Protein Aggregation in Neurodegenerative Diseases: Insights from Computational Analyses

Anita      Sarkar; Sonu      Kumar; Abhinav      Grover; Durai      Sundar

doi:10.2174/157489312799304495

Abstract

Biological aggregation is a process where bio-macromolecules such as proteins, lipids and nucleic acids essentially self-associate in an ordered fashion into functional complexes (that may be normal or pathological) and finally precipitate out due to formation of higher order conglomerates of low solubility. Neurodegenerative diseases are, in general, associated with the deposition of pathogenic aggregates composed of amyloid fibrils/plaques in tissues. Sequence analysis of proteins prone to aggregation has aided the evolution of accurate prediction algorithms now being used in designing aggregation-reducing mutations. Computational and experimental researches have proved that preventing aggregation does not necessarily prevent amyloidosis and vice-versa. Investigation of amyloid fibril formation with the help of these approaches shall lead to the understanding of the mechanism and prevention from various neurodegenerative diseases. We have observed in our past computational studies that there are certain “sequence breaker” amino acid residues which when placed in the aggregation-prone stretches, drastically affect the aggregation propensity and fibrillization activity. The sequence breaker concept is quite similar to that of the “gatekeeper residues” which have been explored earlier [1, 2]. On such grounds, we have studied α-synuclein, which is the pathogenic protein implicated in Parkinsons Disease (PD) as well as other neurodegenerative diseases, and identified a double mutant of A53T (familial PD-causing) mutant that increases its solubility, positively enhances its thermodynamic stability and nearly ends the aggregation propensity in the diseased state (which is a precursor to amyloidosis). Moreover, as protein aggregation is the key to control the symptoms of most neurodegenerative diseases, numerous small peptides (therapeutic drugs) as well as small molecules have been designed to target the aggregation-prone regions in individual experimental studies. This is also an important facet in biotherapeutics where constant efforts are being made to reduce protein aggregation. The focus of this article is to shed light on the recent technologies and developments in bioinformatics to investigate protein aggregation (with α-synuclein as a recurring example).

Keywords: amyloid, fibrillization, aggregation propensity, mutants, neurodegenerative diseases, Parkinson's disease, protein aggregation, protein misfolding, synuclein, cytotoxicity, computational prediction