Protein aggregation has long been associated with a broad range of human maladies ranging from neurodegenerative diseases to systemic amyloidosis. Aggregation prone regions (APRs) in proteins are experimentally shown to drive intermolecular interactions that stabilize aggregates in the form of amyloid fibrils. Despite growing concerns over the years, a proteome-wide understanding of the prevalence of APRs in human proteins is incomplete due to limited experimental studies.
Our review and comparison of various existing in silico APR prediction tools led to the development of a new method ANuPP [1-3]. ANuPP is a sequence-based meta-classifier developed to identify APRs in proteins, and its robust prediction performance has been extensively validated over several experimental datasets. Using the available experimental data and in silico prediction tools, we conducted a systematic survey of the entire human proteome for potential APRs to understand its sequential and spatial distribution in proteins and study molecular-level adaptations that mitigate aggregation [4]. The analysis highlighted the effectiveness of amino acid composition bias, sequence patterning, structural localization, and conformation in controlling and reducing APRs at a molecular level.
Further, we extended the proteome-wide survey to 12 model organisms spanning bacteria to mammals. Proteins with high aggregation propensity are primarily compartmentalized in cellular organelles or embedded in cell membranes. Overall, our analysis suggests that APRs play a role as order-promoting regions by contributing to protein native contacts. However, there are embedded designs such as charged and beta-breakers to mitigate its aggregation propensity. To elucidate the role of charged residues in reducing aggregation propensity, we conducted a comparative molecular dynamic simulation of an aggregation prone region in myostatin inhibiting antibody, Stamulumab, and its mutants [5]. The study revealed that the charged residue point mutation increases the kinetic barrier to oligomerization due to charge-charge repulsions and modulates the solvent-solute interactions.

Publications:
[1] Prabakaran R, Rawat P, Kumar S, Gromiha MM. ANuPP: A Versatile Tool to Predict Aggregation Nucleating Regions in Peptides and Proteins. J Mol Biol. 2021; 433:166707
[2] Prabakaran R, Rawat P, Kumar S, Gromiha MM. Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets. Brief Bioinformatics. 2021; 22:bbab240
[3] Prabakaran R, Rawat P, Thangakani AM, Kumar S, Gromiha MM. Protein aggregation: in silico algorithms and applications. Biophys Rev. 2021; 13:71-89
[4] Prabakaran R, Goel D, Kumar S, Gromiha MM. Aggregation prone regions in human proteome: Insights from large-scale data analyses. Proteins. 2017; 85:1099-1118
[5] Prabakaran R, Rawat P, Yasuo N, Sekijima M, Kumar S, Gromiha MM. Effect of Charged Mutation on Aggregation of a Pentapeptide: Insights from Molecular Dynamics Simulations. Proteins. 2022; 90:405-417