Computational Approaches to Identification of Aggregation Sites and the Mechanism of Amyloid Growth

Abstract

This chapter describes computational approaches to study amyloid formation. The first part addresses identification of potential amyloidogenic regions in the amino acid sequences of proteins and peptides. Next, we discuss nucleation and aggregation sites in protein folding and misfolding. The last part describes up-to-date kinetic models of amyloid fibrils formation. Numerous studies show that protein misfolding is initiated by specific amino acid segments with high amyloid-forming propensity. The ability to identify and, ultimately, block such segments is very important. To this end, many prediction algorithms have been developed which vary greatly in their effectiveness. We compared the predictions for 30 proteins by using different methods and found that, at best, only 50% of residues in amyloidogenic segments were predicted correctly. The best results were obtained by using the meta-servers that combine several independent approaches, and by the method PASTA2. Thus, correct prediction of amyloidogenic segments remains a difficult task. Additional data and new algorithms that are becoming available are expected to improve the accuracy of the prediction methods, particularly if they use 3D structural information on the target proteins. At the same time, our understanding of the kinetics of fibril formation is more advanced. The current kinetic models outlined in this chapter adequately describe the key features of amyloid nucleation and growth. However, the underlying structural details are less clear, not least because of the apparently different mechanisms of amyloid fibril formation which are discussed. Ultimately, the detailed understanding of the structural basis for amyloidogenesis should help develop rational therapies to block this pathogenic process.

Characterization of Amyloid Cores in Prion Domains

“…One of the outcomes of these efforts is the so-called “short-stretch hypothesis”, which postulates that a short amino acid stretch could provide most of the driving force needed to trigger the self-assembly of a protein into an amyloid 46 47 48 . This observation has pushed the development of over twenty different algorithms, aimed to identify these sequence stretches in disease-linked polypeptides 49 50 , whose predictions have been experimentally validated in many instances. Indeed, compounds blocking these regions have been shown to successfully interfere with entire protein aggregation reactions 51 and conversely mutations that increase the amyloid propensity of these stretches usually exacerbate full-length protein deposition 52 .…”

Proteome-scale understanding of relationship between homo-repeat enrichments and protein aggregation properties

“…We describe the aggregating properties of proteins considering such the aggregation values as Spos, Sneg and Sall (see Material and methods ) for each amino acid residue along the protein sequence using the FoldAmyloid program [ 28 , 29 ]. Comparison of the results for 30 proteins [ 30 ] using eight different methods demonstrated that our method is among the best ones (see Table 2 ).…”

Curing bacterial infections with protein aggregates

“…These Aggregation Prone Regions (APRs), also known as "hot spots", are generally composed of 5-15 amino acids, enriched in hydrophobic residues and with a low net charge. A number of computational methods have been developed in recent times to identify the presence of APRs in protein sequences, as a mean to predict a polypeptide's aggregation propensity (Castillo et al, 2011;Dovidchenko and Galzitskaya, 2015). Although these predictive methods rely on different assumptions and exploit different protein properties, they tend to provide consistent predictions and APRs can now be predicted with a high accuracy.…”