Biology subjects

Jing, B.

Publications and source records attributed to Jing, B..

13 recordsLinked to original sources

bioRxiv2026

Deep reinforcement learning-driven discovery of a MsbA-targeted small-molecule antibiotic for the treatment of Acinetobacter baumannii infection

Antibiotics with new mechanisms are highly pursued to address the threat of infections caused by drug-resistant Gram-negative bacteria. Targeting MsbA, a key protein of the lipopolysaccharide biosynthesis pathway, represents a promising strategy to discover new classes of antibiotics. However, currently available MsbA-targeted molecules either lack sufficient potency or have unfavorable properties, necessitating expansion of chemical space. In this study, we chose the most promising cerastecin Cpd 4 as the template, and used two Artificial Intelligence (AI)-based tools, i.e. Link-INVENT and AutoMolDesigner for molecular design, performed chemical derivatization and antibacterial activity evaluation, which led to the discovery of Y-11 (MIC for A. baumannii: 0.5 g/mL). Encouragingly, Y-11 showed equivalent potency to Cpd4 for carbapenem-resistant A. baumannii, and less cytotoxicity and hemolysis as well as lower spontaneous resistance frequency. In vivo efficacy study demonstrated that Y-11 could effectively reduce bacterial loads in the mice infected by A. baumannii. The following mechanism study including molecular dynamics simulation, biochemical assay, and transmission electron microscope (TEM) analysis suggested that Y-11 inhibited the transport of lipooligosaccharide and impaired the formation of outer membrane, probably by competitively binding to the substrate binding site of MsbA and modulating ATPase activity. Taken together, we have discovered a MsbA-targeted small molecule Y-11 via AI-driven drug design, which provides a foundation for future antibiotic development.

biochemistry↗

bioRxiv2026

Promera: a unified model for biomolecular structure prediction, filtering, and design

Generative models have become staple tools for modeling and designing biomolecular structures. However, although these tools have improved in structural prediction accuracy, their ability to filter designed binders--an essential use case--remains insufficient; whereas design methods have focused more on unconstrained binder generation rather than capabilities enabled by controllable design. We introduce Promera, a unified generative model that combines all-atom structure prediction with improved filtering and controllable design. We find that Promeras confidence metrics are more accurate for filtering binders from non-binders for both miniproteins and nanobodies, while its co-folding performance surpasses popular open-source models (OpenFold3-p2, Boltz-2) on therapeutically relevant categories. As a design model, Promera generates binders by predicting masked protein sequences with optional epitope, paratope, and template constraints. Remarkably, our nanobody designs match the in silico success rates from backprop-based techniques (mBER) when evaluated under co-folding confidence filters. We further provide two in silico demonstrations of the the versatile capabilities of our design method: epitope targeting of the Andes hantavirus glycoprotein with VHHs and active state stabilization of the {beta}2 andrenergic GPCR. We conclude by proposing a scaling law for co-folding models, suggesting a path for further performance improvement.

bioinformatics↗

bioRxiv2026

Thousandfold Expansion Microscopy

Biological macromolecules, such as proteins, are made of concatenated building blocks. We hypothesized that individual protein residues could be imaged by anchoring their side chains to a swellable polymer, cleaving backbone amide bonds, and expanding residues away from each other to a degree that enables them to be visualized separately. We introduce thousandfold expansion microscopy (1000ExM), a four-network interpenetrating hydrogel architecture that enables successive expansion from [~]18-fold to >1000-fold (one billion-fold in volume). Protein and peptide structures are maintained across these expansion factors, as verified by analyses of proteins with known structures (nanobodies, GFP) and a well-studied peptide (mCLING). Computational analysis indicates that 1000ExM resolves adjacent amino acid residues, thereby achieving sub-nanometer precision on conventional light microscopes. We anticipate that 1000ExM will find wide utility in protein visualization and identification, potentially even in intact cells and tissues.

molecular biology↗

bioRxiv2026

The abscopal effect of Combination IRE with anti-PD-1 achieves local ablation and systemic control of PDAC

Irreversible electroporation (IRE) has shown promise for treating pancreatic ductal adenocarcinoma (PDAC), but whether IRE can induce an abscopal effect is not established. We demonstrated that the combination of IRE and anti-PD-1 antibody could trigger robust abscopal effects in preclinical models of metastatic PDAC. Data from multiple in vivo models, RNA-seq, scRNA-seq, and spatial immunofluorescence provide compelling evidence that IRE induced mitochondrial dysfunction and cellular stress, which triggered activation of the cGAS-STING pathway and subsequent systemic antitumor effects. IRE also led to inflammatory response characterized by tumor infiltration of myeloid cells and their polarization toward M1 state, turning immunologically "cold" tumors into "hot" tumors. Moreover, the presence of T cell/B cell clusters in tumors from mice treated with IRE plus PD-1 and the lack of antitumor efficacy in B cell knockout mice bearing orthotopic murine PDAC tumors indicate that B cells play an important role in IRE-mediated systemic antitumor immunity. SignificanceThis study shows that IRE plus a checkpoint inhibitor represents a promising therapeutic strategy for PDAC and supports advancing this treatment toward clinical translation. Our data also support potential combination strategies with immunomodulatory agents that can recruit and reprogram B cells to support T cell activation and cytotoxic effector functions.

immunology↗

bioRxiv2025

Advancing Protein Ensemble Predictions Across the Order-Disorder Continuum

While deep learning has transformed structure prediction for ordered proteins, intrinsically disordered proteins remain poorly predicted due to systematic underrepresentation in training data, despite constituting approximately 30% of eukaryotic proteomes. We introduce PeptoneBench, the first benchmark to enable systematic assessment of ensemble generators for both ordered and disordered proteins, integrating diverse experimental observables. Our analysis reveals that existing evaluation metrics exhibit systematic bias toward the structured spectrum of the proteome. Assessment of popular predictors (AlphaFold2, ESMFlow, Boltz2) confirms high accuracy on ordered proteins but shows performance degradation with increasing disorder. We further present PepTron, a flow-matching ensemble generator trained on data augmented with synthetic disordered protein ensembles. On our benchmark PepTron matches BioEmu on disordered regions while maintaining competitive accuracy on ordered protein benchmarks. Our data augmentation approach demonstrates that targeted training strategies can approach the performance of computationally expensive simulation-based methods, establishing a generalizable framework applicable to other protein generative models. All datasets, models, and code are openly available.

biophysics↗

bioRxiv2025

Generating functional and multistate proteins with a multimodal diffusion transformer

Generating proteins with the full diversity and complexity of functions found in nature is a grand challenge in protein design. Here, we present ProDiT, a multimodal diffusion model that unifies sequence and structure modeling paradigms to enable the design of functional proteins at scale. Trained on sequences, 3D structures, and annotations for 214M proteins across the evolutionary landscape, ProDiT generates diverse, novel proteins that preserve known active and binding site motifs and can be successfully conditioned on a wide range of molecular functions, spanning 465 Gene Ontology terms. We introduce a diffusion sampling protocol to design proteins with multiple functional states, and demonstrate this protocol by scaffolding enzymatic active sites from carbonic anhydrase and lysozyme to be allosterically deactivated by a calcium effector. Our results showcase ProDiTs unique capacity to satisfy design specifications inaccessible to existing generative models, thereby expanding the protein design toolkit.

bioinformatics↗

bioRxiv2025

Construction of recombinant Bacillus subtilis spores surface-displaying PRRSV ORF1b, GP5, M, and N proteins in tandem and evaluation of their immunogenicity in mice

Porcine reproductive and respiratory syndrome virus (PRRSV), a single-stranded RNA virus, is a highly contagious pathogen that causes severe reproductive and respiratory disorders in pigs, leading to significant economic losses in the swine industry worldwide. However, current commercial vaccines provide only limited protection against circulating PRRSV strains, highlighting the urgent need for novel vaccine strategies. B. Bacillus subtilis, a well-characterized probiotic, has emerged as a promising platform for mucosal vaccine delivery due to its safety profile and ability to induce robust immune responses. In this study, we screened B cell and T cell linear epitopes from PRRSV-ORF1b, GP5, M, and N proteins to construct a recombinant tandem antigen (ER), which was displayed on the surface of B. subtilis 168 spores. The resulting recombinant strain, designated B. subtilis BE, was evaluated for immunogenicity in mice via oral administration. The results showed that Oral immunization with B. subtilis BE significantly elevated antigen-specific secretory IgA levels in intestinal contents and IgG levels in serum, indicating potent mucosal and systemic humoral responses. Moreover, B. subtilis BE induced measurable neutralizing antibody titers and enhanced cellular immunity, as evidenced by increased frequencies of CD3+ and CD8+ T cells in mesenteric lymph nodes and upregulation of IFN-, TNF-, and IFN-{gamma} expression in intestinal tissues. Collectively, these findings demonstrate that B. subtilis BE elicits both humoral and cellular immune responses and may serve as a novel oral vaccine candidate against PRRSV, offering a promising alternative for controlling the ongoing epidemic.

immunology↗

bioRxiv2025

Learning the language of protein-protein interactions

Protein Language Models (PLMs) trained on large databases of protein sequences have proven effective in modeling protein biology across a wide range of applications. However, while PLMs excel at capturing individual protein properties, they face challenges in natively representing protein-protein interactions (PPIs), which are crucial to understanding cellular processes and disease mechanisms. Here, we introduce MINT, a PLM specifically designed to model sets of interacting proteins in a contextual and scalable manner. Using unsupervised training on a large curated PPI dataset derived from the STRING database, MINT outperforms existing PLMs in diverse tasks relating to protein-protein interactions, including binding affinity prediction and estimation of mutational effects. Beyond these core capabilities, it excels at modeling interactions in complex protein assemblies and surpasses specialized models in antibody-antigen modeling and T cell receptor-epitope binding prediction. MINTs predictions of mutational impacts on oncogenic PPIs align with experimental studies, and it provides reliable estimates for the potential for cross-neutralization of antibodies against SARS-CoV-2 variants of concern. These findings position MINT as a powerful tool for elucidating complex protein interactions, with significant implications for biomedical research and therapeutic discovery.

bioinformatics↗

bioRxiv2022

Proteomic profiling reveals lysosomal heterogeneity in association with longevity

Lysosomes are active sites to integrate cellular metabolism and signal transduction. A collection of proteins associated with the lysosome mediate these metabolic and signaling functions. Both lysosomal metabolism and lysosomal signaling have been linked to longevity regulation; however, how lysosomes adjust their protein composition to accommodate this regulation remains unclear. Using deep proteomic profiling, we systemically profiled lysosome-associated proteins linked with different longevity mechanisms. We further discovered the lysosomal recruitment of AMPK and nucleoporin proteins and their requirements for longevity in response to increased lysosomal lipolysis. Through comparative proteomic analyses of lysosomes from different tissues and labeled with different markers, we discovered lysosomal heterogeneity across tissues as well as the specific enrichment of the Ragulator complex on Cystinosin positive lysosomes. Together, this work uncovers lysosomal proteome heterogeneity at different levels and provides resources for understanding the contribution of lysosomal protein dynamics to signal transduction, organelle crosstalk and organism longevity.

cell biology↗

bioRxiv2021

Drosulfakinin signaling modulates female sexual receptivity in Drosophila

Female sexual behavior as an innate behavior is of prominent biological importance for survival and reproduction. However, molecular and circuit mechanisms underlying female sexual behavior is not well understood. Here, we identify the Cholecystokinin-like peptide Drosulfakinin (DSK) promotes female sexual behavior in Drosophila. Manipulation both Dsk and DSK neuronal activity impact female sexual receptivity. In addition, we reveal that Dsk-expressing neurons receive input signal from R71G01GAL4 neurons to promote female sexual receptivity. Based on intersectional technique, we further found the regulation of female sexual behavior relies mainly on medial DSK neurons rather than lateral DSK neurons, and medial DSK neurons modulate female sexual behavior by acting on its receptor CCKLR-17D3. Thus, we characterized DSK/CCKLR-17D3 as R71G01GAL4 neurons downstream signaling to regulate female sexual behavior.

neuroscience↗

bioRxiv2021

Individual structural MRI abnormality indexes are associated with molecular,histological and cognitive indicators for frontal glioma patients

Although every glioma patient varies in tumor size, location, histological grade and molecular biomarkers, structural abnormalities are commonly conducted in a group-level, leading to the miss of individual structural atrophy. In this study, we introduced an individual-level structural abnormality detection method for glioma patients and proposed several novel abnormality indexes to depict the individual atrophy pattern. Forty-five glioma patients in frontal lobe and fifty-two age-matched healthy controls participated in the study. All patients underwent neurocognitive test and molecular examinations, including 1p/19q co-deletion, isocitrate dehydrogenase (IDH) mutation, telomerase reverse transcriptase (TERT) promoter mutation and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation. Individual structural abnormality maps for every glioma patient were calculated from the preoperative T1 images, and the individual abnormality index were further computed and explored the associations with clinical indicators. The results manifested that: 1. Every glioma patient show unique atrophy pattern; 2. Glioma patients also share consistent atrophy regions; 3. The atrophy pattern is influenced by some molecular biomarkers. Our study provides an effective way to access the individual structural abnormalities in glioma patients, and displays great potentials in individualized precision medicine for glioma patients.

neuroscience↗

bioRxiv2019

Bootstrapping promotes the RSFC-behavior associations: an application of individual cognitive traits prediction

Resting state functional connectivity records enormous functional interaction information between any pair of brain nodes, which enriches the prediction of individual phenotypes. To reduce the high dimensional features in prediction, correlation analysis is a common way for feature selection. However, rs-fMRI signal exhibits typically low signal-to-noise ratio and correlation analysis is sensitive to outliers and data distribution, which may bring unstable and uninformative features to subsequent prediction. To alleviate this problem, a bootstrapping-based feature selection framework was proposed and applied on three widely used regression models: connectome-based predictive model (CPM), support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO). A large open-source dataset from Human Connectome Project (HCP) was adopted in the study and a series of cognitive traits were acted as the prediction targets. To systematically investigate the influences of different parameter settings on the bootstrapping-based framework, a total of 216 parameter combinations were evaluated through the R value between the predicted and real cognitive traits, and the best identified performance among them was chosen out as the final prediction accuracy for each cognitive trait. By using bootstrapping without replacement, the best performances of CPM with positive and negative feature sets, SVR and LASSO averagely increased by 28.0%, 33.2%, 11.6% and 24.3% in R values in contrast to the baseline method without bootstrapping. By using bootstrapping with replacement, these best performances increased by 22.1%, 22.9%, 9.4% and 19.6%. Furthermore, the bootstrapping-based feature selection methods could effectively refine the original feature sets obtained from correlation analysis, which thus retained the more stable and informative feature sets. The results demonstrate that bootstrapping-based feature selection is an easy-to-use and effective method to improve RSFC prediction of cognitive traits and is highly recommended in future RSFC prediction studies.

neuroscience↗

bioRxiv2018

GONST2 transports GDP-Mannose for sphingolipid glycosylation in the Golgi apparatus of Arabidopsis

The Golgi lumen is the site of many different glycosylation events, including cell wall polysaccharide biosynthesis and lipid glycosylation. Transporters are necessary for the import of the substrates required for glycosylation (nucleotide sugars) from the cytosol where they are synthesized. Plants use four GDP-linked sugars to glycosylate macromolecules: GDP-L-Fucose, GDP-D-Mannose, GDP-L-Galactose and GDP-D-Glucose. Of the predicted fifty-one members of the nucleotide sugar transporter/triose phosphate transporter family in Arabidopsis, only four appear to contain the conserved motif needed for the transport of GDP-linked sugars, GOLGI LOCALIZED NUCLEOTIDE SUGAR TRANSPORTER (GONST) 1-4. Previously, we have demonstrated that GONST1 provides GDP-D-Mannose for glycosylation of a class of sphingolipids, the glycosylinositolphosphorylceramides (GIPCs). Here, we characterize its closest homologue, GONST2, and conclude that it also specifically provides substrate for GIPC glycosylation. Expression of GONST2 driven by the GONST1 promoter is able to rescue the severe growth phenotype of gonst1. Loss of GONST2 exacerbates the gonst1 constitutive hypersensitive response, as well as the reduced cell wall cellulose content. The gonst2 mutant grows normally under standard conditions, but has enhanced resistance to the powdery mildew-causing fungus Golovinomyces orontii.

plant biology↗