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

Protein-based bioactive coatings: from nanoarchitectonics to applications

Protein-based bioactive coatings have emerged as a versatile and promising strategy for enhancing the performance and biocompatibility of diverse biomedical materials and devices. Through surface modification, these coatings confer novel biofunctional attributes, rendering the material highly bioactive. Their widespread adoption across various domains in recent years underscores their importance. This review systematically elucidates the behavior of protein-based bioactive coatings in organisms and expounds on their underlying mechanisms. Furthermore, it highlights notable advancements in artificial synthesis methodologies and their functional applications in vitro. A focal point is the delineation of assembly strategies employed in crafting protein-based bioactive coatings, which provides a guide for their expansion and sustained implementation. Finally, the current trends, challenges, and future directions of protein-based bioactive coatings are discussed.

Development of QSAR models for in silico screening of antibody solubility

Although monoclonal antibodies (mAbs) have been shown to be extremely effective in treating a number of diseases, they often suffer from poor developability attributes, such as high viscosity and low solubility at elevated concentrations. Since experimental candidate screening is often materials and labor intensive, there is substantial interest in developing in silico tools for expediting mAb design. Here, we present a strategy using machine learning-based QSAR models for the a priori estimation of mAb solubility. The extrapolated protein solubilities of a set of 111 antibodies in a histidine buffer were determined using a high throughput PEG precipitation assay. 3D homology models of the antibodies were determined, and a large set of in house and commercially available molecular descriptors were then calculated. The resulting experimental and descriptor data were then used for the development of QSAR models of mAb solubilities. After feature selection and training with different machine learning algorithms, the models were evaluated with external test sets. The resulting regression models were able to estimate the solubility values of external test set data with R² of 0.81 and 0.85 for the two regression models developed. In addition, three class and binary classification models were developed and shown to be good estimators of mAb solubility behavior, with overall test set accuracies of 0.70 and 0.95, respectively. The analysis of the selected molecular descriptors in these models was also found to be informative and suggested that several charge-based descriptors and isotype may play important roles in mAb solubility. The combination of high throughput relative solubility experimental techniques in concert with efficient machine learning QSAR models offers an opportunity to rapidly screen potential mAb candidates and to design therapeutics with improved solubility characteristics.

Structural characterization with light scattering: A tool for rationally designing protein formulations

Here we explore the possibility of using light scattering technologies as an analytical tool for understanding structural features of a protein that might be responsible for initiating aggregative interactions. Using widely independent complementary experimental and computational techniques, we found that interaction parameters like K_m in particular possess good correlation with residue specific descriptors for the model protein Bovine Serum Albumin. Such information can help rationally design protein engineering and/or formulation strategies for prolonged shelf-life of such products.

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

The demand on biologics has been constantly rising over the past decades and has become crucial in modern medicine. Promising approaches to cope with widespread diseases like cancer and diabetes are gene therapy, plasmid DNA, virus-like particles, and exosomes. Due to progress that has been made in upstream processing (USP), difficulties arise in downstream processing and demand for innovative solutions. This work focuses on the integration of precipitation using a quality by design (QbD) approach for process development. Selective precipitation is achieved with PEG 4000 resulting in an HCP depletion of ≥80% respectively to IgG. Dissolution was executed with a sodium phosphate buffer (pH = 5/50 mM) reaching an IgG recovery of ≥95%. However, the central challenge in process development is still an optimal process design, which is transferable for a broad molecular variety of new products. This is where rigorous modeling becomes vital in order to generate digital twins to support early-stage process development and reduce the experimental overhead. Therefore, a model development and validation concept for construction of a process model for precipitation is also presented.

In search of bioinspired hydrogels from amphiphilic peptides: a template for nanoparticle stabilization for the sustained release of anticancer drugs

This report presents the efficiency of palmitic acid-based proteolytically stable, biocompatible hydrogelators for the sustained release of anticancer drugs.

An auxin–tyrosine derivative based biocompatible supergelator: a template for fabrication of nanoparticles for sustained release of model drugs

This report reveals the supergelating ability of an auxin-tyrosine derivative based biocompatible hydrogel in drug delivery.