A Review of Fifteen Years Developing Computational Tools to Study Protein Aggregation

Abstract

The presence of insoluble protein deposits in tissues and organs is a hallmark of many human pathologies. In addition, the formation of protein aggregates is considered one of the main bottlenecks to producing protein-based therapeutics. Thus, there is a high interest in rationalizing and predicting protein aggregation. For almost two decades, our laboratory has been working to provide solutions for these needs. We have traditionally combined the core tenets of both bioinformatics and wet lab biophysics to develop algorithms and databases to study protein aggregation and its functional implications. Here, we review the computational toolbox developed by our lab, including programs for identifying sequential or structural aggregation-prone regions at the individual protein and proteome levels, engineering protein solubility, finding and evaluating prion-like domains, studying disorder-to-order protein transitions, or categorizing non-conventional amyloid regions of polar nature, among others. In perspective, the succession of the tools we describe illustrates how our understanding of the protein aggregation phenomenon has evolved over the last fifteen years.

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

“…A3D has been successfully applied in the redesign of less aggregation-prone variants of biotherapeutics, engineering novel self-assembled nanomaterials, understanding of pathological and physiological protein aggregation, and as a tool for teaching protein aggregation in university courses ( 42 ). While mutation protocols are the most straightforward and potent to prevent protein aggregation, they may face limitations such as the impossibility of mutating aggregation-prone but functionally important residues, like CDRs in antibodies or the existence of intellectual protection issues, like in the case of biosimilars.…”

A3DyDB: exploring structural aggregation propensities in the yeast proteome

“…Numerous past studies, mostly performed using simple prokaryotic and eukaryotic model organisms such as bacteria and yeast, have led to a detailed understanding of how highly aggregation-prone proteins form insoluble species and how these proteins are toxic for the cells [ 34 ]. These seminal investigations have allowed the identification of important principles of protein aggregation, which has led during the last decade to the development of a series of predictive algorithms to identify aggregation-prone sites [ 35 ]. Our current understanding of the structural landscape of the yeast proteome has radically changed with the development of deep-learning-based approaches, such as RoseTTA fold [ 36 ] or AF2 [ 21 ].…”

Protein Condensates and Protein Aggregates: In Vitro, in the Cell, and In Silico

“…The old methods only calculate the overall aggregation propensity of a polypeptide chain and do not provide information on the growth of aggregates over time. GAP deficiency is a small data set used in its development [99]; whereas the AGGRESCAN algorithm is simple and fast, the last implementation of the online software was performed in early 2023 [100]. In general, APRs are usually buried in the hydrophobic core of the native protein and enriched with residues that favor the formation of β-strands, contributing to increased hydrophobicity and low charge content [101].…”

“…With the advent of AlphaFold [112] and the establishment of AlphaFoldDB (https://alphafold.ebi.ac.uk/), the limitations due to the number of 3D protein structures identified are disappearing. Consequently, it is likely that in the next few years, we will foresee the development of many new tools for predicting the aggregation of protein 3D structures, which will enable new biomedical applications such as antibodies and beta-sheet-breaking peptides to treat diseases caused by protein aggregation [100]. In any case, the last decade has seen impressive innovations in ARP prediction.…”