Abstract

The ability to generate and design antibodies recognizing specific targets has revolutionized the pharmaceutical industry and medical imaging. Engineering antibody therapeutics in some cases requires modifying their constant domains to enable new and altered interactions. Engineering novel specificities into antibody constant domains has proved challenging due to the complexity of inter-domain interactions. Covarying networks of residues that tend to cluster on the protein surface and near binding sites have been identified in some proteins. However, the underlying role these networks play in the protein resulting in their conservation remains unclear in most cases. Resolving their role is crucial, because residues in these networks are not viable design targets if their role is to maintain the fold of the protein. Conversely, these networks of residues are ideal candidates for manipulating specificity if they are primarily involved in binding, such as the myriad interdomain interactions maintained within antibodies. Here, we identify networks of evolutionarily-related residues in C-class antibody domains by evaluating covariation, a measure of propensity with which residue pairs vary dependently during evolution. We computationally test whether mutation of residues in these networks affects stability of the folded antibody domain, determining their viability as design candidates. We find that members of covarying networks cluster at domain-domain interfaces, and that mutations to these residues are diverse and frequent during evolution, precluding their importance to domain stability. These results indicate that networks of covarying residues exist in antibody domains for functional reasons unrelated to thermodynamic stability, making them ideal targets for antibody design.

Original languageEnglish (US)
Pages (from-to)884-895
Number of pages12
JournalProteins: Structure, Function and Bioinformatics
Volume81
Issue number5
DOIs
StatePublished - May 1 2013

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology

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