Biology subjects

Tubiana, J.

Publications and source records attributed to Tubiana, J..

8 recordsLinked to original sources

bioRxiv2026

A fusion Cell-Permeable C16orf74 Peptide Selectively Disrupts Calcineurin-NFAT Interaction and Inhibits T-cell Activation Without Cytotoxicity

Calcineurin (Cn) is a protein phosphatase that initiates T-cell activation by dephosphorylating the transcription factor NFAT, driving its nuclear translocation and the transcription of immune-related genes. While clinical immunosuppressants like Cyclosporine A (CsA) potently inhibit Cn, they completely block its catalytic site, leading to non-specific inhibition and severe off-target toxicity. Selectively targeting the specific protein-protein interaction (PPI) between Cn and NFAT presents a safer therapeutic strategy. We previously identified the C16orf74 (C16) peptide as a high-affinity Cn-NFAT PPI inhibitor; however, its utility in cellular systems is restricted by poor membrane permeability. In this study, we evaluated cell-penetrating peptide (CPP) conjugates of C16 with an N-terminus transactivator of transcription (TAT) and polyarginine (R11) to enable efficient intracellular delivery. Structural modeling, fluorescence polarization displacement, and pull-down assays confirmed that the CPP-C16 conjugates retain the ability to compete with an NFAT-derived peptide and bind Cn. Fluorescence microscopy demonstrated efficient intracellular entry of TAT-C16 and R11-C16 in mammalian cells, and effective inhibition of NFAT nuclear translocation and attenuation of downstream NFAT-dependent transcriptional activity of the IL-2 gene in human T cells at concentrations of 10 {micro}M or lower. Crucially, unlike CsA, the CPP-C16 peptides exhibited minimal cytotoxicity even at high concentrations of up to 50 {micro}M, establishing a potential safe therapeutic window. These findings establish CPP-C16 conjugates as effective, cell-permeable, and non-toxic inhibitors of the Cn-NFAT signaling axis, providing the basis for the development of PPI-directed immunosuppressants.

bioengineering↗

bioRxiv2026

LocAlign: Local Protein Structural Alignment with Geometric Deep Learning

Identifying common function-determining structural motifs among proteins with different folds is a foundational task in computational biology with no go-to solution. Indeed, standard alignment tools like TM-align are ill-suited for matching small, sequence-order-independent motifs, while specialized tools have limited success. Here, we introduce LocAlign, a local structural alignment algorithm based on geometric deep learning. Given two protein structures, LocAlign iteratively predicts atom-level correspondences and a 3D superimposition. By formulating training as a weakly supervised task on pairs of proteins bound to identical ligands, we bypass the need for ground-truth alignments. We find that LocAlign recovers known functional motifs without explicit supervision, identifying high-quality alignments for 87% and 37% of protein pairs with similar and dissimilar folds, respectively. We show that, equipped with confidence scoring and motif-conditioning capabilities, LocAlign supports diverse applications, including functional annotation of the dark proteome and drug off-target screening. LocAlign is thus a potent, versatile framework for protein functional site comparison.

bioinformatics↗

bioRxiv2022

Reduced antigenicity of Omicron lowers host serologic response

SARS-CoV-2 Omicron variant of concern (VOC) contains fifteen mutations on the receptor binding domain (RBD), evading most neutralizing antibodies from vaccinated sera. Emerging evidence suggests that Omicron breakthrough cases are associated with substantially lower antibody titers than other VOC cases. However, the mechanism remains unclear. Here, using a novel geometric deep-learning model, we discovered that the antigenic profile of Omicron RBD is distinct from the prior VOCs, featuring reduced antigenicity in its remodeled receptor binding sites (RBS). To substantiate our deep-learning prediction, we immunized mice with different recombinant RBD variants and found that the Omicrons extensive mutations can lead to a drastically attenuated serologic response with limited neutralizing activity in vivo, while the T cell response remains potent. Analyses of serum cross-reactivity and competitive ELISA with epitope-specific nanobodies revealed that the antibody response to Omicron was reduced across RBD epitopes, including both the variable RBS and epitopes without any known VOC mutations. Moreover, computational modeling confirmed that the RBS is highly versatile with a capacity to further decrease antigenicity while retaining efficient receptor binding. Longitudinal analysis showed that this evolutionary trend of decrease in antigenicity was also found in hCoV229E, a common cold coronavirus that has been circulating in humans for decades. Thus, our study provided unprecedented insights into the reduced antibody titers associated with Omicron infection, revealed a possible trajectory of future viral evolution and may inform the vaccine development against future outbreaks.

immunology↗

bioRxiv2021

Super-immunity by broadly protective nanobodies to sarbecoviruses

Vaccine boosters and infection can facilitate the development of SARS-CoV-2 antibodies with improved potency and breadth. Here, we observed super-immunity in a camelid extensively immunized with the SARS-CoV-2 receptor-binding domain (RBD). We rapidly isolated a large repertoire of specific ultrahigh-affinity nanobodies that bind strongly to all known sarbecovirus clades using integrative proteomics. These pan-sarbecovirus nanobodies (psNbs) are highly effective against SARS-CoV and SARS-CoV-2 variants including the Omicron, with the best median neutralization potency at single-digit ng/ml. Structural determinations of 13 psNbs with the SARS-CoV-2 spike or RBD revealed five epitope classes, providing insights into the mechanisms and evolution of their broad activities. The highly evolved psNbs target small, flat, and flexible epitopes that contain over 75% of conserved RBD surface residues. Their potencies are strongly and negatively correlated with the distance of the epitopes to the receptor binding sites. A highly potent, inhalable and bispecific psNb (PiN-31) was developed. Our findings inform on the development of broadly protective vaccines and therapeutics. One sentence summarySuccessive immunization of SARS-CoV-2 RBD in a camelid enhanced the development of super-immunity and isolation and systematic characterization of a large repertoire of ultrahigh-affinity pan-sarbecovirus single-chain VHH antibodies to understand the evolution of this potent and broad immune response.

microbiology↗

bioRxiv2021

Compositional Restricted Boltzmann Machines Unveil the Brain-Wide Organization of Neural Assemblies

Patterns of endogenous activity in the brain reflect a stochastic exploration of the neuronal state space that is constrained by the underlying assembly organization of neurons. Yet it remains to be shown that this interplay between neurons and their assembly dynamics indeed suffices to generate whole-brain data statistics. Here we recorded the activity from ~ 40,000 neurons simultaneously in zebrafish larvae, and show that a data-driven generative model of neuron-assembly interactions can accurately reproduce the mean activity and pairwise correlation statistics of their spontaneous activity. This model, the compositional Restricted Boltzmann Machine (cRBM), unveils ~ 200 neural assemblies, which compose neurophysiological circuits and whose various com-binations form successive brain states. We then performed in silico perturbation experiments to determine the interregional functional connectivity, which is conserved across individual animals and correlates well with structural connectivity. Our results showcase how cRBMs can capture the coarse-grained organization of the zebrafish brain. Notably, this generative model can readily be deployed to parse neural data obtained by other large-scale recording techniques.

neuroscience↗

bioRxiv2021

ScanNet: An interpretable geometric deep learning model for structure-based protein binding site prediction

Predicting the functional sites of a protein from its structure, such as the binding sites of small molecules, other proteins or antibodies sheds light on its function in vivo. Currently, two classes of methods prevail: Machine Learning (ML) models built on top of handcrafted features and comparative modeling. They are respectively limited by the expressivity of the handcrafted features and the availability of similar proteins. Here, we introduce ScanNet, an end-to-end, interpretable geometric deep learning model that learns features directly from 3D structures. ScanNet builds representations of atoms and amino acids based on the spatio-chemical arrangement of their neighbors. We train ScanNet for detecting protein-protein and protein-antibody binding sites, demonstrate its accuracy - including for unseen protein folds - and interpret the filters learned. Finally, we predict epitopes of the SARS-CoV-2 spike protein, validating known antigenic regions and predicting previously uncharacterized ones. Overall, ScanNet is a versatile, powerful, and interpretable model suitable for functional site prediction tasks. A webserver for ScanNet is available from http://bioinfo3d.cs.tau.ac.il/ScanNet/

bioinformatics↗

bioRxiv2020

Flexible machine learning prediction of antigen presentation for rare and common HLA-I alleles

The recent increase of immunopeptidomic data, obtained by mass spectrometry or binding assays, opens unprecedented possibilities for investigating endogenous antigen presentation by the highly polymorphic human leukocyte antigen class I (HLA-I) protein. We introduce a flexible and easily interpretable peptide presentation prediction method, RBM-MHC. We validate its performance as a predictor of cancer neoantigens and viral epitopes and we use it to reconstruct peptide motifs presented on specific HLA-I molecules. By benchmarking RBM-MHC performance on a wide range of HLA-I alleles, we show its importance to improve prediction accuracy for rarer alleles.

bioinformatics↗

bioRxiv2017

Blind sparse deconvolution for inferring spike trains from fluorescence recordings

The parallel developments of genetically-encoded calcium indicators and fast fluorescence imaging techniques makes it possible to simultaneously record neural activity of extended neuronal populations in vivo, opening a new arena for systems neuroscience. To fully harness the potential of functional imaging, one needs to infer the sequence of action potentials from fluorescence time traces. Here we build on recently proposed computational approaches to develop a blind sparse deconvolution algorithm (BSD), which we motivate by a theoretical analysis. We demonstrate that this method outperforms existing sparse deconvolution algorithms in terms of robustness, speed and/or accuracy on both synthetic and real fluorescence data. Furthermore, we provide solutions for the practical problems of thresholding and determination of the rise and decay time constants. We provide theoretical bounds on the performance of the algorithm in terms of precision-recall and temporal accuracy. Finally, we extend the computational framework to support temporal superresolution whose performance is established on real data.

neuroscience↗