Unraveling the molecular grammar and the structural transitions underlying the fibrillation of a viral fibrillogenic domain

Hendra virus (HeV) is a biosafety level 4 human pathogen belonging to the Henipavirus genus within the Paramyxoviridae family. In HeV, the phosphoprotein-encoding gene also drives the synthesis of the V and W proteins that are two major players in the host innate immune response evasion. These three proteins share a common intrinsically disordered N-terminal domain (NTD) and have distinct C-terminal domains. We recently reported the ability of a short region (i.e., PNT3), located within the shared NTD, to form fibrils. We subsequently identified a PNT3 motif (EYYY) critically involved in fibrillation and deciphered the contribution of each tyrosine to the process. Herein, we combined mutational studies with various biochemical and biophysical approaches to further investigate the molecular mechanisms underlying PNT3 fibrillation. The results show that (i) lysine residues play a critical role in driving fibrillation, (ii) hydrophobic residues affect the nucleation step, and (iii) charge distribution strongly affects the fibrillation propensities. Vibrational Raman spectroscopy data further validated the role of lysine residues in promoting fibrillation and enabled documenting the formation of cross-β amyloid structures. Altogether, these results illuminate the molecular mechanisms involved in fibril formation and pave the way towards the rational design of inhibitors.

A novel alpha-synuclein K58N missense variant in a patient with Parkinson's disease

Mutations and multiplications in the SNCA gene, encoding alpha-synuclein (aSyn), are associated with familial forms of Parkinson's disease (PD). We report the identification of a novel SNCA missense mutation (NM_000345.4, cDNA 174G>C; protein K58N) in a PD patient using whole exome sequencing, and describe comprehensive molecular and cellular analysss of the effects of this novel mutation. The patient exhibited typical sporadic PD with early onset and a benign disease course. Biophysical studies revealed that the K58N substitution causes local structural effects, disrupts binding to membranes, and enhances aSyn in vitro aggregation. K58N aSyn produces fewer inclusions per cell, and fails to undergo condensate formation. The mutation increases the cytoplasmic distribution of the protein, and has minimal effect on the dynamic reversibility of serine-129 phosphorylation. In total, the identification of this novel mutation advances our understanding of aSyn biology and pathobiology.

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.

Exploring Compactness and Dynamics of Apomyoglobin

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) approach has become a valuable analytical complement to traditional methods. HDX-MS allows the identification of dynamic surfaces in proteins. We have shown that the introduction of various mutations into the amino acid sequence of whale apomyoglobin (apoMb) leads to a change in the number of exchangeable hydrogen atoms, which is associated with a change in its compactness in the native-like condition. Thus, amino acid substitutions V10A, A15S, P120G, and M131A result in an increase in the number of exchangeable hydrogen atoms at the native-like condition, while the mutant form A144S leads to a decrease in the number of exchangeable hydrogen atoms. This may be due to a decrease and increase in the compactness of apoMb structure compared to the wild-type apoMb, respectively. The L9F and L9E mutations did not affect the compactness of the molecule compared to the wild type. We have demonstrated that V10A and M131A substitutions lead to the maximum and large increase correspondently in the average number of exchangeable hydrogen atoms for deuterium, since these substitutions lead to the loss of contacts between important parts of myoglobin structure: helices A, G, and H, which are structured at the early stage of folding.

What Can Be Learned by Knowing Only the Amino Acid Composition of Proteins?

The amino acid composition of proteins depends on many factors. It varies in organisms that are distant in taxonomic position. The amino acid composition of proteins depends on the localization of proteins in cells and tissues and the structure of proteins. The question arises: is it possible to separate different proteomes using only the amino acid composition of proteins? Is it possible to determine, considering only its amino acid composition, to what structural class the protein under study will belong? We have developed a method and a measure that maximally separate two sets of proteins. As a result, we assign each protein an R-value, positive values of which are more characteristic of the first set, and negative ones-of the second. By studying the distribution of R in two sets, we can determine how much these sets differ in composition. Also, when examining a new protein, we can determine if it is more similar to the first set or the second. In this paper, we show that using only amino acid composition, it is possible to separate sets of proteins belonging to different organisms, as well as proteins that differ in function or structure. In all cases, we assign to proteins a measure R that maximally separates the studied sets. This approach can be further used to annotate proteins with unknown functions.

Anti-aggregation Properties of the Mini-Peptides Derived from Alpha Crystallin Domain of the Small Heat Shock Protein, Tpv HSP 14.3

The highly conserved alpha crystallin domain of the small heat shock proteins is essential for dimerization and also implicated in substrate interaction. In this study, we designed four novel mini-peptides from alpha crystallin domain of archaeal Small Heat Shock Protein Tpv HSP 14.3. Among the peptide designs, the mini-peptides 38SDLVLEAEMAGFDKKNIKVS57 and 40LVLEAEMAGFD50 overlapped to the sequences of β3-β4 region. The other two peptides 77YIDQRVDKVYKVVKLPVE94 and 107GILTVRMK114 correspond to β6-β7 region and β9, respectively. Functional activity of the peptides was evaluated by monitoring heat-induced aggregation of the model substrates alcohol dehydrogenase at 43 °C and citrate synthase at 45 °C. Our results showed that the (38-57) and the (77-94) fragments exhibited chaperone activity with both of the substrate proteins. The (40-50) fragment while exhibiting a noticeable protective effect (> 90%) when tested with citrate synthase showed an anti-chaperone property toward alcohol dehydrogenase. Unlike the (40-50) fragment, the (107-114) fragment did not show any chaperone activity with citrate synthase but exhibited the highest chaperone efficiency among four mini-peptides with alcohol dehydrogenase. The selectivity of the (40-50) and the (107-114) fragments in targeting the client proteins is most likely dependent on their surface hydrophobicity and/or charge as revealed by the sequence and exposed surface analyses.

Amyloidogenesis of SARS-CoV-2 delta plus and omicron variants receptor-binding domain (RBD): impact of SUMO fusion tag

PURPOSE

The RBD of SARS-CoV-2 mediates viral entry into host cells by binding to the host receptor ACE2. SARS-CoV-2 infection is linked to various health issues resembling amyloid-related problems, persuading us to investigate the amyloidogenicity of the SARS-CoV-2 spike RBD.

METHODS

The FoldAmyloid program was used to assess the amyloidogenic propensities in the RBD of Delta Plus and RBD of the Omicron variant, with and without the SUMO tag. After the expression of RBDs, purification, and dialysis steps were performed, subsequently the ThT assay, FTIR, and TEM were employed to check the RBD ability to form fibrils.

RESULTS

The ThT assay, TEM, and FTIR revealed the ability of RBD to self-assemble into β-sheet-rich aggregates (48.4% β-sheet content). Additionally, the presence of the SUMO tag reduced the formation of RBD amyloid-like fibrils. The amyloidogenic potential of Omicron RBD was higher than Delta Plus, according to both in silico and experimental analyses.

CONCLUSIONS

The SARS-CoV-2 RBD can assemble itself by forming aggregates containing amyloid-like fibrils and the presence of a SUMO tag can significantly decrease the formation of RBD amyloid-like fibrils. In silico analysis suggested that variation in the ThT fluorescence intensity of amyloid accumulations in the two SARS-CoV-2 strains arises from specific mutations in their RBD regions.

Advanced glycation end-products accelerate amyloid deposits in adipocyte's lipid droplets

Adipose tissue dysfunction is central to insulin resistance, and the emergence of type 2 diabetes (T2D) is associated with elevated levels of carbonyl metabolites from glucose metabolism. In this study, using methylglyoxal (MGO) and glycolaldehyde (GAD) carbonyl metabolites induced protein glycation, leading to misfolding and β-sheet formation and generation of advanced glycation end products (AGEs). The formed AGEs compromise adipocytes activity. Microscopic and spectroscopic assays were used to examine the impact of MGO and GAD on lipid droplet-associated proteins. The results provide information about how these conditions lead to the appearance of glycated and amyloidogenic proteins formation that hinders metabolism and autophagy in adipocytes. We measured the beneficial effects of metformin (MET), an anti-diabetic drug, on misfolded protein as assessed by thioflavin (ThT) spectroscopy and improved autophagy, determined by LC3 staining. In vitro findings were complemented by in vivo analysis of white adipose tissue (WAT), where lipid droplet-associated β-amyloid deposits were predominantly linked to adipose triglyceride lipase (ATGL), a lipid droplet protein. Bioinformatics, imaging, biochemical and MS/MS methods affirm ATGL's glycation and its role in β-sheet secondary structure formation. Our results highlighted the pronounced presence of amyloidogenic proteins in adipocytes treated with carbonyl compounds, potentially reshaping our understanding of adipocyte altered activity in the context of T2D. This in-depth exploration offers novel perspectives on related pathophysiology and underscores the potential of adipocytes as pivotal therapeutic targets, bridging T2D, amyloidosis, protein glycation, and adipocyte malfunction.

Cryo-EM structure of a lysozyme-derived amyloid fibril from hereditary amyloidosis

Systemic ALys amyloidosis is a debilitating protein misfolding disease that arises from the formation of amyloid fibrils from C-type lysozyme. We here present a 2.8 Å cryo-electron microscopy structure of an amyloid fibril, which was isolated from the abdominal fat tissue of a patient who expressed the D87G variant of human lysozyme. We find that the fibril possesses a stable core that is formed by all 130 residues of the fibril precursor protein. There are four disulfide bonds in each fibril protein that connect the same residues as in the globularly folded protein. As the conformation of lysozyme in the fibril is otherwise fundamentally different from native lysozyme, our data provide a structural rationale for the need of protein unfolding in the development of systemic ALys amyloidosis.

Predictive Model Building for Aggregation Kinetics Based on Molecular Dynamics Simulations of an Antibody Fragment

Computational methods including machine learning and molecular dynamics simulations have strong potential to characterize, understand, and ultimately predict the properties of proteins relevant to their stability and function as therapeutics. Such methods would streamline the development pathway by minimizing the current experimental testing required for many protein variants and formulations. The molecular understanding of thermostability and aggregation propensity has advanced significantly along with predictive algorithms based on the sequence-level or structural-level information on a protein. However, these approaches focus largely on a comparison of protein sequence variations to correlate the properties of proteins to their stability, solubility, and aggregation propensity. For therapeutic protein development, it is of equal importance to take into account the impact of the formulation conditions to elucidate and predict the stability of the antibody drugs. At the macroscopic level, changing temperature, pH, ionic strength, and the addition of excipients can significantly alter the kinetics of protein aggregation. The mechanisms controlling aggregation kinetics have been traced back to a combination of molecular features, including conformational stability, partial unfolding to aggregation-prone states, and the colloidal stability governed by surface charges and hydrophobicity. However, very little has been done to evaluate these features in the context of protein dynamics in different formulations. In this work, we have combined a range of molecular features calculated from the Fab A33 protein sequence and molecular dynamics simulations. Using the power of advanced, yet interpretable, statistical tools, it has been possible to uncover greater insights into the mechanisms behind protein stability, validating previous findings, and also develop models that can predict the aggregation kinetics within a range of 49 different solution conditions.

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.

Enhancing protein aggregation prediction: a unified analysis leveraging graph convolutional networks and active learning

Protein aggregation (PA) is a critical phenomenon associated with Alzheimer's and Parkinson's disease. Recent studies have suggested that factors like aggregation-prone regions (APRs) and β-strand interactions are crucial in understanding such behavior. While experimental methods have provided valuable insights, there has been a shift towards computational strategies, particularly machine learning, for their efficacy and speed. The challenge, however, lies in effectively incorporating structural information into these models. This study constructs a Graph Convolutional Network (GCN) to predict PA scores with the expanded and refined Protein Data Bank (PDB) and AlphaFold2.0 dataset. We employed AGGRESCAN3D 2.0 to calculate PA propensity and to enhance the dataset, we systematically separated multi polypeptide chains within PDB data into single polypeptide chains, removing redundancy. This effort resulted in a dataset comprising 302 032 unique PDB entries. Subsequently, we compared sequence similarity and obtained 22 774 Homo sapiens data from AlphaFold2.0. Using this expanded and refined dataset, the trained GCN model for PA prediction achieves a remarkable coefficient of determination (R 2) score of 0.9849 and a low mean absolute error (MAE) of 0.0381. Furthermore, the efficacy of the active learning process was demonstrated through its rapid identification of proteins with high PA propensity. Consequently, the active learning approach achieved an MAE of 0.0291 in expected improvement, surpassing other methods. It identified 99% of the target proteins by exploring merely 29% of the entire search space. This improved GCN model demonstrates promise in selecting proteins susceptible to PA, advancing protein science. This work contributes to the development of efficient computational tools for PA prediction, with potential applications in disease diagnosis and therapy.

Authentic hSAA related with AA amyloidosis: New method of purification, folding and amyloid polymorphism

The secondary amyloidosis of humans is caused by the formation of hSAA fibrils in different organs and tissues. Until now hSAA was thought to have low amyloidogenicity in vitro and the majority of SAA aggregation experiments were done using murine protein or hSAA non-pathogenic isoforms. In this work a novel purification method for recombinant hSAA was introduced, enabling to obtain monomeric protein capable of amyloid aggregation under physiological conditions. The stability and amyloid aggregation of hSAA have been examined using a wide range of biophysical methods. It was shown that the unfolding of monomeric protein occurs through the formation of molten globule-like intermediate state. Polymorphism of hSAA amyloids was discovered to depend on the solution pH. At pH 8.5, rapid protein aggregation occurs, which leads to the formation of twisted short fibrils. Even a slight decrease of the pH to 7.8 results in delayed aggregation with the formation of long straight amyloids composed of laterally associated protofilaments. Limited proteolysis experiments have shown that full-length hSAA is involved in the formation of intermolecular interactions in both amyloid polymorphs. The results obtained, and the experimental approach used in this study can serve as a basis for further research on the mechanism of authentic hSAA amyloid formation.

Unravelling aggregation propensity of rotavirus A VP6 expressed as E. coli inclusion bodies through in silico prediction

The inner capsid protein of rotavirus, VP6, emerges as a promising candidate for next-generation vaccines against rotaviruses owing to its abundance in virion particles and high conservation. However, the formation of inclusion bodies during prokaryotic VP6 expression poses a significant hurdle to rotavirus research and applications. Here, we employed experimental and computational approaches to investigate inclusion body formation and aggregation-prone regions (APRs). Heterologous recombinant VP6 expression in Escherichia coli BL21(DE3) cells resulted in inclusion body formation, confirmed by transmission electron microscopy revealing amorphous aggregates. Thioflavin T assay demonstrated incubation temperature-dependent aggregation of VP6 inclusion bodies. Computational predictions of APRs in rotavirus A VP6 protein were performed using sequence-based tools (TANGO, AGGRESCAN, Zyggregator, Waltz, FoldAmyloid, ANuPP, Camsol intrinsic) and structure-based tools (SolubiS, CamSol structurally corrected, Aggrescan3D). A total of 24 consensus APRs were identified, with 21 of them being surface-exposed in VP6. All identified APRs display a predominance of hydrophobic amino acids, ranging from 33 to 100%. Computational identification of these APRs corroborates our experimental observation of VP6 inclusion body or aggregate formation. Characterization of VP6's aggregation propensity facilitates understanding of its behaviour during prokaryotic expression and opens avenues for protein engineering of soluble variants, advancing research on rotavirus VP6 in pathology, therapy, and diagnostics.

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors

BACKGROUND

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

RESULTS

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

CONCLUSIONS

VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

Insulin fibrillation under physicochemical parameters of bioprocessing and intervention by peptides and surface-active agents

Even after the centenary celebration of insulin discovery, there prevail challenges concerning insulin aggregation, not only after repeated administration but also during industrial production, storage, transport, and delivery, significantly impacting protein quality, efficacy, and effectiveness. The aggregation reduces insulin bioavailability, increasing the risk of heightened immunogenicity, posing a threat to patient health, and creating a dent in the golden success story of insulin therapy. Insulin experiences various physicochemical and mechanical stresses due to modulations in pH, temperature, ionic strength, agitation, shear, and surface chemistry, during the upstream and downstream bioprocessing, resulting in insulin unfolding and subsequent fibrillation. This has fueled research in the pharmaceutical industry and academia to unveil the mechanistic insights of insulin aggregation in an attempt to devise rational strategies to regulate this unwanted phenomenon. The present review briefly describes the impacts of environmental factors of bioprocessing on the stability of insulin and correlates with various intermolecular interactions, particularly hydrophobic and electrostatic forces. The aggregation-prone regions of insulin are identified and interrelated with biophysical changes during stress conditions. The quest for novel additives, surface-active agents, and bioderived peptides in decelerating insulin aggregation, which results in overall structural stability, is described. We hope this review will help tackle the real-world challenges of insulin aggregation encountered during bioprocessing, ensuring safer, stable, and globally accessible insulin for efficient management of diabetes.

Photocontrolled Reversible Amyloid Fibril Formation of Parathyroid Hormone-Derived Peptides

Peptide fibrillization is crucial in biological processes such as amyloid-related diseases and hormone storage, involving complex transitions between folded, unfolded, and aggregated states. We here employ light to induce reversible transitions between aggregated and nonaggregated states of a peptide, linked to the parathyroid hormone (PTH). The artificial light-switch 3-{[(4-aminomethyl)phenyl]diazenyl}benzoic acid (AMPB) is embedded into a segment of PTH, the peptide PTH25-37, to control aggregation, revealing position-dependent effects. Through in silico design, synthesis, and experimental validation of 11 novel PTH25-37-derived peptides, we predict and confirm the amyloid-forming capabilities of the AMPB-containing peptides. Quantum-chemical studies shed light on the photoswitching mechanism. Solid-state NMR studies suggest that β-strands are aligned parallel in fibrils of PTH25-37, while in one of the AMPB-containing peptides, β-strands are antiparallel. Simulations further highlight the significance of π-π interactions in the latter. This multifaceted approach enabled the identification of a peptide that can undergo repeated phototriggered transitions between fibrillated and defibrillated states, as demonstrated by different spectroscopic techniques. With this strategy, we unlock the potential to manipulate PTH to reversibly switch between active and inactive aggregated states, representing the first observation of a photostimulus-responsive hormone.

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.

AggreProt: a web server for predicting and engineering aggregation prone regions in proteins

Recombinant proteins play pivotal roles in numerous applications including industrial biocatalysts or therapeutics. Despite the recent progress in computational protein structure prediction, protein solubility and reduced aggregation propensity remain challenging attributes to design. Identification of aggregation-prone regions is essential for understanding misfolding diseases or designing efficient protein-based technologies, and as such has a great socio-economic impact. Here, we introduce AggreProt, a user-friendly webserver that automatically exploits an ensemble of deep neural networks to predict aggregation-prone regions (APRs) in protein sequences. Trained on experimentally evaluated hexapeptides, AggreProt compares to or outperforms state-of-the-art algorithms on two independent benchmark datasets. The server provides per-residue aggregation profiles along with information on solvent accessibility and transmembrane propensity within an intuitive interface with interactive sequence and structure viewers for comprehensive analysis. We demonstrate AggreProt efficacy in predicting differential aggregation behaviours in proteins on several use cases, which emphasize its potential for guiding protein engineering strategies towards decreased aggregation propensity and improved solubility. The webserver is freely available and accessible at https://loschmidt.chemi.muni.cz/aggreprot/.

Protonation-State Dependent Modulation of Hen Egg-White Lysozyme Fibrillation under the Influence of a Short Synthetic Peptide

Under the influence of various conditions, misfolding of soluble proteins occurs, leading to the formation of toxic insoluble amyloids. The formation and deposition of such amyloids within the body are associated with detrimental biological consequences such as the onset of several amyloid-related diseases. Previously, we established a strategy for the rational design of peptide inhibitors against amyloid formation based on the amyloidogenic-prone region of the protein. In the current study, we have designed and identified an Asp-containing rationally designed hexapeptide (SqP4) as an excellent inhibitor of hen egg-white lysozyme (HEWL) amyloid progression in vitro. First, SqP4 showed strong affinity toward the native monomeric HEWL leading to the stabilization of the native form and restriction in the unfolding process of monomeric HEWL. Second, SqP4 was found to arrest the amyloidogenic misfolded structure of HEWL in a nonfibrillar monomer-like stage. We also observed the differential effect of the protonation state of the charged amino acid (Asp) within the peptide inhibitor on the amyloid formation of HEWL and explored the reason behind the observations. The findings of this study can be implemented in future strategies for the development of potent therapeutics against other amyloid-related diseases.

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.

Amyloidogenic regions in beta-strands II and III modulate the aggregation and toxicity of SOD1 in living cells

Mutations in the protein superoxide dismutase-1 (SOD1) promote its misfolding and aggregation, ultimately causing familial forms of the debilitating neurodegenerative disease amyotrophic lateral sclerosis (ALS). Currently, over 220 (mostly missense) ALS-causing mutations in the SOD1 protein have been identified, indicating that common structural features are responsible for aggregation and toxicity. Using in silico tools, we predicted amyloidogenic regions in the ALS-associated SOD1-G85R mutant, finding seven regions throughout the structure. Introduction of proline residues into β-strands II (I18P) or III (I35P) reduced the aggregation propensity and toxicity of SOD1-G85R in cells, significantly more so than proline mutations in other amyloidogenic regions. The I18P and I35P mutations also reduced the capability of SOD1-G85R to template onto previously formed non-proline mutant SOD1 aggregates as measured by fluorescence recovery after photobleaching. Finally, we found that, while the I18P and I35P mutants are less structurally stable than SOD1-G85R, the proline mutants are less aggregation-prone during proteasome inhibition, and less toxic to cells overall. Our research highlights the importance of a previously underappreciated SOD1 amyloidogenic region in β-strand II (15QGIINF20) to the aggregation and toxicity of SOD1 in ALS mutants, and suggests that β-strands II and III may be good targets for the development of SOD1-associated ALS therapies.

Gut microbiota produces biofilm-associated amyloids with potential for neurodegeneration

Age-related neurodegenerative diseases involving amyloid aggregation remain one of the biggest challenges of modern medicine. Alterations in the gastrointestinal microbiome play an active role in the aetiology of neurological disorders. Here, we dissect the amyloidogenic properties of biofilm-associated proteins (BAPs) of the gut microbiota and their implications for synucleinopathies. We demonstrate that BAPs are naturally assembled as amyloid-like fibrils in insoluble fractions isolated from the human gut microbiota. We show that BAP genes are part of the accessory genomes, revealing microbiome variability. Remarkably, the abundance of certain BAP genes in the gut microbiome is correlated with Parkinson's disease (PD) incidence. Using cultured dopaminergic neurons and Caenorhabditis elegans models, we report that BAP-derived amyloids induce α-synuclein aggregation. Our results show that the chaperone-mediated autophagy is compromised by BAP amyloids. Indeed, inoculation of BAP fibrils into the brains of wild-type mice promote key pathological features of PD. Therefore, our findings establish the use of BAP amyloids as potential targets and biomarkers of α-synucleinopathies.

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.

Sulfation of hyaluronic acid reconfigures the mechanistic pathway of bone morphogenetic protein-2 aggregation

Bone morphogenetic protein-2 (BMP-2) is a critical growth factor of bone extracellular matrix (ECM), pivotal for osteogenesis. Glycosaminoglycans (GAGs), another vital ECM biomolecules, interact with growth factors, affecting signal transduction. Our study primarily focused on hyaluronic acid (HA), a prevalent GAG, and its sulfated derivative (SHA). We explored their impact on BMP-2's conformation, aggregation, and mechanistic pathways of aggregation using diverse optical and rheological methods. In the presence of HA and SHA, the secondary structure of BMP-2 underwent a structured transformation, characterized by a substantial increase in beta sheet content, and a detrimental alteration, manifesting as a shift towards unstructured content, respectively. Although both HA and SHA induced BMP-2 aggregation, their mechanisms differed. SHA led to rapid amorphous aggregates, while HA promoted amyloid fibrils with a lag phase and sigmoidal kinetics. Aggregate size and shape varied; HA produced larger structures, SHA smaller. Each aggregation type followed distinct pathways influenced by viscosity and excluded volume. Higher viscosity, low diffusivity of protein and higher excluded volume In the presence of HA promotes fibrillation having size in micrometer range. Low viscosity, high diffusivity of protein and lesser excluded volume leads to amorphous aggregate of size in nanometer range.

AMYGNN: A Graph Convolutional Neural Network-Based Approach for Predicting Amyloid Formation from Polypeptides

There has been an increasing interest in the use of amyloids for constructing various functional materials. The design of amyloid-associated functional materials requires the identification of the core peptide sequences as the fundamental building block. The existing computational methods are limited in terms of delineating polypeptides, the typical non-Euclidean structural data, and they fail to capture the dynamic interactions between amino acids due to ignoring the contextual information from surrounding amino acids. Here, we first propose the use of a state-of-the-art graph convolutional neural network for predicting the trends of amyloid formation from specific peptide sequences (AMYGNN) by abstracting each polypeptide as a graph, in which the constituting amino acids are viewed as nodes and edges characterizing the connections between pairs of amino acids are established when they meet a given distance threshold (Cα-Cα ≤ 5 Å). Our model achieves high performance with accuracy (0.9208), G-mean (0.9203), MCC (0.8417), and F1 (0.9235) in determining the characteristic peptide sequences to form amyloid. 32 of 534 crucial amino acid properties that greatly contribute to the formation of amyloids are ascertained, and the β-folding-like graph structure of a polypeptide is believed to be essential for the formation of amyloid. Our model enables the mapping of polypeptides with underlying interactions between amino acids and provides a quick and precise predictive framework for directing the construction of amyloid-associated functional materials.

Aggregation of Disrupted in Schizophrenia 1 arises from a central region of the protein

An emerging approach to studying major mental illness is through proteostasis, with the identification of several proteins that form insoluble aggregates in the brains of patients. One of these is Disrupted in Schizophrenia 1 (DISC1), a neurodevelopmentally-important scaffold protein, and product of a classic schizophrenia risk gene. DISC1 aggregates have been detected in post mortem brain tissue from patients with schizophrenia, bipolar disorder and major depressive disorder, as well as various model systems, although the mechanism by which it aggregates is still unclear. Aggregation of two other proteins implicated in mental illness, TRIOBP-1 and NPAS3, was shown to be dependent on very specific structural regions of the protein. We therefore looked at the domain structure of DISC1, and investigated which structural elements are key for its aggregation. While none of the known structured DISC1 regions (named D, I, S and C respectively) formed aggregates individually when expressed in neuroblastoma cells, the combination of the D and I regions, plus the linker region between them, formed visible aggregates. Further refinement revealed that a region of approximately 30 amino acids between these two regions is critical for aggregation, and deletion of this region is sufficient to abolish the aggregation propensity of DISC1. This finding from mammalian cell culture contrasts with the recent determination that the C-region of DISC1 can aggregate in vitro, although some variations of the C-terminal of DISC1 could aggregate in our system. It therefore appears likely that DISC1 aggregation, implicated in mental illness, can occur through at least two distinct mechanisms.

Cryo-EM observation of the amyloid key structure of polymorphic TDP-43 amyloid fibrils

The transactive response DNA-binding protein-43 (TDP-43) is a multi-facet protein involved in phase separation, RNA-binding, and alternative splicing. In the context of neurodegenerative diseases, abnormal aggregation of TDP-43 has been linked to amyotrophic lateral sclerosis and frontotemporal lobar degeneration through the aggregation of its C-terminal domain. Here, we report a cryo-electron microscopy (cryo-EM)-based structural characterization of TDP-43 fibrils obtained from the full-length protein. We find that the fibrils are polymorphic and contain three different amyloid structures. The structures differ in the number and relative orientation of the protofilaments, although they share a similar fold containing an amyloid key motif. The observed fibril structures differ from previously described conformations of TDP-43 fibrils and help to better understand the structural landscape of the amyloid fibril structures derived from this protein.

Formation of amyloid fibrils by the regulatory 14-3-3ζ protein

The 14-3-3 proteins are a highly conserved adaptor protein family with multi-layer functions, abundantly expressed in the brain. The 14-3-3 proteins modulate phosphorylation, regulate enzymatic activity and can act as chaperones. Most importantly, they play an important role in various neurodegenerative disorders due to their vast interaction partners. Particularly, the 14-3-3ζ isoform is known to co-localize in aggregation tangles in both Alzheimer's and Parkinson's diseases as a result of protein-protein interactions. These abnormal clumps consist of amyloid fibrils, insoluble aggregates, mainly formed by the amyloid-β, tau and α-synuclein proteins. However, the molecular basis of if and how 14-3-3ζ can aggregate into amyloid fibrils is unknown. In this study, we describe the formation of amyloid fibrils by 14-3-3ζ using a comprehensive approach that combines bioinformatic tools, amyloid-specific dye binding, secondary structure analysis and atomic force microscopy. The results presented herein characterize the amyloidogenic properties of 14-3-3ζ and imply that the well-folded protein undergoes aggregation to β-sheet-rich amyloid fibrils.

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 .

Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry

Protein-based therapeutics have revolutionized the pharmaceutical industry and become vital components in the development of future therapeutics. They offer several advantages over traditional small molecule drugs, including high affinity, potency and specificity, while demonstrating low toxicity and minimal adverse effects. However, the development and manufacturing processes of protein-based therapeutics presents challenges related to protein folding, purification, stability and immunogenicity that should be addressed. These proteins, like other biological molecules, are prone to chemical and physical instabilities. The stability of protein-based drugs throughout the entire manufacturing, storage and delivery process is essential. The occurrence of structural instability resulting from misfolding, unfolding, and modifications, as well as aggregation, poses a significant risk to the efficacy of these drugs, overshadowing their promising attributes. Gaining insight into structural alterations caused by aggregation and their impact on immunogenicity is vital for the advancement and refinement of protein therapeutics. Hence, in this review, we have discussed some features of protein aggregation during production, formulation and storage as well as stabilization strategies in protein engineering and computational methods to prevent aggregation.

Widespread amyloid aggregates formation by Zika virus proteins and peptides

Viral pathogenesis typically involves numerous molecular mechanisms. Protein aggregation is a relatively unknown characteristic of viruses, despite the fact that viral proteins have been shown to form terminally misfolded forms. Zika virus (ZIKV) is a neurotropic one with the potential to cause neurodegeneration. Its protein amyloid aggregation may link the neurodegenerative component to the pathogenicity associated with the viral infection. Therefore, we investigated protein aggregation in the ZIKV proteome as a putative pathogenic route and one of the alternate pathways. We discovered that it contains numerous anticipated aggregation-prone regions in this investigation. To validate our prediction, we used a combination of supporting experimental techniques routinely used for morphological characterization and study of amyloid aggregates. Several ZIKV proteins and peptides, including the full-length envelope protein, its domain III (EDIII) and fusion peptide, Pr N-terminal peptide, NS1 β-roll peptide, membrane-embedded signal peptide 2K, and cytosolic region of NS4B protein, were shown to be highly aggregating in our study. Because our findings show that viral proteins can form amyloids in vitro, we need to do a thorough functional study of these anticipated APRs to understand better the role of amyloids in the pathophysiology of ZIKV infection.

Enhancing the Antimicrobial Properties of Peptides through Cell-Penetrating Peptide Conjugation: A Comprehensive Assessment

Combining antimicrobial peptides (AMPs) with cell-penetrating peptides (CPPs) has shown promise in boosting antimicrobial potency, especially against Gram-negative bacteria. We examined the CPP-AMP interaction with distinct bacterial types based on cell wall differences. Our investigation focused on AMPs incorporating penetratin CPP and dihybrid peptides containing both cell-penetrating TAT protein fragments from the human immunodeficiency virus and Antennapedia peptide (Antp). Assessment of the peptides TAT-AMP, AMP-Antp, and TAT-AMP-Antp revealed their potential against Gram-positive strains (Staphylococcus aureus, Methicillin-resistant Staphylococcus aureus (MRSA), and Bacillus cereus). Peptides TAT-AMP and AMP-Antp using an amyloidogenic AMP from S1 ribosomal protein Thermus thermophilus, at concentrations ranging from 3 to 12 μM, exhibited enhanced antimicrobial activity against B. cereus. TAT-AMP and TAT-AMP-Antp, using an amyloidogenic AMP from the S1 ribosomal protein Pseudomonas aeruginosa, at a concentration of 12 µM, demonstrated potent antimicrobial activity against S. aureus and MRSA. Notably, the TAT-AMP, at a concentration of 12 µM, effectively inhibited Escherichia coli (E. coli) growth and displayed antimicrobial effects similar to gentamicin after 15 h of incubation. Peptide characteristics determined antimicrobial activity against diverse strains. The study highlights the intricate relationship between peptide properties and antimicrobial potential. Mechanisms of AMP action are closely tied to bacterial cell wall attributes. Peptides with the TAT fragment exhibited enhanced antimicrobial activity against S. aureus, MRSA, and P. aeruginosa. Peptides containing only the Antp fragment displayed lower activity. None of the investigated peptides demonstrated cytotoxic or cytostatic effects on either BT-474 cells or human skin fibroblasts. In conclusion, CPP-AMPs offer promise against various bacterial strains, offering insights for targeted antimicrobial development.

Pathologic polyglutamine aggregation begins with a self-poisoning polymer crystal

A long-standing goal of amyloid research has been to characterize the structural basis of the rate-determining nucleating event. However, the ephemeral nature of nucleation has made this goal unachievable with existing biochemistry, structural biology, and computational approaches. Here, we addressed that limitation for polyglutamine (polyQ), a polypeptide sequence that causes Huntington's and other amyloid-associated neurodegenerative diseases when its length exceeds a characteristic threshold. To identify essential features of the polyQ amyloid nucleus, we used a direct intracellular reporter of self-association to quantify frequencies of amyloid appearance as a function of concentration, conformational templates, and rational polyQ sequence permutations. We found that nucleation of pathologically expanded polyQ involves segments of three glutamine (Q) residues at every other position. We demonstrate using molecular simulations that this pattern encodes a four-stranded steric zipper with interdigitated Q side chains. Once formed, the zipper poisoned its own growth by engaging naive polypeptides on orthogonal faces, in a fashion characteristic of polymer crystals with intramolecular nuclei. We further show that self-poisoning can be exploited to block amyloid formation, by genetically oligomerizing polyQ prior to nucleation. By uncovering the physical nature of the rate-limiting event for polyQ aggregation in cells, our findings elucidate the molecular etiology of polyQ diseases.

Potential of ADAM 17 Signal Peptide To Form Amyloid Aggregates in Vitro

ADAM 17, a disintegrin and metalloproteinase 17 belonging to the adamalysin protein family, is a Zn2+-dependent type-I transmembrane α-secretase protein. As a major sheddase, ADAM 17 acts as an indispensable regulator of chief cellular events and controls diverse cytokines, adhesion molecules, and growth factors. The signal peptide (residues 1-17) of ADAM 17 targets the protein to the secretory pathway and gets cleaved off afterward. No other function is documented for the ADAM 17 signal peptide (ADAM 17-SP) inside the cells. Here, we have taken a reductionist approach to understand the biophysical properties of ADAM 17-SP. Aiming to understand the possibility of aggregation, we found several aggregation-prone segments in the signal peptide. We performed in vitro experiments to show that the signal peptide forms amyloid-like aggregates in buffered conditions. We also studied its aggregation in the presence of sodium tripolyphosphate and heparin to correlate with the cellular conditions, as these biomolecules are naturally present inside cells. Further, we performed seeding experiments to observe the possibility of ADAM 17-SP aggregate interaction with the Aβ42 peptide. The results suggest that its seeds escalate the aggregation kinetics of the Aβ42 peptide and form heteromeric aggregates with it. We believe this finding could further intensify the aggregation studies on other signal peptides and shed light on the potential role of these segments other than signaling.

mt-tRNAs in the polymerase gamma mutant heart

Introduction

Mice harboring a D257A mutation in the proofreading domain of the mitochondrial DNA polymerase, Polymerase Gamma (POLG), experience severe metabolic dysfunction and display hallmarks of accelerated aging. We previously reported a mitochondrial unfolded protein response (UPTmt) - like (UPRmt-like) gene and protein expression pattern in the right ventricular tissue of POLG mutant mice.

Aim

We sought to determine if POLG mutation altered the expression of genes encoded by the mitochondria in a way that might also reduce proteotoxic stress.

Methods and Results

The expression of genes encoded by the mitochondrial DNA was interrogated via RNA-seq and northern blot analysis. A striking, location-dependent effect was seen in the expression of mitochondrial-encoded tRNAs in the POLG mutant as assayed by RNA-seq. These expression changes were negatively correlated with the tRNA partner amino acid's amyloidogenic potential. Direct measurement by northern blot was conducted on candidate mt-tRNAs identified from the RNA-seq. This analysis confirmed reduced expression of MT-TY in the POLG mutant but failed to show increased expression of MT-TP, which was dramatically increased in the RNA-seq data.

Conclusion

We conclude that reduced expression of amyloid-associated mt-tRNAs is another indication of adaptive response to severe mitochondrial dysfunction in the POLG mutant. Incongruence between RNA-seq and northern blot measurement of MT-TP expression points towards the existence of mt-tRNA post-transcriptional modification regulation in the POLG mutant that alters either polyA capture or cDNA synthesis in RNA-seq library generation. Together, these data suggest that 1) evolution has distributed mt-tRNAs across the circular mitochondrial genome to allow chromosomal location-dependent mt-tRNA regulation (either by expression or PTM) and 2) this regulation is cognizant of the tRNA partner amino acid's amyloidogenic properties.

iAMY-RECMFF: Identifying amyloidgenic peptides by using residue pairwise energy content matrix and features fusion algorithm

Various diseases, including Huntington's disease, Alzheimer's disease, and Parkinson's disease, have been reported to be linked to amyloid. Therefore, it is crucial to distinguish amyloid from non-amyloid proteins or peptides. While experimental approaches are typically preferred, they are costly and time-consuming. In this study, we have developed a machine learning framework called iAMY-RECMFF to discriminate amyloidgenic from non-amyloidgenic peptides. In our model, we first encoded the peptide sequences using the residue pairwise energy content matrix. We then utilized Pearson's correlation coefficient and distance correlation to extract useful information from this matrix. Additionally, we employed an improved similarity network fusion algorithm to integrate features from different perspectives. The Fisher approach was adopted to select the optimal feature subset. Finally, the selected features were inputted into a support vector machine for identifying amyloidgenic peptides. Experimental results demonstrate that our proposed method significantly improves the identification of amyloidgenic peptides compared to existing predictors. This suggests that our method may serve as a powerful tool in identifying amyloidgenic peptides. To facilitate academic use, the dataset and codes used in the current study are accessible at https://figshare.com/articles/online_resource/iAMY-RECMFF/22816916.

Prediction of protein aggregation propensity employing SqFt-based logistic regression model

Here we present a novel machine-learning approach to predict protein aggregation propensity (PAP) which is a key factor in the formation of amyloid fibrils based on logistic regression (LR). Amyloid fibrils are associated with various neurodegenerative diseases (ND) such as Alzheimer's disease (AD) and Parkinson's disease (PD), which are caused by oxidative stress and impaired protein homeostasis. Accordingly, the paper uses a dataset of hexapeptides with known aggregation tendencies and eight physiochemical features to train and test the LR model. Also, it evaluates the performance of the LR model using F-measure and Matthews correlation coefficient (MCC) as metrics and compares it with other existing methods. Moreover, it investigates the effect of combining sequence and feature information in the prediction. In conclusion, the LR model with sequence and feature information achieves high F-measure (0.841) and MCC (0.6692), outperforming other methods and demonstrating its efficiency and reliability for PAP prediction. In addition, the overall performance of the concluded method was higher than the other known servers, for instance, Aggrescan, Metamyl, Foldamyloid, and PASTA 2.0. The LR model can be accessed at: https://github.com/KatherineEshari/Protein-aggregation-prediction.

Nanoscopic Elucidation of Spontaneous Self-Assembly of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Open Reading Frame 6 (ORF6) Protein

Open reading frame 6 (ORF6), the accessory protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that suppresses host type-I interferon signaling, possesses amyloidogenic sequences. ORF6 amyloidogenic peptides self-assemble to produce cytotoxic amyloid fibrils. Currently, the molecular properties of the ORF6 remain elusive. Here, we investigate the structural dynamics of the full-length ORF6 protein in a near-physiological environment using high-speed atomic force microscopy. ORF6 oligomers were ellipsoidal and readily assembled into ORF6 protofilaments in either a circular or a linear pattern. The formation of ORF6 protofilaments was enhanced at higher temperatures or on a lipid substrate. ORF6 filaments were sensitive to aliphatic alcohols, urea, and SDS, indicating that the filaments were predominantly maintained by hydrophobic interactions. In summary, ORF6 self-assembly could be necessary to sequester host factors and causes collateral damage to cells via amyloid aggregates. Nanoscopic imaging unveiled the innate molecular behavior of ORF6 and provides insight into drug repurposing to treat amyloid-related coronavirus disease 2019 complications.

First Report of Lysozyme Amyloidosis with p.F21L/T88N Amino Acid Substitutions in a Russian Family

Lysozyme amyloidosis is caused by an amino acid substitution in the sequence of this protein. In our study, we described a clinical case of lysozyme amyloidosis in a Russian family. In our work, we described in detail the histological changes in tissues that appeared as a result of massive deposition of amyloid aggregates that affected almost all organ systems, with the exception of the central nervous system. We determined the type of amyloidosis and mutations using mass spectrometry. Using mass spectrometry, the protein composition of tissue samples of patient 1 (autopsy material) and patient 2 (biopsy material) with histologically confirmed amyloid deposits were analyzed. Amino acid substitutions p.F21L/T88N in the lysozyme sequence were identified in both sets of samples and confirmed by sequencing of the lysozyme gene of members of this family. We have shown the inheritance of these mutations in the lysozyme gene in members of the described family. For the first time, we discovered a mutation in the first exon p.F21L of the lysozyme gene, which, together with p.T88N amino acid substitution, led to amyloidosis in members of the studied family.

Cryo-EM Structure of the Full-length hnRNPA1 Amyloid Fibril

Heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1) is a multifunctional RNA-binding protein that is associated with neurodegenerative diseases, such as amyotrophic lateral sclerosis and multisystem proteinopathy. In this study, we have used cryo-electron microscopy to investigate the three-dimensional structure of amyloid fibrils from full-length hnRNPA1 protein. We find that the fibril core is formed by a 45-residue segment of the prion-like low-complexity domain of the protein, whereas the remaining parts of the protein (275 residues) form a fuzzy coat around the fibril core. The fibril consists of two fibril protein stacks that are arranged into a pseudo-21 screw symmetry. The ordered core harbors several of the positions that are known to be affected by disease-associated mutations, but does not encompass the most aggregation-prone segments of the protein. These data indicate that the structures of amyloid fibrils from full-length proteins may be more complex than anticipated by current theories on protein misfolding.

Investigating the aggregation perspective of Dengue virus proteome

Dengue viruses are human pathogens that are transmitted through mosquitoes. Apart from the typical symptoms associated with viral fevers, DENV infections are known to cause several neurological complications such as meningitis, encephalitis, intracranial haemorrhage, retinopathies along with the more severe, and sometimes fatal, vascular leakage and dengue shock syndrome. This study was designed to investigate, in detail, the predicted viral protein aggregation prone regions among all serotypes. Further, in order to understand the cross-talk between viral protein aggregation and aggregation of cellular proteins, cross-seeding experiments between the DENV NS1 (1-30), corresponding to the β-roll domain and the diabetes hallmark protein, amylin, were performed. Various techniques such as fluorescence spectroscopy, circular dichroism, atomic force microscopy and immunoblotting have been employed for this. We observe that the DENV proteomes have many predicted APRs and the NS1 (1-30) of DENV1-3, 2K and capsid anchor of DENV2 and DENV4 are capable of forming amyloids, in vitro. Further, the DENV NS1 (1-30), aggregates are also able to cross-seed and enhance amylin aggregation and vice-versa. This knowledge may lead to an opportunity for designing suitable inhibitors of protein aggregation that may be beneficial for viral infections and comorbidities.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains

Over 120 FDA-approved antibody-based therapeutics are used to treat a variety of diseases.However, many candidates could fail because of unfavorable physicochemical properties. Light-chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with a less amyloidosis risk at the early stage can not only save the time and cost of antibody development but also improve the safety of antibody drugs. In this study, based on the dipeptide composition of 742 amyloidogenic and 712 non-amyloidogenic antibody light chains, a support vector machine-based model, AB-Amy, was trained to predict the light-chain amyloidogenic risk. The AUC of AB-Amy reaches 0.9651. The excellent performance of AB-Amy indicates that it can be a useful tool for the in silico evaluation of the light-chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development. A web server is freely available at http://i.uestc.edu.cn/AB-Amy/.

The validation of artificial anti-monkeypox antibodies by in silico and experimental approaches

As a result of smallpox immunization programs that ended more than 40 years ago, a significant portion of the world's population is not immune. Moreover, due to the lack of anti-monkeypox drugs and vaccines against monkeypox, the spread of this virus may be the beginning of another challenge. In this study, novel antibodies against monkeypox virus were modeled based on a heavy chain of human antibody and a small peptide fragment. Docking of modeled antibodies with C19L protein showed the range of docking energy, and root-mean-square deviation (RMSD) was from -124 to -154 kcal/mL and 4-6 angstrom, respectively. Also, docking of modeled antibodies-C19L complex with gamma Fc receptor type I illustrated the range of docking energy, and RMSD was from -132 to -155 kcal/ml and 5-7 angstrom, respectively. Moreover, molecular dynamics simulation showed that antibody 62 had the highest stability with the lowest energy level and RMSD. Interestingly, no modeled antibodies had immunogenicity, allergenicity, and toxicity. Although all of them had good stability, only antibodies 25, 28, 54, and 62 had a half-life of >10 h. Moreover, the interaction between C19L protein and anti-C19L antibodies (wild-type and synthetic) was evaluated by the SPR method. We found that KD in synthetic antibodies was lower than wild antibody. In terms of δH°, TδS°, and δG°, the results were consistent with binding parameters. Here, the lowest value of thermodynamic parameters was obtained for antibody 62. These data show that the synthetic antibodies, especially antibody 62, had a higher affinity than the wild-type antibody.

Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis

Cardiac amyloidosis is an uncommon restrictive cardiomyopathy featuring an unregulated amyloid protein deposition that impairs organic function. Early cardiac amyloidosis diagnosis is generally delayed by indistinguishable clinical findings of more frequent hypertrophic diseases. Furthermore, amyloidosis is divided into various groups, according to a generally accepted taxonomy, based on the proteins that make up the amyloid deposits; a careful differentiation between the various forms of amyloidosis is necessary to undertake an adequate therapeutic treatment. Thus, cardiac amyloidosis is thought to be underdiagnosed, which delays necessary therapeutic procedures, diminishing quality of life and impairing clinical prognosis. The diagnostic work-up for cardiac amyloidosis begins with the identification of clinical features, electrocardiographic and imaging findings suggestive or compatible with cardiac amyloidosis, and often requires the histological demonstration of amyloid deposition. One approach to overcome the difficulty of an early diagnosis is the use of automated diagnostic algorithms. Machine learning enables the automatic extraction of salient information from “raw data” without the need for pre-processing methods based on the a priori knowledge of the human operator. This review attempts to assess the various diagnostic approaches and artificial intelligence computational techniques in the detection of cardiac amyloidosis.

Amyloidogenic proteins in the SARS-CoV and SARS-CoV-2 proteomes

The phenomenon of protein aggregation is associated with a wide range of human diseases. Our knowledge of the aggregation behaviour of viral proteins, however, is still rather limited. Here, we investigated this behaviour in the SARS-CoV and SARS-CoV-2 proteomes. An initial analysis using a panel of sequence-based predictors suggested the presence of multiple aggregation-prone regions (APRs) in these proteomes and revealed a strong aggregation propensity in some SARS-CoV-2 proteins. We then studied the in vitro aggregation of predicted aggregation-prone SARS-CoV and SARS-CoV-2 proteins and protein regions, including the signal sequence peptide and fusion peptides 1 and 2 of the spike protein, a peptide from the NSP6 protein, and the ORF10 and NSP11 proteins. Our results show that these peptides and proteins can form amyloid aggregates. We used circular dichroism spectroscopy to reveal the presence of β-sheet rich cores in aggregates and X-ray diffraction and Raman spectroscopy to confirm the formation of amyloid structures. Furthermore, we demonstrated that SARS-CoV-2 NSP11 aggregates are toxic to mammalian cell cultures. These results motivate further studies about the possible role of aggregation of SARS proteins in protein misfolding diseases and other human conditions.

The Strategies of Development of New Non-Toxic Inhibitors of Amyloid Formation

In recent years, due to the aging of the population and the development of diagnostic medicine, the number of identified diseases associated with the accumulation of amyloid proteins has increased. Some of these proteins are known to cause a number of degenerative diseases in humans, such as amyloid-beta (Aβ) in Alzheimer's disease (AD), α-synuclein in Parkinson's disease (PD), and insulin and its analogues in insulin-derived amyloidosis. In this regard, it is important to develop strategies for the search and development of effective inhibitors of amyloid formation. Many studies have been carried out aimed at elucidating the mechanisms of amyloid aggregation of proteins and peptides. This review focuses on three amyloidogenic peptides and proteins-Aβ, α-synuclein, and insulin-for which we will consider amyloid fibril formation mechanisms and analyze existing and prospective strategies for the development of effective and non-toxic inhibitors of amyloid formation. The development of non-toxic inhibitors of amyloid will allow them to be used more effectively for the treatment of diseases associated with amyloid.

A guide to studying protein aggregation

Disrupted protein folding or decreased protein stability can lead to the accumulation of (partially) un- or misfolded proteins, which ultimately cause the formation of protein aggregates. Much of the interest in protein aggregation is associated with its involvement in a wide range of human diseases and the challenges it poses for large-scale biopharmaceutical manufacturing and formulation of therapeutic proteins and peptides. On the other hand, protein aggregates can also be functional, as observed in nature, which triggered its use in the development of biomaterials or therapeutics as well as for the improvement of food characteristics. Thus, unmasking the various steps involved in protein aggregation is critical to obtain a better understanding of the underlying mechanism of amyloid formation. This knowledge will allow a more tailored development of diagnostic methods and treatments for amyloid-associated diseases, as well as applications in the fields of new (bio)materials, food technology and therapeutics. However, the complex and dynamic nature of the aggregation process makes the study of protein aggregation challenging. To provide guidance on how to analyse protein aggregation, in this review we summarize the most commonly investigated aspects of protein aggregation with some popular corresponding methods.