Biology subjects

Nguyen, D.-T.

Publications and source records attributed to Nguyen, D.-T..

6 recordsLinked to original sources

bioRxiv2026

EpiTune: An Accurate Epitope Prediction Model with Mechanistic Insights

The accurate prediction of b-cell epitopes facilitates vaccine development by identifying known antibodies for an antigen. Multiple epitope prediction models use protein language models to enable more accurate predictions with modest results. Here, we present EpiTune, a b-cell epitope prediction model that fine-tunes the underlying protein language model to deliver best-in-class predictions of linear epitopes and competitive predictions for confirmational epitopes. EpiTune achieves this performance from antigen sequence alone, and utilizes ESM-2s RoPE architecture to fine-tune and infer on sequences longer than other sequence-based models currently available in the literature. EpiTunes single-model architecture allows the model to determine the meaningfulness of sequence features for epitope prediction. This avoids the need for assigning importance to intermediates such as structure-based information, while still allowing a high degree of model interpretability.

bioinformatics↗

bioRxiv2026

Polymer-lipid hybrid nanoparticle enhances mRNA delivery and T cell-mediated immunity

mRNA vaccines have transformed prophylactic immunization against infectious diseases as well as therapeutic interventions for cancer. However, their effectiveness against emerging viral variants and a range of malignancies continues to be hindered by suboptimal induction of T cell-mediated immunity. To overcome this limitation, here we developed a polymer-lipid hybrid nanoparticle (PLNP) platform engineered to improve mRNA delivery to antigen-presenting cells (APCs) and to potentiate T cell responses. Relative to conventional mRNA lipid nanoparticle (LNP) vaccines, mRNA PLNP vaccines demonstrated markedly improved lymph node targeting, APC activation, Th1-biased pro-inflammatory cytokine response, and antigen-specific T cell expansion while retaining robust humoral immunity. Remarkably, mRNA-PLNP vaccines generated approximately 50% more antigen-specific CD8+ T cells than mRNA-LNP vaccines across multiple antigens, including SARS-CoV-2 spike, influenza hemagglutinin, and ovalbumin. In prophylactic applications, mRNA PLNP vaccine provided complete protection against SARS-CoV-2 variants. As a therapeutic approach in a melanoma model, mRNA PLNP vaccination resulted in enhanced tumor control and significantly prolonged survival compared to LNP-based formulations. Collectively, these results establish PLNP as a versatile and broadly applicable platform for augmenting mRNA vaccine efficacy through improved mRNA delivery and T cell priming, offering promising implications for infection prevention and cancer immunotherapy.

immunology↗

bioRxiv2025

Low-Strength Type I Interferon Signaling Promotes CAR T-Cell Treatment Efficacy

CD19-directed chimeric antigen receptor (CAR) T-cell therapy has significantly advanced the treatment landscape for relapsed/refractory diffuse large B-cell lymphoma (r/r DLBCL). However, up to 60% of patients do not achieve a complete response. To uncover determinants of therapeutic efficacy, we analyzed the infusion products of eight r/r DLBCL patients with distinct clinical responses to axicabtagene ciloleucel using single-cell transcriptomics. Compared to patients who exhibited progressive disease, infusion products of complete responders demonstrated enriched signatures of type I interferon (IFN-I) signaling. Based on these findings, we developed a novel strategy to improve CD19-directed CAR T-cell treatment efficacy by incorporating IFN-I as an enhancer during the ex vivo manufacturing process, with IFN-I removal before CAR T-cell infusion to avoid in vivo toxicities. For both CD28- and 4-1BB-costimulated second-generation CARs, we found that low-strength IFN-I signaling enhanced CAR T-cell cytotoxicity and treatment efficacy against B-cell lymphoma and leukemia. Our low-strength IFN-I-enhanced CAR T-cell ex vivo manufacturing approach leverages an existing FDA-approved pharmacologic agent, circumvents in vivo interferon-associated toxicities, and remains fully compatible with current CAR constructs and manufacturing workflows. Together, our results establish IFN-I as a potent and costimulation-independent enhancer of CAR T-cell efficacy and provide a translationally feasible approach to enhance CAR T-cell therapies.

immunology↗

bioRxiv2024

epiTCR-KDA: Knowledge Distillation model on Dihedral Angles for TCR-peptide prediction

MotivationAntigen recognition by T-cell receptors (TCRs) triggers cascades of immune responses. Successful predictions of the TCR and antigen (as peptide) bindings therefore signify the advancements in immunotherapy. However, most of current TCR-peptide interaction predictors fail to predict unseen data. This limitation may be derived from the conventional usage of TCR and/or peptide sequences as input, which may not adequately reflect their structural characteristics. Therefore, incorporating the TCR and peptide structural information into the prediction model to improve the generalizability is necessary. ResultsWe presented epiTCR-KDA as a new predictor of TCR-peptide binding that utilises structural information, specifically the dihedral angles between the residues of both the peptide and the TCR. This structural descriptor was integrated into a model constructed using knowledge distillation to enhance its generalizability. The epiTCR-KDA demonstrated competitive prediction performance, with an AUC of 0.99 for seen data and AUC of 0.86 for unseen data. Across multiple public datasets, epiTCR-KDA consistently outperformed other predictors, such as epiTCR, NetTCR, BERTrand, TEIM-Seq, TEINet, and ImRex, maintaining a median AUC of 0.9 (ranging from 0.82 to 0.91). Further analysis of epiTCR-KDA performance indicated that the cosine similarity of the dihedral angle vectors between the unseen testing data and training data is crucial for its stable performance. In conclusion, our epiTCR-KDA model, with its capacity to predict for unseen data, has brought us one step closer toward the development of a highly effective pipeline for affordable antigen-based immunotherapy. Availability and implementationepiTCR-KDA is available on GitHub (https://github.com/ddiem-ri-4D/epiTCR-KDA)

bioinformatics↗

bioRxiv2022

Perception of Rigidity in Three- and Four-Dimensional Spaces

Our brain employs mechanisms to adapt to changing visual conditions. In addition to natural changes in our physiology and those in the environment, our brain is also capable of adapting to "unnatural" changes, such as inverted visual-inputs generated by inverting prisms. In this study, we examined the brains capability to adapt to hyperspaces. We generated four spatial-dimensional stimuli in virtual reality and tested the ability to distinguish between rigid and non-rigid motion. We found that observers are able to differentiate rigid and non-rigid motion of tesseracts (4D) with a performance comparable to that obtained using cubes (3D). Moreover, observers performance improved when they were provided with more immersive 3D experience but remained robust against increasing shape variations. At this juncture, we characterize our findings as "[Formula] perception" since, while we show the ability to extract and use 4D information, we do not have yet evidence of a complete phenomenal 4D experience.

neuroscience↗

bioRxiv2017

Drug Target Ontology to Classify and Integrate Drug Discovery Data

BackgroundOne of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome.\n\nResultsAs part of that effort, we have been developing a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while also supporting manual knowledge acquisition. A modular, hierarchical implementation facilitates development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships.\n\nConclusionsDTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/, Github (https://github.com/DrugTargetOntology/DTO), and the NCBO Bioportal (https://bioportal.bioontology.org/ontologies/DTO). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

bioinformatics↗