A structure-based approach to predict predisposition to amyloidosis

Assessing amyloid fibrils and amorphous aggregates: A review

Protein misfolding and aggregation play a central role in the progression of neurodegenerative diseases such as Alzheimer's and Parkinson's. These aggregates manifest either as structured amyloid fibrils enriched in β-sheet conformations or as irregular amorphous aggregates with diverse morphologies. Understanding their formation, structure, and behavior is critical for deciphering disease mechanisms and developing targeted diagnostics and therapeutics. This review presents an integrated overview of both conventional and advanced techniques used to detect, distinguish, and structurally characterize these protein aggregates. It covers a range of spectroscopic and spectrometric tools, such as fluorescence, Raman, and mass spectrometry that facilitate aggregate identification. Microscopy methods, including atomic force and electron microscopy, are highlighted for morphological analysis. The review also discusses in situ detection strategies using fluorescent dyes, conformation-specific antibodies, enzymatic reporters, and real-time imaging. Separation methods like centrifugation, electrophoresis, and chromatography are outlined alongside structural analysis tools such as X-ray diffraction. Furthermore, the growing utility of computational approaches and artificial intelligence in predicting aggregation propensities and integrating biological data is emphasized. By critically evaluating each method's capabilities and limitations, this review provides a practical and forward-looking resource for researchers studying the complex landscape of protein aggregation.

Environmental sensitivity of amyloidogenic motifs in fungal NOD-like receptor-mediated immunity: Molecular and structural insights into amyloid assembly

This study investigates the aggregation behavior of amyloidogenic motifs associated with fungal NOD-like receptor (NLR) proteins, focusing on their sensitivity to various environmental conditions. We aimed to develop a minimal model that explains amyloid aggregation, aligning with in vivo observations and the expected role of these motifs in amyloid-based signaling. The purpose was to understand how changes in physicochemical conditions influence amyloid formation, which is crucial for fungal immune responses and has potential applications in controlling fungal infections. To achieve this, two amyloidogenic motifs, PUASM_N and PUASM_C, derived from the fungus Colletotrichum gloeosporioides, were synthesized and subjected to different conditions that simulate their natural environment. These conditions included varying pH levels, peptide concentrations, and surface adsorption properties. The aggregation kinetics, morphology, and secondary structures of the peptides were analyzed using Thioflavin T (ThT) fluorescence assay, transmission electron microscopy (TEM), and Fourier transform infrared micro-spectroscopy (micro-FTIR). The results showed that PUASM_N aggregates rapidly without a lag phase, forming long, structured fibers. In contrast, PUASM_C aggregates more slowly, with a significant lag phase, forming shorter, irregular fibers. The aggregation of PUASM_C was highly sensitive to environmental factors, such as alkaline pH and surface hydrophobicity, which accelerated its aggregation. PUASM_N, however, displayed consistent aggregation behavior under different conditions. Our findings suggest that minor environmental changes can modulate the functional roles of PUASM peptides, potentially aiding Colletotrichum gloeosporioides in regulating its antipathogenic activity in response to environmental challenges.

A computational approach to predict the effects of missense mutations on protein amyloidogenicity: A case study in hereditary transthyretin cardiomyopathy

With many amyloidosis-associated missense mutations still unidentified and early diagnostic methods largely unavailable, there is an urgent need for a reliable computational approach to predict hereditary amyloidoses from gene sequencing data. Progress has been made in predicting amyloidosis-triggering sequences within intrinsically disordered regions. However, some diseases are caused by mutations in amyloidogenic regions within structured domains that must unfold for amyloid formation. Accurate prediction of amyloidogenic regions requires tools for detecting amyloidogenicity and assessing mutation effects on protein stability. We developed datasets of mutations linked to hereditary ATTR cardiomyopathy and others likely unrelated, evaluating TTR mutants with amyloidogenicity and stability predictors. Notably, the stability predictors consistently indicated that ATTR-related mutations tend to destabilize the TTR structure more than non-ATTR-associated mutations. Using these datasets and newly generated mutation features, we developed a machine learning model SDAM-TTR to predict mutations leading to ATTR cardiomyopathy.

Prion-like Properties of Short Isoforms of Human Chromatin Modifier PHC3

The formation of self-perpetuating protein aggregates such as amyloids is associated with various diseases and provides a basis for transmissible (infectious or heritable) protein isoforms (prions). Many human proteins involved in the regulation of transcription contain potentially amyloidogenic regions. Here, it is shown that short N-terminal isoforms of the human protein PHC3, a component of the chromatin-modifying complex PRC1 (Polycomb repressive complex 1), can form prion-like aggregates in yeast assays, exhibit amyloid properties in the E. coli-based C-DAG assay, and produce detergent-resistant aggregates when ectopically expressed in cultured human cells. Moreover, aggregates of short isoforms can sequester the full-length PHC3 protein, causing its accumulation in the cytosol instead of the nucleus. The introduction of an aggregating short PHC3 isoform alters the transcriptional profile of cultured human cells. It is proposed that the aggregation of short isoforms is involved in the feedback downregulation of PRC1 activity, leading to more open chromatin configuration.

Developing machine-learning-based amyloidogenicity predictors with Cross-Beta DB

INTRODUCTION

The importance of protein amyloidogenesis, associated with various diseases and functional roles, has driven the creation of computational predictors of amyloidogenicity. The accuracy of these predictors, particularly those utilizing artificial intelligence technologies, heavily depends on the quality of the data.

METHODS

We built Cross-Beta DB, a database containing high-quality data on known cross-β amyloids formed under natural conditions. We used it to train and benchmark several machine-learning (ML) algorithms to predict amyloid-forming potential of proteins.

RESULTS

We developed the Cross-Beta predictor using an Extra trees ML algorithm, which outperforms other amyloid predictors with the highest F1 score (0.852) and accuracy (0.844) compared to existing methods.

DISCUSSION

The development of the Cross-Beta DB database and a new ML-based Cross-Beta predictor may enable the creation of personalized risk profiles for neurodegenerative diseases and other amyloidoses-especially as genome sequencing becomes more affordable.

HIGHLIGHTS

Accuracy of ML-based predictors depends on the quality of training data We built Cross-Beta DB, a database of high-quality data on naturally-occurring amyloids Using this data, we developed an amyloid predictor that outperforms other predictors This computational tool enables the creation of risk profiles for neurodegenerative diseases.

Prediction and Evaluation of Protein Aggregation with Computational Methods

Protein and peptide aggregation has recently become one of the most studied biomedical problems due to its central role in several neurodegenerative disorders and of biotechnological importance. Multiple in silico methods, databases, tools, and algorithms have been developed to predict aggregation of proteins and peptides to better understand fundamental mechanisms of various aggregation diseases. Here, we attempt to provide a brief overview of bioinformatic methods and tools to better understand molecular mechanisms of aggregation disorders. Furthermore, through a better understanding of protein aggregation mechanisms, it might be possible to design novel therapeutic agents to treat and hopefully prevent protein aggregation diseases.

Ab-Amy 2.0: Predicting light chain amyloidogenic risk of therapeutic antibodies based on antibody language model

Therapeutic antibodies have emerged as a promising treatment option for a wide range of diseases. However, the light chain of antibodies can potentially induce amyloidosis, a condition characterized by protein misfolding and aggregation, posing a significant safety concern. Therefore, it is crucial to assess the amyloidogenic risk of therapeutic antibodies during the early stages of drug development. In this study, we introduce AB-Amy 2.0, a new computational model with enhanced performance for assessing the light chain amyloidogenic risk of therapeutic antibodies. By employing pretrained protein language models (PLMs) embeddings, AB-Amy 2.0 achieves higher accuracy in amyloidogenic risk prediction compared with traditional features offering a crucial tool for early-stage identification of antibodies with low aggregation propensity. The AB-Amy 2.0 was trained on antiBERTy embeddings and utilizes the SVM algorithm, resulting in superior performance metrics. On an independent test dataset, the model achieved high sensitivity, specificity, ACC, MCC and AUC of 93.47%, 89.23%, 91.92%, 0.8261 and 0.9739, respectively. These results highlight the effectiveness and robustness of AB-Amy 2.0 in predicting light chain amyloidogenic risk accurately. To facilitate user-friendly access, we have developed an online web server (http://i.uestc.edu.cn/AB-Amy2) and a command line tool (https://github.com/zzyywww/ABAmy2). These resources enable the broader application of this advanced model and promise to enhance the development of safer therapeutic antibodies.

Aggregating amyloid resources: A comprehensive review of databases on amyloid-like aggregation

Protein aggregation is responsible for several degenerative conditions in humans, and it is also a bottleneck in industrial protein production and storage of biotherapeutics. Bioinformatics tools have been developed to predict and redesign protein solubility more efficiently by understanding the underlying principles behind aggregation. As more experimental data become available, dedicated resources for storing, indexing, classifying and consolidating experimental results have emerged. These resources vary in focus, including aggregation-prone regions, 3D patches or protein stretches capable of forming amyloid fibrils. Some of these resources also consider the experimental conditions that cause protein aggregation and how they affect the process. This review article explores how protein aggregation databases have evolved and surveys state-of-the-art resources. We highlight their applications, complementarity and existing limitations. Moreover, we showcase the existing symbiosis between amyloid-related databases and predictive tools. To increase the usefulness of our review, we supplement it with a comprehensive list of present and past amyloid databases: https://biogenies.info/amyloid-database-list/.

Unravelling the Complexity of Amyloid Peptide Core Interfaces

Amyloids, large intermolecular sandwiched β-sheet structures, underlie several protein misfolding diseases but have also been shown to have functional roles and can be a basis for designing smart and responsive nanomaterials. Short segments of proteins, called aggregation-prone regions (APRs), have been identified that nucleate amyloid formation. Here we present the database of 173 APR crystal structures currently available in the PDB, and a tool, ACW, for analyzing their topologies and the 267 inter-β-sheet interfaces of zipper regions assigned in these structures. We defined a new descriptor of zipper interfaces, the surface detail index (SDi), which quantifies the intertwining between the side chains of both β-sheets of the zipper, an important factor for the molecular recognition and self-assembly of these mesostructures. This allowed a comparative analysis of the zipper interfaces and identification of 6 clusters with different intertwining, steric fit, and size characteristics using three complementary descriptors, SDi, shape complementarity, and buried surface area. 60% of the APR structures are formed by parallel β-sheets, of which 52% belong to the topological class 1. This could be explained by the better fit and a deeper entanglement of the zipper regions of the parallel structures than of the antiparallel structures, as the analysis showed that both their shape complementarity (0.79 vs 0.70) and SDi (1.53 vs 1.32) were higher. The higher abundance of certain residues (Asn and Gln in parallel and Leu and Ala in antiparallel β-sheets) can be explained by their ability to form different ladder-like secondary interaction patterns within β-sheets. Analogous to the hierarchy of protein structure, we interpreted the primary, secondary, tertiary, and quaternary structure levels of APRs revealing different characteristics of the zipper regions for both parallel and antiparallel β-sheet structures, which may provide clues to the structural conditions of amyloid core formation and the rational design of amyloid polymorphs.

Amyloid Fibrils of the s36 Protein Modulate the Morphogenesis of Drosophila melanogaster Eggshell

Drosophila melanogaster is the oldest classic model object in developmental genetics. It may seem that various structures of the fruit fly at all developmental stages have been well studied and described. However, recently we have shown that some specialized structures of the D. melanogaster eggshell contain an amyloid fibril network. Here, we demonstrate that this amyloid network is formed by the chorionic protein s36. The s36 protein colocalizes with the amyloid-specific dyes Congo Red and Thioflavin S in the micropyle, dorsal appendages, and pillars. The fibrils of s36 obtained from the eggs demonstrate amyloid properties. In the context of the CG33223 gene deletion, the s36 protein is produced but is not detected in the eggshell. The absence of amyloid fibrils of s36 in the eggshell disrupts the endochorion morphology and blocks the development of the micropyle, dorsal appendages, and pillars, leading to sterility. Our data show for the first time that amyloid fibrils are essential for morphogenesis modulation. We suggest that attachment of follicle cells to the s36 extracellular fibrils triggers signaling to enable subsequent cellular divisions needed for building the specialized eggshell structures.

Proteomic Evidence for Amyloidogenic Cross-Seeding in Fibrinaloid Microclots

In classical amyloidoses, amyloid fibres form through the nucleation and accretion of protein monomers, with protofibrils and fibrils exhibiting a cross-β motif of parallel or antiparallel β-sheets oriented perpendicular to the fibre direction. These protofibrils and fibrils can intertwine to form mature amyloid fibres. Similar phenomena can occur in blood from individuals with circulating inflammatory molecules (and also some originating from viruses and bacteria). Such pathological clotting can result in an anomalous amyloid form termed fibrinaloid microclots. Previous proteomic analyses of these microclots have shown the presence of non-fibrin(ogen) proteins, suggesting a more complex mechanism than simple entrapment. We thus provide evidence against such a simple entrapment model, noting that clot pores are too large and centrifugation would have removed weakly bound proteins. Instead, we explore whether co-aggregation into amyloid fibres may involve axial (multiple proteins within the same fibril), lateral (single-protein fibrils contributing to a fibre), or both types of integration. Our analysis of proteomic data from fibrinaloid microclots in different diseases shows no significant quantitative overlap with the normal plasma proteome and no correlation between plasma protein abundance and their presence in fibrinaloid microclots. Notably, abundant plasma proteins like α-2-macroglobulin, fibronectin, and transthyretin are absent from microclots, while less abundant proteins such as adiponectin, periostin, and von Willebrand factor are well represented. Using bioinformatic tools, including AmyloGram and AnuPP, we found that proteins entrapped in fibrinaloid microclots exhibit high amyloidogenic tendencies, suggesting their integration as cross-β elements into amyloid structures. This integration likely contributes to the microclots' resistance to proteolysis. Our findings underscore the role of cross-seeding in fibrinaloid microclot formation and highlight the need for further investigation into their structural properties and implications in thrombotic and amyloid diseases. These insights provide a foundation for developing novel diagnostic and therapeutic strategies targeting amyloidogenic cross-seeding in blood clotting disorders.

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 β-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 β-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.

Aggrescan4D: structure-informed analysis of pH-dependent protein aggregation

Protein aggregation is behind the genesis of incurable diseases and imposes constraints on drug discovery and the industrial production and formulation of proteins. Over the years, we have been advancing the Aggresscan3D (A3D) method, aiming to deepen our comprehension of protein aggregation and assist the engineering of protein solubility. Since its inception, A3D has become one of the most popular structure-based aggregation predictors because of its performance, modular functionalities, RESTful service for extensive screenings, and intuitive user interface. Building on this foundation, we introduce Aggrescan4D (A4D), significantly extending A3D's functionality. A4D is aimed at predicting the pH-dependent aggregation of protein structures, and features an evolutionary-informed automatic mutation protocol to engineer protein solubility without compromising structure and stability. It also integrates precalculated results for the nearly 500,000 jobs in the A3D Model Organisms Database and structure retrieval from the AlphaFold database. Globally, A4D constitutes a comprehensive tool for understanding, predicting, and designing solutions for specific protein aggregation challenges. The A4D web server and extensive documentation are available at https://biocomp.chem.uw.edu.pl/a4d/. This website is free and open to all users without a login requirement.

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Structural effects of charge destabilization and amino acid substitutions in amyloid fragments of CsgA

The most studied functional amyloid is the CsgA, major curli subunit protein, which is produced by numerous strains of Enterobacteriaceae. Although CsgA sequences are highly conserved, they exhibit species diversity, which reflects the specific evolutionary and functional adaptability of the major curli subunit. Herein, we performed bioinformatics analyses to uncover the differences in the amyloidogenic properties of the R4 fragments in Escherichia coli and Salmonella enterica and proposed four mutants for more detailed studies: M1, M2, M3, and M4. The mutated sequences were characterized by various experimental techniques, such as circular dichroism, ATR-FTIR, FT-Raman, thioflavin T, transmission electron microscopy and confocal microscopy. Additionally, molecular dynamics simulations were performed to determine the role of buffer ions in the aggregation process. Our results demonstrated that the aggregation kinetics, fibril morphology, and overall structure of the peptide were significantly affected by the positions of charged amino acids within the repeat sequences of CsgA. Notably, substituting glycine with lysine resulted in the formation of distinctive spherically packed globular aggregates. The differences in morphology observed are attributed to the influence of phosphate ions, which disrupt the local electrostatic interaction network of the polypeptide chains. This study provides knowledge on the preferential formation of amyloid fibrils based on charge states within the polypeptide chain.

Prediction of protein aggregation

The scientific community is very interested in protein aggregation because of its involvement in several neurodegenerative diseases and its significance in industry. Remarkably, fibrillar aggregates are utilized naturally for constructing structural scaffolds or creating biological switches and may be intentionally designed to construct versatile nanomaterials. Consequently, there is a significant need to rationalize and predict protein aggregation. Researchers have developed various computational methodologies and algorithms to predict protein aggregation and understand its underlying mechanics. This chapter aims to summarize the significant advancements in computational methods, accessible resources, and prospective developments in the field of in silico research. We assess the existing computational tools for predicting protein aggregation propensities, detecting areas that are prone to sequential and structural aggregation, analyzing the effects of mutations on protein aggregation, or identifying prion-like domains.

A sequence-based model for identifying proteins undergoing liquid-liquid phase separation/forming fibril aggregates via machine learning

Liquid-liquid phase separation (LLPS) and the solid aggregate (also referred to as amyloid aggregates) formation of proteins, have gained significant attention in recent years due to their associations with various physiological and pathological processes in living organisms. The systematic investigation of the differences and connections between proteins undergoing LLPS and those forming amyloid fibrils at the sequence level has not yet been explored. In this research, we aim to address this gap by comparing the two types of proteins across 36 features using collected data available currently. The statistical comparison results indicate that, 24 of the selected 36 features exhibit significant difference between the two protein groups. A LLPS-Fibrils binary classification model built on these 24 features using random forest reveals that the fraction of intrinsically disordered residues (FIDR ) is identified as the most crucial feature. While, in the further three-class LLPS-Fibrils-Background classification model built on the same screened features, the composition of cysteine and that of leucine show more significant contributions than others. Through feature ablation analysis, we finally constructed a model FLFB (Feature-based LLPS-Fibrils-Background protein predictor) using six refined features, with an average area under the receiver operating characteristics of 0.83. This work indicates using sequence features and a machine learning model, proteins undergoing LLPS or forming amyloid fibrils can be identified.

PACT - Prediction of amyloid cross-interaction by threading

Amyloid proteins are often associated with the onset of diseases, including Alzheimer's, Parkinson's and many others. However, there is a wide class of functional amyloids that are involved in physiological functions, e.g., formation of microbial biofilms or storage of hormones. Recent studies showed that an amyloid fibril could affect the aggregation of another protein, even from a different species. This may result in amplification or attenuation of the aggregation process. Insight into amyloid cross-interactions may be crucial for better understanding of amyloid diseases and the potential influence of microbial amyloids on human proteins. However, due to the demanding nature of the needed experiments, knowledge of such interactions is still limited. Here, we present PACT (Prediction of Amyloid Cross-interaction by Threading) - the computational method for the prediction of amyloid cross-interactions. The method is based on modeling of a heterogeneous fibril formed by two amyloidogenic peptides. The resulting structure is assessed by the structural statistical potential that approximates its plausibility and energetic stability. PACT was developed and first evaluated mostly on data collected in the AmyloGraph database of interacting amyloids and achieved high values of Area Under ROC (AUC=0.88) and F1 (0.82). Then, we applied our method to study the interactions of CsgA - a bacterial biofilm protein that was not used in our in-reference datasets, which is expressed in several bacterial species that inhabit the human intestines - with two human proteins. The study included alpha-synuclein, a human protein that is involved in Parkinson's disease, and human islet amyloid polypeptide (hIAPP), which is involved in type 2 diabetes. In both cases, PACT predicted the appearance of cross-interactions. Importantly, the method indicated specific regions of the proteins, which were shown to play a central role in both interactions. We experimentally confirmed the novel results of the indicated CsgA fragments interacting with hIAPP based on the kinetic characteristics obtained with the ThT assay. PACT opens the possibility of high-throughput studies of amyloid interactions. Importantly, it can work with fairly long protein fragments, and as a purely physicochemical approach, it relies very little on scarce training data. The tool is available as a web server at https://pact.e-science.pl/pact/ . The local version can be downloaded from https://github.com/KubaWojciechowski/PACT .

Amyloids and prions in the light of evolution

Functional amyloids have been identified in a wide variety of organisms including bacteria, fungi, plants, and vertebrates. Intracellular and extracellular amyloid fibrils of different proteins perform storage, protective, structural, and regulatory functions. The structural organization of amyloid fibrils determines their unique physical and biochemical properties. The formation of these fibrillar structures can provide adaptive advantages that are picked up by natural selection. Despite the great interest in functional and pathological amyloids, questions about the conservatism of the amyloid properties of proteins and the regularities in the appearance of these fibrillar structures in evolution remain almost unexplored. Using bioinformatics approaches and summarizing the data published previously, we have shown that amyloid fibrils performing similar functions in different organisms have been arising repeatedly and independently in the course of evolution. On the other hand, we show that the amyloid properties of a number of bacterial and eukaryotic proteins are evolutionarily conserved. We also discuss the role of protein-based inheritance in the evolution of microorganisms. Considering that missense mutations and the emergence of prions cause the same consequences, we propose the concept that the formation of prions, similarly to mutations, generally causes a negative effect, although it can also lead to adaptations in rare cases. In general, our analysis revealed certain patterns in the emergence and spread of amyloid fibrillar structures in the course of evolution.

Spider Silk Protein Forms Amyloid-Like Nanofibrils through a Non-Nucleation-Dependent Polymerization Mechanism

Amyloid fibrils-nanoscale fibrillar aggregates with high levels of order-are pathogenic in some today incurable human diseases; however, there are also many physiologically functioning amyloids in nature. The process of amyloid formation is typically nucleation-elongation-dependent, as exemplified by the pathogenic amyloid-β peptide (Aβ) that is associated with Alzheimer's disease. Spider silk, one of the toughest biomaterials, shares characteristics with amyloid. In this study, it is shown that forming amyloid-like nanofibrils is an inherent property preserved by various spider silk proteins (spidroins). Both spidroins and Aβ capped by spidroin N- and C-terminal domains, can assemble into macroscopic spider silk-like fibers that consist of straight nanofibrils parallel to the fiber axis as observed in native spider silk. While Aβ forms amyloid nanofibrils through a nucleation-dependent pathway and exhibits strong cytotoxicity and seeding effects, spidroins spontaneously and rapidly form amyloid-like nanofibrils via a non-nucleation-dependent polymerization pathway that involves lateral packing of fibrils. Spidroin nanofibrils share amyloid-like properties but lack strong cytotoxicity and the ability to self-seed or cross-seed human amyloidogenic peptides. These results suggest that spidroins´ unique primary structures have evolved to allow functional properties of amyloid, and at the same time direct their fibrillization pathways to avoid formation of cytotoxic intermediates.

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.

Identification of New FG-Repeat Nucleoporins with Amyloid Properties

Amyloids are fibrillar protein aggregates with a cross-β structure. More than two hundred different proteins with amyloid or amyloid-like properties are already known. Functional amyloids with conservative amyloidogenic regions were found in different organisms. Protein aggregation appears to be beneficial for the organism in these cases. Therefore, this property might be conservative for orthologous proteins. The amyloid aggregates of the CPEB protein were suggested to play an important role in the long-term memory formation in Aplysia californica, Drosophila melanogaster, and Mus musculus. Moreover, the FXR1 protein demonstrates amyloid properties among the Vertebrates. A few nucleoporins (e.g., yeast Nup49, Nup100, Nup116, and human Nup153 and Nup58), are supposed or proved to form amyloid fibrils. In this study, we performed wide-scale bioinformatic analysis of nucleoporins with FG-repeats (phenylalanine-glycine repeats). We demonstrated that most of the barrier nucleoporins possess potential amyloidogenic properties. Furthermore, the aggregation-prone properties of several Nsp1 and Nup100 orthologs in bacteria and yeast cells were analyzed. Only two new nucleoporins, Drosophila melanogaster Nup98 and Schizosaccharomyces pombe Nup98, aggregated in different experiments. At the same time, Taeniopygia guttata Nup58 only formed amyloids in bacterial cells. These results rather contradict the hypothesis about the functional aggregation of nucleoporins.

Cell specialization in cyanobacterial biofilm development revealed by expression of a cell-surface and extracellular matrix protein

Cyanobacterial biofilms are ubiquitous and play important roles in diverse environments, yet, understanding of the processes underlying the development of these aggregates is just emerging. Here we report cell specialization in formation of Synechococcus elongatus PCC 7942 biofilms-a hitherto unknown characteristic of cyanobacterial social behavior. We show that only a quarter of the cell population expresses at high levels the four-gene ebfG-operon that is required for biofilm formation. Almost all cells, however, are assembled in the biofilm. Detailed characterization of EbfG4 encoded by this operon revealed cell-surface localization as well as its presence in the biofilm matrix. Moreover, EbfG1-3 were shown to form amyloid structures such as fibrils and are thus likely to contribute to the matrix structure. These data suggest a beneficial 'division of labor' during biofilm formation where only some of the cells allocate resources to produce matrix proteins-'public goods' that support robust biofilm development by the majority of the cells. In addition, previous studies revealed the operation of a self-suppression mechanism that depends on an extracellular inhibitor, which supresses transcription of the ebfG-operon. Here we revealed inhibitor activity at an early growth stage and its gradual accumulation along the exponential growth phase in correlation with cell density. Data, however, do not support a threshold-like phenomenon known for quorum-sensing in heterotrophs. Together, data presented here demonstrate cell specialization and imply density-dependent regulation thereby providing deep insights into cyanobacterial communal behavior.

An interchangeable prion-like domain is required for Ty1 retrotransposition

Retrotransposons and retroviruses shape genome evolution and can negatively impact genome function. Saccharomyces cerevisiae and its close relatives harbor several families of LTR-retrotransposons, the most abundant being Ty1 in several laboratory strains. The cytosolic foci that nucleate Ty1 virus-like particle (VLP) assembly are not well-understood. These foci, termed retrosomes or T-bodies, contain Ty1 Gag and likely Gag-Pol and the Ty1 mRNA destined for reverse transcription. Here, we report a novel intrinsically disordered N-terminal pr ion-like d omain (PrLD) within Gag that is required for transposition. This domain contains amino-acid composition similar to known yeast prions and is sufficient to nucleate prionogenesis in an established cell-based prion reporter system. Deleting the Ty1 PrLD results in dramatic VLP assembly and retrotransposition defects but does not affect Gag protein level. Ty1 Gag chimeras in which the PrLD is replaced with other sequences, including yeast and mammalian prionogenic domains, display a range of retrotransposition phenotypes from wildtype to null. We examine these chimeras throughout the Ty1 replication cycle and find that some support retrosome formation, VLP assembly, and retrotransposition, including the yeast Sup35 prion and the mouse PrP prion. Our interchangeable Ty1 system provides a useful, genetically tractable in vivo platform for studying PrLDs, complete with a suite of robust and sensitive assays, and host modulators developed to study Ty1 retromobility. Our work invites study into the prevalence of PrLDs in additional mobile elements.

Significance

Retrovirus-like retrotransposons help shape the genome evolution of their hosts and replicate within cytoplasmic particles. How their building blocks associate and assemble within the cell is poorly understood. Here, we report a novel pr ion-like d omain (PrLD) in the budding yeast retrotransposon Ty1 Gag protein that builds virus-like particles. The PrLD has similar sequence properties to prions and disordered protein domains that can drive the formation of assemblies that range from liquid to solid. We demonstrate that the Ty1 PrLD can function as a prion and that certain prion sequences can replace the PrLD and support Ty1 transposition. This interchangeable system is an effective platform to study additional disordered sequences in living cells.

Molecular Determinants of Fibrillation in a Viral Amyloidogenic Domain from Combined Biochemical and Biophysical Studies

The Nipah and Hendra viruses (NiV and HeV) are biosafety level 4 human pathogens classified within the Henipavirus genus of the Paramyxoviridae family. In both NiV and HeV, the gene encoding the Phosphoprotein (P protein), an essential polymerase cofactor, also encodes the V and W proteins. These three proteins, which share an intrinsically disordered N-terminal domain (NTD) and have unique C-terminal domains (CTD), are all known to counteract the host innate immune response, with V and W acting by either counteracting or inhibiting Interferon (IFN) signaling. Recently, the ability of a short region within the shared NTD (i.e., PNT3) to form amyloid-like structures was reported. Here, we evaluated the relevance of each of three contiguous tyrosine residues located in a previously identified amyloidogenic motif (EYYY) within HeV PNT3 to the fibrillation process. Our results indicate that removal of a single tyrosine in this motif significantly decreases the ability to form fibrils independently of position, mainly affecting the elongation phase. In addition, we show that the C-terminal half of PNT3 has an inhibitory effect on fibril formation that may act as a molecular shield and could thus be a key domain in the regulation of PNT3 fibrillation. Finally, the kinetics of fibril formation for the two PNT3 variants with the highest and the lowest fibrillation propensity were studied by Taylor Dispersion Analysis (TDA). The results herein presented shed light onto the molecular mechanisms involved in fibril formation.

Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins

NLR proteins are intracellular receptors constituting a conserved component of the innate immune system of cellular organisms. In fungi, NLRs are characterized by high diversity of architectures and presence of amyloid signaling. Here, we explore the diverse world of effector and signaling domains of fungal NLRs using state-of-the-art bioinformatic methods including MMseqs2 for fast clustering, probabilistic context-free grammars for sequence analysis, and AlphaFold2 deep neural networks for structure prediction. In addition to substantially improving the overall annotation, especially in basidiomycetes, the study identifies novel domains and reveals the structural similarity of MLKL-related HeLo- and Goodbye-like domains forming the most abundant superfamily of fungal NLR effectors. Moreover, compared to previous studies, we found several times more amyloid motif instances, including novel families, and validated aggregating and prion-forming properties of the most abundant of them in vitro and in vivo. Also, through an extensive in silico search, the NLR-associated amyloid signaling was identified in basidiomycetes. The emerging picture highlights similarities and differences in the NLR architectures and amyloid signaling in ascomycetes, basidiomycetes and other branches of life.

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.

Human RAD51 Protein Forms Amyloid-like Aggregates In Vitro

RAD51 is a central protein of homologous recombination and DNA repair processes that maintains genome stability and ensures the accurate repair of double-stranded breaks (DSBs). In this work, we assessed amyloid properties of RAD51 in vitro and in the bacterial curli-dependent amyloid generator (C-DAG) system. Resistance to ionic detergents, staining with amyloid-specific dyes, polarized microscopy, transmission electron microscopy (TEM), X-ray diffraction and other methods were used to evaluate the properties and structure of RAD51 aggregates. The purified human RAD51 protein formed detergent-resistant aggregates in vitro that had an unbranched cross-β fibrillar structure, which is typical for amyloids, and were stained with amyloid-specific dyes. Congo-red-stained RAD51 aggregates demonstrated birefringence under polarized light. RAD51 fibrils produced sharp circular X-ray reflections at 4.7 Å and 10 Å, demonstrating that they had a cross-β structure. Cytoplasmic aggregates of RAD51 were observed in cell cultures overexpressing RAD51. We demonstrated that a key protein that maintains genome stability, RAD51, has amyloid properties in vitro and in the C-DAG system and discussed the possible biological relevance of this observation.

Computational approaches for predicting variant impact: An overview from resources, principles to applications

One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.

NOS1AP Interacts with α-Synuclein and Aggregates in Yeast and Mammalian Cells

The NOS1AP gene encodes a cytosolic protein that binds to the signaling cascade component neuronal nitric oxide synthase (nNOS). It is associated with many different disorders, such as schizophrenia, post-traumatic stress disorder, autism, cardiovascular disorders, and breast cancer. The NOS1AP (also known as CAPON) protein mediates signaling within a complex which includes the NMDA receptor, PSD-95, and nNOS. This adapter protein is involved in neuronal nitric oxide (NO) synthesis regulation via its association with nNOS (NOS1). Our bioinformatics analysis revealed NOS1AP as an aggregation-prone protein, interacting with α-synuclein. Further investigation showed that NOS1AP forms detergent-resistant non-amyloid aggregates when overproduced. Overexpression of NOS1AP was found in rat models for nervous system injury as well as in schizophrenia patients. Thus, we can assume for the first time that the molecular mechanisms underlying these disorders include misfolding and aggregation of NOS1AP. We show that NOS1AP interacts with α-synuclein, allowing us to suggest that this protein may be implicated in the development of synucleinopathies and that its aggregation may explain the relationship between Parkinson’s disease and schizophrenia.

Amyloid Properties of the FXR1 Protein Are Conserved in Evolution of Vertebrates

Functional amyloids are fibrillary proteins with a cross-β structure that play a structural or regulatory role in pro- and eukaryotes. Previously, we have demonstrated that the RNA-binding FXR1 protein functions in an amyloid form in the rat brain. This RNA-binding protein plays an important role in the regulation of long-term memory, emotions, and cancer. Here, we evaluate the amyloid properties of FXR1 in organisms representing various classes of vertebrates. We show the colocalization of FXR1 with amyloid-specific dyes in the neurons of amphibians, reptiles, and birds. Moreover, FXR1, as with other amyloids, forms detergent-resistant insoluble aggregates in all studied animals. The FXR1 protein isolated by immunoprecipitation from the brains of different vertebrate species forms fibrils, which show yellow-green birefringence after staining with Congo red. Our data indicate that in the evolution of vertebrates, FXR1 acquired amyloid properties at least 365 million years ago. Based on the obtained data, we discuss the possible role of FXR1 amyloid fibrils in the regulation of vital processes in the brain of vertebrates.

Computational methods to predict protein aggregation

In most cases, protein aggregation stems from the establishment of non-native intermolecular contacts. The formation of insoluble protein aggregates is associated with many human diseases and is a major bottleneck for the industrial production of protein-based therapeutics. Strikingly, fibrillar aggregates are naturally exploited for structural scaffolding or to generate molecular switches and can be artificially engineered to build up multi-functional nanomaterials. Thus, there is a high interest in rationalizing and forecasting protein aggregation. Here, we review the available computational toolbox to predict protein aggregation propensities, identify sequential or structural aggregation-prone regions, evaluate the impact of mutations on aggregation or recognize prion-like domains. We discuss the strengths and limitations of these algorithms and how they can evolve in the next future.

Bioinformatics Methods in Predicting Amyloid Propensity of Peptides and Proteins

Several computational methods have been developed to predict amyloid propensity of a protein or peptide. These bioinformatics tools are time- and cost-saving alternatives to expensive and laborious experimental methods which are used to confirm self-aggregation of a protein. Computational approaches not only allow preselection of reliable candidates for amyloids but, most importantly, are capable of a thorough and informative analysis of a protein, indicating the sequence determinants of protein aggregation, identifying the potential causal mutations and likely mechanisms. Bioinformatics modeling applies several different approaches, which most typically include physicochemical or structure-based modeling, machine learning, or statistics based modeling. Bioinformatics methods typically use the amino acid sequence of a protein as an input, some also include additional information, for example, an available structure. This chapter describes the methods currently used to computationally predict amyloid propensity of a protein or peptide. Since the accuracy of bioinformatics methods may be highly dependent on reference data used to develop and evaluate the predictors, we also briefly present the main databases of amyloids used by the authors of bioinformatics tools.

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).

The Human NUP58 Nucleoporin Can Form Amyloids In Vitro and In Vivo

Amyloids are fibrillar protein aggregates with a cross-β structure and unusual features, including high resistance to detergent or protease treatment. More than two hundred different proteins with amyloid or amyloid-like properties are already known. Several examples of nucleoporins (e.g., yeast Nup49, Nup100, Nup116, and human NUP153) are supposed to form amyloid fibrils. In this study, we demonstrated an ability of the human NUP58 nucleoporin to form amyloid aggregates in vivo and in vitro. Moreover, we found two forms of NUP58 aggregates: oligomers and polymers stabilized by disulfide bonds. Bioinformatic analysis revealed that all known orthologs of this protein are potential amyloids which possess several regions with conserved ability to aggregation. The biological role of nucleoporin amyloid formation is debatable. We suggest that it is a rather abnormal process, which is characteristic for many proteins implicated in phase separation.

Δ133p53β isoform pro-invasive activity is regulated through an aggregation-dependent mechanism in cancer cells

The p53 isoform, Δ133p53β, is critical in promoting cancer. Here we report that Δ133p53β activity is regulated through an aggregation-dependent mechanism. Δ133p53β aggregates were observed in cancer cells and tumour biopsies. The Δ133p53β aggregation depends on association with interacting partners including p63 family members or the CCT chaperone complex. Depletion of the CCT complex promotes accumulation of Δ133p53β aggregates and loss of Δ133p53β dependent cancer cell invasion. In contrast, association with p63 family members recruits Δ133p53β from aggregates increasing its intracellular mobility. Our study reveals novel mechanisms of cancer progression for p53 isoforms which are regulated through sequestration in aggregates and recruitment upon association with specific partners like p63 isoforms or CCT chaperone complex, that critically influence cancer cell features like EMT, migration and invasion.

Identification of a Region in the Common Amino-terminal Domain of Hendra Virus P, V, and W Proteins Responsible for Phase Transition and Amyloid Formation

Henipaviruses are BSL-4 zoonotic pathogens responsible in humans for severe encephalitis. Their V protein is a key player in the evasion of the host innate immune response. We previously showed that the Henipavirus V proteins consist of a long intrinsically disordered N-terminal domain (NTD) and a β-enriched C-terminal domain (CTD). These terminals are critical for V binding to DDB1, which is a cellular protein that is a component of the ubiquitin ligase E3 complex, as well as binding to MDA5 and LGP2, which are two host sensors of viral RNA. Here, we serendipitously discovered that the Hendra virus V protein undergoes a liquid-to-hydrogel phase transition and identified the V region responsible for this phenomenon. This region, referred to as PNT3 and encompassing residues 200-310, was further investigated using a combination of biophysical and structural approaches. Congo red binding assays, together with negative-staining transmisison electron microscopy (TEM) studies, show that PNT3 forms amyloid-like fibrils. Fibrillation abilities are dramatically reduced in a rationally designed PNT3 variant in which a stretch of three contiguous tyrosines, falling within an amyloidogenic motif, were replaced by three alanines. Worthy to note, Congo red staining experiments provided hints that these amyloid-like fibrils form not only in vitro but also in cellula after transfection or infection. The present results set the stage for further investigations aimed at assessing the functional role of phase separation and fibrillation by the Henipavirus V proteins.

Determination of amyloid core regions of insulin analogues fibrils

A rapid-acting insulin lispro and long-acting insulin glargine are commonly used for the treatment of diabetes. Clinical cases have described the formation of injectable amyloidosis with these insulin analogues, but their amyloid core regions of fibrils were unknown. To reveal these regions, we have analysed the hydrolyzates of insulin fibrils and its analogues using high-performance liquid chromatography and mass spectrometry methods and found that insulin and its analogues have almost identical amyloid core regions that intersect with the predicted amyloidogenic regions. The obtained results can be used to create new insulin analogues with a low ability to form fibrils.

Abbreviations

a.a., amino acid residues; HPLC-MS, high-performance liquid chromatography/mass spectrometry; m/z, mass-to-charge ratio; TEM, transmission electron microscopy.

Disentangling the complexity of low complexity proteins

There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs.

Short abstract

There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.

Variability of Amyloid Propensity in Imperfect Repeats of CsgA Protein of Salmonella enterica and Escherichia coli

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.

Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars

Background

Amyloid signaling motifs are a class of protein motifs which share basic structural and functional features despite the lack of clear sequence homology. They are hard to detect in large sequence databases either with the alignment-based profile methods (due to short length and diversity) or with generic amyloid- and prion-finding tools (due to insufficient discriminative power). We propose to address the challenge with a machine learning grammatical model capable of generalizing over diverse collections of unaligned yet related motifs.

Results

First, we introduce and test improvements to our probabilistic context-free grammar framework for protein sequences that allow for inferring more sophisticated models achieving high sensitivity at low false positive rates. Then, we infer universal grammars for a collection of recently identified bacterial amyloid signaling motifs and demonstrate that the method is capable of generalizing by successfully searching for related motifs in fungi. The results are compared to available alternative methods. Finally, we conduct spectroscopy and staining analyses of selected peptides to verify their structural and functional relationship.

Conclusions

While the profile HMMs remain the method of choice for modeling homologous sets of sequences, PCFGs seem more suitable for building meta-family descriptors and extrapolating beyond the seed sample.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04139-y.

Chromosome-level genome assembly and structural variant analysis of two laboratory yeast strains from the Peterhof Genetic Collection lineage

Thousands of yeast genomes have been sequenced with both traditional and long-read technologies, and multiple observations about modes of genome evolution for both wild and laboratory strains have been drawn from these sequences. In our study, we applied Oxford Nanopore and Illumina technologies to assemble complete genomes of two widely used members of a distinct laboratory yeast lineage, the Peterhof Genetic Collection (PGC), and investigate the structural features of these genomes including transposable element content, copy number alterations, and structural rearrangements. We identified numerous notable structural differences between genomes of PGC strains and the reference S288C strain. We discovered a substantial enrichment of mid-length insertions and deletions within repetitive coding sequences, such as in the SCH9 gene or the NUP100 gene, with possible impact of these variants on protein amyloidogenicity. High contiguity of the final assemblies allowed us to trace back the history of reciprocal unbalanced translocations between chromosomes I, VIII, IX, XI, and XVI of the PGC strains. We show that formation of hybrid alleles of the FLO genes during such chromosomal rearrangements is likely responsible for the lack of invasive growth of yeast strains. Taken together, our results highlight important features of laboratory yeast strain evolution using the power of long-read sequencing.

Protein aggregation: in silico algorithms and applications

Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications.

iAMY-SCM: Improved prediction and analysis of amyloid proteins using a scoring card method with propensity scores of dipeptides

Fast, accurate identification and characterization of amyloid proteins at a large-scale is essential for understating their role in therapeutic intervention strategies. As a matter of fact, there exist only one in silico model for amyloid protein identification using the random forest (RF) model in conjunction with various feature types namely the RFAmy. However, it suffers from low interpretability for biologists. Thus, it is highly desirable to develop a simple and easily interpretable prediction method with robust accuracy as compared to the existing complicated model. In this study, we propose iAMY-SCM, the first scoring card method-based predictor for predicting and analyzing amyloid proteins. Herein, the iAMY-SCM made use of a simple weighted-sum function in conjunction with the propensity scores of dipeptides for the amyloid protein identification. Cross-validation results indicated that iAMY-SCM provided an accuracy of 0.895 that corresponded to 10-22% higher performance than that of widely used machine learning models. Furthermore, iAMY-SCM achieving an accuracy of 0.827 as evaluated by an independent test, which was found to be comparable to that of RFAmy and was approximately 9-13% higher than widely used machine learning models. Furthermore, the analysis of estimated propensity scores of amino acids and dipeptides were performed to provide insights into the biophysical and biochemical properties of amyloid proteins. As such, this demonstrates that the proposed iAMY-SCM is efficient and reliable in terms of simplicity, interpretability and implementation. To facilitate ease of use of the proposed iAMY-SCM, a user-friendly and publicly accessible web server at http://camt.pythonanywhere.com/iAMY-SCM has been established. We anticipate that that iAMY-SCM will be an important tool for facilitating the large-scale prediction and characterization of amyloid protein.

Amyloid Signaling in Filamentous Fungi and Bacteria

Amyloids are implicated in many protein misfolding diseases. Amyloid folds, however, also display a range of functional roles particularly in the microbial world. The templating ability of these folds endows them with specific properties allowing their self-propagation and protein-to-protein transmission in vivo. This property, the prion principle, is exploited by specific signaling pathways that use transmission of the amyloid fold as a way to convey information from a receptor to an effector protein. I describe here amyloid signaling pathways involving fungal nucleotide binding and oligomerization domain (NOD)-like receptors that were found to control nonself recognition and programmed cell death processes. Studies on these fungal amyloid signaling motifs stem from the characterization of the fungal [Het-s] prion protein and have led to the identification in fungi but also in multicellular bacteria of several distinct families of signaling motifs, one of which is related to RHIM [receptor-interacting protein (RIP) homotypic interaction motif], an amyloid motif regulating mammalian necroptosis.

New Human Islet Amyloid Polypeptide Fragments Susceptible to Aggregation

Human Islet Amyloid Polypeptide (hIAPP) plays a key role in the pathogenesis of type II diabetes. The aim of this research was to search for new amyloidogenic fragments of hIAPP. An initial attempt to predict the amyloidogenic cores of polypeptides/proteins using five different computer programs did not provide conclusive results. Therefore, we synthesized hIAPP fragments covering the entire hormone. The fragments were assessed for their aggregation ability, using recommended methods to search for the amyloidogenic fragments of the polypeptides/proteins. It was found that fragments (18-22) H-HSSNN-OH and (33-37) H-GSNTY-NH₂ aggregate and form stable amyloid-like structures. Both of these fragments have a much higher antiproliferative activity relative to the RIN-5F cell compared to the (23-27) H-FGAIL-OH fragment widely regarded as the amyloidogenic core of amylin. The analog of (33-37) H-GSNTY-NH₂ containing a free carboxy group on the C-terminal amino acid (H-GSNTY-OH) does not have amyloidogenic properties and can therefore be considered as a potential inhibitor of amylin aggregation. Research on the use of non-aggregating amylin fragments as potential hormone aggregation inhibitors is ongoing.

The hot sites of α-synuclein in amyloid fibril formation

The role of alpha-synuclein (αS) amyloid fibrillation has been recognized in various neurological diseases including Parkinson's Disease (PD). In early stages, fibrillation occurs by the structural transition from helix to extended states in monomeric αS followed by the formation of beta-sheets. This alpha-helix to beta-sheet transition (αβT) speeds up the formation of amyloid fibrils through the formation of unstable and temporary configurations of the αS. In this study, the most important regions that act as initiating nuclei and make unstable the initial configuration were identified based on sequence and structural information. In this regard, a Targeted Molecular Dynamics (TMD) simulation was employed using explicit solvent models under physiological conditions. Identified regions are those that are in the early steps of structural opening. The trajectory was clustered the structures characterized the intermediate states. The findings of this study would help us to better understanding of the mechanism of amyloid fibril formation.

Aggregation and Prion-Inducing Properties of the G-Protein Gamma Subunit Ste18 are Regulated by Membrane Association

Yeast prions and mnemons are respectively transmissible and non-transmissible self-perpetuating protein assemblies, frequently based on cross-β ordered detergent-resistant aggregates (amyloids). Prions cause devastating diseases in mammals and control heritable traits in yeast. It was shown that the de novo formation of the prion form [PSI⁺] of yeast release factor Sup35 is facilitated by aggregates of other proteins. Here we explore the mechanism of the promotion of [PSI⁺] formation by Ste18, an evolutionarily conserved gamma subunit of a G-protein coupled receptor, a key player in responses to extracellular stimuli. Ste18 forms detergent-resistant aggregates, some of which are colocalized with de novo generated Sup35 aggregates. Membrane association of Ste18 is required for both Ste18 aggregation and [PSI⁺] induction, while functional interactions involved in signal transduction are not essential for these processes. This emphasizes the significance of a specific location for the nucleation of protein aggregation. In contrast to typical prions, Ste18 aggregates do not show a pattern of heritability. Our finding that Ste18 levels are regulated by the ubiquitin-proteasome system, in conjunction with the previously reported increase in Ste18 levels upon the exposure to mating pheromone, suggests that the concentration-dependent Ste18 aggregation may mediate a mnemon-like response to physiological stimuli.