No abstract

No abstract

Information about protein-protein interactions provides valuable knowledge about the phase behavior of protein solutions during the biopharmaceutical production process. Up to date it is possible to capture their overall impact by an experimentally determined potential of mean force. For the description of this potential, the second virial coefficient B22, the diffusion interaction parameter kD, the storage modulus G', or the diffusion coefficient D is applied. In silico methods do not only have the potential to predict these parameters, but also to provide deeper understanding of the molecular origin of the protein-protein interactions by correlating the data to the protein's three-dimensional structure. This methodology furthermore allows a lower sample consumption and less experimental effort. Of all in silico methods, QSAR modeling, which correlates the properties of the molecule's structure with the experimental behavior, seems to be particularly suitable for this purpose. To verify this, the study reported here dealt with the determination of a QSAR model for the diffusion coefficient of proteins. This model consisted of diffusion coefficients for six different model proteins at various pH values and NaCl concentrations. The generated QSAR model showed a good correlation between experimental and predicted data with a coefficient of determination R2 = 0.9 and a good predictability for an external test set with R2 = 0.91. The information about the properties affecting protein-protein interactions present in solution was in agreement with experiment and theory. Furthermore, the model was able to give a more detailed picture of the protein properties influencing the diffusion coefficient and the acting protein-protein interactions. Biotechnol. Bioeng. 2017;114: 821-831. © 2016 Wiley Periodicals, Inc.

No abstract

In this study, influenza A/Puerto Rico/8/34 H1N1 virus particles (VP) produced in adherent and suspension Madin Darby canine kidney cells were investigated with a broad analytical toolbox to obtain more information on the VP's surface properties potentially affecting their aggregation behavior. First, differences in aggregation behavior were revealed by VP size distributions obtained via differential centrifugal sedimentation and confirmed by dynamic light scattering. The VP produced in adherent cells showed increased levels of aggregation in a 20 mM NaCl 10 mM Tris‐HCl pH 7.4 low‐salt buffer. This included the formation of multimers (dimers up to pentamers), whereas VP produced in suspension cells displayed no tendency toward aggregate formation. To investigate the cause of these differences in aggregation behavior, the VP samples were compared based on their zeta potential, their surface hydrophobicity, their lipid composition, and the N‐glycosylation of their major VP surface protein hemagglutinin. The zeta potential and the hydrophobicity of the VP produced in the adherent cells was significantly decreased compared to the VP produced in the suspension cells. The lipid composition of both VP systems was approximately identical. The hemagglutinin of the VP produced in adherent cells included more of the larger N‐glycans, whereas the VP produced in suspension cells included more of the smaller N‐glycans. These results indicate that differences in the glycosylation of viral surface proteins should be monitored to characterize VP hydrophobicity and aggregation behavior, and to avoid aggregate formation and product losses in virus purification processes for vaccines and gene therapy.