BetaSerpentine: a bioinformatics tool for reconstruction of amyloid structures

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.

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.

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.

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.

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.

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.

From Prions to Stress Granules: Defining the Compositional Features of Prion-Like Domains That Promote Different Types of Assemblies

Stress granules are ribonucleoprotein assemblies that form in response to cellular stress. Many of the RNA-binding proteins found in stress granule proteomes contain prion-like domains (PrLDs), which are low-complexity sequences that compositionally resemble yeast prion domains. Mutations in some of these PrLDs have been implicated in neurodegenerative diseases, including amyotrophic lateral sclerosis and frontotemporal dementia, and are associated with persistent stress granule accumulation. While both stress granules and prions are macromolecular assemblies, they differ in both their physical properties and complexity. Prion aggregates are highly stable homopolymeric solids, while stress granules are complex dynamic biomolecular condensates driven by multivalent homotypic and heterotypic interactions. Here, we use stress granules and yeast prions as a paradigm to examine how distinct sequence and compositional features of PrLDs contribute to different types of PrLD-containing assemblies.

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.

Structure-based machine-guided mapping of amyloid sequence space reveals uncharted sequence clusters with higher solubilities

The amyloid conformation can be adopted by a variety of sequences, but the precise boundaries of amyloid sequence space are still unclear. The currently charted amyloid sequence space is strongly biased towards hydrophobic, beta-sheet prone sequences that form the core of globular proteins and by Q/N/Y rich yeast prions. Here, we took advantage of the increasing amount of high-resolution structural information on amyloid cores currently available in the protein databank to implement a machine learning approach, named Cordax (https://cordax.switchlab.org), that explores amyloid sequence beyond its current boundaries. Clustering by t-Distributed Stochastic Neighbour Embedding (t-SNE) shows how our approach resulted in an expansion away from hydrophobic amyloid sequences towards clusters of lower aliphatic content and higher charge, or regions of helical and disordered propensities. These clusters uncouple amyloid propensity from solubility representing sequence flavours compatible with surface-exposed patches in globular proteins, functional amyloids or sequences associated to liquid-liquid phase transitions.

An increasing number of amyloid structures are determined. Here, the authors present the structure-based amyloid core sequence prediction method Cordax that is based on machine learning and allows the detection of aggregation-prone regions with higher solubility, disorder and surface exposure, and furthermore predicts the structural topology, orientation and overall architecture of the resulting putative fibril core.

Protein Co-Aggregation Related to Amyloids: Methods of Investigation, Diversity, and Classification

Amyloids are unbranched protein fibrils with a characteristic spatial structure. Although the amyloids were first described as protein deposits that are associated with the diseases, today it is becoming clear that these protein fibrils play multiple biological roles that are essential for different organisms, from archaea and bacteria to humans. The appearance of amyloid, first of all, causes changes in the intracellular quantity of the corresponding soluble protein(s), and at the same time the aggregate can include other proteins due to different molecular mechanisms. The co-aggregation may have different consequences even though usually this process leads to the depletion of a functional protein that may be associated with different diseases. The protein co-aggregation that is related to functional amyloids may mediate important biological processes and change of protein functions. In this review, we survey the known examples of the amyloid-related co-aggregation of proteins, discuss their pathogenic and functional roles, and analyze methods of their studies from bacteria and yeast to mammals. Such analysis allow for us to propose the following co-aggregation classes: (i) titration: deposition of soluble proteins on the amyloids formed by their functional partners, with such interactions mediated by a specific binding site; (ii) sequestration: interaction of amyloids with certain proteins lacking a specific binding site; (iii) axial co-aggregation of different proteins within the same amyloid fibril; and, (iv) lateral co-aggregation of amyloid fibrils, each formed by different proteins.