Biology subjects

Mishra, A. R.

Publications and source records attributed to Mishra, A. R..

3 recordsLinked to original sources

bioRxiv2026

Duration of Initial Viremia Modulates Functional Properties of HIV-specific T Cell Receptors

Virus-specific CD8+ T cells are crucial in controlling chronic human viral infections such as HIV-1, but the effect of persistent antigen exposure on T cell repertoire formation is not well understood. In this study, we examined epitope-specific CD8+ T cell repertoires in people living with HIV-1, where duration of viremia following hyperacute infection was modulated by the time of initiation of continuous suppressive antiretroviral therapy (ART). After ART-induced undetectable viremia in persons expressing the same HLA class I allele, we analyzed the impact of early (n=6) versus delayed (n=6) ART initiation on the clonotypic composition, clonotypic cross-reactivity, functional avidity and memory differentiation profile of the HIV-specific T cell repertoire restricted by HLA-B*58:01. Using a panel of barcoded tetramers, we mapped T cell receptor (TCR) clonotypes specific for three dominant epitopes and their variants. Both groups exhibited polyclonal TCR repertoires with evidence of cross-reactivity, which was significantly enriched in donors with prolonged antigen exposure. Within this cohort, broadly cross-reactive clonotypes capable of recognizing all autologous variants were identified, but these were rare (<1%). Early ART initiation preserved repertoires characterized by higher-avidity TCRs and a relative enrichment of transitional memory CD8+ T cell subsets. These functional differences were not associated with differences in TRBV gene sharing, indicating that ART timing shapes repertoire quality and memory differentiation without altering TRBV gene bias. These findings demonstrate how antigen suppression dynamics differentially shape the breadth, functional sensitivity, and memory composition of the HIV-specific TCR repertoire, with implications for T cell-directed immunotherapies and HIV cure strategies. One Sentence SummaryThe duration of viral antigen exposure during early HIV infection shapes the functional quality, breadth, and memory composition of virus-specific CD8 T cell receptor repertoires. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/702605v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@9a5a6dorg.highwire.dtl.DTLVardef@1a6bbdaorg.highwire.dtl.DTLVardef@17707e8org.highwire.dtl.DTLVardef@1a84d9e_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2026

TubercuProbe: A Cross-Attention Graph-Sequence Model for Cross-Species Chemoproteomic Discovery in Mycobacterium tuberculosis

Activity-based protein profiling (ABPP) and residue-specific chemoproteomics have transformed human chemical biology, yet applying these approaches to pathogens remains limited by biosafety constraints, low throughput, and the absence of reusable atlases. We present TubercuProbe, a cross-species machine learning framework that leverages large-scale human chemoproteomic knowledge to prioritize compound-protein interactions in Mycobacterium tuberculosis (Mtb) and other pathogens. Our model integrates a graph isomorphism network (GINE) for ligand encoding with frozen ESM-C (600M) protein embeddings via bidirectional cross-attention. Trained on >2M ChEMBL compound-protein pairs (predominantly human targets), TubercuProbe achieves R2=0.77 (MSE=0.45) for continuous affinity prediction and transfers effectively to binary cysteine reactivity prediction (CysDB AUPRC=0.63). Ablation studies reveal that pretrained features are highly transferable across freezing strategies ({Delta}AUPRC<0.03), suggesting the model captures fundamental protein-molecule interaction patterns. As a case study, we prioritize cysteine-reactive electrophiles and molecular glues for three Mtb virulence proteins (PtpB, SapM, Rv3671c), providing candidate probes for prospective ABPP validation. Orthogonal comparison with Boltz-2 structure predictions shows moderate correlation (Pearson r{approx}0.69). TubercuProbe provides a lightweight, sequence-driven first-pass ranker that enables pre-experimental prioritization--reducing the time, cost, and experimental burden of chemoproteomic discovery in biosafety-restricted systems. We discuss extensions toward multitask learning that jointly predicts non-covalent binding and covalent reactivity, recognizing that effective covalent probes must both reach their target and react once there.

biochemistry↗

bioRxiv2023

SignEEG v1.0 : Multimodal Electroencephalography and Signature Database for Biometric Systems

Handwritten signatures in biometric authentication leverage unique individual characteristics for identification, offering high specificity through dynamic and static properties. However, this modality faces significant challenges from sophisticated forgery attempts, underscoring the need for enhanced security measures in common applications. To address forgery in signature-based biometric systems, integrating a forgery-resistant modality, namely, noninvasive electroencephalography (EEG), which captures unique brain activity patterns, can significantly enhance system robustness by leveraging multimodalitys strengths. By combining EEG, a physiological modality, with handwritten signatures, a behavioral modality, our approach capitalizes on the strengths of both, significantly fortifying the robustness of biometric systems through this multimodal integration. In addition, EEGs resistance to replication offers a high-security level, making it a robust addition to user identification and verification. This study presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms based on mental, & motor imagery, and physical execution: i) thinking of the signatures image, (ii) drawing the signature mentally, and (iii) drawing a signature physically. Extensive experiments have been conducted to establish a baseline with machine learning classifiers. The results demonstrate that multimodality in biometric systems significantly enhances robustness, achieving high reliability even with limited sample sizes. We release the raw, pre-processed data and easy-to-follow implementation details.

neuroscience↗