Influence of structure properties on protein–protein interactions—QSAR modeling of changes in diffusion coefficients

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.

Development of QSAR models for in silico screening of antibody solubility

“…Features that have VIP values over 1 are considered important in the model whereas features with values less than 1 have a minor effect on the projections. 38 , 39 , 41 A comparison of the feature coefficients and their VIP scores from the regression model based on all the mAb data is presented in Figure 4 , which includes a horizontal dashed line for better identification of positive/negative characteristics of the coefficients. In Figure 4 , the top four descriptors that had VIP values equal or greater than one were: 1) isotype identifier, 2) average electrical potential of the top 25% strongest negative clusters(EPL_str), 3) protein isoelectric point (pI) based on three-dimensional structure(pro_pI_3D) and 4) the charge symmetry parameter of the variable region(FvCSP).…”

“… 34–38 Recently, QSAR models have also been used to estimate protein diffusion coefficients in formulation applications. 39 …”

Protein-based bioactive coatings: from nanoarchitectonics to applications

“…143 Moreover, chemoinformatics plays a pivotal role in drug discovery by allowing the prediction of binding affinities between protein-protein and protein–interface interactions through techniques like quantitative structure-activity relationship (QSAR) 144 models and molecular docking. 145,146 In the realm of materials science, it contributes to the rational design of surfaces and materials, optimizing protein interactions for various applications, including medical implants. Additionally, chemoinformatics facilitates virtual high-throughput screening, accelerating the identification of potential drug compounds 143 or materials with desired protein–interface interactions, thereby reducing time and costs.…”

Accelerating Biologics Manufacturing by Modeling: Process Integration of Precipitation in mAb Downstream Processing

“…To begin with, correlations describing equilibrium and solubility of proteins have been found [40,58] and extended for the change in solubility in presence of PEG molecules [32,34,36,59]. Further, empirical models like quantitative structure-activity relationship (QSAR) are applied to predict the discontinuity point of proteins in presence of PEG molecules [35,36]. Moreover, model approaches derived from a mechanistic model approach for crystallization are described also for precipitation [60][61][62][63][64][65].…”