Aggrescan3D (A3D) 2.0: prediction and engineering of protein solubility

An in vivo platform to select and evolve aggregation-resistant proteins

Protein biopharmaceuticals are highly successful, but their utility is compromised by their propensity to aggregate during manufacture and storage. As aggregation can be triggered by non-native states, whose population is not necessarily related to thermodynamic stability, prediction of poorly-behaving biologics is difficult, and searching for sequences with desired properties is labour-intensive and time-consuming. Here we show that an assay in the periplasm of E. coli linking aggregation directly to antibiotic resistance acts as a sensor for the innate (un-accelerated) aggregation of antibody fragments. Using this assay as a directed evolution screen, we demonstrate the generation of aggregation resistant scFv sequences when reformatted as IgGs. This powerful tool can thus screen and evolve ‘manufacturable’ biopharmaceuticals early in industrial development. By comparing the mutational profiles of three different immunoglobulin scaffolds, we show the applicability of this method to investigate protein aggregation mechanisms important to both industrial manufacture and amyloid disease.

Protein aggregation remains a significant challenge for manufacturing of protein biopharmaceuticals. Here, the authors demonstrate the use of directed evolution and an assay for in vivo innate protein aggregation-propensity to generate aggregation-resistant scFv fragments.

Effects of Mutating Trp42 Residue on γD-Crystallin Stability

Oligomerization and aggregation of γD-crystallins (HγDC) in the eye lens is one of the main causes of cataract development. To date, several congenital mutations related to this protein are known to promote the formation of aggregates. Previous studies have demonstrated that mutations in W42 residue of HγDC lead to the generation of partially unfolded intermediates that are more prone to aggregate. To understand the role of W42 in the stability of HγDC, we performed alchemical free-energy calculations and all-atom molecular dynamics simulations of different W42 mutant models. Our results suggest that substitution of W42 by small size and/or polar residues promotes HγDC denaturation due to the entry of water molecules into the hydrophobic core of the N-terminal domain. Similar behavior was observed in the C-terminal domain of HγDC when mutating the W130 residue located in a homologous position. Moreover, the exposure of the hydrophobic core residues could lead to the formation of aggregation-prone partially unfolded species. Overall, this study takes a step toward understanding the role of HγDC in cataract development.

Using protein engineering to understand and modulate aggregation

Protein aggregation occurs through a variety of mechanisms, initiated by the unfolded, non-native, or even the native state itself. Understanding the molecular mechanisms of protein aggregation is challenging, given the array of competing interactions that control solubility, stability, cooperativity and aggregation propensity. An array of methods have been developed to interrogate protein aggregation, spanning computational algorithms able to identify aggregation-prone regions, to deep mutational scanning to define the entire mutational landscape of a protein's sequence. Here, we review recent advances in this exciting and emerging field, focussing on protein engineering approaches that, together with improved computational methods, hold promise to predict and control protein aggregation linked to human disease, as well as facilitating the manufacture of protein-based therapeutics.

pH-Dependent Aggregation in Intrinsically Disordered Proteins Is Determined by Charge and Lipophilicity

Protein aggregation is associated with an increasing number of human disorders and premature aging. Moreover, it is a central concern in the manufacturing of recombinant proteins for biotechnological and therapeutic applications. Nevertheless, the unique architecture of protein aggregates is also exploited by nature for functional purposes, from bacteria to humans. The relevance of this process in health and disease has boosted the interest in understanding and controlling aggregation, with the concomitant development of a myriad of algorithms aimed to predict aggregation propensities. However, most of these programs are blind to the protein environment and, in particular, to the influence of the pH. Here, we developed an empirical equation to model the pH-dependent aggregation of intrinsically disordered proteins (IDPs) based on the assumption that both the global protein charge and lipophilicity depend on the solution pH. Upon its parametrization with a model IDP, this simple phenomenological approach showed unprecedented accuracy in predicting the dependence of the aggregation of both pathogenic and functional amyloidogenic IDPs on the pH. The algorithm might be useful for diverse applications, from large-scale analysis of IDPs aggregation properties to the design of novel reversible nanofibrillar materials.

Computational Assessment of Bacterial Protein Structures Indicates a Selection Against Aggregation

The aggregation of proteins compromises cell fitness, either because it titrates functional proteins into non-productive inclusions or because it results in the formation of toxic assemblies. Accordingly, computational proteome-wide analyses suggest that prevention of aggregation upon misfolding plays a key role in sequence evolution. Most proteins spend their lifetimes in a folded state; therefore, it is conceivable that, in addition to sequences, protein structures would have also evolved to minimize the risk of aggregation in their natural environments. By exploiting the AGGRESCAN3D structure-based approach to predict the aggregation propensity of >600 Escherichia coli proteins, we show that the structural aggregation propensity of globular proteins is connected with their abundance, length, essentiality, subcellular location and quaternary structure. These data suggest that the avoidance of protein aggregation has contributed to shape the structural properties of proteins in bacterial cells.