Motivation: LIBRA-seq (linking B cell receptor to antigen specificity by sequencing) provides a powerful tool for interrogating the antigen-specific B cell compartment and identifying antibodies against antigen targets of interest. Identification of noise in single-cell B cell receptor sequencing data, such as LIBRA-seq, is critical for improving antigen binding predictions for downstream applications including antibody discovery and machine learning technologies.
Results: In this study, we present a method for denoising LIBRA-seq data by clustering antigen counts into signal and noise components with a negative binomial mixture model.
Human respiratory syncytial virus (RSV) and human metapneumovirus (hMPV) are frequent drivers of morbidity and mortality in susceptible populations, most often infantile, older adults, and immunocompromised. The primary target of neutralizing antibodies is the fusion (F) glycoprotein on the surface of the RSV and hMPV virion. As a result of the structural conservation between RSV and hMPV F, three antigenic regions are known to induce cross-neutralizing responses: sites III, IV, and V.
View Article and Find Full Text PDFAn antibody-based HIV-1 vaccine will require the induction of potent cross-reactive HIV-1-neutralizing responses. To demonstrate feasibility toward this goal, we combined vaccination targeting the fusion-peptide site of vulnerability with infection by simian-human immunodeficiency virus (SHIV). In four macaques with vaccine-induced neutralizing responses, SHIV infection boosted plasma neutralization to 45%-77% breadth (geometric mean 50% inhibitory dilution [ID] ∼100) on a 208-strain panel.
View Article and Find Full Text PDFInfluenza virus is a highly contagious respiratory pathogen causing between 9.4 and 41 million infections per year in the United States in the last decade. Annual vaccination is recommended by the World Health Organization, with the goal to reduce influenza severity and transmission.
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