AmyloComp: A Bioinformatic Tool for Prediction of Amyloid Co-aggregation.

Typically, amyloid fibrils consist of multiple copies of the same protein. In these fibrils, each polypeptide chain adopts the same ß-arc-containing conformation and these chains are stacked in a parallel and in-register manner. In the last few years, however, a considerable body of data has been accumulated about co-aggregation of different amyloid-forming proteins. Among known examples of the co-aggregation are heteroaggregates of different yeast prions and human proteins Rip1 and Rip3. Since the co-aggregation is linked to such important phenomena as infectivity of amyloids and molecular mechanisms of functional amyloids, we analyzed its structural aspects in more details. An axial stacking of different proteins within the same amyloid fibril is one of the most common type of co-aggregation. By using an approach based on structural similarity of the growing tips of amyloids, we developed a computational method to predict amyloidogenic ß-arch structures that are able to interact with each other by the axial stacking. Furthermore, we compiled a dataset consisting of 26 experimentally known pairs of proteins capable or incapable to co-aggregate. We utilized this dataset to test and refine our algorithm. The developed method opens a way for a number of applications, including the identification of microbial proteins capable triggering amyloidosis in humans. AmyloComp is available on the website: https://bioinfo.crbm.cnrs.fr/index.php?route=tools&tool=30.

Census of exposed aggregation-prone regions in proteomes.

Loss of solubility usually leads to the detrimental elimination of protein function. In some cases, the protein aggregation is also required for beneficial functions. Given the duality of this phenomenon, it remains a fundamental question how natural selection controls the aggregation. The exponential growth of genomic sequence data and recent progress with in silico predictors of the aggregation allows approaching this problem by a large-scale bioinformatics analysis. Most of the aggregation-prone regions are hidden within the 3D structure, rendering them inaccessible for the intermolecular interactions responsible for aggregation. Thus, the most realistic census of the aggregation-prone regions requires crossing aggregation prediction with information about the location of the natively unfolded regions. This allows us to detect so-called 'exposed aggregation-prone regions' (EARs). Here, we analyzed the occurrence and distribution of the EARs in 76 reference proteomes from the three kingdoms of life. For this purpose, we used a bioinformatics pipeline, which provides a consensual result based on several predictors of aggregation. Our analysis revealed a number of new statistically significant correlations about the presence of EARs in different organisms, their dependence on protein length, cellular localizations, co-occurrence with short linear motifs and the level of protein expression. We also obtained a list of proteins with the conserved aggregation-prone sequences for further experimental tests. Insights gained from this work led to a deeper understanding of the relationship between protein evolution and aggregation.

The Difference in Structural States between Canonical Proteins and Their Isoforms Established by Proteome-Wide Bioinformatics Analysis.

Alternative splicing is an important means of generating the protein diversity necessary for cellular functions. Hence, there is a growing interest in assessing the structural and functional impact of alternative protein isoforms. Typically, experimental studies are used to determine the structures of the canonical proteins ignoring the other isoforms. Therefore, there is still a large gap between abundant sequence information and meager structural data on these isoforms. During the last decade, significant progress has been achieved in the development of bioinformatics tools for structural and functional annotations of proteins. Moreover, the appearance of the AlphaFold program opened up the possibility to model a large number of high-confidence structures of the isoforms. In this study, using state-of-the-art tools, we performed in silico analysis of 58 eukaryotic proteomes. The evaluated structural states included structured domains, intrinsically disordered regions, aggregation-prone regions, and tandem repeats. Among other things, we found that the isoforms have fewer signal peptides, transmembrane regions, or tandem repeat regions in comparison with their canonical counterparts. This could change protein function and/or cellular localization. The AlphaFold modeling demonstrated that frequently isoforms, having differences with the canonical sequences, still can fold in similar structures though with significant structural rearrangements which can lead to changes of their functions. Based on the modeling, we suggested classification of the structural differences between canonical proteins and isoforms. Altogether, we can conclude that a majority of isoforms, similarly to the canonical proteins are under selective pressure for the functional roles.

TAPASS: Tool for annotation of protein amyloidogenicity in the context of other structural states.

Numerous studies have demonstrated that the propensity of a protein to form amyloids or amorphous aggregates is encoded by its amino acid sequence. This led to the emergence of several computational programs to predict amyloidogenicity from amino acid sequences. However, a growing number of studies indicate that an accurate prediction of the protein aggregation can only be achieved when also accounting for the overall structural context of the protein, and the likelihood of transition between the initial state and the aggregate. Here, we describe a computational pipeline called TAPASS, which was designed to do just that. The pipeline assigns each residue of a protein as belonging to a structured region or an intrinsically disordered region (IDR). For this purpose, TAPASS uses either several state-of-the-art programs for prediction of IDRs, of transmembrane regions and of structured domains or the artificial intelligence program AlphaFold. In the next step, this assignment is crossed with amyloidogenicity prediction. As a result, TAPASS allows the detection of Exposed Amyloidogenic Regions (EARs) located within intrinsically disordered regions (IDRs) and carrying high amyloidogenic potential. TAPASS can substantially improve the prediction of amyloids and be used in proteome-wide analysis to discover new amyloid-forming proteins. Its results, combined with clinical data, can create individual risk profiles for different amyloidoses, opening up new opportunities for personalised medicine. The architecture of the pipeline is designed so that it makes it easy to add new individual predictors as they become available. TAPASS can be used through the web interface (https://bioinfo.crbm.cnrs.fr/index.php?route=tools&tool=32).