-Quadratic (LQ) framework, reliably predict macroscopic population survival, they obscure the intrinsic single-cell stochasticity that governs critical rare events like tumor recurrence or the emergence of radioresistant persisters. To bridge this divide, we develop a mathematically exact stochastic differential equation (SDE) framework that models continuous DSB induction and repair as a Feller square-root process. By deriving exact closed-form expressions for the foci moments, we establish a highly efficient Maximum Likelihood Estimation (MLE) pipeline that circumvents computationally exhaustive Monte Carlo simulations, allowing the direct extraction of deterministic repair velocities and intrinsic molecular noise from empirical single-cell $\gamma$-H2AX data. Integrating this kinetic model with a cumulative damage hazard via the Feynman-Kac formalism, our framework seamlessly recovers the classic macroscopic LQ survival topology from microscopic first principles. Furthermore, systematic sensitivity analysis uncovers a fundamental evolutionary duality: while initial physical damage operates additively, ultimate cellular fate is driven by a nonlinear survival response governed by the trade-off between the damage hazard rate and intrinsic molecular noise strength. Crucially, we demonstrate that this molecular noise inherently enhances population survival. Governed by Jensen's inequality, stochastic variance acts as a non-genetic bet-hedging mechanism that buffers the population by favoring cells with transiently low damage loads. Ultimately, this exact stochastic framework bridges microscopic biophysics and macroscopic demographics, offering deep mechanistic insights into the evolutionary roots of radioresistance.

biophysics↗
bioRxiv2026

Polarized neutrons for the study of individual and collective fast dynamics in proteins

Neutron scattering is a powerful technique to investigate atomic structures and molecular dynamics of proteins at the nano-scale. When it comes to dynamics, incoherent and coherent scattering respectively provide information on the single and collective dynamics of nuclei. In proteins, hydrogen has the highest incoherent cross-section, and it is common practice to overlook the contribution of coherent terms stemming from all nuclei. However, the fast collective dynamics of heavier nuclei could also be studied if coherent scattering and incoherent scattering were experimentally separated. The recent advent of polarized neutron spectroscopy with sufficient flux and energy resolution has made it possible, and opens new perspectives to investigate the relative importance of coherent scattering and the information it provides on biological samples. The present study reports on the use of polarized quasi-elastic neutron scattering (QENS) and the application of a minimalistic model adapted to both individual and collective dynamics. Using a perdeuterated green fluorescent protein as a model globular protein, the study provides an interpretation of the dynamical parameters obtained with QENS, and a comparative study of the Elastic Coherent and Incoherent Scattering Factor. Based on both experiments and calculations, we discuss the relative importance of distinct and self components of coherent scattering, which is often wrongly assumed to be representative of collective dynamics only. The results highlight the current impediments rendering complicated a straightforward analysis of fast collective dynamics in hydrated protein samples.

biophysics↗
bioRxiv2026

Red and blue light cues drive contrasting remodeling of lipophilic metabolites and photophysiology in natural benthic diatom biofilms

Intertidal mudflats are low hydrodynamic energy environments hosting microphytobenthic communities that experience strong spatiotemporal variability in light regimes, including changes in spectral quality and light intensity that can lead to cellular photooxidative stress. To cope with these fluctuations, autotrophs exhibit diverse and highly plastic adaptations that are often species-dependent and shaped by their ecological niches. This study investigates photophysiological responses and metabolic remodeling in a diatom assemblage originating from a natural winter microphytobenthic biofilm under contrasting red and blue light intensities. To this end, photosynthetic parameters were monitored alongside changes in lipophilic metabolites, including untargeted lipids and lipophilic pigments. While few metabolites showed temporal remodeling, rapid and contrasting changes were observed within 30 minutes in response to both spectral quality and light intensity. Red light treatments induced broader remodeling of lipophilic metabolites than blue light, whereas blue light appeared to have a greater impact on photosynthetic parameters. Moreover, red light induced xanthophyll-cycle responses comparable to those observed under blue light at equivalent incident intensity. We discuss these metabolic responses in relation to diatom photoadaptive strategies, placing these findings within the intertidal environmental framework. This work further underlines the importance of understanding rapid metabolic plasticity in coping with light fluctuations, providing new insights into the photoregulatory strategies of natural microphytobenthic communities.

cell biology↗
bioRxiv2026

Learning and forecasting shared evolutionary pathways to multi-drug resistance across global pathogens

Infections with bacteria which have evolved multi-drug resistance (MDR) cause millions of deaths worldwide. Large-scale efforts are gathering genotypic and phenotypic data on MDR bacteria, but methods for learning the structure, diversity, and predictors of evolutionary pathways to MDR have yet to take full advantage of these data. Here, we use evolutionary accumulation modelling (EvAM), an emerging class of machine learning methods with roots in cancer progression, to infer these evolutionary pathways across ESKAPEE pathogens (seven bacterial species that dominate health burdens), using a database of over 635k genotyped phenotypic observations from around the world. We identify global patterns in MDR evolutionary pathways, remarkably shared across multiple ESKAPEE species. Species-specific deviations from these stereotypical pathways are connected with geographical and demographic covariates, facilitating predictions of future MDR evolution. We verify these predictions with several hundred new phenotypes from ESKAPEE samples spanning decades of clinical infections in sub-Saharan Africa, demonstrating the capacity to forecast future MDR evolution from these inferred shared pathways.

evolutionary biology↗
bioRxiv2026

Historical squid biomass increase is not explained by rising temperature but rather by loss of top predators.

Squid abundance has been reported to increase globally between 1970 and 2010. This increase has been hypothesized to result from two primary factors: the loss of top predators due to overfishing and rising ocean temperatures. The decline in apex predators may lead to the expansion of squid populations either through reduced predation pressure or diminished competition with juvenile predators. Concurrently, increased temperatures could enhance the somatic growth rates of squid, thereby accelerating their population growth. However, empirically disentangling the impacts of predator loss and temperature on squid biomass remains challenging, especially in a food-web context. In this study, we used a size- and trait-based model of upper trophic levels that resolves the ecosystem structure -- biomass and trophic interactions of fish and squid -- for varying depth, temperature, and secondary production, to investigate two hypotheses of the historical expansion of squid, i.e., the effects of predator depletion from fishing and rising temperatures on squid biomass. Our model reveals that intensified fishing of squid predators -- specifically large demersal fish in shelf systems and large pelagic fish in open oceans -- leads to a slight increase in squid biomass. Conversely, elevated temperatures are associated with a decline in squid biomass. This temperature-driven reduction in biomass is attributed to an increased metabolism of squids beyond the available food supply. If historic overfishing on large marine predators continues to be curtailed, we expect a corresponding reduction in global squid biomass and fisheries potential, which could be further exacerbated by rising temperatures.

ecology↗
bioRxiv2026

Antibody co-administration robustly improves proton therapy with radiosensitizing nanoparticles: a mathematical modeling study

Radiosensitizing nanoparticles represent a promising approach for enhancing the efficacy of proton radiotherapy; however, their performance is constrained by restricted penetration into tumor tissue, resulting in preferential perivascular accumulation. Here, we develop a spatially distributed mathematical model of a growing tumor undergoing proton therapy with intravenously administered radiosensitizing nanoparticles to investigate treatment optimization strategies. Using physiologically plausible parameter ranges informed by our own experimental measurements and published data, we demonstrate that co-administration of targeted nanoparticles with antibodies binding to the same tumor receptors can overcome transport-induced localization and promote a more uniform intratumoral redistribution of nanoparticles before irradiation. Population-level simulations across heterogeneous parameter sets suggest that moderate antibody doses consistently prolong tumor regrowth time, whereas higher antibody doses produce a pronounced and robust increase in tumor cure probability under a single high-dose irradiation regimen representative of preclinical settings. A key conceptual result of our analysis is the asymmetric risk associated with antibody co-administration. In contrast to antibody--drug conjugates, for which excessive dosing of unconjugated antibodies may severely compromise therapeutic efficacy, co-administration of antibodies with nanoparticle-based radiosensitizers constitutes a "safe-by-design" strategy with respect to tumor cell kill in the modeled single high-dose irradiation setting: although excessive antibody doses may yield suboptimal outcomes, they cannot reduce tumor cell kill below that achieved with targeted nanoparticles administered without antibodies. These findings identify antibody-mediated spatial redistribution of radiosensitizing nanoparticles as a favorable strategy that is expected to provide robust therapeutic benefit despite substantial variability in tumor characteristics.

cancer biology↗
Compare source metadata on this page
WorkPublishedSource identifierSource
Mind the gap between functional groups and surface of magnetic nanoparticles for highly specific magnetic-based protein assays in biological medium2026-09-0110.64898/2026.08.29.747976v1biorxiv
The Gordian Knot Enhances Ubiquitin Binding in UCH-L12026-09-0110.64898/2026.08.29.747984v1biorxiv
Microsecond molecular dynamics of SOD1 variants suggest a structural basis for divergent ALS clinical outcomes2026-09-0110.64898/2026.08.29.747999v1biorxiv
Evolutionary origins of protein novelty across an entire yeast subphylum2026-09-0110.64898/2026.08.29.748005v1biorxiv
Quantifying sprint force-velocity elasticity: implications for individualized training decisions2026-09-0110.64898/2026.08.29.748040v1biorxiv
TigerAI: An AI-powered genetic evidence platform to support clinical development2026-09-0110.64898/2026.08.29.748046v1biorxiv
Multiscale modelling of drug-host-pathogen interaction: quantifying drug and immune contributions to treatment response2026-09-0110.64898/2026.08.30.744418v1biorxiv
Dissecting the TMEM132A-EGFR Dependency to Unlock Translational Therapeutic Opportunities for Pan-Solid Tumor2026-09-0110.64898/2026.08.30.746586v1biorxiv
miR-34/449 miRNAs regulate choroid plexus ciliogenesis to control cerebrospinal fluid production2026-09-0110.64898/2026.08.30.747939v1biorxiv
A Metabolic Labeling Strategy for Tracking Protein Synthesis in Complex Biological Systems2026-09-0110.64898/2026.08.30.747940v1biorxiv
Local mechanical heterogeneity drives epidermal cell delamination2026-09-0110.64898/2026.08.30.747990v1biorxiv
Structure-inspired design of Nsp8-based protein inhibitors to suppress SARS-CoV-2 replication2026-09-0110.64898/2026.08.30.747997v1biorxiv
Stochastic Biophysics of Cellular Radiosensitivity: From Molecular Noise and Repair Kinetics to Evolutionary Demographics2026-09-0110.64898/2026.08.30.748070v1biorxiv
Polarized neutrons for the study of individual and collective fast dynamics in proteins2026-09-0110.64898/2026.08.30.748099v1biorxiv
Red and blue light cues drive contrasting remodeling of lipophilic metabolites and photophysiology in natural benthic diatom biofilms2026-09-0110.64898/2026.08.30.748109v1biorxiv
Learning and forecasting shared evolutionary pathways to multi-drug resistance across global pathogens2026-09-0110.64898/2026.08.30.748110v1biorxiv
Historical squid biomass increase is not explained by rising temperature but rather by loss of top predators.2026-09-0110.64898/2026.08.30.748117v1biorxiv
Antibody co-administration robustly improves proton therapy with radiosensitizing nanoparticles: a mathematical modeling study2026-09-0110.64898/2026.08.30.748121v1biorxiv

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