Amyloids are proteins associated with several clinical disorders, including Alzheimer’s, and Creutzfeldt-Jakob’s. Despite their diversity, all amyloid proteins can undergo aggregation initiated by short segments called hot spots. To find the patterns defining the hot spots, we trained predictors of amyloidogenicity, using n-grams and random forest classifiers. Since the amyloidogenicity may not depend on the exact sequence of amino acids but on their more general properties, we tested 524,284 reduced amino acid alphabets of different lengths (three to six letters) to find the alphabet providing the best performance in cross-validation. The predictor based on this alphabet, called AmyloGram, was benchmarked against the most popular tools for the detection of amyloid peptides using an external data set and obtained the highest values of performance measures (AUC: 0.90, MCC: 0.63). Our results showed sequential patterns in the amyloids which are strongly correlated with hydrophobicity, a tendency to form β-sheets, and lower flexibility of amino acid residues. Among the most informative n-grams of AmyloGram we identified 15 that were previously confirmed experimentally. AmyloGram is available as the web-server: http://smorfland.uni.wroc.pl/shiny/AmyloGram/ and as the R package AmyloGram. R scripts and data used to produce the results of this manuscript are available at http://github.com/michbur/AmyloGramAnalysis.

Analyses of amyloidogenic sequence fragments are essential in studies of neurodegenerative diseases. However, there is no one internet dataset that collects all the sequences that have been investigated for their amyloidogenicity. Therefore, we have created the AmyLoad website which collects the amyloidogenic sequences from all major sources. The website allows for filtration of the fragments and provides detailed information about each of them. Registered users can both personalize their work with the website and submit their own sequences into the database. To maintain database reliability, submitted sequences are reviewed before making them available to the public. Finally, we re-implemented several amyloidogenic sequence predictors, thus the AmyLoad website can be used as a sequence analysis tool. We encourage researchers working on amyloid proteins to contribute to our service. Availability and implementation: The AmyLoad website is freely available at http://comprec-lin.iiar.pwr.edu.pl/amyload/. Contact:malgorzata.kotulska@pwr.edu.pl

Amyloids are protein aggregates observed in several diseases, for example in Alzheimer’s and Parkinson’s diseases. An aggregate has a very regular beta structure with a tightly packed core, which spontaneously assumes a steric zipper form. Experimental methods enable studying such peptides, however they are tedious and costly, therefore inappropriate for genomewide studies. Several bioinformatic methods have been proposed to evaluate protein propensity to form an amyloid. However, the knowledge of aggregate structures is usually not taken into account. We propose PATH (Prediction of Amyloidogenicity by THreading) - a novel structure-based method for predicting amyloidogenicity and show that involving available structures of amyloidogenic fragments enhances classification performance. Experimental aggregate structures were used in templatebased modeling to recognize the most stable representative structural class of a query peptide. Several machine learning methods were then applied on the structural models, using their energy terms. Finally, we identified the most important terms in classification of amyloidogenic peptides. The proposed method outperforms most of the currently available methods for predicting amyloidogenicity, with its area under ROC curve equal to 0.876. Furthermore, the method gave insight into significance of selected structural features and the potentially most stable structural class of a peptide fragment if subjected to crystallization.

CsgA is an aggregating protein from bacterial biofilms, representing a class of functional amyloids. Its amyloid propensity is defined by five fragments (R1–R5) of the sequence, representing non-perfect repeats. Gate-keeper amino acid residues, specific to each fragment, define the fragment’s propensity for self-aggregation and aggregating characteristics of the whole protein. We study the self-aggregation and secondary structures of the repeat fragments of Salmonella enterica and Escherichia coli and comparatively analyze their potential effects on these proteins in a bacterial biofilm. Using bioinformatics predictors, ATR-FTIR and FT-Raman spectroscopy techniques, circular dichroism, and transmission electron microscopy, we confirmed self-aggregation of R1, R3, R5 fragments, as previously reported for Escherichia coli, however, with different temporal characteristics for each species. We also observed aggregation propensities of R4 fragment of Salmonella enterica that is different than that of Escherichia coli. Our studies showed that amyloid structures of CsgA repeats are more easily formed and more durable in Salmonella enterica than those in Escherichia coli.

How the cuticles of the roughly 4.5 million species of ecdysozoan animals are constructed is not well understood. Here, we systematically mine gene expression datasets to uncover the spatiotemporal blueprint for how the chitin-based pharyngeal cuticle of the nematode Caenorhabditis elegans is built. We demonstrate that the blueprint correctly predicts expression patterns and functional relevance to cuticle development. We find that as larvae prepare to molt, catabolic enzymes are upregulated and the genes that encode chitin synthase, chitin cross-linkers, and homologs of amyloid regulators subsequently peak in expression. Forty-eight percent of the gene products secreted during the molt are predicted to be intrinsically disordered proteins (IDPs), many of which belong to four distinct families whose transcripts are expressed in overlapping waves. These include the IDPAs, IDPBs, and IDPCs, which are introduced for the first time here. All four families have sequence properties that drive phase separation and we demonstrate phase separation for one exemplar in vitro. This systematic analysis represents the first blueprint for cuticle construction and highlights the massive contribution that phase-separating materials make to the structure.

Information about the impact of interactions between amyloid proteins on their fibrillization propensity is scattered among many experimental articles and presented in unstructured form. We manually curated information located in almost 200 publications (selected out of 562 initially considered), obtaining details of 883 experimentally studied interactions between 46 amyloid proteins or peptides. We also proposed a novel standardized terminology for the description of amyloid–amyloid interactions, which is included in our database, covering all currently known types of such a cross-talk, including inhibition of fibrillization, cross-seeding and other phenomena. The new approach allows for more specific studies on amyloids and their interactions, by providing very well-defined data. AmyloGraph, an online database presenting information on amyloid–amyloid interactions, is available at (http://AmyloGraph.com/). Its functionalities are also accessible as the R package (https://github.com/KotulskaLab/AmyloGraph). AmyloGraph is the only publicly available repository for experimentally determined amyloid–amyloid interactions.