Chameleon sequences in neurodegenerative diseases

A unifying framework for amyloid-mediated membrane damage: The lipid-chaperone hypothesis

Over the past thirty years, researchers have highlighted the role played by a class of proteins or polypeptides that forms pathogenic amyloid aggregates in vivo, including i) the amyloid Aβ peptide, which is known to form senile plaques in Alzheimer's disease; ii) α-synuclein, responsible for Lewy body formation in Parkinson's disease and iii) IAPP, which is the protein component of type 2 diabetes-associated islet amyloids. These proteins, known as intrinsically disordered proteins (IDPs), are present as highly dynamic conformational ensembles. IDPs can partially (mis) fold into (dys) functional conformations and accumulate as amyloid aggregates upon interaction with other cytosolic partners such as proteins or lipid membranes. In addition, an increasing number of reports link the toxicity of amyloid proteins to their harmful effects on membrane integrity. Still, the molecular mechanism underlying the amyloidogenic proteins transfer from the aqueous environment to the hydrocarbon core of the membrane is poorly understood. This review starts with a historical overview of the toxicity models of amyloidogenic proteins to contextualize the more recent lipid-chaperone hypothesis. Then, we report the early molecular-level events in the aggregation and ion-channel pore formation of Aβ, IAPP, and α-synuclein interacting with model membranes, emphasizing the complexity of these processes due to their different spatial-temporal resolutions. Next, we underline the need for a combined experimental and computational approach, focusing on the strengths and weaknesses of the most commonly used techniques. Finally, the last two chapters highlight the crucial role of lipid-protein complexes as molecular switches among ion-channel-like formation, detergent-like, and fibril formation mechanisms and their implication in fighting amyloidogenic diseases.

AlphaFold and the amyloid landscape

Protein aggregation is a widespread phenomenon with important implications in many scientific areas. Although amyloid formation is typically considered as detrimental, functional amyloids that perform physiological roles have been identified in all kingdoms of life. Despite their functional and pathological relevance, the structural details of the majority of molecular species involved in the amyloidogenic process remains elusive. Here, we explore the application of AlphaFold, a highly accurate protein structure predictor, in the field of protein aggregation. While we envision a straightforward application of AlphaFold in assisting the design of globular proteins with improved solubility for biomedical and industrial purposes, the use of this algorithm for predicting the structure of aggregated species seems far from trivial. First, in amyloid diseases, the presence of multiple amyloid polymorphs and the heterogeneity of aggregation intermediates challenges the "one sequence, one structure" paradigm, inherent to sequence-based predictions. Second, aberrant aggregation is not the subject of positive selective pressure, precluding the use of evolutionary-based approaches, which are the core of the AlphaFold pipeline. Instead, amyloid polymorphism seems to be constrained by the need for a defined structure-activity relationship in functional amyloids. They may thus provide a starting point for the application of AlphaFold in the amyloid landscape.

The 'Shape-Shifter' Peptide from the Disulphide Isomerase PmScsC Shows Context-Dependent Conformational Preferences

Multiple crystal structures of the homo-trimeric protein disulphide isomerase PmScsC reveal that the peptide which links the trimerization stalk and catalytic domain can adopt helical, β-strand and loop conformations. This region has been called a 'shape-shifter' peptide. Characterisation of this peptide using NMR experiments and MD simulations has shown that it is essentially disordered in solution. Analysis of the PmScsC crystal structures identifies the role of intermolecular contacts, within an assembly of protein molecules, in stabilising the different linker peptide conformations. These context-dependent conformational properties may be important functionally, allowing for the binding and disulphide shuffling of a variety of protein substrates to PmScsC. They also have a relevance for our understanding of protein aggregation and misfolding showing how intermolecular quaternary interactions can lead to β-sheet formation by a sequence that in other contexts adopts a helical structure. This 'shape-shifting' peptide region within PmScsC is reminiscent of one-to-many molecular recognition features (MoRFs) found in intrinsically disordered proteins which are able to adopt different conformations when they fold upon binding to their protein partners.

Turncoat Polypeptides: We Adapt to Our Environment

A common interpretation of Anfinsen's hypothesis states that one amino acid sequence should fold into a single, native, ordered state, or a highly similar set thereof, coinciding with the global minimum in the folding-energy landscape, which, in turn, is responsible for the function of the protein. However, this classical view is challenged by many proteins and peptide sequences, which can adopt exchangeable, significantly dissimilar conformations that even fulfill different biological roles. The similarities and differences of concepts related to these proteins, mainly chameleon sequences, metamorphic proteins, and switch peptides, which are all denoted herein "turncoat" polypeptides, are reviewed. As well as adding a twist to the conventional view of protein folding, the lack of structural definition adds clear versatility to the activity of proteins and can be used as a tool for protein design and further application in biotechnology and biomedicine.

Importance of Fluctuating Amino Acid Residues in Folding and Binding of Proteins

BACKGROUND

Conformational flexibility of proteins remains as one of the major events in protein-protein/DNA/ligand/small molecule binding to achieve its biological function in the cell. The availability of high-resolution structures of protein complexes is a valuable resource for researchers to understand the mechanisms behind such interactions and it is found that the flexibility of amino acid residues at binding sites is crucial for many important functions in the cell.

METHODS

In this article, our statistical method (PreFRP) developed based on fluctuating amino acid residues and various amino acid indices related to flexibility/rigidity were used to study the importance of fluctuating amino acid residues in thermonucleases from pathogenic bacteria, cell penetrating peptides and intrinsically disordered proteins responsible for many neural disorders.

RESULTS

The results from our analysis reveal the importance of fluctuating amino acid residues in folding and binding of proteins. The role of moderate and high fluctuating residues in themonucleases, cell penetrating peptide and disordered regions are discussed in detail.

CONCLUSION

Therefore, our analysis will help in understanding the importance of fluctuating amino acid residues in proteins which undergo a conformation change phenomenon.

A human body physiological feature selection algorithm based on filtering and improved clustering

Research

The body composition model is closely related to the physiological characteristics of the human body. At the same time there can be a large number of physiological characteristics, many of which may be redundant or irrelevant. In existing human physiological feature selection algorithms, it is difficult to overcome the impact that redundancy and irrelevancy may have on human body composition modeling. This suggests a role for selection algorithms, where human physiological characteristics are identified using a combination of filtering and improved clustering. To do this, a feature filtering method based on Hilbert-Schmidt dependency criteria is first of all used to eliminate irrelevant features. After this, it is possible to use improved Chameleon clustering to increase the combination of sub-clusters amongst the characteristics, thereby removing any redundant features to obtain a candidate feature set for human body composition modeling. Method

We report here on the use of an algorithm to filter the characteristic parameters in INBODY770 (this paper used INBODY 770 as body composition analyzer.) measurement data, which has three commonly-used impedance bands (1 kHZ, 250 kHZ, 500 kHZ). This algorithm is able to filter out parameters that have a low correlation with body composition BFM. The algorithm is also able to draw upon improved clustering techniques to reduce the initial feature set from 29 parameters to 10 parameters for any parameters of the 250 kHZ band that remain after filtering. In addition, we also examined the impact of different sample sizes on feature selection.

Result

The proposed algorithm is able to remove irrelevant and redundant features and the resulting correlation between the model and the body composition (BFM which is a whole body fat evaluation can better assess the body's overall fat and muscle composition.) is 0.978, thereby providing an improved model for prediction with a relative error of less than 0.12.

Sixty-five years of the long march in protein secondary structure prediction: the final stretch?

Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new methods breathe new life into this field. The highest three-state accuracy without relying on structure templates is now at 82-84%, a number unthinkable just a few years ago. These improvements came from increasingly larger databases of protein sequences and structures for training, the use of template secondary structure information and more powerful deep learning techniques. As we are approaching to the theoretical limit of three-state prediction (88-90%), alternative to secondary structure prediction (prediction of backbone torsion angles and Cα-atom-based angles and torsion angles) not only has more room for further improvement but also allows direct prediction of three-dimensional fragment structures with constantly improved accuracy. About 20% of all 40-residue fragments in a database of 1199 non-redundant proteins have <6 Å root-mean-squared distance from the native conformations by SPIDER2. More powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques for secondary structure prediction. The time has come to finish off the final stretch of the long march towards protein secondary structure prediction.