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Human Germline Antibody Gene Segments Encode Polyspecific Antibodies

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  • Jordan R Willis
  • Bryan S Briney
  • Samuel L DeLuca
  • James E Crowe Jr
  • Jens Meiler

Abstract

Structural flexibility in germline gene-encoded antibodies allows promiscuous binding to diverse antigens. The binding affinity and specificity for a particular epitope typically increase as antibody genes acquire somatic mutations in antigen-stimulated B cells. In this work, we investigated whether germline gene-encoded antibodies are optimal for polyspecificity by determining the basis for recognition of diverse antigens by antibodies encoded by three VH gene segments. Panels of somatically mutated antibodies encoded by a common VH gene, but each binding to a different antigen, were computationally redesigned to predict antibodies that could engage multiple antigens at once. The Rosetta multi-state design process predicted antibody sequences for the entire heavy chain variable region, including framework, CDR1, and CDR2 mutations. The predicted sequences matched the germline gene sequences to a remarkable degree, revealing by computational design the residues that are predicted to enable polyspecificity, i.e., binding of many unrelated antigens with a common sequence. The process thereby reverses antibody maturation in silico. In contrast, when designing antibodies to bind a single antigen, a sequence similar to that of the mature antibody sequence was returned, mimicking natural antibody maturation in silico. We demonstrated that the Rosetta computational design algorithm captures important aspects of antibody/antigen recognition. While the hypervariable region CDR3 often mediates much of the specificity of mature antibodies, we identified key positions in the VH gene encoding CDR1, CDR2, and the immunoglobulin framework that are critical contributors for polyspecificity in germline antibodies. Computational design of antibodies capable of binding multiple antigens may allow the rational design of antibodies that retain polyspecificity for diverse epitope binding. Author Summary: Human antibodies are critical for eradication of viral and bacterial infections, while providing the basis for immunological memory. Antibody protein molecules are encoded by several recombined germline gene segments prior to antigen exposure. The initial set of antibodies that are generated by recombination in the bone marrow is the antigen-naïve antibody repertoire. It is of great interest to know how a finite set of such germline gene-encoded antibodies can recognize the large number of possible foreign antigens. A current hypothesis in the field suggests that antibodies encoded by germline gene segments are structurally flexible and able to accommodate binding to many antigens, much like one glove fitting the shape of many hands. The phenomenon of one structure binding to many targets is known as polyspecificity. Here we further support this hypothesis by showing that entire antibody protein variable regions encoded by germline gene segments are close to ideal for polyspecificity. We used computational design algorithms to explore antibody sequence space rapidly and predict optimal sequences to achieve polyspecificity. The resulting designed sequences recapitulated the germline gene segment sequences and highlighted residues critical for achieving polyspecificity. These results suggest how a finite set of antibody germline gene segments can encode antibodies that can engage a large number of antigens.

Suggested Citation

  • Jordan R Willis & Bryan S Briney & Samuel L DeLuca & James E Crowe Jr & Jens Meiler, 2013. "Human Germline Antibody Gene Segments Encode Polyspecific Antibodies," PLOS Computational Biology, Public Library of Science, vol. 9(4), pages 1-14, April.
  • Handle: RePEc:plo:pcbi00:1003045
    DOI: 10.1371/journal.pcbi.1003045
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    References listed on IDEAS

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    1. Aroop Sircar & Jeffrey J Gray, 2010. "SnugDock: Paratope Structural Optimization during Antibody-Antigen Docking Compensates for Errors in Antibody Homology Models," PLOS Computational Biology, Public Library of Science, vol. 6(1), pages 1-13, January.
    2. Andrew Leaver-Fay & Ron Jacak & P Benjamin Stranges & Brian Kuhlman, 2011. "A Generic Program for Multistate Protein Design," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-17, July.
    3. Elisabeth L Humphris & Tanja Kortemme, 2007. "Design of Multi-Specificity in Protein Interfaces," PLOS Computational Biology, Public Library of Science, vol. 3(8), pages 1-14, August.
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    1. Steven Schulz & Sébastien Boyer & Matteo Smerlak & Simona Cocco & Rémi Monasson & Clément Nizak & Olivier Rivoire, 2021. "Parameters and determinants of responses to selection in antibody libraries," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-24, March.
    2. Alexander M Sevy & Swetasudha Panda & James E Crowe Jr & Jens Meiler & Yevgeniy Vorobeychik, 2018. "Integrating linear optimization with structural modeling to increase HIV neutralization breadth," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-18, February.
    3. Alexander M Sevy & Tim M Jacobs & James E Crowe Jr. & Jens Meiler, 2015. "Design of Protein Multi-specificity Using an Independent Sequence Search Reduces the Barrier to Low Energy Sequences," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-23, July.

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