Biology subjects

Abelin, J. G.

Publications and source records attributed to Abelin, J. G..

10 recordsLinked to original sources

bioRxiv2026

Peptide-HLA II interaction prediction for post-translationally modified peptides

CD4+ T cells recognize peptides presented by human leukocyte antigen (HLA) II, implementing a fundamental mediation mechanism of the adaptive immune system. Although post-translational modifications (PTMs) alter immune responses, PTM-peptide-HLA interaction prediction remains challenging due to data scarcity resulting from substoichiometric levels of PTMs. To overcome this, we developed PepChem, a deep learning model utilizing novel, molecular-level peptide representations that enable predictions for sidechain modifications. Using monoallelic datasets that we reanalyze for PTMs of interest, we show accurate predictions on PTMs that were unseen during training. Furthermore, we introduce a novel training protocol that improves PTM-peptide generalization compared to conventional methods. We predict and experimentally validate citrullination-induced binding increase of rheumatoid arthritis (RA)-linked peptides to HLA II risk allele DRB1*04:01. This framework bridges the critical gap in PTM-aware immune recognition prediction, with immediate applications in autoimmunity, cancer, and infectious disease.

bioinformatics↗

bioRxiv2025

Conserved programs and specificities of T cells targeting hematological malignancies

T cell-mediated immune surveillance is critical for cancer control, yet its endogenous effectiveness in hematological malignancies remains limited and poorly understood. Here, we integrate single-cell T cell receptor (TCR) profiling, HLA immunopeptidomics and functional antigen mapping to dissect the specificity landscape of bone marrow lymphocytes (BMLs) in multiple myeloma (MM) and acute myeloid leukemia (AML). We identify a rare subset of tumor-reactive T cells that exhibit a stereotyped transcriptional state distinct from bystander and virus-specific populations. Across both malignancies, immunopeptidomic profiling uncovers a partially conserved antigen repertoire enriched for noncanonical peptides, including products of novel or unannotated open reading frames (nuORFs), pseudogenes, and clonotypic immunoglobulin sequences. Several of these epitopes are recurrently presented and associated with convergent TCR responses across individuals. Based on this immune architecture, we develop a TCR-intrinsic fitness model that infers BML tumor specificity from transcriptional cues and stratifies immunotherapy response across three independent patient cohorts. Together, these findings map the latent potential of endogenous anti-tumor immunity in two biologically distinct diseases and provide a framework for decoding and restoring productive immune surveillance of hematological malignancies. HighlightsO_LISingle-cell resolved TCR profiling maps rare tumor-reactive T cells in the bone marrow of multiple myeloma (MM) and acute myeloid leukemia (AML) reveals conserved transcriptional programs C_LIO_LIA shared immunopeptidome across MM and AML includes noncanonical epitopes from nuORFs and idiotype sequences C_LIO_LIConserved tumor antigens elicit convergent T cell responses across patients C_LI O_LIA TCR fitness model predicts tumor specificity in bone marrow lymphocytes and stratifies immunotherapy response in both hematological malignancies C_LI

immunology↗

bioRxiv2024

High-quality peptide evidence for annotating non-canonical open reading frames as human proteins

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.

molecular biology↗

bioRxiv2023

Pan-viral ORFs discovery using Massively Parallel Ribosome Profiling

Unveiling the complete proteome of viruses is crucial to our understanding of the viral life cycle and interaction with the host. We developed Massively Parallel Ribosome Profiling (MPRP) to experimentally determine open reading frames (ORFs) in 20,170 designed oligonucleotides across 679 human-associated viral genomes. We identified 5,381 ORFs, including 4,208 non-canonical ORFs, and show successful detection of both annotated coding sequences (CDSs) and reported non-canonical ORFs. By examining immunopeptidome datasets of infected cells, we found class I human leukocyte antigen (HLA-I) peptides originating from non-canonical ORFs identified through MPRP. By inspecting ribosome occupancies on the 5UTR and CDS regions of annotated viral genes, we identified hundreds of upstream ORFs (uORFs) that negatively regulate the synthesis of canonical viral proteins. The unprecedented source of viral ORFs across a wide range of viral families, including highly pathogenic viruses, expands the repertoire of vaccine targets and exposes new cis-regulatory sequences in viral genomes.

systems biology↗

bioRxiv2023

The HLA-II immunopeptidome of SARS-CoV-2

Targeted synthetic vaccines have the potential to transform our response to viral outbreaks; yet the design of these vaccines requires a comprehensive knowledge of viral immunogens, including T-cell epitopes. Having previously mapped the SARS-CoV-2 HLA-I landscape, here we report viral peptides that are naturally processed and loaded onto HLA-II complexes in infected cells. We identified over 500 unique viral peptides from canonical proteins, as well as from overlapping internal open reading frames (ORFs), revealing, for the first time, the contribution of internal ORFs to the HLA-II peptide repertoire. Most HLA-II peptides co-localized with the known CD4+ T cell epitopes in COVID-19 patients. We also observed that two reported immunodominant regions in the SARS-CoV-2 membrane protein are formed at the level of HLA-II presentation. Overall, our analyses show that HLA-I and HLA-II pathways target distinct viral proteins, with the structural proteins accounting for most of the HLA-II peptidome and non-structural and non-canonical proteins accounting for the majority of the HLA-I peptidome. These findings highlight the need for a vaccine design that incorporates multiple viral elements harboring CD4+ and CD8+ T cell epitopes to maximize the vaccine effectiveness.

microbiology↗

bioRxiv2023

What can Ribo-seq and proteomics tell us about the non-canonical proteome?

Ribosome profiling (Ribo-seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of non-canonical sites of ribosome translation outside of the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7,000 non-canonical open reading frames (ORFs) are translated, which, at first glance, has the potential to expand the number of human protein-coding sequences by 30%, from [~]19,500 annotated CDSs to over 26,000. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of non-canonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome, but searching for guidance on how to proceed. Here, we discuss the current state of non-canonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein-coding". In briefThe human genome encodes thousands of non-canonical open reading frames (ORFs) in addition to protein-coding genes. As a nascent field, many questions remain regarding non-canonical ORFs. How many exist? Do they encode proteins? What level of evidence is needed for their verification? Central to these debates has been the advent of ribosome profiling (Ribo-seq) as a method to discern genome-wide ribosome occupancy, and immunopeptidomics as a method to detect peptides that are processed and presented by MHC molecules and not observed in traditional proteomics experiments. This article provides a synthesis of the current state of non-canonical ORF research and proposes standards for their future investigation and reporting. HighlightsO_LICombined use of Ribo-seq and proteomics-based methods enables optimal confidence in detecting non-canonical ORFs and their protein products. C_LIO_LIRibo-seq can provide more sensitive detection of non-canonical ORFs, but data quality and analytical pipelines will impact results. C_LIO_LINon-canonical ORF catalogs are diverse and span both high-stringency and low-stringency ORF nominations. C_LIO_LIA framework for standardized non-canonical ORF evidence will advance the research field. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/541049v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@4878d0org.highwire.dtl.DTLVardef@1f3c9e4org.highwire.dtl.DTLVardef@4ba1f7org.highwire.dtl.DTLVardef@17695a6_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2021

MONTE enables serial immunopeptidome, ubiquitylome, proteome, phosphoproteome, acetylome analyses of sample-limited tissues

Serial multiomic analyses of proteome, phosphoproteome and acetylome provides functional insights into disease pathology and drug effects while conserving precious human material. To date, ubiquitylome and HLA peptidome analyses have required separate samples for parallel processing each using distinct protocols. Here we present MONTE, a highly-sensitive multi-omic native tissue enrichment workflow that enables serial, deepscale analysis of HLA-I and HLA-II immunopeptidome, ubiquitylome, proteome, phosphoproteome and acetylome from the same tissue samples. We demonstrate the capabilities of MONTE in a proof-of-concept study of primary patient lung adenocarcinoma(LUAD) tumors. Depth of coverage and quantitative precision at each of the omes is not compromised by serialization, and the addition of HLA immunopeptidomics enables identification of putative immunotherapeutic targets such as cancer/testis antigens and neoantigens. MONTE can provide insights into disease-specific changes in antigen presentation, protein expression, protein degradation, cell signaling, cross-talk and epigenetic pathways involved in disease pathology and treatment.

cancer biology↗

bioRxiv2021

Optimized liquid and gas phase fractionation increase HLA-peptidome coverage for primary cell and tissue samples

Mass spectrometry is the most effective method to directly identify peptides presented on HLA molecules. However, current standard approaches often require many millions of cells for input material to achieve high coverage of the immunopeptidome and are therefore not compatible with the often limited amounts of tissue available from clinical tumor samples. Here, we evaluated microscaled basic reversed-phase fractionation to separate HLA peptide samples off-line followed by ion mobility coupled to LC-MS/MS for analysis. The combination of these two separation methods enabled identification of 20% to 50% more peptides compared to samples analyzed without either prior fractionation or use of ion mobility alone. We demonstrate coverage of HLA immunopeptidomes with up to 8,107 distinct peptides starting with as few as 100 million cells or 150 milligrams of wet weight tumor tissue. This increased sensitivity can improve HLA binding prediction algorithms and enable detection of clinically relevant epitopes such as neoantigens.

biochemistry↗

bioRxiv2020

SARS-CoV-2 infected cells present HLA-I peptides from canonical and out-of-frame ORFs

T cell-mediated immunity may play a critical role in controlling and establishing protective immunity against SARS-CoV-2 infection; yet the repertoire of viral epitopes responsible for T cell response activation remains mostly unknown. Identification of viral peptides presented on class I human leukocyte antigen (HLA-I) can reveal epitopes for recognition by cytotoxic T cells and potential incorporation into vaccines. Here, we report the first HLA-I immunopeptidome of SARS-CoV-2 in two human cell lines at different times post-infection using mass spectrometry. We found HLA-I peptides derived not only from canonical ORFs, but also from internal out-of-frame ORFs in Spike and Nucleoprotein not captured by current vaccines. Proteomics analyses of infected cells revealed that SARS-CoV-2 may interfere with antigen processing and immune signaling pathways. Based on the endogenously processed and presented viral peptides that we identified, we estimate that a pool of 24 peptides would provide one or more peptides for presentation by at least one HLA allele in 99% of the human population. These biological insights and the list of naturally presented SARS-CoV-2 peptides will facilitate data-driven selection of peptides for immune monitoring and vaccine development.

immunology↗

bioRxiv2017

A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations

Though the added value of proteomic measurements to gene expression profiling has been demonstrated, profiling of gene expression on its own remains the dominant means of understanding cellular responses to perturbation. Direct protein measurements are typically limited due to issues of cost and scale; however, the recent development of high-throughput, targeted sentinel mass spectrometry assays provides an opportunity for proteomics to contribute at a meaningful scale in high-value areas for drug development. To demonstrate the feasibility of a systematic and comprehensive library of perturbational proteomic signatures, we profiled 90 drugs (in triplicate) in six cell lines using two different proteomic assays -- one measuring global changes of epigenetic marks on histone proteins and another measuring a set of peptides reporting on the phosphoproteome -- for a total of more than 3,400 samples. This effort represents a first-of-its-kind resource for proteomics. The majority of tested drugs generated reproducible responses in both phosphosignaling and chromatin states, but we observed differences in the responses that were cell line-and assay-specific. We formalized the process of comparing response signatures within the data using a concept called connectivity, which enabled us to integrate data across cell types and assays. Furthermore, it facilitated incorporation of transcriptional signatures. Consistent connectivity among cell types revealed cellular responses that transcended cell-specific effects, while consistent connectivity among assays revealed unexpected associations between drugs that were confirmed by experimental follow-up. We further demonstrated how the resource could be leveraged against public domain external datasets to recognize therapeutic hypotheses that are consistent with ongoing clinical trials for the treatment of multiple myeloma and acute lymphocytic leukemia (ALL). These data are available for download via the Gene Expression Omnibus (accession GSE101406), and web apps for interacting with this resource are available at https://clue.io/proteomics.\n\nHighlightsO_LIFirst-of-its-kind public resource of proteomic responses to systematically administered perturbagens\nC_LIO_LIDirect proteomic profiling of phosphosignaling and chromatin states in cells for 90 drugs in six different cell lines\nC_LIO_LIExtends Connectivity Map concept to proteomic data for integration with transcriptional data\nC_LIO_LIEnables recognition of unexpected, cell type-specific activities and potential translational therapeutic opportunities\nC_LI

systems biology↗