Biology subjects

Nielsen, M.

Publications and source records attributed to Nielsen, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Genesis: A Modular Protein Language Modelling Approach to Immunogenicity Prediction

Neoantigen immunogenicity prediction is a highly challenging problem in the development of personalised medicines. Low reactivity rates in called neoantigens result in a difficult prediction scenario with limited training datasets. Here we describe Genesis, a modular protein language modelling approach to immunogenicity prediction for CD8+ reactive epitopes. Genesis comprises of a pMHC encoding module trained on three pMHC prediction tasks, an optional TCR encoding module and a set of context specific immunogenicity prediction head modules. Compared with state-of-the-art models for each task, Genesis encoding module performs comparably or better on pMHC binding affinity, eluted ligand prediction and stability tasks. Genesis outperforms all compared models on pMHC immunogenicity prediction (Area under the receiver operating characteristic curve=0.619, average precision: 0.514), with a 7% increase in average precision compared to the next best model. Genesis shows further improved performance on immunogenicity prediction with the integration of TCR context information. Genesis performance is further analysed for interpretability, which locates areas of weakness found across existing immunogenicity models and highlight possible biases in public datasets.

bioinformatics↗

bioRxiv2023

Taxometer: Improving taxonomic classification of metagenomics contigs

For taxonomy based classification of metagenomics assembled contigs, current methods use sequence similarity to identify their most likely taxonomy. However, in the related field of metagenomics binning contigs are routinely clustered using information from both the contig sequences and their abundance. We introduce Taxometer, a neural network based method that improves the annotations and estimates the quality of any taxonomic classifier by combining contig abundance profiles and tetra-nucleotide frequencies. When applied to five short-read CAMI2 datasets, it increased the average share of correct species-level contig annotations of the MMSeqs2 tool from 66.6% to 86.2% and reduced the share of wrong species-level annotations in the CAMI2 Rhizosphere dataset two-fold on average for Metabuli, Centrifuge, and Kraken2. Finally, we applied Taxometer to two complex long-read metagenomics data sets for benchmarking taxonomic classifiers. Taxometer is available as open-source software and can enhance any taxonomic annotation of metagenomic contigs.

bioinformatics↗

bioRxiv2023

NetTCR 2.2 - Improved TCR specificity predictions by combining pan- and peptide-specific training strategies, loss-scaling and integration of sequence similarity

The ability to predict binding between peptides presented by the Major Histocompatibility Complex (MHC) class I molecules and T-cell receptors (TCR) is of great interest in areas of vaccine development, cancer treatment and treatment of autoimmune diseases. However, the scarcity of paired-chain data, combined with the bias towards a few well-studied epitopes, has challenged the development of pan-specific machine-learning (ML) models with accurate predictive power towards peptides characterized by little or no TCR data. To deal with this, we here benefit from a larger paired-chain peptide-TCR dataset and explore different ML model architectures and training strategies to better deal with imbalanced data. We show that while simple changes to the architecture and training strategies results in greatly improved performance, particularly for peptides with little available data, predictions on unseen peptides remain challenging, especially for peptides distant to the training peptides. We also demonstrate that ML models can be used to detect potential outliers, and that the removal of such outliers from training further improves the overall performance. Furthermore, we show that a model combining the properties of pan-specific and peptide-specific models achieves improved performance, and that performance can be further improved by integrating similarity-based predictions, especially when a low false positive rate is desirable. Moreover, in the context of the IMMREP 2022 benchmark, this updated modeling framework archived state-of-the-art performance. Finally, we show that combining all these approaches results in acceptable predictive accuracy for peptides characterized with as little as 15 positive TCRs. This observation thus places great promise on rapidly expanding the peptide covering of the current models for predicting TCR specificity. The final NetTCR 2.2 models are available at https://github.com/mnielLab/NetTCR-2.2, and as a web server at https://services.healthtech.dtu.dk/services/NetTCR-2.2/.

bioinformatics↗

bioRxiv2023

Antisense transcription and PRC2 repression function in parallel during vernalization

Non-coding transcription induces chromatin changes that can mediate environmental responsiveness, but the causes and consequences of these mechanisms are still unclear. Here, we investigate how antisense transcription interfaces with Polycomb Repressive Complex 2 silencing during winter-induced epigenetic regulation of Arabidopsis FLOWERING LOCUS C (FLC). Through genetic, chromatin, and computational analyses, we show that FLC is silenced through pathways that function with different dynamics: an antisense transcription-mediated pathway capable of fast response; and in parallel a slow Polycomb Repressive Complex 2 (PRC2) switching mechanism that maintains each allele in an epigenetically silenced state. Components of both the antisense and PRC2 pathways are regulated by a common transcriptional regulator (NTL8), which accumulates slowly due to reduced growth at low temperatures. The parallel activities of the regulatory steps, which we encapsulate in a mathematical model, creates a flexible system for registering widely fluctuating natural temperature conditions that change year on year, and yet ensure robust epigenetic silencing of FLC. SignificanceThe role of non-coding transcription in establishing and maintaining chromatin states is controversial, mainly because of extensive feedbacks complicating analysis of the relationship between co-transcriptional processing, chromatin state and transcription. This controversy has extended to the role of antisense transcription in the Polycomb-mediated epigenetic silencing of Arabidopsis FLC, a key step in the process of vernalization. Here, we show that antisense transcription and PRC2 silence FLC in parallel pathways that are affected by growth dynamics and temperature fluctuations. These features explain the varied importance of antisense transcription in cold-induced FLC epigenetic silencing seen in various studies using different environmental and growth conditions. The parallel repressive inputs and extensive feedbacks make the mechanism counter-intuitive but provide great flexibility to the plant.

molecular biology↗

bioRxiv2023

Integration of HLA-DR linkagedisequilibrium to MHC class II predictions

Insights into peptide binding to HLA class II molecules is essential when studying the biological mechanisms behind cellular immunity, autoimmune diseases, and the development of immunotherapies and peptide vaccines. Currently, most of the publicly available data used to train state-of-the-art binding prediction methods for HLA-DR only includes DRB1 information. The role of the paralogue alleles, HLA-DRB3/4/5, and their strong linkage disequilibrium to DRB1 is often omitted when typing HLA-II alleles. This leads to ambiguities when making disease associations and interpreting HLA-restricted immune data. To resolve this issue, we present HLAAssoc-1.0, a method to infer HLA-DRB3/4/5 alleles by linkage disequilibrium to HLA-DRB1. We illustrate the usage of the tool and the importance of the integration of HLA-DRB3/4/5 alleles in the data analysis in different case studies including the interpretation of immunopetidomics data. Additionally, we infer allele information for the data used for training of NetMHCIIpan lacking HLA-DRB3/4/5 allele information and demonstrate that the retrained method achieved improved performance. In all cases, inferring HLA-DRB3/4/5 allele presence in non-fully typed HLA-II assays resulted in improved allele and motif deconvolutions. HLAAssoc-1.0 is available at https://services.healthtech.dtu.dk/service.php?HLAAssoc-1.0.

bioinformatics↗

bioRxiv2023

Distinct accessory roles of Arabidopsis VEL proteins in Polycomb silencing

Polycomb Repressive Complex 2 (PRC2) mediates epigenetic silencing of target genes in animals and plants. In Arabidopsis, PRC2 is required for the cold-induced epigenetic silencing of the FLC floral repressor locus to align flowering with spring. During this process, PRC2 relies on VEL accessory factors, including the constitutively expressed VRN5 and the cold-induced VIN3. The VEL proteins are physically associated with PRC2, but their individual functions remain unclear. Here, we show an intimate association between recombinant VRN5 and multiple components within a reconstituted PRC2, dependent on a compact conformation of VRN5 central domains. Key residues mediating this compact conformation are conserved amongst VRN5 orthologs across the plant kingdom. By contrast, VIN3 interacts with VAL1, a transcriptional repressor that binds directly to FLC. These associations differentially affect their role in H3K27me deposition: both proteins are required for H3K27me3, but only VRN5 is necessary for H3K27me2. Although originally defined as vernalization regulators, VIN3 and VRN5 co-associate with many targets in the Arabidopsis genome that are modified with H3K27me3. Our work, therefore, reveals the distinct accessory roles for VEL proteins in conferring cold-induced silencing on FLC, with broad relevance for PRC2 targets generally.

plant biology↗

bioRxiv2023

Extraordinary peptide-binding mode of a songbird MHC class-I molecule suggests mechanism to counter pathogen immune evasion

Long-distance migratory animals such as birds and bats have evolved to withstand selection imposed by pathogens across the globe, and pathogen richness is known to be particularly high in tropical regions. Immune genes, so-called Major Histocompatibility Complex (MHC) genes, are highly duplicated in songbirds compared to other vertebrates, and this high MHC diversity has been hypothesised to result in a unique adaptive immunity. To understand the rationale behind the evolution of the high MHC genetic diversity in songbirds, we determined the structural properties of an MHC class I protein, Acar3, from a long-distance migratory songbird, the great reed warbler Acrocephalus arundinaceus (in short: Acar). The structure of Acar3 was studied in complex with pathogen-derived antigens and shows an overall antigen presentation similar to human MHC class I. However, the peptides bound to Acar3 display an unusual conformation: Whereas the N-terminal ends of the peptides display enhanced flexibility, the conformation of their C-terminal halves is rather static. This uncommon peptide-binding mode in Acar3 is facilitated by a central Arg residue within the peptide-binding groove that fixes the backbone of the peptide at its central position, and potentially permits successful interactions between MHC class I and innate immune receptors. Our study highlights the importance of investigating the immune system of wild animals, such as birds and bats, to uncover unique immune mechanisms which may neither exist in humans nor in model organisms.

ecology↗

bioRxiv2023

A new inverse probability of selection weightedCox model to deal with outcome-dependentsampling in survival analysis

Motivated by the study of genetic effect modifiers of cancer, we examined weighting approaches to correct for ascertainment bias in survival analysis. Family-based outcome-dependent sampling is common in genetic epidemiology leading to study samples with too many events in comparison to the population and an overrepresentation of young, affected subjects. A usual approach to correct for ascertainment bias in this setting is to use an inverse probability-weighted Cox model, using weights based on external available population-based age-specific incidence rates of the type of cancer under investigation. However, the current approach is not general enough leading to invalid weights in relevant practical settings if oversampling of cases is not observed in all age groups. Based on the same principle of weighting observations by their inverse probability of selection, we propose a new, more general approach. We show the advantage of our new method using simulations and two real datasets. In both applications the goal is to assess the association between common susceptibility loci identified in Genome Wide Association Studies (GWAS) and cancer (colorectal and breast) using data collected through genetic testing in clinical genetics centers.

genetics↗

bioRxiv2023

DiscoTope-3.0 - Improved B-cell epitope prediction using AlphaFold2 modeling and inverse folding latent representations

Accurate computational identification of B-cell epitopes is crucial for the development of vaccines, therapies, and diagnostic tools. However, current structure-based prediction methods face limitations due to the dependency on experimentally solved structures. Here, we introduce DiscoTope-3.0, a markedly improved B-cell epitope prediction tool that innovatively employs inverse folding structure representations and a positive-unlabelled learning strategy, and is explicitly adapted for both solved and predicted structures. Our tool demonstrates a considerable improvement in performance over existing methods, accurately predicting linear and conformational epitopes across multiple independent datasets. Most notably, DiscoTope-3.0 maintains high predictive performance across solved, relaxed and predicted structures, alleviating the need for experimental validation and extending the general applicability of accurate B-cell epitope prediction by more than 3 orders of magnitude. DiscoTope-3.0 is made widely accessible on two web servers, processing over 100 structures per submission, and as a downloadable package. In addition, the servers interface with RCSB and AlphaFoldDB, facilitating large-scale prediction across over 200 million cataloged proteins. DiscoTope-3.0 is available at: https://services.healthtech.dtu.dk/service.php?DiscoTope-3.0

immunology↗

bioRxiv2023

Data-driven filtering for denoising of TCRpMHC single-cell data: a benchmark

Pairing of the T cell receptor (TCR) with its cognate peptide-MHC (pMHC) is a cornerstone in T cell-mediated immunity. Recently, single-cell sequencing coupled with DNA-barcoded MHC multimer staining has enabled high-throughput studies of T cell specificities. However, the immense variability of TCR-pMHC interactions combined with the relatively low signal-to-noise ratio in the data generated using current technologies are complicating these studies. Several approaches have been proposed for denoising single-cell TCR-pMHC specificity data. Here, we present a benchmark evaluating two such denoising methods, ICON and ITRAP. We applied and evaluated the methods on publicly available immune profiling data provided by 10x Genomics. We find that both methods identified approximately 75% of the raw data as noise. We analyzed both internal metrics developed for the purpose and performance on independent data using machine learning methods trained on the raw and denoised 10x data. We find an increased signal-to-noise ratio comparing the denoised to the raw data for both methods, and demonstrate an overall superior performance of the ITRAP method in terms of both data consistency and performance. In conclusion, this study demonstrates that Improving the data quality by optimizing signal yield from high throughput studies of TCRpMHC-specificity is paramount in increasing our understanding of T cell-mediated immunity.

bioinformatics↗

bioRxiv2022

Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report

Many different solutions to predicting the cognate epitope target of a T-cell receptor (TCR) have been proposed. However several questions on the advantages and disadvantages of these different approaches remain unresolved, as most methods have only been evaluated within the context of their initial publications and data sets. Here, we report the findings of the first public TCR-epitope prediction benchmark performed on 23 prediction models in the context of the ImmRep 2022 TCR-epitope specificity workshop. This benchmark revealed that the use of paired-chain alpha-beta, as well as CDR1/2 or V/J information, when available, improves classification obtained with CDR3 data, independent of the underlying approach. In addition, we found that straight-forward distance-based approaches can achieve a respectable performance when compared to more complex machine-learning models. Finally, we highlight the need for a truly independent follow-up benchmark and provide recommendations for the design of such a next benchmark.

bioinformatics↗

bioRxiv2022

NetAllergen, a random forest model integrating MHC-II presentation propensity for improved allergenicity prediction

Allergy is a pathological immune reaction towards innocuous protein antigens. Although only a narrow fraction of plant or animal proteins induce allergy, atopic disorders affect millions of children and adults and cost billions in healthcare systems worldwide. In-silico predictors can aid in the development of more innocuous food sources. Previous allergenicity predictors used sequence similarity, common structural domains, and amino acid physicochemical features. However, these predictors strongly rely on sequence similarity to known allergens and fail to predict protein allergenicity accurately when similarity diminishes. In addition, allergen is a broad terminology that may include different compounds, hindering the classification task. To overcome these limitations, we collected allergens from AllergenOnline, a curated database of IgE-inducing allergens, carefully removed allergen redundancy with a novel protein partitioning pipeline, and developed a new allergen prediction method, introducing MHC presentation propensity as a novel feature. NetAllergen outperformed a sequence similarity-based BLAST baseline approach, and previous allergenicity predictor AlgPred 2 when similarity to known allergens is limited. NetAllergen is available as a web service (services.healthtech.dtu.dk/service.php?NetAllergen-1.0) and can predict allergenicity from a protein sequence.

bioinformatics↗

bioRxiv2022

Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome by accurate and comprehensive HLA-DQ antigen presentation prediction

HLA class II antigen presentation is key for controlling and triggering T cell immune responses. HLA-DQ molecules, which are believed to play a major role in autoimmune diseases, are heterodimers that can be formed as both cis and trans variants depending on whether the - and {beta}-chains are encoded on the same (cis) or opposite (trans) chromosomes. So far, limited progress has been made for predicting HLA-DQ antigen presentation. In addition, the contribution of trans-only variants (i.e. variants not observed in the population as cis) in shaping the HLA-DQ immunopeptidome remains largely unresolved. Here, we seek to address these issues by integrating state-of-the-art immunoinformatics data mining models with large volumes of high-quality HLA-DQ specific MS-immunopeptidomics data. The analysis demonstrated a highly improved predictive power and molecular coverage for models trained including these novel HLA-DQ data. More importantly, investigating the role of trans-only HLA-DQ variants revealed a limited to no contribution to the overall HLA-DQ immunopeptidome. In conclusion, this study has furthered our understanding of HLA-DQ specificities and has for the first time cast light on the relative role of cis versus trans-only HLA-DQ variants in the HLA class II antigen presentation space. The developed method, NetMHCIIpan-4.2, is available at https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.2.

bioinformatics↗

bioRxiv2022

ATRAP - Accurate T cell Receptor Antigen Pairing through data-driven filtering of sequencing information from single-cells

Novel single-cell based technologies hold the promise of matching T cell receptor (TCR) sequences with their cognate peptide-MHC recognition motif in a high-throughput manner. Parallel capture of TCR transcripts and peptide-MHC is enabled through the use of reagents labeled with DNA barcodes. However, analysis and annotation of such single-cell sequencing (SCseq) data is challenged by dropout, random noise, and other technical artifacts that must be carefully handled in the downstream processing steps. We here propose a rational, data-driven method termed ATRAP (Accurate T cell Receptor Antigen Paring) to deal with these challenges, filtering away likely artifacts, and enable the generation of large sets of TCR-pMHC sequence data with a high degree of specificity and sensitivity, thus outputting the most likely pMHC target per T cell. We have validated this approach across 10 different virus-specific T cell responses in 16 healthy donors. Across these samples we have identified up to 1494 high-confident TCR-pMHC pairs derived from 4135 single-cells.

immunology↗

bioRxiv2022

BepiPred-3.0: Improved B-cell epitope prediction using protein language models

B-cell epitope prediction tools are of great medical and commercial interest due to their practical applications in vaccine development. The introduction of protein language models (LM) trained on unprecedented large datasets of protein sequences and structures, tap into a powerful numeric representation that can be exploited to accurately predict local and global protein structural features from amino acid sequences only. In this paper, we present BepiPred 3.0, a sequence-based epitope prediction tool that, by exploiting LM embeddings, greatly improves the prediction accuracy for both linear and conformational epitope prediction on several independent test sets. Furthermore, by carefully selecting additional input variables and epitope residue annotation strategy, performance can be further improved, thus achieving extraordinary results. Our tool can predict epitopes across hundreds of sequences in mere minutes. It is freely available as a web server with a user-friendly interface to navigate the results, as well as a standalone downloadable package.

bioinformatics↗

bioRxiv2021

Defective proinsulin handling modulates the MHC I bound peptidome and activates the inflammasome in β-cells

BackgroundHow immune-tolerance is lost to pancreatic {beta}-cell peptides triggering autoimmune type 1 diabetes is enigmatic. We have shown that loss of the proinsulin ER chaperone glucose-regulated protein (GRP) 94 leads to mishandling of proinsulin, ER stress and activation of the inducible proteasome. We hypothesize that inadequate ER proinsulin folding capacity relative to biosynthetic need may lead to an altered {beta}-cell MHC-I bound peptidome and inflammasome activation, sensitizing {beta}-cells to immune attack. MethodsWe used INS-1E cells with or without GRP94 knockout (KO), or in the presence or absence of GRP94 inhibitor PU-WS13 (GRP94i, 20{micro}M), or exposed to proinflammatory cytokines interleukin (IL)-1{beta} or IFN{gamma} (15 pg/ml and 10 ng/ml, respectively) for 24 hours. RT1.A (rat MHC I) expression was evaluated using flow cytometry. The total RT1.A-bound peptidome analysis was performed on cell lysates fractionated by reverse phase high performance liquid chromatography (RP-HPLC) followed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). NALP1, nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor alpha (I{kappa}B), and (pro) IL-1{beta} expression and secretion were investigated by Western blotting. ResultsGRP94 KO increased RT1.A expression in {beta}-cells as did cytokine exposure compared to relevant controls. Immunopeptidome analysis showed increased RT1.A-bound peptide repertoire in GRP94 KO/i cells as well as in the cells exposed to cytokines. The GRP94 KO/cytokine exposure groups showed partial overlap in their peptide repertoire. Notably, proinsulin-derived peptides diversity increased among the total RT1.A peptidome in GRP94 KO/i along with cytokines exposure. NALP1 expression was upregulated in GRP94 deficient cells along with decreased I{kappa}B content while proIL-1{beta} cellular levels declined, coupled with an increased secretion of mature IL-1{beta}. Our results suggest that limiting {beta}-cell proinsulin chaperoning enhances RT1.A expression, alters the MHC-I peptidome including proinsulin peptides and activates inflammatory pathways, suggesting that stress impeding proinsulin handling may sensitize {beta}-cells to immune-attack.

molecular biology↗

bioRxiv2021

Accurate MHC Motif Deconvolution of Immunopeptidomics Data Reveals a Significant Contribution of DRB3, 4 and 5 to the Total DR Immunopeptidome

Mass spectrometry (MS) based immunopeptidomics is used in several biomedical applications including neo-epitope discovery in oncology and next-generation vaccine development. Immunopeptidome data are highly complex given the expression of multiple HLA alleles on the cell membrane and presence of co-immunoprecipitated contaminants. The absence of tools that accurately deal with these challenges is currently a major bottleneck for the large-scale application of this technique. Here, we present the MHCMotifDecon that benefits from state-of-the-art HLA class-I and class-II predictions to accurately deconvolute immunopeptidome datasets and assign individual ligands to the most likely HLA allele while discarding co-purified contaminants. We have benchmarked the tool against other state-of-the-art methods and illustrated its application on experimental datasets for HLA-DR demonstrating a previously underappreciated role for HLA-DRB3/4/5 molecules in defining HLA class II immune repertoires. With its ease of use MHCMotifDecon can efficiently guide interpretation of immunopeptidome datasets, serving the discovery of novel T cell targets.

immunology↗

bioRxiv2021

Bi-modal Variational Autoencoders for Metabolite Identification Using Tandem Mass Spectrometry

A grand challenge of analytical chemistry is the identification of unknown molecules based on tandem mass spectrometry (MS/MS) spectra. Current metabolite annotation approaches are often manual or partially automated, and commonly rely on a spectral database to search from or train machine learning classifiers on. Unfortunately, spectral databases are often instrument specific and incomplete due to the limited availability of compound standards or a molecular database, which limits the ability of methods utilizing them to predict novel molecule structures. We describe a generative modeling approach that can leverage the vast amount of unpaired and/or unlabeled molecule structures and MS/MS spectra to learn general rules for synthesizing molecule structures and MS/MS spectra. The approach is based on recent work using semi-supervised deep variational autoencoders to learn joint latent representations of multiple and complex modalities. We show that adding molecule structures with no spectra to the training set improves the prediction quality on spectra from a structure disjoint dataset of new molecules, which is not possible using bi-modal supervised approaches. The described methodology provides a demonstration and framework for how recent advances in semi-supervised machine learning can be applied to overcome bottlenecks in missing annotations and noisy data to tackle unaddressed problems in the life sciences where large volumes of data are available.

biochemistry↗