MIRRAGGE - Minimum Information Required for Reproducible AGGregation Experiments

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.

Allosteric Modulation of Pathological Ataxin-3 Aggregation: A Path to Spinocerebellar Ataxia Type-3 Therapies

Spinocerebellar ataxia type 3 (SCA3) is a rare inherited neurodegenerative disease caused by the expansion of a polyglutamine repeat in the protease ataxin-3 (Atx3). Despite extensive knowledge of the downstream pathophysiology, no disease-modifying therapies are currently available to halt disease progression. The accumulation of protein inclusions enriched in the polyQ-expanded Atx3 in neurons suggests that inhibiting its self-assembly may yield targeted therapeutic approaches. Here it is shown that a supramolecular tweezer, CLR01, binds to a lysine residue on a positively charged surface patch of the Atx3 catalytic Josephin domain. At this site, the binding of CLR01 decreases the conformational fluctuations of the distal flexible hairpin. This results in reduced exposure of the nearby aggregation-prone region, which overlaps with the substrate ubiquitin binding site and primes Atx3 self-assembly, ultimately delaying Atx3 amyloid fibril formation and reducing the secondary nucleation rate, a process linked to fibril proliferation and toxicity. These effects translate into the reversal of synapse loss in a SCA3 cultured cortical neuron model, an improved locomotor function in a C. elegans SCA3 model, and a delay in disease onset, accompanied by reduced severity of motor symptoms in a SCA3 mouse model. This study provides critical insights into Atx3 self-assembly, revealing a novel allosteric site for designing CLR01-inspired therapies targeting pathological aggregation pathways while sparing essential functional sites. These findings emphasize that targeting allosteric sites in amyloid-forming proteins may offer unique opportunities to develop safe therapeutic strategies for various protein misfolding disorders.

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/.

Challenges in Peptide Solubilization - Amyloids Case Study

Peptide science has been a rapidly growing research field because of the enormous potential application of these biocompatible and bioactive molecules. However, many factors limit the widespread use of peptides in medicine, and low solubility is among the most common problems that hamper drug development in the early stages of research. Solubility is a crucial, albeit poorly understood, feature that determines peptide behavior. Several different solubility predictors have been proposed, and many strategies and protocols have been reported to dissolve peptides, but none of them is a one-size-fits-all method for solubilization of even the same peptide. In this review, we look for the reasons behind the difficulties in dissolving peptides, analyze the factors influencing peptide aggregation, conduct a critical analysis of solubilization strategies and protocols available in the literature, and give some tips on how to deal with the so-called difficult sequences. We focus on amyloids, which are particularly difficult to dissolve and handle such as amyloid beta (Aβ), insulin, and phenol-soluble modulins (PSMs).

NAGPKin: Nucleation-and-growth parameters from the kinetics of protein phase separation

The assembly of biomolecular condensate in eukaryotic cells and the accumulation of amyloid deposits in neurons are processes involving the nucleation and growth (NAG) of new protein phases. To therapeutically target protein phase separation, drug candidates are tested in in vitro assays that monitor the increase in the mass or size of the new phase. Limited mechanistic insight is, however, provided if empirical or untestable kinetic models are fitted to these progress curves. Here we present the web server NAGPKin that quantifies NAG rates using mass-based or size-based progress curves as the input data. A report is generated containing the fitted NAG parameters and elucidating the phase separation mechanisms at play. The NAG parameters can be used to predict particle size distributions of, for example, protein droplets formed by liquid-liquid phase separation (LLPS) or amyloid fibrils formed by protein aggregation. Because minimal intervention is required from the user, NAGPKin is a good platform for standardized reporting of LLPS and protein self-assembly data. NAGPKin is useful for drug discovery as well as for fundamental studies on protein phase separation. NAGPKin is freely available (no login required) at https://nagpkin.i3s.up.pt.

Understanding and controlling the molecular mechanisms of protein aggregation in mAb therapeutics

In antibody development and manufacturing, protein aggregation is a common challenge that can lead to serious efficacy and safety issues. To mitigate this problem, it is important to investigate its molecular origins. This review discusses (1) our current molecular understanding and theoretical models of antibody aggregation, (2) how various stress conditions related to antibody upstream and downstream bioprocesses can trigger aggregation, and (3) current mitigation strategies employed towards inhibiting aggregation. We discuss the relevance of the aggregation phenomenon in the context of novel antibody modalities and highlight how in silico approaches can be exploited to mitigate it.

Quantification of Surface Tension Effects and Nucleation-and-Growth Rates during Self-Assembly of Biological Condensates

Liquid-solid and liquid-liquid phase separation (PS) drives the formation of functional and disease-associated biological assemblies. Principles of phase equilibrium are here employed to derive a general kinetic solution that predicts the evolution of the mass and size of biological assemblies. Thermodynamically, protein PS is determined by two measurable concentration limits: the saturation concentration and the critical solubility. Due to surface tension effects, the critical solubility can be higher than the saturation concentration for small, curved nuclei. Kinetically, PS is characterized by the primary nucleation rate constant and a combined rate constant accounting for growth and secondary nucleation. It is demonstrated that the formation of a limited number of large condensates is possible without active mechanisms of size control and in the absence of coalescence phenomena. The exact analytical solution can be used to interrogate how the elementary steps of PS are affected by candidate drugs.

Dendrimers and Derivatives as Multifunctional Nanotherapeutics for Alzheimer's Disease

Alzheimer's disease (AD) is the most prevalent form of dementia. It affects more than 30 million people worldwide and costs over US$ 1.3 trillion annually. AD is characterized by the brain accumulation of amyloid β peptide in fibrillar structures and the accumulation of hyperphosphorylated tau aggregates in neurons, both leading to toxicity and neuronal death. At present, there are only seven drugs approved for the treatment of AD, of which only two can slow down cognitive decline. Moreover, their use is only recommended for the early stages of AD, meaning that the major portion of AD patients still have no disease-modifying treatment options. Therefore, there is an urgent need to develop efficient therapies for AD. In this context, nanobiomaterials, and dendrimers in particular, offer the possibility of developing multifunctional and multitargeted therapies. Due to their intrinsic characteristics, dendrimers are first-in-class macromolecules for drug delivery. They have a globular, well-defined, and hyperbranched structure, controllable nanosize and multivalency, which allows them to act as efficient and versatile nanocarriers of different therapeutic molecules. In addition, different types of dendrimers display antioxidant, anti-inflammatory, anti-bacterial, anti-viral, anti-prion, and most importantly for the AD field, anti-amyloidogenic properties. Therefore, dendrimers can not only be excellent nanocarriers, but also be used as drugs per se. Here, the outstanding properties of dendrimers and derivatives that make them excellent AD nanotherapeutics are reviewed and critically discussed. The biological properties of several dendritic structures (dendrimers, derivatives, and dendrimer-like polymers) that enable them to be used as drugs for AD treatment will be pointed out and the chemical and structural characteristics behind those properties will be analysed. The reported use of these nanomaterials as nanocarriers in AD preclinical research is also presented. Finally, future perspectives and challenges that need to be overcome to make their use in the clinic a reality are discussed.

Exploring cryptic amyloidogenic regions in prion-like proteins from plants

Prion-like domains (PrLDs) are intrinsically disordered regions (IDRs) of low sequence complexity with a similar composition to yeast prion domains. PrLDs-containing proteins have been involved in different organisms' regulatory processes. Regions of moderate amyloid propensity within IDRs have been shown to assemble autonomously into amyloid fibrils. These sequences tend to be rich in polar amino acids and often escape from the detection of classical bioinformatics screenings that look for highly aggregation-prone hydrophobic sequence stretches. We defined them as cryptic amyloidogenic regions (CARs) and recently developed an integrated database that collects thousands of predicted CARs in IDRs. CARs seem to be evolutionary conserved among disordered regions because of their potential to stablish functional contacts with other biomolecules. Here we have focused on identifying and characterizing CARs in prion-like proteins (pCARs) from plants, a lineage that has been poorly studied in comparison with other prionomes. We confirmed the intrinsic amyloid potential for a selected pCAR from Arabidopsis thaliana and explored functional enrichments and compositional bias of pCARs in plant prion-like proteins.

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.

AmyloGraph: a comprehensive database of amyloid-amyloid interactions

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.

CARs-DB: A Database of Cryptic Amyloidogenic Regions in Intrinsically Disordered Proteins

Proteome-wide analyses suggest that most globular proteins contain at least one amyloidogenic region, whereas these aggregation-prone segments are thought to be underrepresented in intrinsically disordered proteins (IDPs). In recent work, we reported that intrinsically disordered regions (IDRs) indeed sustain a significant amyloid load in the form of cryptic amyloidogenic regions (CARs). CARs are widespread in IDRs, but they are necessarily exposed to solvent, and thus they should be more polar and have a milder aggregation potential than conventional amyloid regions protected inside globular proteins. CARs are connected with IDPs function and, in particular, with the establishment of protein-protein interactions through their IDRs. However, their presence also appears associated with pathologies like cancer or Alzheimer’s disease. Given the relevance of CARs for both IDPs function and malfunction, we developed CARs-DB, a database containing precomputed predictions for all CARs present in the IDPs deposited in the DisProt database. This web tool allows for the fast and comprehensive exploration of previously unnoticed amyloidogenic regions embedded within IDRs sequences and might turn helpful in identifying disordered interacting regions. It contains >8,900 unique CARs identified in a total of 1711 IDRs. CARs-DB is freely available for users and can be accessed at http://carsdb.ppmclab.com. To validate CARs-DB, we demonstrate that two previously undescribed CARs selected from the database display full amyloidogenic potential. Overall, CARs-DB allows easy access to a previously unexplored amyloid sequence space.

Ultrasound-induced protein restructuring and ordered aggregation to form amyloid crystals

Amyloid crystals, a form of ordered protein aggregates documented relatively recently, have not been studied as extensively as amyloid fibres. This study investigates the formation of amyloid crystals with low frequency ultrasound (20 kHz) using β-lactoglobulin, as a model protein for amyloid synthesis. Acoustic cavitation generates localised zones of intense shear, with extreme heat and pressure that could potentially drive the formation of amyloid structures at ambient bulk fluid temperatures (20 ± 1 °C). Thioflavin T fluorescence and electron microscopy showed that low-frequency ultrasound at 20 W/cm3 input power induced β-stacking to produce amyloid crystals in the mesoscopic size range, with a mean length of approximately 22 µm. FTIR spectroscopy indicated a shift towards increased intermolecular antiparallel β-sheet content. An increase in sonication time (0-60 min) and input power (4-24 W/cm3) increased the mean crystal length, but this increase was not linearly proportional to sonication time and input power due to the delayed onset of crystal growth. We propose that acoustic cavitation causes protein unfolding and aggregation and imparts energy to aggregates to cross the torsion barrier, to achieve their lowest energy state as amyloid crystals. The study contributes to a further understanding of protein chemistry relating to the energy landscape of folding and aggregation. Ultrasound presents opportunities for practical applications of amyloid structures, presenting a more adaptable and scalable approach for synthesis.

A Robust Assay to Monitor Ataxin-3 Amyloid Fibril Assembly

Spinocerebellar ataxia type 3 (SCA3) is caused by the expansion of a glutamine repeat in the protein ataxin-3, which is deposited as intracellular aggregates in affected brain regions. Despite the controversial role of ataxin-3 amyloid structures in SCA3 pathology, the identification of molecules with the capacity to prevent aberrant self-assembly and stabilize functional conformation(s) of ataxin-3 is a key to the development of therapeutic solutions. Amyloid-specific kinetic assays are routinely used to measure rates of protein self-assembly in vitro and are employed during screening for fibrillation inhibitors. The high tendency of ataxin-3 to assemble into oligomeric structures implies that minor changes in experimental conditions can modify ataxin-3 amyloid assembly kinetics. Here, we determine the self-association rates of ataxin-3 and present a detailed study of the aggregation of normal and pathogenic ataxin-3, highlighting the experimental conditions that should be considered when implementing and validating ataxin-3 amyloid progress curves in different settings and in the presence of ataxin-3 interactors. This assay provides a unique and robust platform to screen for modulators of the first steps of ataxin-3 aggregation—a starting point for further studies with cell and animal models of SCA3.

Aggregation Time Machine: A Platform for the Prediction and Optimization of Long-Term Antibody Stability Using Short-Term Kinetic Analysis

Monoclonal antibodies are the fastest growing class of therapeutics. However, aggregation limits their shelf life and can lead to adverse immune responses. Assessment and optimization of the long-term antibody stability are therefore key challenges in the biologic drug development. Here, we present a platform based on the analysis of temperature-dependent aggregation data that can dramatically shorten the assessment of the long-term aggregation stability and thus accelerate the optimization of antibody formulations. For a set of antibodies used in the therapeutic areas from oncology to rheumatology and osteoporosis, we obtain an accurate prediction of aggregate fractions for up to three years using the data obtained on a much shorter time scale. Significantly, the strategy combining kinetic and thermodynamic analysis not only contributes to a better understanding of the molecular mechanisms of antibody aggregation but has already proven to be very effective in the development and production of biological therapeutics.

The Disease Associated Tau35 Fragment has an Increased Propensity to Aggregate Compared to Full-Length Tau

Tau35 is a truncated form of tau found in human brain in a subset of tauopathies. Tau35 expression in mice recapitulates key features of human disease, including progressive increase in tau phosphorylation, along with cognitive and motor dysfunction. The appearance of aggregated tau suggests that Tau35 may have structural properties distinct from those of other tau species that could account for its pathological role in disease. To address this hypothesis, we performed a structural characterization of monomeric and aggregated Tau35 and compared the results to those of two longer isoforms, 2N3R and 2N4R tau. We used small angle X-ray scattering to show that Tau35, 2N3R and 2N4R tau all behave as disordered monomeric species but Tau35 exhibits higher rigidity. In the presence of the poly-anion heparin, Tau35 increases thioflavin T fluorescence significantly faster and to a greater extent than full-length tau, demonstrating a higher propensity to aggregate. By using atomic force microscopy, circular dichroism, transmission electron microscopy and X-ray fiber diffraction, we provide evidence that Tau35 aggregation is mechanistically and morphologically similar to previously reported tau fibrils but they are more densely packed. These data increase our understanding of the aggregation inducing properties of clinically relevant tau fragments and their potentially damaging role in the pathogenesis of human tauopathies.

Toward the equilibrium and kinetics of amyloid peptide self-assembly

Several devastating human diseases are linked to peptide self-assembly, but our understanding their onset and progression is not settled. This is a sign of the complexity of the aggregation process, which is prevented, catalyzed, or retarded by numerous factors in body fluids and cells, varying in time and space. Biophysical studies of pure peptide solutions contribute insights into the underlying steps in the process and quantitative parameters relating to rate constants (energy barriers) and equilibrium constants (population distributions). This requires methods to quantify the concentration of at least one species in the process. Translation to an in vivo situation poses an enormous challenge, and the effects of selected components (bottom up) or entire body fluids (top down) need to be quantified.

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.

Bioinformatics methods for identification of amyloidogenic peptides show robustness to misannotated training data

Several disorders are related to amyloid aggregation of proteins, for example Alzheimer's or Parkinson's diseases. Amyloid proteins form fibrils of aggregated beta structures. This is preceded by formation of oligomers-the most cytotoxic species. Determining amyloidogenicity is tedious and costly. The most reliable identification of amyloids is obtained with high resolution microscopies, such as electron microscopy or atomic force microscopy (AFM). More frequently, less expensive and faster methods are used, especially infrared (IR) spectroscopy or Thioflavin T staining. Different experimental methods are not always concurrent, especially when amyloid peptides do not readily form fibrils but oligomers. This may lead to peptide misclassification and mislabeling. Several bioinformatics methods have been proposed for in-silico identification of amyloids, many of them based on machine learning. The effectiveness of these methods heavily depends on accurate annotation of the reference training data obtained from in-vitro experiments. We study how robust are bioinformatics methods to weak supervision, encountering imperfect training data. AmyloGram and three other amyloid predictors were applied. The results proved that a certain degree of misannotation in the reference data can be eliminated by the bioinformatics tools, even if they belonged to their training set. The computational results are supported by new experiments with IR and AFM methods.