Aggrescan4D: structure-informed analysis of pH-dependent protein aggregation

Protein aggregation is a multifactorial process, directed by the intrinsic properties of proteins and heavily influenced by environmental factors. This phenomenon accounts for the onset of highly debilitating proteinopathies, which pose a significant challenge to human health. Additionally, it imposes constraints on developing and implementing protein-based biotechnological and biomedical applications.

Classical computational methods that predict protein aggregation rely on sequential information to assess the propensity of a protein to aggregate. While highlighting APRs provides information on the regions driving aggregation, this strategy assumes that the proximity between residues is directly proportional to their sequential distance. This presumption might apply to disordered or denatured protein states but is an imprecise approximation for the native state of globular proteins. Moreover, these methods treat the aggregation contribution of amino acids residing in collapsed or inaccessible regions (such as the hydrophobic core) equally to exposed residues. To overcome these limitations, we developed a three-dimensional structure aggregation predictor, Aggrescan3D (A3D), which projects classical Aggrescan scoring to proteins" native structure. Over the years, we have been advancing the A3D method, aiming to deepen our comprehension of protein aggregation and assist the engineering of protein solubility. Since its inception, A3D has become one of the most popular structure-based aggregation predictors because of its performance, modular functionalities, RESTful service for extensive screenings, and intuitive user interface.

Expanding on our previous work, we introduce Aggrescan4D (A4D), a tool that significantly enhances A3D"s capabilities. A4D is designed to predict pH-dependent protein structure aggregation and incorporates an evolutionary-informed automatic mutation protocol for engineering protein solubility and maintaining structure and stability. It also integrates precalculated results from the A3D Model Organisms Database, covering nearly 500,000 jobs, and allows structure retrieval from the AlphaFold database. Globally, A4D is a comprehensive tool for understanding, predicting, and addressing specific protein aggregation challenges. The A4D web server and extensive documentation are accessible at https://biocomp.chem.uw.edu.pl/a4d/.