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Current proteomic technologies focus on the quantification of protein levels, while little effort is dedicated to the development of system approaches to simultaneously monitor proteome variability and abundance. Protein variants may display different immunogenic epitopes detectable by monoclonal antibodies. Epitope variability results from alternative splicing, posttranslational modifications, processing, degradation, and complex formation and possesses dynamically changing availability of interacting surface structures that frequently serve as reachable epitopes and often carry different functions. Thus, it is highly likely that the presence of some of the accessible epitopes correlates with function under physiological and pathological conditions. To enable the exploration of the impact of protein variation on the immunogenic epitome first, here, we present a robust and analytically validated PEP technology for characterizing immunogenic epitopes of the plasma. To this end, we prepared mAb libraries directed against the normalized human plasma proteome as a complex natural immunogen. Antibody producing hybridomas were selected and cloned. Monoclonal antibodies react with single epitopes, thus profiling with the libraries is expected to profile many epitopes which we define by the mimotopes, as we present here. Screening blood plasma samples from control subjects (n = 558) and cancer patients (n = 598) for merely 69 native epitopes displayed by 20 abundant plasma proteins resulted in distinct cancer-specific epitope panels that showed high accuracy (AUC 0.826-0.966) and specificity for lung, breast, and colon cancer. Deeper profiling (≈290 epitopes of approximately 100 proteins) showed unexpected granularity of the epitope-level expression data and detected neutral and lung cancer-associated epitopes of individual proteins. Biomarker epitope panels selected from a pool of 21 epitopes of 12 proteins were validated in independent clinical cohorts. The results demonstrate the value of PEP as a rich and thus far unexplored source of protein biomarkers with diagnostic potential.
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http://dx.doi.org/10.1016/j.mcpro.2023.100580 | DOI Listing |
Sci Adv
September 2025
The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Influenza A viruses remain a global health threat, yet no universal antibody therapy exists. Clinical programs have centered on neutralizing mAbs, only to be thwarted by strain specificity and rapid viral escape. We instead engineered three non-neutralizing IgG2a mAbs that target distinct, overlapping epitopes within the conserved N terminus of the M2 ectodomain (M2e).
View Article and Find Full Text PDFSci Transl Med
September 2025
Vaccine Research Center (VRC), National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
Human B cell immunity to the influenza hemagglutinin (HA) stem, a universal vaccine target, is often stereotyped and immunogenetically restricted, posing hurdles to study outside of humans. Here, we show that cynomolgus macaques vaccinated with an HA stem immunogen elicit humanlike public B cell lineages targeting two major conserved sites of vulnerability, the central stem and anchor epitopes. Central stem antibodies were predominantly derived from V1-138, the macaque homolog of human V1-69, a V gene preferentially used in human central stem broadly neutralizing antibodies (bnAbs).
View Article and Find Full Text PDFEmerg Microbes Infect
December 2025
School of Global Health, Chinese Centre for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
There is no vaccine for severe malaria. STEVOR antigens on the surface of -infected red blood cells are implicated in severe malaria and are targeted by neutralizing antibodies, but their epitopes remain unknown. Using computational immunology, we identified highly immunogenic overlapping B- and T-cell epitopes (referred to as multiepitopes, 7-27 amino acids) in the semiconserved domain of four STEVORs linked with severe malaria and clinical immunity.
View Article and Find Full Text PDFMol Biol Rep
September 2025
Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Cancer Med
September 2025
Department of Computer Engineering, Social and Biological Network Analysis Laboratory, University of Kurdistan, Sanandaj, Iran.
Background: Ovarian cancer (OC) remains the most lethal gynecological malignancy, largely due to its late-stage diagnosis and nonspecific early symptoms. Advances in biomarker identification and machine learning offer promising avenues for improving early detection and prognosis. This review evaluates the role of biomarker-driven ML models in enhancing the early detection, risk stratification, and treatment planning of OC.
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