Census of exposed aggregation-prone regions in proteomes

Abstract

Loss of solubility usually leads to the detrimental elimination of protein function. In some cases, the protein aggregation is also required for beneficial functions. Given the duality of this phenomenon, it remains a fundamental question how natural selection controls the aggregation. The exponential growth of genomic sequence data and recent progress with in silico predictors of the aggregation allows approaching this problem by a large-scale bioinformatics analysis. Most of the aggregation-prone regions are hidden within the 3D structures and, therefore, they cannot realize their potential to aggregate. Thus, the most realistic census of the aggregation prone regions requires crossing aggregation prediction with information about the location of the natively unfolded regions. This allows us to detect so-called "Exposed Aggregation-prone Regions" (EARs). Here, we analyzed the occurrence and distribution of the EARs in 76 full reference proteomes from the three kingdoms of life. For this purpose, we used a bioinformatics pipeline, which provides a consensual result based on several predictors of aggregation. Our analysis revealed a number of new statistically significant correlations about the presence of EARs in different organisms, their dependence on protein length, cellular localizations, co-occurrence with short linear motifs, and the level of protein expression. We also obtained a list of proteins with the conserved aggregation-prone sequences for further experimental tests. Insights gained from this work led to a deeper understanding of the functional and evolutionary relations of the protein aggregation.

Developing machine-learning-based amyloid predictors with Cross-Beta DB

“…The entries of the Cross-beta DB also have several predicted characteristics such as AR and Exposed Amyloidogenic Regions (EAR) detected by ArchCandy2.0 (Falgarone et al, 2023), disorder predictions using IUPred3 (Erdős et al, 2021) and structure prediction by ESM fold (Rives et al, 2021).…”

Aggregation‐prone peptides from within a non‐self‐protein homoaggregate are preferred for MHC association: Historical overview

“…70 An aggregation-prone peptide can remain hidden within its donating protein but may contribute a little to whatever degree of homoaggregation propensity that protein displays (Figure 2). 71 'Thus, in addition to determining specificity for antigen, the non-germline encoded elements of T cell receptors may help the proteins cope with the extremely polymorphic nature of major histocompatibility complex products within the species'. 35…”