Large-scale sequence and structural comparisons of human naive and antigen-experienced antibody repertoires

By B. DeKosky, Oana I. Lungu, Daechan Park, Erik L. Johnson, Wissam Charab, C. Chrysostomou, Daisuke Kuroda, A. Ellington, G. Ippolito, Jeffrey J. Gray and 1 more. 2016

191 citations recorded by the source corpus.

Subfields: Immunology, Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

TCRmodel: high resolution modeling of T cell receptors from sequence (2018, background): “As was recently illustrated in the context of antibody modeling, the combination of immune repertoire sequencing data with structural modeling can be highly informative (29).”
Characterization of neutralizing antibodies from a SARS-CoV-2 infected individual (2020, background): “Rather, they were derived from a diverse VH/VL gene repertoire, with frequencies similar to those reported in healthy uninfected individuals (Briney et al., 2019; DeKosky et al., 2016; Soto et al., 2019; Vazquez Bernat et al., 2019).”
Repertoire Analysis of Antibody CDR-H3 Loops Suggests Affinity Maturation Does Not Typically Result in Rigidification (2017, methodology): “used RosettaAntibody to model the structures of ~2,000 common 232 antibodies found in the peripheral blood cells of two human donors (37).”
Accurate structure prediction of CDR H3 loops enabled by a novel structure-based C-terminal constraint (2016, background): “toire is ripe for extracting an unprecedented amount of immunological information (9).”
Five computational developability guidelines for therapeutic antibody profiling (2019, background): “(38) also found, across their 2,000 natively paired models of mAbs, that lambda CDRL3 loops are significantly more hydrophobic than their kappa equivalents.”
Potent Neutralizing Antibodies against SARS-CoV-2 Identified by High-Throughput Single-Cell Sequencing of Convalescent Patients’ B Cells (2020, unknown): “Previously, people have used antibody structure prediction to annotate Ig-seq data for better mAbs selection (DeKosky et al., 2016).”
Protein design and variant prediction using autoregressive generative models (2021, background): “Previous statistical and structural modeling of antibody repertoires 11 – 18 have addressed the characterization of sequences of natural antibodies or predicted higher af ﬁ nity sequences from immunization or selection experiments.”