Biology subjects

He, F.

Publications and source records attributed to He, F..

At least 19 recordsLinked to original sources

bioRxiv2026

Growth rate structures the dynamics of bacterial trait evolution

Bacterial species span orders of magnitude in growth rate, yet how its evolution is embedded within the broader evolutionary dynamics of other bacterial traits remains poorly understood. We integrated experimentally measured growth rates for 283 bacterial species with 11 genomic, metabolic, ecological and environmental traits, and used multivariate Ornstein-Uhlenbeck models to characterize how growth rate and associated traits coevolve across evolutionary time. Growth rate was best explained by models assuming distinct slow- and fast-growing evolutionary regimes, and evolved toward adaptive optima more rapidly than any other trait examined over longer evolutionary timescales, both when modelled independently and jointly with other traits. Coevolution between growth rate and associated traits was primarily structured by growth-rate regimes, suggesting growth rate serves as a pacemaker of evolution. At instantaneous timescales, six of the eight traits coevolving with growth rate showed unidirectional coupling, including all four environmental traits, with the direction varying among traits. In contrast, ribosomal RNA operon copy number and GC percentage exhibited reciprocal coupling with growth rate. These findings recast bacterial growth strategy from a correlate of trait variation to a major organizing axis shaping bacterial trait evolution across distinct evolutionary timescales.

evolutionary biology↗

bioRxiv2026

Glymphatic System Is Enhanced in Anti-NMDAR Encephalitis and Facilitates Antibody Influx and Clearance in the Brain

ABSTRACT Background: Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is an antibody-mediated neuroinflammatory disease. How pathogenic autoantibodies enter and are cleared from the brain parenchyma remains poorly understood. The glymphatic system (GS) mediates cerebrospinal fluid (CSF)-interstitial fluid (ISF) exchange and waste removal, yet its role in anti-NMDAR encephalitis is undefined. Methods: We used polypeptides GluN1356-385 induced active-immunization anti-NMDAR encephalitis mouse model. Glymphatic influx and efflux were evaluated via fluorescent tracer delivery combined with real-time imaging and ex-vivo quantification. Perivascular aquaporin-4 (AQP4) polarization, brain IgG deposition, NMDAR levels and blood-brain barrier (BBB) integrity were analyzed. GS function was pharmacologically modulated with AER-271 (an AQP4 inhibitor) or dexmedetomidine (Dex), and deep cervical lymphatic vessels (dCLV) were ligated in a passive-transfer model (PTM). Behavioral performance was characterized by multiple neurobehavioral tests. Results: Both glymphatic influx and efflux were enhanced in anti-NMDAR encephalitis mice, accompanied by upregulated antibody entry into and clearance from the brain. However, the cerebral IgG deposition was increased. These changes were associated with increased perivascular AQP4 polarization, with no alterations in overall AQP4 abundance or BBB tight-junction integrity. AQP4 inhibition with AER-271 attenuated glymphatic influx and efflux, reduced hippocampus IgG deposition, and partially ameliorated behavioral deficits without restoring hippocampus NMDAR levels. Conversely, dCLV ligation in the PTM mice increased brain retention of patient-derived IgG and exacerbated anxiety-like behavior. Dex enhanced antibody clearance and reduced hippocampus IgG deposition but did not improve behavioral outcomes. Conclusion: The GS is dynamically upregulated in anti-NMDAR encephalitis, driven by increased perivascular AQP4 polarization. The enhanced glymphatic influx facilitates antibody entry into the brain parenchyma and exacerbates neuronal injury, whereas enhanced efflux provides a compensatory route for antibody clearance. Rather than broadly enhancing glymphatic activity, precise tuning of this pathway may present a promising non-immunological therapeutic strategy for autoimmune encephalitis. Keywords: anti-NMDAR encephalitis; antibody influx and clearance; glymphatic system; AQP4 polarization; AER-271; dexmedetomidine

neuroscience↗

bioRxiv2026

A chromosome-scale genome and transcriptomic atlas of taro (Colocasia esculenta) corm development

Aims: Taro corm development depends on carbohydrate metabolism, but cultivar-level annotation differences complicate the integration of genome comparisons and expression data. We assembled a GY2 reference genome and analysed developmental and tissue transcriptomes to characterise corm-associated transcription in a sequence-aware comparative framework. Data resources generated: The 2.309-Gb GY2 assembly contains 14 pseudomolecules and 20,063 primary gene models. Comparisons with LXY and Bun Long were integrated with six-stage corm and four-tissue RNA-seq datasets. Gene annotations, genomic comparisons, processed expression matrices and analysis scripts are supplied in Data S1, with figure source data in Data S2. Key results: The 14 GY2 pseudomolecules represent 98.83% of the assembly; contig and scaffold N50 values are 11.55 and 182.12 Mb, and liliopsid BUSCO completeness is 97.7%. Genome sequence searches reduced candidate assembly-specific orthogroups from 1,820 to 168. A total of 11,884 genes varied significantly across corm stages. Among 593 strictly corm-enriched genes, 522 were also development-responsive, including nine of 20 selected carbohydrate-associated genes. Utility of the resource: Consistent GY2 v3 gene identifiers link genomic homology, collinearity and gene-level expression, supporting cross-cultivar comparisons and prioritisation of corm-associated candidate genes.

plant biology↗

bioRxiv2026

Predictive Modeling of Cancer Cell Growth Kinetics with Machine Learning

Quantitative characterization of cell proliferation is central to preclinical drug discovery. Here, we evaluated Random Forest (RF) regression for predicting confluence-based cell growth trends using data from human cancer cell lines and benchmarked its performance against the widely used logistic and Gompertz models. The RF model achieved higher predictive accuracy within the evaluated dataset, particularly at higher initial seeding densities. We further extended the RF framework to predict growth curves across seeding densities. Collectively, these findings highlight the potential of data-driven modeling to complement conventional mathematical approaches and support more informed cell-culture design.

bioinformatics↗

bioRxiv2026

Novel Dissymmetric Ionizable Lipid-Assembled Lipid Nanoparticles for Delivery of Ferroptosis-Related siRNA in Diabetic Treatment

Small interfering RNA (siRNA) enables precise post-transcriptional gene silencing for refractory diseases, yet its clinical translation remains limited by the lack of safe and efficient delivery vectors. Inspired by the dissymmetric alkyl chain architecture of natural membrane phospholipids, we designed and synthesized 34 novel ionizable lipids with dissymmetric hydrophobic tails and formulated them into lipid nanoparticles (LNPs). Through systematic physicochemical and biological assessments, we established clear structure-activity relationships and identified two lead LNPs (O14-LNP, H18a-LNP) with superior endosomal escape capacity, enhanced in vivo gene silencing potency, and favorable biosafety relative to the clinical benchmark MC3-LNP. In both streptozotocin-induced and spontaneous db/db type 2 diabetes (T2D) mouse models, lead LNPs delivering ferroptosis-related siRNAs effectively ameliorated glucose and lipid metabolic disorders, restored islet function, and alleviated hepatic steatosis. This study not only lays a theoretical foundation for the rational design of novel ionizable lipids, but also validates the therapeutic potential of siRNA therapy targeting ferroptosis, providing a versatile delivery platform and targeted therapeutic strategy for the treatment of T2D.

pharmacology and toxicology↗

bioRxiv2026

Benchmarking confidence estimation and rescoring for cyclic peptide-protein complex predictions

Cyclic peptides are a rapidly expanding class of therapeutics, but the reliability of deep-learning structure prediction for cyclic peptide-protein complexes has not been systematically evaluated. We assembled a curated benchmark of 111 nonredundant complexes spanning five cyclization chemistries and assessed two co-folding models, Boltz and Protenix, each generating 100 poses per target (22,200 total). Stratifying all poses by complex attributes, we found that disulfidecyclized peptides and small protein targets (200 or fewer target residues) were predicted significantly worse by both tools, with target size the largest and most consistent effect; overall accuracy nevertheless remained high (median top-pose DockQ of about 0.89, 96-98% of targets Acceptable or better), indicating that pose generation is rarely the bottleneck. Conversely, native model ranking scores correlated only moderately with pose quality (Spearman rank correlations of 0.53-0.66): approximately 12% of poses showed high model ranking score/confidence despite poor pose DockQ quality, and the highest-quality pose was not ranked first for nearly every target. We therefore augmented the native score with externally computed interface descriptors normalized by chain length, principally the per-residue density of inter-chain hydrogen bonds, in a gradient-boosted rescoring model evaluated under target-grouped cross-validation that prevents leakage, improving out-of-fold ROC-AUC for both tools, significantly so for Protenix. Together, these findings identify pose ranking, rather than pose generation, as the major limitation of current cyclic peptide-protein complex prediction and demonstrate that complementary structural features can improve confidence-based pose selection.

bioinformatics↗

bioRxiv2026

Plasma titration provides a physical ruler for cross-platform proteomics

Plasma proteomics is expanding across platforms and cohorts, and integrating these data for AI demands comparability at the protein level, not merely concordant associations1,2. Affinity and MS platforms use distinct probes (antibodies, aptamers, or peptides) and signal readouts, yielding contradictory cross-platform results3,4. Without a known quantitative truth, we cannot distinguish biology from measurement distortion, leaving no gold standard for integration. Here we introduce Plasmix--a plasma reference suite with predefined male:female ratios (M, 1:0; Y, 3:1; P, 1:1; X, 1:3; F, 0:1)--and show that preserving this quantitative titration gradient, not just technical repeatability, predicts cross-platform concordance and identifies protein measurements suitable for integration. Profiling Plasmix across five platforms (Olink, SomaScan, NULISA, AAgAtlas, and MS-DIA) and 12 protocols across 17 batches, we found discordance is dominated by signal generation, not sample identity, and platforms distort signals in a protein-specific manner. Crucially, proteins retaining the titration response showed stronger agreement in an independent cohort; anchoring to the Plasmix midpoint (P) via sample-to-reference ratios reduced distortions, extending harmonizable coverage by 10-20%. Plasmix thus provides a physical ruler to benchmark accuracy, identifying genuinely integrable measurements before pooling datasets or training AI models--a critical bottleneck for plasma proteomics.

molecular biology↗

bioRxiv2026

MEGA-ODE: Learning Biologically Structured and Navigable Continuous Perturbation Dynamics from Sparse Omics

Perturbation-omics experiments usually measure only a subset of molecular feature, intervention and time space, leaving many response trajectories, perturbation effects and disease- or differentiation-associated transitions unobserved. Here we present MEGA-ODE, a graph-constrained continuous-time framework for reconstructing sparse dynamic omics landscapes, predicting unmeasured molecular states and prioritizing virtual perturbations toward defined biological endpoints. MEGA-ODE integrates molecular-network priors, graph neural ordinary differential equations and context-adaptive mixture-of-experts routing. In L1000 transcriptomic perturbations and CPPA proteomic drug-response data, MEGA-ODE improved held-out-feature and unseen-perturbation prediction over baseline methods, and in SARS-CoV-2 infection time-series data it remained competitive for future-time-point forecasting. In a COVID-19 patient cohort, predicted intermediate profiles improved retrospective disease-stage stratification relative to observed profiles alone, while expert programs highlighted immune and inflammatory signals associated with severity. Across the MAPK drug-response and stem-cell differentiation case studies, graph- and expert-level attributions prioritized perturbation-associated MAPK edges, developmental regulators and TF-target relationships supported by independent promoter-proximal ChIP-seq overlap. In hESC-to-definitive-endoderm differentiation, MEGA-ODE prioritized candidate transcription-factor perturbations predicted to shift 12-36 h profiles toward 96 h definitive-endoderm marker signatures, framing trajectory navigation as a concrete hypothesis-generation task. Together, these results support biologically structured continuous-time modeling for prediction, interpretation and virtual-perturbation prioritization from sparse temporal omics data.

bioinformatics↗

bioRxiv2026

Optimized Multiple Circular Sequence Alignment for Cyclic Peptide Motif Discovery

Head-to-tail (H2T) cyclized peptides are an increasingly important modality in drug discovery, combining high target affinity and selectivity with metabolic sta-bility. Because their underlying chemistry is still that of a linear amino-acid chain, their linear sequence representation is the native input format of main- stream sequence generative models now driving de novo peptide design (Slough et al., 2018; Rettie et al., 2025a;b). Discovering the conserved motifs responsible for a family's function across a library of such candidates requires a multiple sequence alignment (MSA). Because a cyclic peptide can be linearised at any residue, the alignment must additionally solve for the unknown rotation of each sequence, which is the multiple circular sequence alignment (MCSA) problem. However, leading MCSA heuristics (e.g. Ayad & Pissis, 2017) were tuned for the genomic regime (a few tens of long sequences) and become prohibitively slow on the cyclic peptide library regime (hundreds to thousands of shorter sequences). We close this gap by identifying quality-preserving optimisation opportunities, notably the library-scale preset tailored to short-sequence inputs (algorithmic details in Appendix A), and by adding an orthogonal multi-core and SIMD backend for further performance tuning, which gives near-linear thread scaling on the pairwise-comparison stage. We validate the pipeline on a library of 1,000 H2T cyclized peptides of length 18 targeting the oncoprotein Mouse double minute 2 human homolog (MDM2) produced by an internal peptide-design engine. In this practical setup, the optimised MCSA recovers the underlying positional motif of MDM2 binders at the same fidelity as the original MCSA implementation while running over 650x faster. Our optimised MCSA tool thus enables library-scale cyclic peptide sequence alignment and is publicly available at https://github.com/IVB-Generative-Biology/mars-turbo.

bioinformatics↗

bioRxiv2026

Effect of nuclear electrostatic potential on the nucleoplasmic distribution of histones and nucleosome stability

Differences in the density of heterochromatic and euchromatic regions are often assumed to play an important role in determining the transcriptional state of chromatin by influencing its accessibility to transcription factors. Yet, experiments show that even the most densely packed chromatin domains are readily accessible to transcription factors. Thus, the molecular mechanisms underlying differences in the transcriptional states of hetero- and euchromatin remain poorly understood. In this study, using electrically charged mEGFP probes, we demonstrated that heterochromatin and euchromatin differ not only in density but also in the magnitude of the local electrostatic potential. Furthermore, the nucleoplasmic distribution of electrically charged proteins, such as histone-chaperone complexes, was found to correlate with the local electrostatic potential. Estimates based on experimental data have also shown that the difference in electrostatic potentials between heterochromatic and euchromatic regions could lead to unequal nucleosome stability in them, which was successfully confirmed experimentally. Subsequent theoretical calculations showed that this could shift the balance in the DNA-binding competition between transcription factors and nucleosomes, thereby explaining experimental observations that heterochromatin is less transcriptionally active than euchromatin. Overall, our study suggests the existence of a nuclear electrostatic potential-mediated pathway that may be involved in the regulation of gene transcription.

biophysics↗

bioRxiv2026

False discovery rate control for trustworthy AI-based de novo peptide sequencing

AI-based de novo peptide sequencing predicts peptide sequences from tandem mass spectra, enabling identification beyond predefined databases but leaving prediction reliability difficult to assess, particularly with respect to false discovery rate (FDR). In database search, FDR control is provided by target-decoy competition over a finite search space, whereas de novo predictions are generated in open sequence space and lack naturally matched sequence-level decoys. Here we introduce Counterpart Calibration Theory (CCT), a theory-guided framework that reframes de novo FDR control as a four-group score-ranking and threshold-selection problem over target-side predictions and matched counterpart-side comparators. Implemented in {pi}-NovoQC, CCT provides dual-level FDR control at the peptide-spectrum match and peptide levels. Across models, datasets, instruments and acquisition modes, {pi}-NovoQC achieves stable FDR control while preserving identification yield. In large-scale proteomic applications, {pi}-NovoQC recovers low-abundance in-database peptides missed by database search and provides de novo-supported protein-group and variant evidence.

bioinformatics↗

bioRxiv2026

Electrolytic-Microbubble Dynamics Delineate Safety Thresholds During Intracortical Microstimulation with Flexible Neural Interfaces

Intracortical microstimulation (ICMS) with ultraflexible neural electrodes enables low-threshold, chronically stable, and high-resolution modulation of neural circuits, providing a promising strategy for sensory restoration and closed-loop neuromodulation. However, the microscopic mechanisms delineating its safe and effective current range remain unclear. Here, we combine intravital two-photon (2P) imaging and electrophysiology in awake mice to examine the current-dependent neurovascular outcomes of charge-balanced stimulation via ultraflexible arrays. We observed gas bubbles formed along the electrode during ICMS, with bubble size increasing quadratically with current amplitude, consistent with a Faradaic bubble-growth model. Intravital 2P imaging reveals that at low-to-moderate currents (20-40 {micro}A), vascular leakage is small, spatially confined, and largely reversible, whereas higher currents ([≥]60 {micro}A) induce a sharp transition to extensive, field-dominated extravasation and secondary vessel disruption. This transition coincides with immediate, stimulus-locked motor responses and the onset of electrode degradation. Multiphysics simulations reproduce the observed nonlinear leakage-current relationship by incorporating gas bubble-induced electric field redistribution and voltage-dependent vessel wall permeability. The model indicates that gas bubbles act as local electric-field modulators, concentrating suprathreshold fields near the bubble boundary at lower currents while shielding more distant vessel segments; at higher currents, this confinement breaks down and the system enters a field-dominated damage regime. Collectively, these findings define a mechanistically informed safety window for ICMS with flexible neural interfaces and identify bubble-assisted vascular permeabilization as a key failure mode at high currents, crucial for the design of future bidirectional brain-computer interfaces and high-precision neuroprosthetic protocols.

neuroscience↗

bioRxiv2026

SciCore-Omics: a tri-modal foundation model unifying histology, spatial transcriptomics and language for spatial biology

Histomorphology and spatial transcriptomics capture complementary aspects of tissue biology, but their relationships remain difficult to extract, align, and interpret at scale. Existing foundation models typically connect histology, omics, or language only pairwise, which limits their capacity to jointly infer molecular states, decode spatial tissue organization, and generate biologically grounded explanations. Here, we show SciCore-Omics, the first tri-modal foundation model linking histology images, spatial transcriptomics, and biological language. We constructed a spatially paired image-gene-text dataset comprising 151,182 spots across multiple tissues and performed a three-stage progressive training of SciCore-Omics on this dataset. Across gene expression prediction and spatial domain recognition, SciCore-Omics achieved 23.6-80.9% relative gains in task-specific metrics over the strongest external baselines. It further showed robust zero-shot generalization in histopathology classification, outperforming GPT-5 by 6.16 percentage points in mean accuracy across four benchmarks. Expert evaluation in 10 breast cancer cases confirmed its H&E-only case-level molecular reasoning capability. Together, our method demonstrates that a tri-modal framework can effectively bridge histomorphology and molecular state, providing a more general and interpretable foundation model for computational pathology and omics analysis.

bioinformatics↗

bioRxiv2026

Differential and compensatory roles for type I phosphatidylinositol-4-phosphate-5-kinase isoforms in retinal function and health.

Phosphatidylinositol (4,5) bisphosphate (PI(4,5)P2) plays important roles in development, signaling, intracellular trafficking and regulation throughout the nervous system. Using selective and combined gene ablation strategies, we have determined the roles of this lipid and the kinase isoforms of the PIP5KI family primarily responsible for its synthesis in mouse retina. In rod cells, PI(4,5)P2 localizes predominantly to the plasma membrane of inner and outer segments and is enriched in membranes near the synaptic termini. Disruption of the gene encoding the {gamma} PIP5KI isoform, Pip5k1c, throughout the developing retina, using Cre expression driven by a Six3 transcription factor-dependent promoter, yields dramatic, but not complete, loss of the protein, with no apparent effects on morphology or function through the first 3-4 months after birth. Slowly progressing photoreceptor degeneration is observed at later ages. Complete loss of the {gamma} isoform in rods, driven by the rhodopsin promoter-based iCre75 transgene, leads to no obvious developmental defects, but results in an earlier-onset rod degeneration. Germ-line ablation of neither the Pip5k1a nor the Pip5k1b gene leads to any observable morphological defects. Homozygous Pip5k1a ablation leads to functional defects in photoreceptors as revealed by reduced a-wave and b-wave amplitudes in the electroretinograms. On the background of rod-specific Pip5k1c ablation, Pip5k1a deficiency greatly accelerates retinal degeneration. These results reveal a complex interplay among PIP5KI isoforms in ensuring proper photoreceptor function and health, with apparent partial redundancy in fulfilling their critical functions. They underscore the important role of PI(4,5)P2 in neuronal signaling and homeostasis. Significance StatementPhosphatidylinositol(4,5)P2, PI(4,5)P2, plays essential roles in nervous system development and function, but its roles in retina have been unknown. This study combines biochemistry, mouse genetics, light- and electron microscopy to reveal both specific and redundant functions for PIP2 formed by different kinase isoforms in the mammalian retina. It has implications for retinal function, disease and therapy, and for the broader field of phosphoinositide regulation.

neuroscience↗

bioRxiv2026

Targeting THOC2-Mediated mRNA Export Induces PARP Inhibitor Vulnerability in DNA Repair-Competent Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) remains a lethal malignancy with limited therapeutic options. While Poly (ADP-ribose) polymerase inhibitors (PARPi) exploit synthetic lethality in tumors with DNA repair defects, their clinical utility in HCC is hindered by the low prevalence of canonical repair gene mutations and the enhancing DNA repair capacity. Through proteomic analysis of two independent cohorts (n=260), we identified the THO complex component THOC2 as a master regulator of DNA damage response (DDR) via mRNA nuclear export control. Clinically, THOC2 overexpression predicted poor survival (HR=2.68-6.84, P<0.001) and correlated with enhanced DDR gene expression. Mechanistically, THOC2 chaperones mRNA nuclear export of DDR effectors (MDC1, PRKDC, MSH6) and proliferation drivers (TOP2A), thereby establishing a dual pro-repair/pro-growth program. Targeting this vulnerability, THOC2 knockdown induced synthetic lethality with PARPi, reducing Olaparib IC50 by up to 61% and suppressing tumor growth by 76% (P<0.001). Our study illuminates mRNA transport as a druggable DDR modulator and establishes THOC2 as both a prognostic biomarker and a therapeutic target to overcome PARPi resistance in HCC. This work pioneers a strategy to expand synthetic lethality beyond genetic defects by targeting post-transcriptional regulation.

cancer biology↗

bioRxiv2026

Hippocampal Glucuronyl C5-epimerase promotes stress resilience by directly engaging PI3K through a non-enzymatic mechanism

Major depressive disorder (MDD) seriously affects human physical and mental health and causes global socioeconomic burdens. Stress-induced depressive etiology is linked to dendritic atrophy in the hippocampus, however, the underlying mechanism was poorly understood. Here, we identify that glucuronyl C5-epimerase (Glce), a heparan sulfae-modifying enzyme, as a critical regulator of hippocampal dendritic integrity and stress resilience. Genetic ablation of Glce in the hippocampal excitatory neurons is sufficient to induce dendritic atrophy and depressive-like behaviors, whereas its restoration rescues these deficits. Notably, Glce levels are reduced in the plasma of depressed individuals. We further find that Glce functions independently of its enzymatic activity, instead binding directly to and activating PI3K (p85/p110), thereby triggering an Akt/CREB/BDNF signaling cascade. Together, our findings uncover a non-canonical role for Glce and establish that Glce-PI3K-BDNF axis is essential for maintaining hippocampal structure and behavioral resilience, thereby highlighting a critical role of Glce in the pathophysiology of depression.

neuroscience↗

bioRxiv2026

Leptin Acts as a Peripheral Tropic Signal to Tune Steroidogenesis

The integration of metabolic status with reproductive and developmental timing is a cornerstone of animal physiology, yet how steroidogenesis rapidly adapts to abrupt environmental changes remains poorly understood. Here, we identify a peptide hormone circuit in which leptin or its analogs act as peripheral tropic signals, directly coupling systemic metabolic state to steroid hormone production through dynamic remodeling of intracellular lipid pools. Mechanistically, spatiotemporal fluctuations in leptin activate JAK/STAT signaling within steroidogenic tissues to reprogram the balance among lipid droplets, cholesteryl esters, and free cholesterol, thereby tuning the amplitude and timing of steroid hormone pulses via hormone-sensitive lipase (Hsl), a rate-limiting determinant of steroidogenic flux. Cross-species analyses in Drosophila melanogaster, Blattella germanica, and Mus musculus suggest a partially conserved leptin-JAK/STAT-lipase axis that functions as a metabolic "sterol rheostat", enabling rapid modulation of steroidogenesis in response to systemic metabolic and stress cues. These findings reveal a metabolic-endocrine mechanism by which leptin can act directly on steroidogenic organs to regulate hormonal output.

developmental biology↗

bioRxiv2026

π-MSNet: A billion-scale, AI-ready living proteomics data portal

Artificial intelligence (AI) is reshaping proteomics workflows, delivering remarkable gains in both peptide identification sensitivity and quantitative performance. However, the potential of deep learning models in proteomics has not been fully exploited due to the scarcity of large-scale, high-quality and consistently labeled datasets. Here, we present {pi}-MSNet, a billion-scale, AI-ready living mass spectrometry (MS) data portal. Using a uniform identification and quality control workflow, it comprises over 1.66 billion MS/MS spectra, 501 million peptide-spectrum matches (PSMs), and 9 million precursors from 36,356 LC-MS/MS runs across ten instrument types and 55 diverse species. Through community collaboration, the data are shared via international, interactive, and living web resources. Enabled by the built-in MSNetLoader Python API for seamless and scalable data access--with native support for PyTorch and TensorFlow--{pi}-MSNet provides an AI-ready data framework for efficient training and systematic benchmarking of multiple models across three representative tasks (e.g., MS/MS spectrum prediction, retention time prediction, and de novo peptide sequencing). In particular, by retraining multiple models on {pi}-MSNet, we achieved consistent performance improvements over their original versions. These improved models were subsequently integrated into the {pi}-MSNet agent to enable interactive, deployment-free use. Through SDRF (Sample and Data Relationship Format) metadata, an open-source cloud analysis workflow, and a community-driven interactive data portal that supports continuous data submission, {pi}-MSNet serves as a living, AI-ready resource for reproducible benchmarking, robust model training, and accelerated AI innovation in proteomics.

bioinformatics↗