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

Protein Science

Volumn 20, Issue 9, 2011, Pages 1546-1557

  King, Amy C ^a   Woods, Matthew ^a,b   Liu, Wei ^a,c   Lu, Zhijian ^a   Gill, Davinder ^a   Krebs, Mark R H ^a  

^a PFIZER INC   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c AGIOS PHARMACEUTICALS   (United States)

Author keywords

Biophysics; DSC; Machine learning; pH stability; Sypro orange; Thermal stability

Indexed keywords

AMINO ACID SEQUENCE; ANTIBODY ENGINEERING; ANTIBODY STRUCTURE; AREA UNDER THE CURVE; ARTICLE; CIRCULAR DICHROISM; CORRELATION ANALYSIS; DIFFERENTIAL SCANNING CALORIMETRY; FLUORESCENCE ANALYSIS; HIGH THROUGHPUT SCREENING; MACHINE LEARNING; PH; PREDICTION; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN CONFORMATION; PROTEIN STABILITY; PROTEIN UNFOLDING; THERMOSTABILITY;

ANTIBODIES; HYDROGEN-ION CONCENTRATION; PROTEIN DENATURATION; PROTEIN FOLDING; PROTEIN STABILITY; THERMODYNAMICS;

EID: 84860389579     PISSN: 09618368     EISSN: 1469896X     Source Type: Journal    
DOI: 10.1002/pro.680     Document Type: Article

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