Biology subjects

Zhang, O.

Publications and source records attributed to Zhang, O..

At least 19 recordsLinked to original sources

bioRxiv2026

RFOptimization: Guiding Design Optimization with All-Atom Structure Prediction

Biomolecular interactions, including protein--protein interactions, protein--nucleic acid recognition, and protein--small molecule binding, underlie a wide range of biological processes and therapeutic mechanisms. Although recent \emph{de novo} design methods can generate candidate binders for diverse molecular targets, practical design campaigns remain limited by low filter-passing rates and model-specific biases that arise when designs are optimized against a single predictor. Here, we present RFOptimization (RFO), a training-free framework for all-atom biomolecular binder optimization. RFO formulates binder improvement as a residue-wise mutational search problem, sampling candidate substitutions alternately based on gradient-guided sequence optimization using all-atom structure prediction models and a cycling-based sequence redesign strategy that alternates structure generation with an orthogonal predictor and MPNN-based sequence design to improve the \emph{in silico} success rate of RFdiffusion-generated binders within minutes of computation. To reduce overfitting to any individual structure model, candidate mutations are further evaluated with orthogonal AlphaFold3 metrics as final filters. We demonstrate the generality of RFO across diverse design settings, including classical protein binder design, ligand-binding biosensor design, cyclic peptide design, and active site-aware enzyme design.

biochemistry↗

bioRxiv2026

NACraft: Programmatic nucleic-acid aptamer design via all-atom structure-model feedback

Protein-nucleic-acid interactions underpin diverse biological processes and provide a basis for molecular sensing, regulation and therapeutic intervention. However, the coupled dependence of aptamer function on nucleotide sequence, three-dimensional folding and target binding makes rational RNA and DNA binder design challenging. Here we present NACraft, a training-free and programmatic framework for all-atom nucleic-acid aptamer design based on backpropagation through structure-model feedback. By composing binding, sequence-similarity and anti-binding constraints, NACraft supports de novo generation, similarity-guided sampling and target-selective design within a unified optimization framework, without task-specific training or fine-tuning. Computational experiments showed that NACraft generated high-confidence candidates de novo across diverse protein targets, with further improvements achieved through similarity-guided design for both RNA and DNA complexes. Its target-selective design capability was further validated in silico, with 69.44% of paired candidates generated to favour the positive target EGFR over the off-target HER2. Under matched independent AlphaFold3 evaluation, NACraft achieved better performance than ODesign in 10 of 11 NA-12 targets and 17 of 20 protein target-length settings. Together, these results demonstrate the effectiveness and versatility of NACraft and extend structure-model hallucination toward programmatic nucleic-acid aptamer design. Codehttps://github.com/OTEAM-AI4S/NACraft

bioinformatics↗

bioRxiv2026

IDPForge: Deep Learning of Proteins with Global and Local Regions of Disorder

Although machine learning has transformed protein structure prediction of folded protein ground states with remarkable accuracy, intrinsically disordered proteins and regions (IDPs/IDRs) are defined by diverse and dynamical structural ensembles that are predicted with low confidence by algorithms such as AlphaFold and RoseTTAFold. We present a new machine learning method, IDPForge (Intrinsically Disordered Protein, FOlded and disordered Region GEnerator), that exploits a transformer protein language diffusion model to create all-atom IDP ensembles and IDR disordered ensembles that maintains the folded domains. IDPForge does not require sequence-specific training, back transformations from coarse-grained representations, nor ensemble reweighting, as in general the created IDP/IDR conformational ensembles show good agreement with bot NMR and SAXS solution experimental data, and options for biasing with experimental restraints are provided if desired. We envision that IDPForge with these diverse capabilities will facilitate integrative and structural studies for proteins that contain intrinsic disorder, and is available as an open source resource for general use.

biophysics↗

bioRxiv2026

A feed-forward loop between niche adenosine and Gzmk⁺ CD8 T cells propagates systemic inflammaging

The causal link between the aging microenvironment and T cell aging remains elusive. Here, we demonstrate that adenosine within aging tissues actively reprograms CD8+ T cells into a pro-aging Granzyme K+ (Gzmk+) population. Mechanistically, senescent cells create an adenosine-rich niche via p16-dependent CD39 upregulation, triggering A2aR signaling to induce Gzmk+ T cell differentiation. Once released, Gzmk promotes systemic inflammaging through PAR1 and complement activation. Crucially, targeting this axis--either via genetic Gzmk ablation or pharmacological A2aR blockade--reverses multi-organ aging phenotypes and significantly extends healthy lifespan in mice. Human analysis reveals age-dependent Gzmk+ T cell accumulation in multi organs, while coffee intake (an A2aR antagonist) inversely correlates with plasma Gzmk levels. Our findings uncover how metabolic niche changes drive T cell aging and establish the adenosine-Gzmk axis as a pivotal therapeutic target for combating age-related diseases.

immunology↗

bioRxiv2025

AlphaFlex: Ensembles of the human proteome representing disordered regions

More than two thirds of proteins in the human proteome are predicted to contain intrinsically disordered regions (IDRs), which lack stable folded structure. IDRs are critical for biological regulation and organization, as targets for post-translational modifications, and as mediators of biomolecular condensates. To address the pressing need for better structural models enabling functional insight, we developed AlphaFlex to model fully atomistic conformer ensembles for proteins predicted to have IDRs, modeled in the context of AlphaFold folded domains and an implicit bilayer for transmembrane proteins. The AlphaFlex resource provides conformational ensembles of human proteins from the AlphaFold database with identified IDRs in the Protein Ensemble Database that is mirrored in UniProt. This transformative resource of AlphaFlex ensembles provides physically and biologically relevant full-length models for IDR proteins, including scaffold proteins, those with IDR:folded-domain interactions, regulatory and condensate proteins requiring exposed binding elements, conditionally folding IDRs, and transmembrane proteins containing IDRs.

biochemistry↗

bioRxiv2025

Phosphorus stress and spatial confinement lower the threshold for quorum-sensing activation of redox-active metabolite production in Pseudomonas synxantha

Bacteria often coordinate collective behaviors such as biofilm formation and secondary metabolite production through quorum sensing (QS), a regulatory system traditionally linked to high cell density. However, in environments like soil, where microbial populations are spatially fragmented, sparse, nutrient-limited, and subject to mass transport, the mechanisms that enable QS-dependent processes remain incompletely understood. Here, we investigate the regulation of a secreted redox-active metabolite, phenazine-1-carboxylic acid (PCA), in Pseudomonas synxantha 2-79, a model rhizobacterium, under phosphorus (P) limitation, a persistent stress in many soils. Using a combination of microscopy and molecular genetic approaches, we show that P limitation sensitizes the QS activation threshold by an order of magnitude, enabling phenazine induction at relatively low population densities in comparison to P replete conditions. This induction is abolished in QS-deficient mutants and restored by the addition of exogenous acyl-homoserine lactone (AHL), demonstrating that quorum sensing remains essential but its threshold becomes environmentally tuned. Under P limitation, spatial confinement and pore saturation levels further shape the timing and location of induction, illustrating how physical structure and nutrient stress can modulate bacterial activities. Moreover, phosphorus stress confers both collaborative and competitive advantages, enabling P. synxantha to undergo low-cell density AHL cross-talk between related Pseudomonas spp. and to suppress other rhizobacteria. Lastly, on plant roots, PCA genes are more predominantly induced under P limitation. These findings illustrate how the nutrient status of an environment can modulate the onset of quorum sensing, enabling quorum-regulated behaviors to activate at lower thresholds.

microbiology↗

bioRxiv2025

De novo Design of All-atom Biomolecular Interactions with RFdiffusion3

Deep learning has accelerated protein design, but most existing methods are restricted to generating protein backbone coordinates and often neglect interactions with other biomolecules. We present RFdiffusion3 (RFD3), a diffusion model that generates protein structures in the context of ligands, nucleic acids and other non-protein constellations of atoms. Because all polymer atoms are modeled explicitly, conditioning the model on complex sets of atom-level constraints for enzyme design and other challenges is both simpler and more effective than previous approaches. RFD3 achieves improved performance compared to prior approaches on a range of in silico benchmarks with one tenth the computational cost. Finally, we demonstrate the broad applicability of RFD3 by designing and experimentally characterizing DNA binding proteins and cysteine hydrolases. The ability to rapidly generate protein structures guided by complex sets of atom-level constraints in the context of arbitrary non-protein atoms should further expand the range of functions attainable through protein design.

biochemistry↗

bioRxiv2025

Accelerating Biomolecular Modeling with AtomWorks andRF3

Deep learning methods trained on protein structure databases have revolutionized biomolecular structure prediction, but developing and training new models remains a considerable challenge. To facilitate the development of new models, we present AtomWorks: a broadly applicable data framework for developing state-of-the-art biomolecular foundation models spanning diverse tasks, including structure prediction, generative protein design, and fixed backbone sequence design. We use AtomWorks to train RosettaFold-3 (RF3), a structure prediction network capable of predicting arbitrary biomolecular complexes with an improved treatment of chirality that narrows the performance gap between closed-source AlphaFold3 (AF3) and existing open-source implementations. We expect that AtomWorks will accelerate the next generation of open-source biomolecular machine learning models and that RF3 will be broadly useful as a structure prediction tool. To this end, we release the AtomWorks framework (https://github.com/RosettaCommons/atomworks), together with curated training data, code and model weights for RF3 (https://github.com/RosettaCommons/modelforge) under a permissive BSD license.

biochemistry↗

bioRxiv2025

Spatial transcriptomics elucidates the central role of cerebral vasculatures in the progression of Japanese encephalitis

The cellular and molecular mechanisms underlying the neuropathology of Japanese encephalitis virus (JEV) remain obscure. Herein, we designed Stereo-seq chips to simultaneously capture the in situ transcriptomes of both the host and JEV, constructing a comprehensive spatiotemporal pathological landscape for Japanese encephalitis (JE). This study reveals the central role of the vascular system in JE pathogenesis, particularly the meninges, which displayed the strongest signal of inflammation and cell death in the JEV-infected brain. The activation of the Ackr1+ endothelial cells, disruption of the blood-brain barrier, the migration of JEV-infected monocytes, secretion of immune factors by the infected cells, and the occurrence of pyroptosis and necroptosis form a positive feedback loop, resulting in an increasing influx of immune cells and tissue damage. JEV infection leads to neurological impairments, which may be attributed to the interaction between viral proteins and host cellular factors such as Stat1, Stat3, Nfkb1, and Sp1. As the vascular system serves as a central receiver and amplifier of inflammatory signals, regulating inflammation within the vascular system is essential in mitigating JE progression.

immunology↗

bioRxiv2024

ECloudGen: Access to Broader Chemical Space for Structure-based Molecule Generation

Structure-based molecule generation represents a significant advancement in AI-aided drug design (AIDD). However, progress in this domain is constrained by the scarcity of structural data on protein-ligand complexes, a challenge we term the Paradox of Sparse Chemical Space Generation. To address this limitation, we propose a novel latent variable approach that bridges the data gap between ligand-only and protein-ligand complexes, enabling the target-aware generative models to explore a broader chemical space and enhancing the quality of molecular generation. Drawing inspiration from quantum molecular simulations, we introduce ECloudGen, a generative model that leverages electron clouds as meaningful latent variables--an innovative integration of physical principles into deep learning frameworks. ECloudGen incorporates modern techniques, including latent diffusion models, Llama architectures, and a newly proposed contrastive learning task, which organizes the chemical space into a structured and highly interpretable latent representation. Benchmark studies demonstrate that ECloudGen outperforms state-of-the-art methods by generating more potent binders with superior physiochemical properties and by covering a significantly broader chemical space. The incorporation of electron clouds as latent variables not only improves generative performance but also introduces model-level interpretability, as illustrated in a case study designing V2R inhibitors. Furthermore, ECloudGens structurally ordered modeling of chemical space enables the development of a model-agnostic optimizer, extending its utility to molecular optimization tasks. This capability has been validated through a single-objective oracle benchmark and a complex multi-objective optimization scenario involving the redesign of endogenous BRD4 ligands. In conclusion, ECloudGen effectively addresses the Paradox of Sparse Chemical Space Generation through its integration of theoretical insights, advanced generative techniques, and real-world validation. The newly proposed technique of leveraging physical entities (such as electron clouds) as latent variables within a deep learning framework may prove useful for computational biology fields beyond AIDD.

bioinformatics↗

bioRxiv2024

Long-term imaging of three-dimensional hyphal development using the ePetri dish

Imaging three-dimensional microbial development and behavior over extended periods is crucial for advancing microbiological studies. Here, we introduce an upgraded ePetri dish system specifically designed for extended microbial culturing and 3D imaging, addressing the limitations of existing methods. Our approach includes a sealed growth chamber to enable long-term culturing, and a multi-step reconstruction algorithm that integrates 3D deconvolution, image filtering, ridge, and skeleton detection for detailed visualization of the hyphal network. The system effectively monitored the development of Aspergillus brasiliensis hyphae over a seven-day period, demonstrating the growth mediums stability within the chamber. The systems 3D imaging capability was validated in a volume of 5.5 mm x 4 mm x 0.5 mm, revealing a radial growth pattern of fungal hyphae. Additionally, we show that the system can identify potential filter failures that are undetectable with 2D imaging. With these capabilities, the upgraded ePetri dish represents a significant advancement in long-term 3D microbial imaging, promising new insights into microbial development and behavior across various microbiological research areas.

microbiology↗

bioRxiv2024

GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation

Increasing works for antibody design are emerging to generate sequences and structures in Complementarity Determining Regions (CDRs), but problems still exist. We focus on two of them: (i) authenticity of the generated structure and (ii) rationality of the affinity maturation, and propose GEOAB as a solution. In specific, GeoABDesigner generates CDR structures with realistic internal geometries, composed of a generative geometry initializer (Geo-Initializer) and a position refiner (Geo-Refiner); GeoAB-Optimizer achieves affinity maturation by accurately predicting both the mutation effects and structures of mutant antibodies with the same network architecture as Geo-Refiner. Experiments show that GEOAB achieves state-of-the-art performance in CDR co-design and mutation effect predictions, and fulfills the discussed tasks effectively.

bioinformatics↗

bioRxiv2024

Asymmetric Engagement of Dimeric CRL3KBTBD4 by the Molecular Glue UM171 Licenses Degradation of HDAC1/2 Complexes

UM171 is a potent small molecule agonist of ex vivo human hematopoietic stem cell (HSC) self-renewal1, a process that is tightly controlled by epigenetic regulation. By co-opting KBTBD4, a substrate receptor of the CULLIN3-RING E3 ubiquitin ligase complex, UM171 promotes the degradation of members of the CoREST transcriptional corepressor complex, thereby limiting HSC attrition2,3. However, the direct target and mechanism of action of UM171 remain unclear. Here, we reveal that UM171 acts as a molecular glue to induce high-affinity interactions between KBTBD4 and HDAC1 to promote the degradation of select HDAC1/2 corepressor complexes. Through proteomics and chemical inhibitor studies, we discover that the principal target of UM171 is HDAC1/2. Cryo-electron microscopy (cryo-EM) analysis of dimeric KBTBD4 bound to UM171 and the LSD1-HDAC1-CoREST complex unveils an unexpected asymmetric assembly, in which a single UM171 molecule enables a pair of KBTBD4 KELCH-repeat propeller domains to recruit HDAC1 by clamping on its catalytic domain. One of the KBTBD4 propellers partially masks the rim of the HDAC1 active site pocket, which is exploited by UM171 to extend the E3-neo-substrate interface. The other propeller cooperatively strengthens HDAC1 binding via a separate and distinct interface. The overall neomorphic interaction is further buttressed by an endogenous cofactor of HDAC1-CoREST, inositol hexakisphosphate, which makes direct contacts with KBTBD4 and acts as a second molecular glue. The functional relevance of the quaternary complex interaction surfaces defined by cryo-EM is demonstrated by in situ base editor scanning of KBTBD4 and HDAC1. By delineating the direct target of UM171 and its mechanism of action, our results reveal how the cooperativity offered by a large dimeric CRL E3 family can be leveraged by a small molecule degrader and establish for the first time a dual molecular glue paradigm.

molecular biology↗

bioRxiv2024

KBTBD4 Cancer Hotspot Mutations Drive Neomorphic Degradation of HDAC1/2 Corepressor Complexes

Cancer mutations can create neomorphic protein-protein interactions to drive aberrant function1. As a substrate receptor of the CULLIN3-RBX1 E3 ubiquitin ligase complex, KBTBD4 is recurrently mutated in medulloblastoma (MB)2, the most common embryonal brain tumor in children, and pineoblastoma3. These mutations impart gain-of-function to KBTBD4 to induce aberrant degradation of the transcriptional corepressor CoREST4. However, their mechanism of action remains unresolved. Here, we elucidate the mechanistic basis by which KBTBD4 mutations promote CoREST degradation through engaging HDAC1/2, the direct neomorphic target of the substrate receptor. Using deep mutational scanning, we systematically map the mutational landscape of the KBTBD4 cancer hotspot, revealing distinct preferences by which insertions and substitutions can promote gain-of-function and the critical residues involved in the hotspot interaction. Cryo-electron microscopy (cryo-EM) analysis of two distinct KBTBD4 cancer mutants bound to LSD1-HDAC1-CoREST reveals that a KBTBD4 homodimer asymmetrically engages HDAC1 with two KELCH-repeat propeller domains. The interface between HDAC1 and one of the KBTBD4 propellers is stabilized by the MB mutations, which directly insert a bulky side chain into the active site pocket of HDAC1. Our structural and mutational analyses inform how this hotspot E3-neo-substrate interface can be chemically modulated. First, our results unveil a converging shape complementarity-based mechanism between gain-of-function E3 mutations and a molecular glue degrader, UM171. Second, we demonstrate that HDAC1/2 inhibitors can block the mutant KBTBD4-HDAC1 interface, the aberrant degradation of CoREST, and the growth of KBTBD4-mutant MB models. Altogether, our work reveals the structural and mechanistic basis of cancer mutation-driven neomorphic protein-protein interactions and pharmacological strategies to modulate their action for therapeutic applications.

molecular biology↗

bioRxiv2024

PPFlow: Target-aware Peptide Design with Torsional Flow Matching

Therapeutic peptides have proven to have great pharmaceutical value and potential in recent decades. However, methods of AI-assisted peptide drug discovery are not fully explored. To fill the gap, we propose a target-aware peptide design method called PPFO_SCPLOWLOWC_SCPLOW, based on conditional flow matching on torus manifolds, to model the internal geometries of torsion angles for the peptide structure design. Besides, we establish a protein-peptide binding dataset named PPBench2024 to fill the void of massive data for the task of structure-based peptide drug design and to allow the training of deep learning methods. Extensive experiments show that PPFO_SCPLOWLOWC_SCPLOW reaches state-of-the-art performance in tasks of peptide drug generation and optimization in comparison with baseline models, and can be generalized to other tasks including docking and side-chain packing.

molecular biology↗

bioRxiv2024

Investigating 3D microbial community dynamics in the rhizosphere using complex-field and fluorescence microscopy

Microbial interactions in the rhizosphere contribute to soil health. Understanding these interactions thus has the potential to advance sustainable agriculture, ecosystem management, and environmental stewardship. Yet it is difficult to understand what we cannot see; amongst the limitations in rhizosphere imaging are challenges associated with rapidly and non-invasively imaging microbial cells over field depths relevant to plant roots. Here, we present a novel bimodal imaging technique called Complex-field and Fluorescence microscopy using the Aperture Scanning Technique (CFAST) that begins to address these limitations by integrating quantitative phase and 3D fluorescence imaging. We showcase CFASTs practicality and versatility in two ways. First, by harnessing its depth of field of more than 100 microns, we significantly reduce the number of captures required for 3D imaging of plant roots and bacteria in the rhizoplane, thereby minimizing potential photobleaching and phototoxicity. Second, by leveraging CFASTs phase sensitivity and fluorescence specificity, we track early bacterial aggregate development, bacterial competition, and gene expression under varying environmental conditions. Specifically, we resolve bacterial growth dynamics of mixed populations at the early stages of colonization without the need for genetically labeling environmental isolates. Moreover, we find that the expression of genes of interest to rhizosphere chemistry (e.g. representative genes involved in phosphorus-sensing and antibiotic production) varies spatiotemporally within microbial populations that are surface-attached and appears distinct from their expression in planktonic cultures. Together, CFASTs attributes overcome commercial imaging platform limitations and enable new insights to be gained into microbial behavioral dynamics in experimental systems of relevance to the rhizosphere.

microbiology↗

bioRxiv2023

Resolving the nanoscale structure of β-sheet assemblies using single-molecule orientation-localization microscopy

Synthetic peptides that self-assemble into cross-{beta} fibrils have remarkable utility as engineered biomaterials due to their modularity and biocompatibility, but their structural and morphological similarity to amyloid species has been a long-standing concern for their translation. Further, their polymorphs are difficult to characterize using spectroscopic and imaging techniques that rely on ensemble averaging to achieve high resolution. Here, we utilize single-molecule orientation-localization microscopy (SMOLM) to characterize fibrils formed by the designed amphipathic enantiomers, KFE8L and KFE8D, and the pathological amyloid-beta peptide A{beta}42. SMOLM reveals that the orientations of Nile red, as it transiently binds to both KFE8 and A{beta}42, are consistent with a helical (bilayer) ribbon structure and convey the precise tilt of the fibrils inner and outer backbones. SMOLM also finds polymorphic branched and curved morphologies of KFE8 whose backbones exhibit much more heterogeneity than those of more typical straight fibrils. Thus, SMOLM is a powerful tool to interrogate the structural differences and polymorphism between engineered and pathological cross {beta}-rich fibrils.

biophysics↗

bioRxiv2023

Local Disordered Region Sampling (LDRS) for Ensemble Modeling of Proteins with Experimentally Undetermined or Low Confidence Prediction Segments

STRUCTURED ABSTRACTO_ST_ABSSUMMARYC_ST_ABSThe Local Disordered Region Sampling (LDRS, pronounced loaders) tool, developed for the IDPConformerGenerator platform (Teixeira et al. 2022), provides a method for generating all-atom conformations of intrinsically disordered regions (IDRs) at N- and C-termini of and in loops or linkers between folded regions of an existing protein structure. These disordered elements often lead to missing coordinates in experimental structures or low confidence in predicted structures. Requiring only a pre-existing PDB structure of the protein with missing coordinates or with predicted confidence scores and its full-length primary sequence, LDRS will automatically generate physically meaningful conformational ensembles of the missing flexible regions to complete the full-length protein. The capabilities of the LDRS tool of IDPConformerGenerator include modeling phosphorylation sites using enhanced Monte Carlo Side Chain Entropy (MC-SCE) (Bhowmick and Head-Gordon 2015), transmembrane proteins within an all-atom bilayer, and multi-chain complexes. The modeling capacity of LDRS capitalizes on the modularity, ability to be used as a library and via command-line, and computational speed of the IDPConformerGenerator platform. AVAILABILITY AND IMPLEMENTATIONThe LDRS module is part of the IDPConformerGenerator modeling suite, which can be downloaded from GitHub at https://github.com/julie-forman-kay-lab/IDPConformerGenerator. IDPConformerGenerator is written in Python and works on Linux, Microsoft Windows, and Mac OS versions that support DSSP. Users can utilize LDRSs Python API for scripting the same way they can use any part of IDPConformerGenerators API, by importing functions from the idpconfgen.ldrs_helper library. Otherwise, LDRS can be used as a command line interface application within IDPConformerGenerator. Full documentation is available within the command-line interface (CLI) as well as on IDPConformerGenerators official documentation pages (https://idpconformergenerator.readthedocs.io/en/latest/). CONTACTFor support with LDRS please contact Zi Hao (Nemo) Liu via nemo.liu@sickkids.ca or submit an issue in the IDPConformerGenerator repository on GitHub (https://github.com/julie-forman-kay-lab/IDPConformerGenerator/issues). SUPPLEMENTARY INFORMATIONThe supplementary information document contains, or links to, all the conformer ensembles generated for this publication, the generalized Python scripts using the LDRS Python API, figures of detailed methods, fractional secondary structure information, torsion angle sampling, and the time required to generate the different protein cases.

biophysics↗