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High‐throughput measurement, correlation analysis, and machine‐learning predictions for pH and thermal stabilities of Pfizer‐generated antibodies
Author(s) -
King Amy C.,
Woods Matthew,
Liu Wei,
Lu Zhijian,
Gill Davinder,
Krebs Mark R. H.
Publication year - 2011
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.680
Subject(s) - differential scanning calorimetry , chemistry , thermal stability , protein stability , stability (learning theory) , chemical stability , equilibrium unfolding , antibody , chromatography , thermodynamics , analytical chemistry (journal) , crystallography , physics , biochemistry , computer science , machine learning , organic chemistry , circular dichroism , biology , immunology
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.