Highlights d ''Re-epitoping'' of an existing antibody by in silico design d Engineering a functional antibody to IL-17A d Crystal structure of the antibody-antigen complex confirms the targeted epitope

Determining which parts of the Ab are essential for Ag recognition and binding is crucial for understanding B cell–mediated immunity. Identification of fragments of Abs that maintain specificity to the Ag will also allow for the development of improved Ab-based therapy and diagnostics. In this article, we show that structural analysis of Ab–Ag complexes reveals which fragments of the Ab may bind the Ag on their own. In particular, it is possible to predict whether a given CDR is likely to bind the Ag as a peptide by analyzing the energetic contribution of each CDR to Ag binding and by assessing to what extent the interaction between that CDR and the Ag depends on other CDRs. To demonstrate this, we analyzed five Ab–Ag complexes and predicted for each of them which of the CDRs may bind the Ag on its own as a peptide. We then show that these predictions are in agreement with our experimental analysis and with previously published experimental results. These findings promote our understanding of the modular nature of Ab–Ag interactions and lay the foundation for the rational design of active CDR-derived peptides.

Of the currently identified protein sequences, 99.6% have never been observed in the laboratory as proteins and their molecular function has not been established experimentally. Predicting the function of such proteins relies mostly on annotated homologs. However, this has resulted in some erroneous annotations, and many proteins have no annotated homologs. Here we propose a de-novo function prediction approach based on identifying biophysical features that underlie function. Using our approach, we discover DNA and RNA binding proteins that cannot be identified based on homology and validate these predictions experimentally. For example, FGF14, which belongs to a family of secreted growth factors was predicted to bind DNA. We verify this experimentally and also show that FGF14 is localized to the nucleus. Mutating the predicted binding site on FGF14 abrogated DNA binding. These results demonstrate the feasibility of automated de-novo function prediction based on identifying function-related biophysical features.

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