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Protein Sci. 2011 Sep;20(9):1546-57. doi: 10.1002/pro.680. Epub 2011 Jul 13.

High-throughput measurement, correlation analysis, and machine-learning predictions for pH and thermal stabilities of Pfizer-generated antibodies.

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  • 1Pfizer Global BioTherapeutic Technologies, Cambridge, Massachusetts 02140, USA.


Generating stable antibodies is an important goal in the development of antibody-based drugs. Often, thermal stability is assumed predictive of overall stability. To test this, we used different internally created antibodies and first studied changes in antibody structure as a function of pH, using the dye ANS. Comparison of the pH(50) values, the midpoint of the transition from the high-pH to the low-pH conformation, allowed us for the first time to rank antibodies based on their pH stability. Next, thermal stability was probed by heating the protein in the presence of the dye Sypro Orange. A new data analysis method allowed extraction of all three antibody unfolding transitions and showed close correspondence to values obtained by differential scanning calorimetry. T(1%) , the temperature at which 1% of the protein is unfolded, was also determined. Importantly, no correlations could be found between thermal stability and pH(50) , suggesting that to accurately quantify antibody stability, different measures of protein stability are necessary. The experimental data were further analyzed using a machine-learning approach with a trained model that allowed the prediction of biophysical stability using primary sequence alone. The pH stability predictions proved most successful and were accurate to within pH ±0.2.

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