Biology subjects

Karthikeyan, D.

Publications and source records attributed to Karthikeyan, D..

5 recordsLinked to original sources

bioRxiv2026

Peptide:MHC Binding Stability Prediction Using Protein Language Models

AO_SCPLOWBSTRACTC_SCPLOWPeptide:MHC class I (pMHC-I) binding stability governs the persistence of antigenic complexes at the cell surface and plays a key role in facilitating downstream immunological signals such as antigen presentation, T-cell activation, and immunodominance. However, methods for in silico stability prediction remain underexplored relative to binding affinity prediction, in part because available half-life datasets are sparse and expensive to collect. Here, we perform a systematic reassessment of pMHC-I stability prediction using controlled, similarity-aware data splits and apply a recently introduced supervised transfer-learning strategy to MINT, an interaction-aware protein language model, pre-trained on binding affinity and fine-tuned for quantitative half-life prediction. We show that MINT improves stability prediction over standard ESM-2 representations and existing predictors, and that assay-conditioned recalibration corrects systematic shifts across experimental measurement modalities. Across eluted ligand, immunogenicity, and personalized neoantigen prioritization benchmarks, predicted stability provides signal beyond binding affinity, enriching for naturally presented and immunogenic peptides within affinity-filtered candidate sets. These results establish pMHC-I half-life as an orthogonal and transferable biophysical signal connecting peptide binding, surface presentation, and T-cell recognition, and provide a leakage-aware, assay-aware framework for future antigen-presentation modeling.

bioinformatics↗

bioRxiv2026

Immunodominance Hierarchy of Endogenous BBN963 Bladder Cancer Antigens Remains Stable Under Anti-PD1 and Anti-CTLA4 Immunotherapy

Immune checkpoint inhibition (ICI) is clinically active against multiple cancers, including urothelial cancer at the non-muscle invasive, muscle-invasive, and metastatic stages. Despite this, large numbers of patients experience disease progression and relapse after treatment with ICI-containing regimens. Tumor antigen-specific T cells are critical to ICI response, however few studies have evaluated the breadth and magnitude of tumor antigen-specific T cell responses with ICI therapy. In this study, we mapped the tumor antigen immunodominance hierarchy in the BBN963 model of murine basal-like bladder cancer for endogenous tumor neoantigens expressed physiologically. We used a high-throughput matrixed ELISpot assay to detect CD8+ T cell responses to predicted BBN963 tumor antigens derived from multiple mutational genomic sources. We found CD8+ T cell responses were directed against a subset of tumor antigens forming a stable and reproducible immunodominance hierarchy across individual mice. Treatment with anti-PD-1 or anti-CTLA-4 did not substantially reshape this hierarchy or broadly shift dominant responses to previously defined subdominant epitopes. Predicted peptide MHC binding stability and affinity was associated with antigen immunogenicity. Cancer-testis antigens, endogenous retroviral antigens, and SNV-derived tumor antigens that were immunogenic were found across tumor subclones. By diversifying the immunogenic antigen repertoire beyond SNVs, we achieved nearly 100% tumor subclone coverage, suggesting that broader antigen selection could help immunotherapy target more tumor subclones. In conclusion, this study supports the stability of the immunodominance hierarchy under ICI therapy and a role for broadening antigen discovery to multiple expressional sources in immunotherapy design.

cancer biology↗

bioRxiv2024

TCR-TRANSLATE: Conditional Generation of Real Antigen Specific T-cell Receptor Sequences

The paradoxical nature of T-cell receptor (TCR) specificity, which requires both precise recognition and adequate coverage of antigenic peptide-MHCs (pMHCs), poses a fundamental challenge in immunology. Efforts at modeling this complex many-to-many mapping have been greatly impeded by a severe lack of experimental data. To address this, we present TCR-TRANSLATE, a novel framework that adapts low-resource machine translation techniques to the TCR:pMHC specificity domain. Here, we explore sequence-to-sequence (seq2seq) modeling with various training strategies, including semi-synthetic data augmentation and multi-task objectives to generate antigen specific TCR sequences for a given target of interest. We benchmark twelve model variants derived from the BART and T5 model architectures on a target-rich validation set of well-studied pMHCs, finding an optimal model, TCRT5, that generated validated antigen-specific CDR3{beta} sequences for previously unseen antigens. While current limitations include a narrow validation set and a focus on the CDR3{beta} loop, our approach demonstrates the potential of seq2seq models in rapidly generating antigen-specific TCR repertoires, offering a promising avenue for increasing throughput in precision immunotherapies. Our findings highlight both the capabilities and limitations of sequence-based conditional TCR design, emphasizing the need for experimental validation to bridge the gaps between predictions, metrics, and functional capacity.

synthetic biology↗

bioRxiv2023

ACE Configurator for ELISpot (ACE): Optimizing Combinatorial Design of Pooled ELISpot Assays with an Epitope Similarity Model

The ELISpot assay is a powerful in vitro immunoassay that enables cost-effective quantification of antigen-specific T-cell reactivity. It is widely used in the context of cancer and infectious diseases to validate the immunogenicity of epitopes. While technological advances in hardware and software have kept pace with the need for increased throughput, assay design and deconvolution methodology have largely remained stagnant. Current methods for designing multiplexed ELISpot assays are restricted to preset configurations, lack support for high-throughput scenarios, and ignore peptide identity during pool assignment. We introduce the ACE Configurator for ELISpot (ACE) to address these gaps. ACE generates optimized peptide-pool assignments from highly customizable user inputs and handles positive peptide deconvolution using assay readouts. We present a novel sequence-aware pooling strategy, powered by a fine-tuned ESM-2 deep sequence model to identify immunologically similar peptides, reducing the number of false positives and subsequent confirmatory assays. To validate the performance of ACE using real-world datasets, we conducted a comprehensive benchmark study against various design heuristics, deconvolution methods, and experimental conditions, contextualizing design parameter choices with their impact on precision and number of total pools. Our results demonstrate ACEs capacity to further increase precision of identified immunogenic peptides, maximizing experimental efficiency at the bench-side. ACE is freely available as an executable with a graphical user interface and command-line interfaces at https://github.com/pirl-unc/ace.

bioinformatics↗

bioRxiv2020

Developing human fetal skin demonstrates a unique lymphocyte signature

Lymphocytes in barrier tissues play critical roles in host defense and homeostasis. These cells take up residence in tissues during defined developmental windows, when they may demonstrate distinct phenotypes and functions. Here, we utilized mass and flow cytometry to elucidate early features of human skin immunity, demonstrating a unique fetal skin lymphocyte signature. While most conventional {beta} T (Tconv) cells in fetal skin have a naive, proliferative phenotype, a subset of CD4+ Tconv and CD8+ cells demonstrate memory-like features and a propensity for IFN{gamma} production. Skin regulatory T cells dynamically accumulate over the second trimester in temporal and regional association with hair follicle development. These fetal skin Tregs demonstrate an effector memory phenotype while differing from their adult counterparts in expression of key effector molecules. Thus, we identify features of prenatal skin lymphocytes that may have key implications for understanding antigen and allergen encounters in utero and in infancy.

immunology↗