The efficient development
of new therapeutic antibodies relies
on developability assessment with biophysical and computational methods
to find molecules with drug-like properties such as resistance to
aggregation. Despite the many novel approaches to select well-behaved
proteins, antibody aggregation during storage is still challenging
to predict. For this reason, there is a high demand for methods that
can identify aggregation-resistant antibodies. Here, we show that
three straightforward techniques can select the aggregation-resistant
antibodies from a dataset with 13 molecules. The ReFOLD assay provided
information about the ability of the antibodies to refold to monomers
after unfolding with chemical denaturants. Modulated scanning fluorimetry
(MSF) yielded the temperatures that start causing irreversible unfolding
of the proteins. Aggregation was the main reason for poor unfolding
reversibility in both ReFOLD and MSF experiments. We therefore performed
temperature ramps in molecular dynamics (MD) simulations to obtain
partially unfolded antibody domains in silico and
used CamSol to assess their aggregation potential. We compared the
information from ReFOLD, MSF, and MD to size-exclusion chromatography
(SEC) data that shows whether the antibodies aggregated during storage
at 4, 25, and 40 °C. Contrary to the aggregation-prone molecules,
the antibodies that were resistant to aggregation during storage at
40 °C shared three common features: (i) higher tendency to refold
to monomers after unfolding with chemical denaturants, (ii) higher
onset temperature of nonreversible unfolding, and (iii) unfolding
of regions containing aggregation-prone sequences at higher temperatures
in MD simulations.