Abstract

The self-assembly of proteins is an essential process for a variety of cellular functions including cell respiration, mobility and division. On the other hand, protein or peptide misfolding and aggregation is related to the development of Parkinson’s disease and Alzheimer’s disease, among other aggregopathies. As a consequence, significant research efforts are directed towards the understanding of this process. In this review, we are focused on the use of UV-Visible Absorption Spectroscopy, Fluorescence Spectroscopy and Circular Dichroism to evaluate the self-organization of proteins and peptides in solution. These spectroscopic techniques are commonly available in most chemistry and biochemistry research laboratories, and together they are a powerful approach for initial as well as routine evaluation of protein and peptide self-assembly and aggregation under different environmental stimulus. Furthermore, these spectroscopic techniques are even suitable for studying complex systems like those in the food industry or pharmaceutical formulations, providing an overall idea of the folding, self-assembly, and aggregation processes, which is challenging to obtain with high-resolution methods. Here, we compiled and discussed selected examples, together with our results and those that helped us better to understand the process of protein and peptide aggregation. We put particular emphasis on the basic description of the methods as well as on the experimental considerations needed to obtain meaningful information, to help those who are just getting into this exciting area of research. Moreover, this review is particularly useful to those out of the field who would like to improve reproducibility in their cellular and biomedical experiments, especially while working with peptide and protein systems as an external stimulus. Our final aim is to show the power of these low-resolution techniques to improve our understanding of the self-assembly of peptides and proteins and translate this fundamental knowledge in biomedical research or food applications.

Highlights

  • The Aβ-peptide [104], the Ac-Phe-Phe-Cys-NH2 (Ac-FFC-NH2 ) amyloid peptide model [61], and proteins such as alphasynuclein [152] and β2-microglobulin [153] have been shown to change their conformation from random coil to β-sheet structure over time during the aggregation process, which is compatible with the formation of fibrils

  • The aggregation of peptides and proteins into non-amyloid fibrils has been extensively studiedthe by aggregation

  • The 33-mer and the and p31–43 gliadin peptides, whichfibrils are related to celiac

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Summary

Setting the Frame and Initial Considerations

Protein self-assembly plays a pivotal role in cellular physiology It is a necessary process for the formation of any ordered protein structure [1]. To obtain molecular information, the use of specialized core facilities and specific training on them is required In this sense, spectroscopic methods like UV-Vis, fluorescence and circular dichroism together make a simple approach for evaluating proteins and peptides and assessing their self-assembly processes. Spectroscopic methods like UV-Vis, fluorescence and circular dichroism together make a simple approach for evaluating proteins and peptides and assessing their self-assembly processes In this regard, these spectroscopic techniques provide unique information in the case of complex systems, such as those composed by a mixture of proteins, where their analysis by sophisticated techniques as NMR (Nuclear Magnetic Resonance) is not easy to implement.

Principles and General Considerations
Intrinsic Chromophores
Extrinsic
Direct Methods
Intrinsic
Extrinsic Fluorophore
Method
Steady-State Fluorescence Methods
Time-Resolved Methods
Main Principles and Structural Protein Features
Experimental Considerations of CD Spectroscopy
Analysis of Amyloidogenic Proteins
Evaluation of Non-Amyloid Systems
Near-UV CD Analysis of Protein Aggregation
Some Considerations in Order to Avoid Protein Aggregation
Conclusions
Methods
A Crucial Triggering Factor in Josephin Domain Aggregation
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