PLoS Computational Biology (May 2019)

Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design.

  • Daniel A Cannon,
  • Lu Shan,
  • Qun Du,
  • Lena Shirinian,
  • Keith W Rickert,
  • Kim L Rosenthal,
  • Martin Korade,
  • Lilian E van Vlerken-Ysla,
  • Andrew Buchanan,
  • Tristan J Vaughan,
  • Melissa M Damschroder,
  • Bojana Popovic

DOI
https://doi.org/10.1371/journal.pcbi.1006980
Journal volume & issue
Vol. 15, no. 5
p. e1006980

Abstract

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Antibodies are an important class of therapeutics that have significant clinical impact for the treatment of severe diseases. Computational tools to support antibody drug discovery have been developing at an increasing rate over the last decade and typically rely upon a predetermined co-crystal structure of the antibody bound to the antigen for structural predictions. Here, we show an example of successful in silico affinity maturation of a hybridoma derived antibody, AB1, using just a homology model of the antibody fragment variable region and a protein-protein docking model of the AB1 antibody bound to the antigen, murine CCL20 (muCCL20). In silico affinity maturation, together with alanine scanning, has allowed us to fine-tune the protein-protein docking model to subsequently enable the identification of two single-point mutations that increase the affinity of AB1 for muCCL20. To our knowledge, this is one of the first examples of the use of homology modelling and protein docking for affinity maturation and represents an approach that can be widely deployed.