Abstract
Antibodies are critical components of adaptive immunity, binding with high affinity to pathogenic epitopes. Antibodies undergo rigorous selection to achieve this high affinity, yet some maintain an additional basal level of low affinity, broad reactivity to diverse epitopes, a phenomenon termed 'polyreactivity'. While polyreactivity has been observed in antibodies isolated from various immunological niches, the biophysical properties that allow for promiscuity in a protein selected for high-affinity binding to a single target remain unclear. Using a database of over 1000 polyreactive and non-polyreactive antibody sequences, we created a bioinformatic pipeline to isolate key determinants of polyreactivity. These determinants, which include an increase in inter-loop crosstalk and a propensity for a neutral binding surface, are sufficient to generate a classifier able to identify polyreactive antibodies with over 75% accuracy. The framework from which this classifier was built is generalizable, and represents a powerful, automated pipeline for future immune repertoire analysis.
Highlights
Antibodies are immunogenic proteins expressed by B cells that play a major role in the adaptive immune response against non-self
Using an ELISA-based assay, the reactivity of each antibody is tested against a panel of 4–7 biochemically diverse target antigens: DNA, insulin, lipopolysaccharide (LPS), flagellin, albumin, cardiolipin, and keyhole limpet hemocyanin (KLH)
Previous research has highlighted the importance of hydrophobicity, charge, and complementarity determining region (CDR) loop flexibility on antibody specificity
Summary
Antibodies are immunogenic proteins expressed by B cells that play a major role in the adaptive immune response against non-self. One hypothesized mechanism for the capability of polyreactive antibodies to confer this broad neutralization in the face of a changing viral epitope is heteroligation, the ability of a single antibody to bind the primary target with one binding domain and use the other binding domain to bind in a polyreactive manner (Mouquet et al, 2010) This heteroligation allows the antibody to take advantage of the significant avidity increase afforded by bivalent binding, despite the low envelope protein density of HIV or a geometry which does not readily lend itself to bivalent binding on the surface of influenza viruses (Klein and Bjorkman, 2010). A machine learning-based classification software was developed with the capability to determine the polyreactivity status of a given sequence This software is generalizable and can be retrained on any binary classification problem and identify the key differences between two distinct populations of antibodies, T cell receptors, or MHC-like molecules at the amino acid level
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