bioRxiv Science⌕ Search

Biology subjects

Malone, B.

Publications and source records attributed to Malone, B..

11 recordsLinked to original sources

Machine Learning-Driven Antigen Selection Reveals Conserved T-Cell Targets for Broad Coronavirus Vaccination

BackgroundCoronavirus outbreaks remain a persistent threat to global health, and vaccines based primarily on spike-specific immune responses are susceptible to antigenic variation. T-cell immunity directed against conserved internal viral proteins may provide a complementary and more variant-tolerant strategy for next-generation coronavirus vaccines. MethodsWe combined machine learning-guided antigen prioritization with ex vivo functional immunological validation to identify conserved non-spike T-cell targets across betacoronaviruses. Candidate sequences were screened for immunogenicity using primary human peripheral blood mononuclear cells from healthy donors using intracellular cytokine staining and activation-induced marker assays. Top-ranked conserved regions were incorporated into multiepitope mRNA constructs, and their intracellular expression and HLA class I presentation were confirmed by immunopeptidomics. Immunogenicity was further evaluated ex vivo and in vivo using mRNA immunization of mice and T-cell FluoroSpot assays. FindingsAcross a panel of 97 peptides derived from 19 viral proteins, evolutionary conservation across distinct betacoronavirus taxa was strongly associated with functional T-cell immunogenicity in human donors. Highly conserved peptides elicited significantly stronger and more frequent CD4 and CD8 T-cell responses than taxon-restricted peptides. Multiepitope mRNA constructs encoding conserved regions were efficiently expressed and presented on HLA class I molecules and induced T-cell responses in human PBMCs. In mice, mRNA immunization with conserved multiepitope constructs generated robust interferon-{gamma}- and interleukin-2-producing T-cell responses that exceeded those induced by unconserved control constructs. InterpretationThese results link evolutionary conservation to functional cellular immunogenicity and demonstrate the feasibility of multiepitope mRNA delivery for inducing conserved coronavirus-directed T-cell responses. Although protective efficacy remains to be established, conservation-guided antigen selection represents a scalable strategy for developing T-cell-focused vaccines with broad lineage coverage, supporting pandemic preparedness beyond spike-centered immunity. FundingThe research was supported by CEPI, NEC, University of Oslo and Oslo university hospital. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSPrior coronavirus vaccine development has focused predominantly on spike protein-directed neutralizing antibodies. While highly effective against matched strains, spike-centered immunity is vulnerable to antigenic drift and lineage-specific escape. Multiple observational and experimental studies have shown that T-cell responses, particularly against internal viral proteins, are more conserved and correlate with reduced disease severity and cross-variant recognition. Epitope prediction algorithms and immunoinformatics approaches have been widely used to nominate candidate T-cell targets; however, systematic functional validation of conserved non-spike antigens across betacoronaviruses in primary human immune systems, combined with antigen presentation data and in vivo vaccine testing, has remained limited. Searches of PubMed and bioRxiv up to December 2025 using terms including "coronavirus T-cell vaccine," "conserved coronavirus epitopes," "betacoronavirus cross-reactive T cells," and "mRNA T-cell vaccine" identified studies demonstrating cross-reactive T-cell immunity and computational epitope selection, but few integrated machine-learning-guided antigen prioritization with ex vivo human functional screening, immunopeptidomics, and in vivo mRNA immunization in a unified workflow. Added value of this studyThis study provides an integrated experimental and computational framework for identifying and validating conserved non-spike T-cell antigens across betacoronaviruses. We functionally screened a panel of candidate peptides derived from multiple viral proteins and demonstrated that evolutionary conservation across species is strongly associated with T-cell immunogenicity. We further demonstrate that multiepitope mRNA constructs encoding these top-ranked conserved regions can be intracellularly expressed, presented on HLA class I molecules to induce polyfunctional T-cell responses in primary human PBMCs. Finally, in vivo mRNA immunization in mice induces robust interferon-{gamma} and interleukin-2 T-cell responses exceeding those induced by unconserved control constructs. Together, these findings link evolutionary conservation to functional cellular immunogenicity and extend beyond in silico prediction by demonstrating antigen processing, presentation, and immunogenicity across human and murine systems. Implications of all the available evidenceCollectively, the available evidence indicates that T-cell immunity directed toward conserved internal coronavirus proteins represents a complementary and potentially more variant-tolerant axis of vaccine design than spike-only strategies. Our findings suggest that evolutionary conservation can serve as a practical selection principle for prioritizing T-cell antigens with broad lineage coverage and that multiepitope mRNA delivery is a feasible platform for inducing such responses. While direct protection and heterologous challenge studies will be required to establish clinical efficacy, the integration of computational prioritization with functional validation supports a scalable approach to pandemic preparedness that may be applicable to other rapidly evolving viral families.

immunology↗

A potent and selective TNKS2 inhibitor for tumor-selective WNT suppression

Hyperactive WNT signaling is a potent cancer driver, but clinical translation of WNT inhibitors has been hampered by on-target toxicities. WNT signaling can be constrained through inhibition of the PARP family enzymes Tankyrase 1 (TNKS1) and Tankyrase 2 (TNKS2), however, existing TNKS inhibitors suppress WNT signaling in both tumor and healthy tissues. In this study, we show that the loss of chromosome 8p that occurs in approximately half of advanced epithelial malignancies, creates a collateral vulnerability that enables tumor-selective inhibition of Tankyrase activity. 8p loss depletes expression of TNKS1 and creates a tumor-specific dependency on the functionally redundant TNKS2 protein. Through structure-guided drug design, we identify a first-in-class TNKS2-selective inhibitor that can drive selective WNT inhibition in TNKS1-deficient oncogenic cell and organoid models. This work demonstrates a targetable vulnerability in multiple cancer types, providing a new approach to potent and selective WNT-targeted therapies.

cancer biology↗

Early intermediates in bacterial RNA polymerase promoter melting visualized by time-resolved cryo-electron microscopy

During formation of the transcription-competent open complex (RPo) by bacterial RNA polymerases (RNAP), transient intermediates pile up before overcoming a rate-limiting step. Structural descriptions of these interconversions in real time are unavailable. To address this gap, time-resolved cryo-electron microscopy (cryo-EM) was used to capture four intermediates populated 120 or 500 milliseconds (ms) after mixing Escherichia coli {sigma}70-RNAP and the PR promoter. Cryo-EM snapshots revealed the upstream edge of the transcription bubble unpairs rapidly, followed by stepwise insertion of two conserved nontemplate strand (nt-strand) bases into RNAP pockets. As nt-strand "read-out" extends, the RNAP clamp closes, expelling an inhibitory {sigma}70 domain from the active-site cleft. The template strand is fully unpaired by 120 ms but remains dynamic, indicating yet unknown conformational changes load it in subsequent steps. Because these events likely describe DNA opening at many bacterial promoters, this study provides needed insights into how DNA sequence regulates steps of RPo formation.

biophysics↗

Ensemble cryo-electron microscopy reveals conformational states of the nsp13 helicase in the SARS-CoV-2 helicase replication-transcription complex

The SARS-CoV-2 nonstructural proteins coordinate genome replication and gene expression. Structural analyses revealed the basis for coupling of the essential nsp13 helicase with the RNA dependent RNA polymerase (RdRp) where the holo-RdRp and RNA substrate (the replication-transcription complex, or RTC) associated with two copies of nsp13 (nsp132-RTC). One copy of nsp13 interacts with the template RNA in an opposing polarity to the RdRp and is envisaged to drive the RdRp backwards on the RNA template (backtracking), prompting questions as to how the RdRp can efficiently synthesize RNA in the presence of nsp13. Here, we use cryo-electron microscopy and molecular dynamics simulations to analyze the nsp132-RTC, revealing four distinct conformational states of the helicases. The results suggest a mechanism for the nsp132-RTC to turn backtracking on and off, using an allosteric mechanism to switch between RNA synthesis or backtracking in response to stimuli at the RdRp active site.

biophysics↗

Structural origins of Escherichia coli RNA polymerase open promoter complex stability

The first step of gene expression in all organisms requires opening the DNA duplex to expose one strand for templated RNA synthesis. In Escherichia coli, promoter DNA sequence fundamentally determines how fast the RNA polymerase (RNAP) forms "open" complexes (RPo), whether RPo persists for seconds or hours, and how quickly RNAP transitions from initiation to elongation. These rates control promoter strength in vivo but their structural origins remain largely unknown. Here we use cryo-electron microscopy to determine structures of RPo formed de novo at three promoters with widely differing lifetimes at 37{degrees}C: {lambda}PR (t1/2 [~] 10 hours), T7A1 (t1/2 [~] 4 minutes), and a point mutant in {lambda}PR ({lambda}PR-5C) (t1/2 [~] 2 hours). Two distinct RPo conformers are populated at {lambda}PR, likely representing productive and unproductive forms of RPo observed in solution studies. We find that changes in the sequence and length of DNA in the transcription bubble just upstream of the start site (+1) globally alter the network of DNA-RNAP interactions, base stacking, and strand order in the single-stranded DNA of the transcription bubble; these differences propagate beyond the bubble to upstream and downstream DNA. After expanding the transcription bubble by one base (T7A1), the nontemplate-strand "scrunches" inside the active site cleft; the template-strand bulges outside the cleft at the upstream edge of the bubble. The structures illustrate how limited sequence changes trigger global alterations in the transcription bubble that modulate RPo lifetime and affect the subsequent steps of the transcription cycle.

biophysics↗

Structural basis for backtracking by the SARS-CoV-2 replication-transcription complex

Backtracking, the reverse motion of the transcriptase enzyme on the nucleic acid template, is a universal regulatory feature of transcription in cellular organisms but its role in viruses is not established. Here we present evidence that backtracking extends into the viral realm, where backtracking by the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) may aid viral transcription and replication. Structures of SARS-CoV-2 RdRp bound to the essential nsp13 helicase and RNA suggested the helicase facilitates backtracking. We use cryo-electron microscopy, RNA-protein crosslinking, and unbiased molecular dynamics simulations to characterize SARS-CoV-2 RdRp backtracking. The results establish that the single-stranded 3-segment of the product-RNA generated by backtracking extrudes through the RdRp NTP-entry tunnel, that a mismatched nucleotide at the product-RNA 3-end frays and enters the NTP-entry tunnel to initiate backtracking, and that nsp13 stimulates RdRp backtracking. Backtracking may aid proofreading, a crucial process for SARS-CoV-2 resistance against antivirals. Significance StatementThe COVID-19 pandemic is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The SARS-CoV-2 genome is replicated and transcribed by its RNA-dependent RNA polymerase (RdRp), which is the target for antivirals such as remdesivir. We use a combination of approaches to show that backtracking (backwards motion of the RdRp on the template RNA) is a feature of SARS-CoV-2 replication/transcription. Backtracking may play a critical role in proofreading, a crucial process for SARS-CoV-2 resistance against many antivirals.

biochemistry↗

BERTMHC: Improves MHC-peptide class II interaction prediction with transformer and multiple instance learning

MotivationIncreasingly comprehensive characterisation of cancer associated genetic alteration has paved the way for the development of highly specific therapeutic vaccines. Predicting precisely binding and presentation of peptides by MHC alleles is an important step towards such therapies. Recent data suggest that presentation of both class I and II epitopes is critical for the induction of a sustained effective immune response. However, the prediction performance for MHC class II has been limited compared to class I. ResultsWe present a transformer neural network model which leverages on self-supervised pretraining from a large corpus of protein sequences. We also propose a multiple instance learning (MIL) framework to deconvolve mass spectrometry data where multiple potential MHC alleles may have presented each peptide. We show that pretraining boosted the performance for these tasks. Combining pretraining and the novel MIL approach, our model outperforms state-of-the-art models for both binding and mass spectrometry presentation predictions. AvailabilityOur model is available at https://github.com/s6juncheng/BERTMHC Contactjun.cheng@neclab.eu, brandon.malone@neclab.eu

bioinformatics↗

DOUBLER: Unified Representation Learning of Biological Entities and Documents for Predicting Protein-Disease Relationships

MotivationWe propose a system that learns consistent representations of biological entities, such as proteins and diseases, based on a knowledge graph and additional data modalities, like structured annotations and free text describing the entities. In contrast to similar approaches, we explicitly incorporate the consistency of the representations into the learning process. In particular, we use these representations to identify novel proteins associated with diseases; these novel relationships could be used to prioritize protein targets for new drugs. ResultsWe show that our approach outperforms state-of-the-art link prediction algorithms for predicting unknown protein-disease associations. Detailed analysis demonstrates that our approach is most beneficial when additional data modalities, such as free text, are informative. AvailabilityCode and data are available at: https://github.com/nle-sztyler/research-doubler Contacttimo.sztyler@neclab.eu

bioinformatics↗

Time-resolved cryoEM using Spotiton

We present an approach for preparing cryoEM grids to study short-lived molecular states. Using piezo electric dispensing, two independent streams of ~50 pL sample drops are deposited within 10 ms of each other onto a nanowire EM grid surface, and the mixing reaction stops when the grid is vitrified in liquid ethane, on the order of ~100 ms later. We demonstrate the utility of this approach for four biological systems where short-lived states are of high interest.

biophysics↗

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goals of this study were to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant "epitope hotspot" regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA genotypes of approximately 22 000 individuals to develop a "digital twin" type simulation to model how effective different combinations of hotspots would work in a diverse human population, and used the approach to identify an optimal constellation of epitopes hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have managed to profile the entire SARS-CoV-2 proteome and identify a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

bioinformatics↗

dom2vec: Assessable domain embeddings and their use for protein prediction tasks

MotivationWord embedding approaches have revolutionized Natural Language Processing NLP research. These approaches aim to map words to a low-dimensional vector space in which words with similar linguistic features are close in the vector space. These NLP approaches also preserve local linguistic features, such as analogy. Embedding-based approaches have also been developed for proteins. To date, such approaches treat amino acids as words, and proteins are treated as sentences of amino acids. These approaches have been evaluated either qualitatively, via visual inspection of the embedding space, or extrinsically, via performance on a downstream task. However, it is difficult to directly assess the intrinsic quality of the learned embeddings. ResultsIn this paper, we introduce dom2vec, an approach for learning protein domain embeddings. We also present four intrinsic evaluation strategies which directly assess the quality of protein domain embeddings. We leverage the hierarchy relationship of InterPro domains, known secondary structure classes, Enzyme Commission class information, and Gene Ontology annotations in these assessments. These evaluations allow us to assess the quality of learned embeddings independently of a particular downstream task. Importantly, allow us to draw an analog between the local linguistic features in nature languages and the domain structure and function information in domain architectures, thus providing data-driven insights into the context found in the language of domain architectures. We also show that dom2vec embeddings outperform, or are comparable with, state-of-the-art approaches on downstream tasks. AvailabilityThe protein domain embeddings vectors and the entire code to reproduce the results are available at https://github.com/damianosmel/dom2vec. Contactmelidis@l3s.uni-hannover.de

bioinformatics↗