Census of exposed aggregation-prone regions in proteomes

Area of Science:

• Proteomics and Bioinformatics
• Molecular Evolution
• Structural Biology

Background:

• Protein aggregation can be detrimental to function but is also essential for certain biological roles.
• Understanding how natural selection governs protein aggregation is a fundamental biological question.
• In silico prediction tools for protein aggregation have advanced, enabling large-scale bioinformatics analyses.

Purpose of the Study:

• To investigate the evolutionary control of protein aggregation by analyzing exposed aggregation-prone regions (EARs).
• To determine the occurrence and distribution of EARs across diverse organisms and protein types.
• To uncover correlations between EARs and various protein characteristics, including length, localization, and expression.

Main Methods:

• Utilized a bioinformatics pipeline integrating multiple aggregation predictors with natively unfolded region data.
• Analyzed 76 reference proteomes from bacteria, archaea, and eukaryotes to identify and map EARs.
• Cross-referenced EARs with protein length, cellular localization, short linear motifs, and expression levels.

Main Results:

• Identified statistically significant correlations regarding the presence and distribution of EARs across different organisms.
• Found relationships between EARs and protein length, cellular localization, co-occurrence with short linear motifs, and protein expression levels.
• Generated a catalog of proteins exhibiting conserved aggregation-prone sequences, suitable for experimental validation.

Conclusions:

• Protein aggregation is under evolutionary control, with exposed aggregation-prone regions (EARs) playing a key role.
• The distribution and characteristics of EARs provide insights into the functional and evolutionary dynamics of proteins.
• This study enhances our understanding of the interplay between protein evolution and aggregation phenomena.