Influence of Repeats in the Protein Chain on its Aggregation Capacity for ALS-Associated Proteins

Studies of diseases associated with pathological irreversible aggregation of proteins have become of special relevance and attracted the attention of researchers throughout the world because of the appearance of a new conceptual model based on the capacity of some proteins to self-assemble by the prion mechanism. Along with direct prion diseases, such as bovine rabies and Creutzfeldt-Jakob disease in humans, a great number of neurodegenerative disorders associated with the formation of aggregates through the prion mechanism are revealed. These disorders include Alzheimer’s and Parkinson’s diseases, amyotrophic lateral sclerosis, Huntington disease, and mucoviscidosis, some types of diabetes and hereditary cataracts. The listed diseases are caused by transition of a “healthy” protein or peptide molecule from the native conformation to a very stable “pathological” form. In this case, molecules in the “pathological” conformation aggregate specifically, forming amyloid fibrils that can multiply infinitely. An important result of studying the molecular mechanisms of prion diseases and different proteinopathies, associated with the formation of pathological aggregations by the prion mechanism, is the discovery of protein chain regions responsible for their aggregation. The ability to regulate aggregation (fibrillation) of proteins can be the focal tool for the drug development. Herein by the example of 29 RNA-binding proteins with prion-like domains, we demonstrate what role the amino acid repeats in prion-like domains can play. For these proteins, quite different repeats are revealed in the disordered part of the protein chain predicted with bioinformatics methods. Ten proteins of the 29 RNA-binding proteins are involved in the development of some diseases. The prion-like domains of FUS, TAF15, and EWS are critical for the aggregation of proteins associated with human neurodegenerative diseases. Proteins of this family are involved not only in neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS), Huntington disease, spinocerebral ataxy, and dentatorubral pallidoluysian atrophy, but also in the formation of human mixoid liposarcoma. It can be suggested that the presence of a great number of repeats in prion-like domains of RNA-binding proteins can accelerate the formation of a dynamic beta-structure and pathological aggregates, which are crucibles of amyotrophic lateral sclerosis (ALS) pathogenesis

Regions which are Responsible for Swapping are also Responsible for Folding and Misfolding

Domain swapping is a term used to describe a process when two or more protein chains exchange identical structural elements. Some cases of amyloid formation can be explained through a domain swapping mechanism therefore this deserves theoretical consideration and studying. It has been demonstrated that diverse proteins in sequence and structure are able to oligomerize via domain swapping. This allows us to suggest that the exchangeable regions are important in folding and misfolding processes of proteins, i.e. the residues from the swapping regions are typically incorporated into the native structure early during its formation. The modeling of folding of the proteins with swapped domains demonstrates that the regions exchanged in the oligomeric form in most cases are also responsible for folding and misfolding. For 11 out of 17 proteins, swapping regions intersect with the predicted amyloidogenic regions. Moreover, for 10 out of 17 proteins, high Φ-values (>0.5) belong to residues from the swapping regions. Our data confirm that the exchangeable regions are important in folding, misfolding, and domain swapping processes of the proteins, therefore the suggestion that domain swapping can serve as a mechanism for functional interconversion between monomers and oligomers is likely to be correct

Kinetics of Amyloid Formation by Different Proteins and Peptides: Polymorphism and Sizes of Folding Nuclei of Fibrils

Aggregation of peptides and proteins into amyloid structure is one of the most intensively studied biological phenomena at the moment. To date, there is no developed theory that would allow one to determine what kind of mechanism presents in the given experiment on the basis of aggregation kinetic data. Debates concerning the mechanism of the amyloid fibrils formation and, in particular, the size of the amyloidogenic nucleus are still going on. We created such a theory on the basis of the kinetics of amyloid aggregates formation. In the presented chapter, theoretical and experimental approaches were employed for studding the process of amyloid formation by different proteins and peptides. The current kinetic models described in this chapter adequately describe the key features of amyloid nucleation and growth

Multiple Unfolding Intermediates Obtained by Molecular Dynamic Simulations under Stretching for Immunoglobulin-Binding Domain of Protein G

We have studied the mechanical properties of the immunoglobulin-binding domain of protein G at the atomic level under stretching at constant velocity using molecular dynamics simulations. We have found that the unfolding process can occur either in a single step or through intermediate states. Analysis of the trajectories from the molecular dynamic simulations showed that the mechanical unfolding of the immunoglobulin-binding domain of protein G is triggered by the separation of the terminal β-strands and the order in which the secondary-structure elements break is practically the same in two- and multi-state events and at the different extension velocities studied. It is seen from our analysis of 24 trajectories that the theoretical pathway of mechanical unfolding for the immunoglobulin-binding domain of protein G does not coincide with that proposed in denaturant studies in the absence of force

Disordered Patterns in Clustered Protein Data Bank and in Eukaryotic and Bacterial Proteomes

We have constructed the clustered Protein Data Bank and obtained clusters of chains of different identity inside each cluster, http://bioinfo.protres.ru/st_pdb/. We have compiled the largest database of disordered patterns (141) from the clustered PDB where identity between chains inside of a cluster is larger or equal to 75% (version of 28 June 2010) by using simple rules of selection. The results of these analyses would help to further our understanding of the physicochemical and structural determinants of intrinsically disordered regions that serve as molecular recognition elements. We have analyzed the occurrence of the selected patterns in 97 eukaryotic and in 26 bacterial proteomes. The disordered patterns appear more often in eukaryotic than in bacterial proteomes. The matrix of correlation coefficients between numbers of proteins where a disordered pattern from the library of 141 disordered patterns appears at least once in 9 kingdoms of eukaryota and 5 phyla of bacteria have been calculated. As a rule, the correlation coefficients are higher inside of the considered kingdom than between them. The patterns with the frequent occurrence in proteomes have low complexity (PPPPP, GGGGG, EEEED, HHHH, KKKKK, SSTSS, QQQQQP), and the type of patterns vary across different proteomes, http://bioinfo.protres.ru/fp/search_new_pattern.html

Prediction of Amyloidogenic and Disordered Regions in Protein Chains

The determination of factors that influence protein conformational changes is very important for the identification of potentially amyloidogenic and disordered regions in polypeptide chains. In our work we introduce a new parameter, mean packing density, to detect both amyloidogenic and disordered regions in a protein sequence. It has been shown that regions with strong expected packing density are responsible for amyloid formation. Our predictions are consistent with known disease-related amyloidogenic regions for eight of 12 amyloid-forming proteins and peptides in which the positions of amyloidogenic regions have been revealed experimentally. Our findings support the concept that the mechanism of amyloid fibril formation is similar for different peptides and proteins. Moreover, we have demonstrated that regions with weak expected packing density are responsible for the appearance of disordered regions. Our method has been tested on datasets of globular proteins and long disordered protein segments, and it shows improved performance over other widely used methods. Thus, we demonstrate that the expected packing density is a useful value with which one can predict both intrinsically disordered and amyloidogenic regions of a protein based on sequence alone. Our results are important for understanding the structural characteristics of protein folding and misfolding

ComSin: database of protein structures in bound (complex) and unbound (single) states in relation to their intrinsic disorder

Most of the proteins in a cell assemble into complexes to carry out their function. In this work, we have created a new database (named ComSin) of protein structures in bound (complex) and unbound (single) states to provide a researcher with exhaustive information on structures of the same or homologous proteins in bound and unbound states. From the complete Protein Data Bank (PDB), we selected 24 910 pairs of protein structures in bound and unbound states, and identified regions of intrinsic disorder. For 2448 pairs, the proteins in bound and unbound states are identical, while 7129 pairs have sequence identity 90% or larger. The developed server enables one to search for proteins in bound and unbound states with several options including sequence similarity between the corresponding proteins in bound and unbound states, and validation of interaction interfaces of protein complexes. Besides that, through our web server, one can obtain necessary information for studying disorder-to-order and order-to-disorder transitions upon complex formation, and analyze structural differences between proteins in bound and unbound states. The database is available at http://antares.protres.ru/comsin/

Critical assessment of protein intrinsic disorder prediction

Abstract: Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude