Improving antibody thermostability based on statistical analysis of sequence and structural consensus data

The use of Monoclonal Antibodies (MAbs) as therapeutics has been increasing over the past 30 years due to their high specificity and strong affinity toward the target. One of the major challenges toward their use as drugs is their low thermostability, which impacts both efficacy as well as manufacturing and delivery.

To aid the design of thermally more stable mutants, consensus sequence-based method has been widely used. These methods typically have a success rate of about 50% with maximum melting temperature increment ranging from 10 to 32°C. To improve the prediction performance, we have developed a new and fast MAbs specific method by adding a 3D structural layer to the consensus sequence method. This is done by analyzing the close-by residue pairs which are conserved in >800 MAbs' 3D structures.

Combining consensus sequence and structural residue pair covariance methods, we developed an in-house application for predicting human MAb thermostability to guide protein engineers to design stable molecules. Major advantage of this structural level assessment is in significantly reducing the false positives by almost half from the consensus sequence method alone. This application has shown success in designing MAb engineering panels in multiple biologics programs.

Our data science-based method shows impacts in Mab engineering.