HighlightsAggregation of an exemplar therapeutic antibody fragment (scFv) enhances immunogenicity in vivo.Epitope mapping reveals immunogenicity is directed to a specific epitope in aggregate species.Molecular simulation demonstrates biophysical stress enhances epitope presentation.Protein aggregates have distinct immunological profiles to their native counterparts.

Methods to optimize the solution behavior of therapeutic proteins are frequently time-consuming, provide limited information, and often use milligram quantities of material. Here, we present a simple, versatile method that provides valuable information to guide the identification and comparison of formulation conditions for, in principle, any biopharmaceutical drug. The subject protein is incubated with a designed synthetic peptide microarray; the extent of binding to each peptide is dependent on the solution conditions. The array is washed, and the adhesion of the subject protein is detected using a secondary antibody. We exemplify the method using a well-characterized human single-chain Fv and a selection of human monoclonal antibodies. Correlations of peptide adhesion profiles can be used to establish quantitative relationships between different solution conditions, allowing subgrouping into dendrograms. Multidimensional reduction methods, such as t-distributed stochastic neighbor embedding, can be applied to compare how different monoclonals vary in their adhesion properties under different solution conditions. Finally, we screened peptide binding profiles using a selection of monoclonal antibodies for which a range of biophysical measurements were available under specified buffer conditions. We used a neural network method to train the data against aggregation temperature, k D , percentage recovery after incubation at 25 °C, and melting temperature. The results demonstrate that peptide binding profiles can indeed be effectively trained on these indicators of protein stability and self-association in solution. The method opens up multiple possibilities for the application of machine learning methods in therapeutic protein formulation.