This work focused on the development of a combined experimental and computational tool set to study protein-mAb interactions. A model protein library was first screened using cross interaction chromatography to identify proteins with the strongest retention. Fluorescence polarization was then employed to study the interactions and thermodynamics of the selected proteins-lactoferrin, pyruvate kinase, and ribonuclease B with the mAb. Binding affinities of lactoferrin and pyruvate kinase to the mAb were seen to be relatively salt insensitive in the range examined. Further, a strong entropic contribution was observed, suggesting the importance of hydrophobic interactions. On the other hand, ribonuclease B-mAb binding was seen to be enthalpically driven and salt sensitive, indicating the importance of electrostatic interactions. Protein-protein docking was then carried out and the results identified the CDR region on the mAb as an important binding site for all three proteins. The binding interfaces identified for the mAb-lactoferrin and mAb-pyruvate kinase systems were found to contain complementary hydrophobic and oppositely charged clusters on the interacting regions which were indicative of both hydrophobic and electrostatic interactions. On the other hand, the binding site on ribonuclease B was predominantly positively charged with minimal hydrophobicity. This resulted in an alignment with negatively charged clusters on the mAb, supporting the contention that these interactions were primarily electrostatic in nature. Importantly, these computational results were found to be consistent with the fluorescence polarization data and this combined approach may have utility in examining mAb-HCP interactions which can often complicate the downstream processing of biologics. © 2019 American Institute of Chemical Engineers.
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http://dx.doi.org/10.1002/btpr.2825 | DOI Listing |
Hepatology
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Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
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Department of Plant Pathology, The Ohio State University, Columbus, Ohio, United States of America.
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Departament de Ciències Matemàtiques i Informàtica, Universitat de les Illes Balears, Palma, Spain.
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Accurate detection of fabric defects is crucial for quality control in the textile industry. However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. To address the issues of inaccurate localization and false positives caused by complex textures and varying defect sizes, this paper proposes an improved YOLOv8-based fabric defect detection method.
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