PASTA 2.0: an improved server for protein aggregation prediction

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.

ENAM Mutations Can Cause Hypomaturation Amelogenesis Imperfecta

Amelogenesis imperfecta (AI) is a diverse group of inherited diseases featured by various presentations of enamel malformations that are caused by disturbances at different stages of enamel formation. While hypoplastic AI suggests a thickness defect of enamel resulting from aberrations during the secretory stage of amelogenesis, hypomaturation AI indicates a deficiency of enamel mineralization and hardness established at the maturation stage. Mutations in ENAM, which encodes the largest enamel matrix protein, enamelin, have been demonstrated to cause generalized or local hypoplastic AI. Here, we characterized 2 AI families with disparate hypoplastic and hypomaturation enamel defects and identified 2 distinct indel mutations at the same location of ENAM, c588+1del and c.588+1dup. Minigene splicing assays demonstrated that they caused frameshifts and truncation of ENAM proteins, p.Asn197Ilefs*81 and p.Asn197Glufs*25, respectively. In situ hybridization of Enam on mouse mandibular incisors confirmed its restricted expression in secretory stage ameloblasts and suggested an indirect pathogenic mechanism underlying hypomaturation AI. In silico analyses indicated that these 2 truncated ENAMs might form amyloid structures and cause protein aggregation with themselves and with wild-type protein through the added aberrant region at their C-termini. Consistently, protein secretion assays demonstrated that the truncated proteins cannot be properly secreted and impede secretion of wild-type ENAM. Moreover, compared to the wild-type, overexpression of the mutant proteins significantly increased endoplasmic reticulum stress and upregulated the expression of unfolded protein response (UPR)-related genes and TNFRSF10B, a UPR-controlled proapoptotic gene. Caspase, terminal deoxynucleotidyl transferase UTP nick-end labeling (TUNEL), and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assays further revealed that both truncated proteins, especially p.Asn197Ilefs*81, induced cell apoptosis and decreased cell survival, suggesting that the 2 ENAM mutations cause AI through ameloblast cell pathology and death rather than through a simple loss of function. This study demonstrates that an ENAM mutation can lead to generalized hypomaturation enamel defects and suggests proteinopathy as a potential pathogenesis for ENAM-associated AI.

The effects of histidine substitution of aromatic residues on the amyloidogenic properties of the fragment 264-277 of nucleophosmin 1

Histidine (His) plays a key role in mediating protein interactions and its unique side chain determines pH responsive self-assembling processes and thus in the formation of nanostructures. In this study, To identify novel self-assembling bioinspired sequences, we analyzed a series of peptide sequences obtained through the point mutation of aromatic residues of 264-277 fragment of nucleophosmin 1 (NPM1) with single and double histidines. Through several orthogonal biophysical techniques and under different pH and ionic strength conditions we evaluated the effects of these substitutions in the amyloidogenic features of derived peptides. The results clearly indicate that both the type of aromatic mutated residue and its position can have different effect on amyloid-like behaviors. They corroborate the crucial role exerted by Tyr271 in the self-assembling process of CTD of NPM1 in AML mutated form and add novel insights in the accurate investigation of how side chain orientations can determine successful design of innovative bioinspired materials.

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.

CORDAX web server: an online platform for the prediction and 3D visualization of aggregation motifs in protein sequences

MOTIVATION

Proteins, the molecular workhorses of biological systems, execute a multitude of critical functions dictated by their precise three-dimensional structures. In a complex and dynamic cellular environment, proteins can undergo misfolding, leading to the formation of aggregates that take up various forms, including amorphous and ordered aggregation in the shape of amyloid fibrils. This phenomenon is closely linked to a spectrum of widespread debilitating pathologies, such as Alzheimer's disease, Parkinson's disease, type-II diabetes, and several other proteinopathies, but also hampers the engineering of soluble agents, as in the case of antibody development. As such, the accurate prediction of aggregation propensity within protein sequences has become pivotal due to profound implications in understanding disease mechanisms, as well as in improving biotechnological and therapeutic applications.

RESULTS

We previously developed Cordax, a structure-based predictor that utilizes logistic regression to detect aggregation motifs in protein sequences based on their structural complementarity to the amyloid cross-beta architecture. Here, we present a dedicated web server interface for Cordax. This online platform combines several features including detailed scoring of sequence aggregation propensity, as well as 3D visualization with several customization options for topology models of the structural cores formed by predicted aggregation motifs. In addition, information is provided on experimentally determined aggregation-prone regions that exhibit sequence similarity to predicted motifs, scores, and links to other predictor outputs, as well as simultaneous predictions of relevant sequence propensities, such as solubility, hydrophobicity, and secondary structure propensity.

AVAILABILITY AND IMPLEMENTATION

The Cordax webserver is freely accessible at https://cordax.switchlab.org/.

Formation of Calprotectin Inhibits Amyloid Aggregation of S100A8 and S100A9 Proteins

Calcium-binding S100A8 and S100A9 proteins play a significant role in various disorders due to their pro-inflammatory functions. Substantially, they are also relevant in neurodegenerative disorders via the delivery of signals for the immune response. However, at the same time, they can aggregate and accelerate the progression of diseases. Natively, S100A8 and S100A9 exist as homo- and heterodimers, but upon aggregation, they form amyloid-like oligomers, fibrils, or amorphous aggregates. In this study, we aimed to elucidate the aggregation propensities of S100A8, S100A9, and their heterodimer calprotectin by investigating aggregation kinetics, secondary structures, and morphologies of the aggregates. For the first time, we followed the in vitro aggregation of S100A8, which formed spherical aggregates, unlike the fibrillar structures of S100A9 under the same conditions. The aggregates were sensitive to amyloid-specific ThT and ThS dyes and had a secondary structure composed of β-sheets. Similarly to S100A9, S100A8 protein was stabilized by calcium ions, resulting in aggregation inhibition. Finally, the formation of S100A8 and S100A9 heterodimers stabilized the proteins in the absence of calcium ions and prevented their aggregation.

Molecular Dynamics Insights into the Aggregation Behavior of N-Terminal β-Lactoglobulin Peptides

β-lactoglobulin (BLG) forms amyloid-like aggregates at high temperatures, low pH, and low ionic strengths. At a pH below 2, BLG undergoes hydrolysis into peptides, with N-terminal peptides 1-33 and 1-52 being prone to fibrillization, forming amyloid-like fibrils. Due to their good mechanical properties, BLG amyloids demonstrate great potential for diverse applications, including biosensors, nanocomposites, and catalysts. Consequently, further studies are essential to comprehensively understand the factors governing the formation of BLG amyloid-like morphologies. In this study, all-atom molecular dynamics simulations were employed to explore the aggregation of N-terminal 1-33 and 1-52 BLG peptides under conditions of pH 2 and at 10 mM NaCl concentration. The simulations revealed that the peptides spontaneously assembled into aggregates of varying sizes. The aggregation process was enabled by the low charge of peptides and the presence of hydrophobic residues within them. As the peptides associated into aggregates, there was a concurrent increase in β-sheet structures and the establishment of hydrogen bonds, enhancing the stability of the aggregates. Notably, on average, 1-33 peptides formed larger aggregates compared to their 1-52 counterparts, while the latter exhibited a slightly higher content of β-sheets and higher cluster orderliness. The applied approach facilitated insights into the early stages of amyloid-like aggregation and molecular-level insight into the formation of β-sheets, which serve as nucleation points for further fibril growth.

Interaction and simulation studies suggest the possible molecular targets of intrinsically disordered amyloidogenic antimicrobial peptides in Acinetobacter baumannii

Acinetobacter baumannii is one of the causing agents of nosocomial infections. A wide range of antibiotics fails to work against these pathogens. Hence, there is an urgent requirement to develop other therapeutics to solve this problem. Antimicrobial peptides (AMPs) are a diverse group of naturally occurring peptides that have the ability to kill diverse groups of microorganisms. The major challenge of using AMPs as therapeutics is their unstable nature and the fact that most of their molecular targets are still unknown. In this study, we have selected intrinsically disordered and amyloidogenic AMPs, showing activity against A. baumannii, that is, Bactenecin, Cath BF, Citropin 1.1, DP7, NA-CATH, Tachyplesin, and WAM-1. To identify the probable target of these AMPs in A. baumannii, calculation of docking score, binding energy, dissociation constant, and molecular dynamics analysis was performed with selected seventeen possible molecular targets. The result showed that the most probable molecular targets of most of the intrinsically disordered amyloidogenic AMPs were UDP-N-acetylenol-pyruvoyl-glucosamine reductase (MurB), followed by 33-36 kDa outer membrane protein (Omp 33-36), UDP-N-acetylmuramoyl-l-alanyl-d-glutamate-2,6-diaminopimelate ligase (MurE), and porin Subfamily Protein (PorinSubF). Further, molecular dynamics analysis concluded that the target of antimicrobial peptide Bactenecin is MurB of A. baumannii, and identified other molecular targets of selected AMPs. Additionally, the oligomerization capacity of the selected AMPs was also investigated, and it was shown that the selected AMPs form oligomeric states, and interact with their molecular targets in that state. Experimental validation using purified AMPs and molecular targets needs to be done to confirm the interaction.Communicated by Ramaswamy H. Sarma.

Structural determinants of odorant-binding proteins affecting their ability to form amyloid fibrils

The formation of amyloid fibrils is associated with many severe pathologies as well as the execution of essential physiological functions by proteins. Despite the diversity, all amyloids share a similar morphology and consist of stacked β-strands, suggesting high amyloidogenicity of native proteins enriched with β-structure. Such proteins include those with a β-barrel-like structure with β-strands arranged into a cylindrical β-sheet. However, the mechanisms responsible for destabilization of the native state and triggering fibrillogenesis have not thoroughly explored yet. Here we analyze the structural determinants of fibrillogenesis in proteins with β-barrel structures on the example of odorant-binding protein (OBP), whose amyloidogenicity was recently demonstrated in vitro. We reveal a crucial role in the fibrillogenesis of OBPs for the "open" conformation of the molecule. This conformation is achieved by disrupting the interaction between the β-barrel and the C-terminus of protein monomers or dimers, which exposes "sticky" amyloidogenic sites for interaction. The data suggest that the "open" conformation of OBPs can be induced by destabilizing the native β-barrel structure through the disruption of: 1) intramolecular disulfide cross-linking and non-covalent contacts between the C-terminal fragment and β-barrel in the protein's monomeric form, or 2) intermolecular contacts involved in domain swapping in the protein's dimeric form.

Influenza A virus propagation requires the activation of the unfolded protein response and the accumulation of insoluble protein aggregates

Influenza A virus (IAV) employs multiple strategies to manipulate cellular mechanisms and support proper virion formation and propagation. In this study, we performed a detailed analysis of the interplay between IAV and the host cells' proteostasis throughout the entire infectious cycle. We reveal that IAV infection activates the inositol requiring enzyme 1 (IRE1) branch of the unfolded protein response, and that this activation is important for an efficient infection. We further observed the accumulation of virus-induced insoluble protein aggregates, containing both viral and host proteins, associated with a dysregulation of the host cell RNA metabolism. Our data indicate that this accumulation is important for IAV propagation and favors the final steps of the infection cycle, more specifically the virion assembly. These findings reveal additional mechanisms by which IAV disrupts host proteostasis and uncovers new cellular targets that can be explored for the development of host-directed antiviral strategies.

Essential factors, advanced strategies, challenges, and approaches involved for efficient expression of recombinant proteins in Escherichia coli

Producing recombinant proteins is a major accomplishment of biotechnology in the past century. Heterologous hosts, either eukaryotic or prokaryotic, are used for the production of these proteins. The utilization of microbial host systems continues to dominate as the most efficient and affordable method for biotherapeutics and food industry productions. Hence, it is crucial to analyze the limitations and advantages of microbial hosts to enhance the efficient production of recombinant proteins on a large scale. E. coli is widely used as a host for the production of recombinant proteins. Researchers have identified certain obstacles with this host, and given the growing demand for recombinant protein production, there is an immediate requirement to enhance this host. The following review discusses the elements contributing to the manifestation of recombinant protein. Subsequently, it sheds light on innovative approaches aimed at improving the expression of recombinant protein. Lastly, it delves into the obstacles and optimization methods associated with translation, mentioning both cis-optimization and trans-optimization, producing soluble recombinant protein, and engineering the metal ion transportation. In this context, a comprehensive description of the distinct features will be provided, and this knowledge could potentially enhance the expression of recombinant proteins in E. coli.

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

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

Computational Screening for mAb Colloidal Stability with Coarse-Grained, Molecular-Scale Simulations

Monoclonal antibodies (mAbs) are an important modality of protein therapeutics with broad applications for numerous diseases. However, colloidal instabilities occurring at high protein concentrations can limit the ability to develop stable, high-concentration liquid dosage forms that are required for patient-centric, device-mediated products. Therefore, it is advantageous to identify colloidally stable mAbs early in the discovery process to ensure that they are selected for development. Experimental screening for colloidal stability can be time- and resource-consuming and is most feasible at the later stages of drug development due to material requirements. Alternatively, computational approaches have emerging potential to provide efficient screening and focus developmental efforts on mAbs with the greatest developability potential, while providing mechanistic relationships for colloidal instability. In this work, coarse-grained, molecular-scale models were fine-tuned to screen for colloidal stability at amino-acid resolution. This model parameterization provides a framework to screen for mAb self-interactions and extrapolate to bulk solution behavior. This approach was applied to a wide array of mAbs under multiple buffer conditions, demonstrating the utility of the presented computational approach to augment early candidate screening and later formulation strategies for protein therapeutics.

Structural studies of catalytic peptides using molecular dynamics simulations

Many self-assembling peptides can form amyloid like structures with different sizes and morphologies. Driven by non-covalent interactions, their aggregation can occur through distinct pathways. Additionally, they can bind metal ions to create enzyme like active sites that allow them to catalyze diverse reactions. Due to the non-crystalline nature of amyloids, it is quite challenging to elucidate their structures using experimental spectroscopic techniques. In this aspect, molecular dynamics (MD) simulations provide a useful tool to derive structures of these macromolecules in solution. They can be further validated by comparing with experimentally measured structural parameters. However, these simulations require a multi-step process starting from the selection of the initial structure to the analysis of MD trajectories. There are multiple force fields, parametrization protocols, equilibration processes, software and analysis tools available for this process. Therefore, it is complicated for non-experts to select the most relevant tools and perform these simulations effectively. In this chapter, a systematic methodology that covers all major aspects of modeling of catalytic peptides is provided in a user-friendly manner. It will be helpful for researchers in this critical area of research.

Four classic "de novo" genes all have plausible homologs and likely evolved from retro-duplicated or pseudogenic sequences

Despite being previously regarded as extremely unlikely, the idea that entirely novel protein-coding genes can emerge from non-coding sequences has gradually become accepted over the past two decades. Examples of "de novo origination", resulting in lineage-specific "orphan" genes, lacking coding orthologs, are now produced every year. However, many are likely cases of duplicates that are difficult to recognize. Here, I re-examine the claims and show that four very well-known examples of genes alleged to have emerged completely "from scratch"- FLJ33706 in humans, Goddard in fruit flies, BSC4 in baker's yeast and AFGP2 in codfish-may have plausible evolutionary ancestors in pre-existing genes. The first two are likely highly diverged retrogenes coding for regulatory proteins that have been misidentified as orphans. The antifreeze glycoprotein, moreover, may not have evolved from repetitive non-genic sequences but, as in several other related cases, from an apolipoprotein that could have become pseudogenized before later being reactivated. These findings detract from various claims made about de novo gene birth and show there has been a tendency not to invest the necessary effort in searching for homologs outside of a very limited syntenic or phylostratigraphic methodology. A robust approach is used for improving detection that draws upon similarities, not just in terms of statistical sequence analysis, but also relating to biochemistry and function, to obviate notable failures to identify homologs.

The blobulator: a webtool for identification and visual exploration of hydrophobic modularity in protein sequences

Motivation

Clusters of hydrophobic residues are known to promote structured protein stability and drive protein aggregation. Recent work has shown that identifying contiguous hydrophobic residue clusters (termed "blobs") has proven useful in both intrinsically disordered protein (IDP) simulation and human genome studies. However, a graphical interface was unavailable.

Results

Here, we present the blobulator: an interactive and intuitive web interface to detect intrinsic modularity in any protein sequence based on hydrophobicity. We demonstrate three use cases of the blobulator and show how identifying blobs with biologically relevant parameters provides useful information about a globular protein, two orthologous membrane proteins, and an IDP. Other potential applications are discussed, including: predicting protein segments with critical roles in tertiary interactions, providing a definition of local order and disorder with clear edges, and aiding in predicting protein features from sequence.

Availability

The blobulator GUI can be found at www.blobulator.branniganlab.org, and the source code with pip installable command line tool can be found on GitHub at www.GitHub.com/BranniganLab/blobulator.

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.

Biophysical evolution of the receptor-binding domains of SARS-CoVs

With hundreds of coronaviruses (CoVs) identified in bats that can infect humans, it is essential to understand how CoVs that affected the human population have evolved. Seven known CoVs have infected humans, of which three CoVs caused severe disease with high mortalities: severe acute respiratory syndrome (SARS)-CoV emerged in 2002, Middle East respiratory syndrome-CoV in 2012, and SARS-CoV-2 in 2019. SARS-CoV and SARS-CoV-2 belong to the same family, follow the same receptor pathway, and use their receptor-binding domain (RBD) of spike protein to bind to the angiotensin-converting enzyme 2 (ACE2) receptor on the human epithelial cell surface. The sequence of the two RBDs is divergent, especially in the receptor-binding motif that directly interacts with ACE2. We probed the biophysical differences between the two RBDs in terms of their structure, stability, aggregation, and function. Since RBD is being explored as an antigen in protein subunit vaccines against CoVs, determining these biophysical properties will also aid in developing stable protein subunit vaccines. Our results show that, despite RBDs having a similar three-dimensional structure, they differ in their thermodynamic stability. RBD of SARS-CoV-2 is significantly less stable than that of SARS-CoV. Correspondingly, SARS-CoV-2 RBD shows a higher aggregation propensity. Regarding binding to ACE2, less stable SARS-CoV-2 RBD binds with a higher affinity than more stable SARS-CoV RBD. In addition, SARS-CoV-2 RBD is more homogenous in terms of its binding stoichiometry toward ACE2 compared to SARS-CoV RBD. These results indicate that SARS-CoV-2 RBD differs from SARS-CoV RBD in terms of its stability, aggregation, and function, possibly originating from the diverse receptor-binding motifs. Higher aggregation propensity and decreased stability of SARS-CoV-2 RBD warrant further optimization of protein subunit vaccines that use RBD as an antigen by inserting stabilizing mutations or formulation screening.

Butyrylcholinesterase signal sequence self-aggregates and enhances amyloid fibril formation in vitro

Alzheimer's disease (AD) pathogenesis has been attributed to extracellular aggregates of amyloid β (Aβ) plaques and neurofibrillary tangles in the human brain. It has been reported that butyrylcholinesterase (BChE) also accumulates in the brain Aβ plaques in AD. We have previously found that the BChE substitution in 5'UTR caused an in-frame N-terminal extension of 41 amino acids of the BChE signal peptide. The resultant variant with a 69 amino acid signal peptide, designated N-BChE, could play a role in AD development. Here, we report that the signal sequence of the BChE, if produced in an extended 69 aa version, can self-aggregate and could form seeds that enhance amyloid fibril formation in vitro in a dose-dependent manner and create larger co-aggregates. Similar phenomena could have been observed in the human brain if such an extended form of the signal sequence had been, in some circumstances, translated.

Optimized peptide nanofibrils as efficient transduction enhancers for in vitro and ex vivo gene transfer

Chimeric antigen receptor (CAR)-T cell therapy is a groundbreaking immunotherapy for cancer. However, the intricate and costly manufacturing process remains a hurdle. Improving the transduction rate is a potential avenue to cut down costs and boost therapeutic efficiency. Peptide nanofibrils (PNFs) serve as one such class of transduction enhancers. PNFs bind to negatively charged virions, facilitating their active engagement by cellular protrusions, which enhances virion attachment to cells, leading to increased cellular entry and gene transfer rates. While first-generation PNFs had issues with aggregate formation and potential immunogenicity, our study utilized in silico screening to identify short, endogenous, and non-immunogenic peptides capable of enhancing transduction. This led to the discovery of an 8-mer peptide, RM-8, which forms PNFs that effectively boost T cell transduction rates by various retroviral vectors. A subsequent structure-activity relationship (SAR) analysis refined RM-8, resulting in the D4 derivative. D4 peptide is stable and assembles into smaller PNFs, avoiding large aggregate formation, and demonstrates superior transduction rates in primary T and NK cells. In essence, D4 PNFs present an economical and straightforward nanotechnological tool, ideal for refining ex vivo gene transfer in CAR-T cell production and potentially other advanced therapeutic applications.

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.

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

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

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.

ATP-independent substrate recruitment to proteasomal degradation in mycobacteria

Mycobacteria and other actinobacteria possess proteasomal degradation pathways in addition to the common bacterial compartmentalizing protease systems. Proteasomal degradation plays a crucial role in the survival of these bacteria in adverse environments. The mycobacterial proteasome interacts with several ring-shaped activators, including the bacterial proteasome activator (Bpa), which enables energy-independent degradation of heat shock repressor HspR. However, the mechanism of substrate selection and processing by the Bpa-proteasome complex remains unclear. In this study, we present evidence that disorder in substrates is required but not sufficient for recruitment to Bpa-mediated proteasomal degradation. We demonstrate that Bpa binds to the folded N-terminal helix-turn-helix domain of HspR, whereas the unstructured C-terminal tail of the substrate acts as a sequence-specific threading handle to promote efficient proteasomal degradation. In addition, we establish that the heat shock chaperone DnaK, which interacts with and co-regulates HspR, stabilizes HspR against Bpa-mediated proteasomal degradation. By phenotypical characterization of Mycobacterium smegmatis parent and bpa deletion mutant strains, we show that Bpa-dependent proteasomal degradation supports the survival of the bacterium under stress conditions by degrading HspR that regulates vital chaperones.

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.

AggBERT: Best in Class Prediction of Hexapeptide Amyloidogenesis with a Semi-Supervised ProtBERT Model

The prediction of peptide amyloidogenesis is a challenging problem in the field of protein folding. Large language models, such as the ProtBERT model, have recently emerged as powerful tools in analyzing protein sequences for applications, such as predicting protein structure and function. In this article, we describe the use of a semisupervised and fine-tuned ProtBERT model to predict peptide amyloidogenesis from sequences alone. Our approach, which we call AggBERT, achieved state-of-the-art performance, demonstrating the potential for large language models to improve the accuracy and speed of amyloid fibril prediction over simple heuristics or structure-based approaches. This work highlights the transformative potential of machine learning and large language models in the fields of chemical biology and biomedicine.

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.

A3DyDB: exploring structural aggregation propensities in the yeast proteome

BACKGROUND

The budding yeast Saccharomyces cerevisiae (S. cerevisiae) is a well-established model system for studying protein aggregation due to the conservation of essential cellular structures and pathways found across eukaryotes. However, limited structural knowledge of its proteome has prevented a deeper understanding of yeast functionalities, interactions, and aggregation.

RESULTS

In this study, we introduce the A3D yeast database (A3DyDB), which offers an extensive catalog of aggregation propensity predictions for the S. cerevisiae proteome. We used Aggrescan 3D (A3D) and the newly released protein models from AlphaFold2 (AF2) to compute the structure-based aggregation predictions for 6039 yeast proteins. The A3D algorithm exploits the information from 3D protein structures to calculate their intrinsic aggregation propensities. To facilitate simple and intuitive data analysis, A3DyDB provides a user-friendly interface for querying, browsing, and visualizing information on aggregation predictions from yeast protein structures. The A3DyDB also allows for the evaluation of the influence of natural or engineered mutations on protein stability and solubility. The A3DyDB is freely available at http://biocomp.chem.uw.edu.pl/A3D2/yeast .

CONCLUSION

The A3DyDB addresses a gap in yeast resources by facilitating the exploration of correlations between structural aggregation propensity and diverse protein properties at the proteome level. We anticipate that this comprehensive database will become a standard tool in the modeling of protein aggregation and its implications in budding yeast.

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.

Spike Protein Fragments Promote Alzheimer's Amyloidogenesis

Alzheimer's disease (AD) is a major cause of dementia inducing memory loss, cognitive decline, and mortality among the aging population. While the amyloid aggregation of peptide Aβ has long been implicated in neurodegeneration in AD, primarily through the production of toxic polymorphic aggregates and reactive oxygen species, viral infection has a less explicit role in the etiology of the brain disease. On the other hand, while the COVID-19 pandemic is known to harm human organs and function, its adverse effects on AD pathobiology and other human conditions remain unclear. Here we first identified the amyloidogenic potential of 1058HGVVFLHVTYV1068, a short fragment of the spike protein of SARS-CoV-2 coronavirus. The peptide fragment was found to be toxic and displayed a high binding propensity for the amyloidogenic segments of Aβ, thereby promoting the aggregation and toxicity of the peptide in vitro and in silico, while retarding the hatching and survival of zebrafish embryos upon exposure. Our study implicated SARS-CoV-2 viral infection as a potential contributor to AD pathogenesis, a little explored area in our quest for understanding and overcoming Long Covid.

Chaotic aging: intrinsically disordered proteins in aging-related processes

The development of aging is associated with the disruption of key cellular processes manifested as well-established hallmarks of aging. Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) have no stable tertiary structure that provide them a power to be configurable hubs in signaling cascades and regulate many processes, potentially including those related to aging. There is a need to clarify the roles of IDPs/IDRs in aging. The dataset of 1702 aging-related proteins was collected from established aging databases and experimental studies. There is a noticeable presence of IDPs/IDRs, accounting for about 36% of the aging-related dataset, which is however less than the disorder content of the whole human proteome (about 40%). A Gene Ontology analysis of the used here aging proteome reveals an abundance of IDPs/IDRs in one-third of aging-associated processes, especially in genome regulation. Signaling pathways associated with aging also contain IDPs/IDRs on different hierarchical levels, revealing the importance of "structure-function continuum" in aging. Protein-protein interaction network analysis showed that IDPs present in different clusters associated with different aging hallmarks. Protein cluster with IDPs enrichment has simultaneously high liquid-liquid phase separation (LLPS) probability, "nuclear" localization and DNA-associated functions, related to aging hallmarks: genomic instability, telomere attrition, epigenetic alterations, and stem cells exhaustion. Intrinsic disorder, LLPS, and aggregation propensity should be considered as features that could be markers of pathogenic proteins. Overall, our analyses indicate that IDPs/IDRs play significant roles in aging-associated processes, particularly in the regulation of DNA functioning. IDP aggregation, which can lead to loss of function and toxicity, could be critically harmful to the cell. A structure-based analysis of aging and the identification of proteins that are particularly susceptible to disturbances can enhance our understanding of the molecular mechanisms of aging and open up new avenues for slowing it down.

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.

Census of exposed aggregation-prone regions in proteomes

Loss of solubility usually leads to the detrimental elimination of protein function. In some cases, the protein aggregation is also required for beneficial functions. Given the duality of this phenomenon, it remains a fundamental question how natural selection controls the aggregation. The exponential growth of genomic sequence data and recent progress with in silico predictors of the aggregation allows approaching this problem by a large-scale bioinformatics analysis. Most of the aggregation-prone regions are hidden within the 3D structure, rendering them inaccessible for the intermolecular interactions responsible for aggregation. Thus, the most realistic census of the aggregation-prone regions requires crossing aggregation prediction with information about the location of the natively unfolded regions. This allows us to detect so-called 'exposed aggregation-prone regions' (EARs). Here, we analyzed the occurrence and distribution of the EARs in 76 reference proteomes from the three kingdoms of life. For this purpose, we used a bioinformatics pipeline, which provides a consensual result based on several predictors of aggregation. Our analysis revealed a number of new statistically significant correlations about the presence of EARs in different organisms, their dependence on protein length, cellular localizations, co-occurrence with short linear motifs and the level of protein expression. We also obtained a list of proteins with the conserved aggregation-prone sequences for further experimental tests. Insights gained from this work led to a deeper understanding of the relationship between protein evolution and aggregation.

SARS-CoV-2 vaccine breakthrough infections (VBI) by Omicron variant (B.1.1.529) and consequences in structural and functional impact

This study investigated the efficacy of existing vaccines against hospitalization and infection due to the Omicron variant of COVID-19, particularly for those who received two doses of Moderna or Pfizer vaccines and one dose of Johnson & Johnson vaccine or who were vaccinated more than five months before. A total of 36 variants in Omicron's spike protein, targeted by all three vaccinations, have made antibodies less effective at neutralizing the virus. The genotyping of the SARS-CoV-2 viral sequence revealed clinically significant variants such as E484K in three genetic mutations (T95I, D614G, and del142-144). A woman showed two of these mutations, indicating a potential risk of infection after successful immunization, as recently reported by Hacisuleyman (2021). We examine the effects of mutations on domains (NID, RBM, and SD2) found at the interfaces of the spike domains Omicron B.1.1529, Delta/B.1.1529, Alpha/B.1.1.7, VUM B.1.526, B.1.575.2, and B.1.1214 (formerly VOI Iota). We tested the affinity of Omicron for ACE2 and found that the wild- and mutant-spike proteins were using atomistic molecular dynamics simulations. According to the binding free energies calculated during mutagenesis, the ACE2 bound Omicron spikes more strongly than the wild strain SARS-CoV-2. T95I, D614G, and E484K are three substitutions that significantly contribute to RBD, corresponding to ACE2 binding energies and a doubling of the electrostatic potential of Omicron spike proteins. The Omicron appears to bind to ACE2 with greater affinity, increasing its infectivity and transmissibility. The spike virus was designed to strengthen antibody immune evasion through binding while boosting receptor binding by enhancing IgG and IgM antibodies that stimulate human β-cell, as opposed to the wild strain, which has more vital stimulation of both antibodies.

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/.

Sequence-targeted Peptides Divert Functional Bacterial Amyloid Towards Destabilized Aggregates and Reduce Biofilm Formation

Functional bacterial amyloid provides structural stability in biofilm, making it a promising target for anti-biofilm therapeutics. Fibrils formed by CsgA, the major amyloid component in E. coli are extremely robust and can withstand very harsh conditions. Like other functional amyloids, CsgA contains relatively short aggregation-prone regions (APR) which drive amyloid formation. Here, we demonstrate the use of aggregation-modulating peptides to knock down CsgA protein into aggregates with low stability and altered morphology. Remarkably, these CsgA-peptides also modulate fibrillation of the unrelated functional amyloid protein FapC from Pseudomonas, possibly through recognition of FapC segments with structural and sequence similarity with CsgA. The peptides also reduce the level of biofilm formation in E. coli and P. aeruginosa, demonstrating the potential for selective amyloid targeting to combat bacterial biofilm.

Mechanistic Insight into the Suppression of Polyglutamine Aggregation by SRCP1

Protein aggregation is a hallmark of the polyglutamine diseases. One potential treatment for these diseases is suppression of polyglutamine aggregation. Previous work identified the cellular slime mold Dictyostelium discoideum as being naturally resistant to polyglutamine aggregation. Further work identified serine-rich chaperone protein 1 (SRCP1) as a protein that is both necessary in Dictyostelium and sufficient in human cells to suppress polyglutamine aggregation. Therefore, understanding how SRCP1 suppresses aggregation may be useful for developing therapeutics for the polyglutamine diseases. Here we utilized a de novo protein modeling approach to generate predictions of SRCP1's structure. Using our best-fit model, we generated mutants that were predicted to alter the stability of SRCP1 and tested these mutants' stability in cells. Using these data, we identified top models of SRCP1's structure that are consistent with the C-terminal region of SRCP1 forming a β-hairpin with a highly dynamic N-terminal region. We next generated a series of peptides that mimic the predicted β-hairpin and validated that they inhibit aggregation of a polyglutamine-expanded mutant huntingtin exon 1 fragment in vitro. To further assess mechanistic details of how SRCP1 inhibits polyglutamine aggregation, we utilized biochemical assays to determine that SRCP1 inhibits secondary nucleation in a manner dependent upon the regions flanking the polyglutamine tract. Finally, to determine if SRCP1 more could generally suppress protein aggregation, we confirmed that it was sufficient to inhibit aggregation of polyglutamine-expanded ataxin-3. Together these studies provide details into the structural and mechanistic basis of the inhibition of protein aggregation by SRCP1.

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

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

Identification of novel amyloidosis in dogs: α-S1-casein acquires amyloidogenicity in mammary tumor by overexpression and N-terminal truncation

Mammary tumor-associated amyloidosis (MTAA) in dogs is characterized by amyloid deposition in the stroma of mammary adenoma or carcinoma; however, the amyloid precursor protein remains unknown. We attempted to identify an amyloid precursor protein and elucidated its etiology by characterizing 5 cases of canine MTAA. Proteomic analyses of amyloid extracts from formalin-fixed paraffin-embedded specimens revealed α-S1-casein (CASA1) as a prime candidate and showed the N-terminal truncation of canine CASA1. Both immunohistochemistry and immunoelectron microscopy showed that amyloid deposits or fibrils in MTAA cases were positive for CASA1. Reverse transcription-polymerase chain reaction and quantitative polymerase chain reaction revealed the complete mRNA sequence encoding CASA1, whose expression was significantly higher in the amyloid-positive group. The recombinant protein of the N-terminal-truncated canine CASA1 and the synthetic peptides derived from canine and human CASA1 formed amyloid-like fibrils in vitro. Structural prediction suggested that the N-terminal region of CASA1 was disordered. Previously, full-length CASA1 was reported to inhibit the amyloidogenesis of other proteins; however, we demonstrated that CASA1 acquires amyloidogenicity via excessive synthesis followed by truncation of its disordered N-terminal region. By identifying a novel in vivo amyloidogenic protein in animals and revealing key mechanistic details of its associated pathology, this study provides valuable insights into the integrated understanding of related proteopathies.

Network of hotspot interactions cluster tau amyloid folds

Cryogenic electron microscopy has revealed unprecedented molecular insight into the conformations of β-sheet-rich protein amyloids linked to neurodegenerative diseases. It remains unknown how a protein can adopt a diversity of folds and form multiple distinct fibrillar structures. Here we develop an in silico alanine scan method to estimate the relative energetic contribution of each amino acid in an amyloid assembly. We apply our method to twenty-seven ex vivo and in vitro fibril structural polymorphs of the microtubule-associated protein tau. We uncover networks of energetically important interactions involving amyloid-forming motifs that stabilize the different fibril folds. We evaluate our predictions in cellular and in vitro aggregation assays. Using a machine learning approach, we classify the structures based on residue energetics to identify distinguishing and unifying features. Our energetic profiling suggests that minimal sequence elements control the stability of tau fibrils, allowing future design of protein sequences that fold into unique structures.

Autooxidation of curcumin in physiological buffer causes an enhanced synergistic anti-amyloid effect

Curcumin is an important food additive that shows multiple medical-benefits including anticarcinogenic, anti-inflammatory, antibiotic and antiamyloid properties. However, understanding the mechanism of curcumin-mediated effects becomes rather complicated since it has low bio-viability and it undergoes autooxidation, influenced by temperature, pH and buffer. We find that curcumin's antiamyloid-potential is not primarily due to curcumin alone, rather due to a synergistic action of curcumin and its autooxidized-products generated during inhibition reactions. In physiological buffer curcumin undergoes thermally induced autooxidation and yields stable compounds which can synergistically work for both inhibition of amyloid aggregation and promotion of amyloid-disaggregation into soluble protein species. Curcumin also showed substantial inhibition effect against coaggregation of different food proteins. Curcumin's strong affinity for the hydrophobic moieties of the aggregation-prone partially-folded insulin structures seems crucial for the inhibition mechanism. Further, autooxidized curcumin products were found to protect UV-induced protein damage. The results provide conceptual foundations highlighting the link between chemistry and antiamyloid-activity of curcumin and may inspire curcumin-based therapeutics against amyloidogenesis.

Widespread amyloidogenicity potential of multiple myeloma patient-derived immunoglobulin light chains

BACKGROUND

In a range of human disorders such as multiple myeloma (MM), immunoglobulin light chains (IgLCs) can be produced at very high concentrations. This can lead to pathological aggregation and deposition of IgLCs in different tissues, which in turn leads to severe and potentially fatal organ damage. However, IgLCs can also be highly soluble and non-toxic. It is generally thought that the cause for this differential solubility behaviour is solely found within the IgLC amino acid sequences, and a variety of individual sequence-related biophysical properties (e.g. thermal stability, dimerisation) have been proposed in different studies as major determinants of the aggregation in vivo. Here, we investigate biophysical properties underlying IgLC amyloidogenicity.

RESULTS

We introduce a novel and systematic workflow, Thermodynamic and Aggregation Fingerprinting (ThAgg-Fip), for detailed biophysical characterisation, and apply it to nine different MM patient-derived IgLCs. Our set of pathogenic IgLCs spans the entire range of values in those parameters previously proposed to define in vivo amyloidogenicity; however, none actually forms amyloid in patients. Even more surprisingly, we were able to show that all our IgLCs are able to form amyloid fibrils readily in vitro under the influence of proteolytic cleavage by co-purified cathepsins.

CONCLUSIONS

We show that (I) in vivo aggregation behaviour is unlikely to be mechanistically linked to any single biophysical or biochemical parameter and (II) amyloidogenic potential is widespread in IgLC sequences and is not confined to those sequences that form amyloid fibrils in patients. Our findings suggest that protein sequence, environmental conditions and presence and action of proteases all determine the ability of light chains to form amyloid fibrils in patients.

Keratinic amyloid deposition in canine hair follicle tumors

Keratinic primary localized cutaneous amyloidosis is a disease in humans; however, no similar condition has been reported in animals. This study aimed to investigate cutaneous keratinic amyloid deposition in dogs and elucidate its etiology. Canine hair follicle tumor tissues were histopathologically analyzed. Immunohistochemistry and mass spectrometry-based proteomic analyses were performed to identify precursor protein candidates. Structural prediction and in vitro fibrillization analyses were conducted to determine the amyloidogenic region and gene sequencing analysis was performed to assess mutations. Of the 266 samples, 16 had amyloid deposition. Amyloid deposits were found in the stroma of tumors and in the margins of keratin debris and around normal hair follicles. Cytokeratin 5 (CK5) was identified as a precursor protein candidate. C-terminal truncation of CK5 was observed in amyloid deposits, and the truncation sites varied depending on the deposition pattern. There was a significantly higher incidence of amyloid deposition in Shiba dogs, and CK5 amino acid polymorphisms were identified in these dogs. A part of the C-terminal region of both canine and human CK5 exhibited highly amyloidogenic properties in vitro. This study revealed the existence of cutaneous keratinic amyloid deposition in animals and identified CK5 as an amyloid precursor protein, providing novel insights into understanding the etiology of cutaneous amyloidosis.

ENTAIL: yEt aNoTher amyloid fIbrils cLassifier

BACKGROUND

This research aims to increase our knowledge of amyloidoses. These disorders cause incorrect protein folding, affecting protein functionality (on structure). Fibrillar deposits are the basis of some wellknown diseases, such as Alzheimer, Creutzfeldt-Jakob diseases and type II diabetes. For many of these amyloid proteins, the relative precursors are known. Discovering new protein precursors involved in forming amyloid fibril deposits would improve understanding the pathological processes of amyloidoses.

RESULTS

A new classifier, called ENTAIL, was developed using over than 4000 molecular descriptors. ENTAIL was based on the Naive Bayes Classifier with Unbounded Support and Gaussian Kernel Type, with an accuracy on the test set of 81.80%, SN of 100%, SP of 63.63% and an MCC of 0.683 on a balanced dataset.

CONCLUSIONS

The analysis carried out has demonstrated how, despite the various configurations of the tests, performances are superior in terms of performance on a balanced dataset.

Lysozyme amyloidosis-a report on a large German cohort and the characterisation of a novel amyloidogenic lysozyme gene variant

Lysozyme-derived (ALys) amyloidosis is a rare type of hereditary amyloidosis. Nine amyloidogenic variants and ∼30 affected families have been described worldwide. The most common manifestations are renal dysfunction, gastrointestinal tract symptoms, and sicca syndrome. We report on the clinical course of ten patients from six families representing one of the largest cohorts published so far. Seven patients carried the W64R variant showing the whole spectrum of ALys-associated symptoms. Two patients-a mother-son pair-carried a novel lysozyme variant, which was associated with nephropathy and peripheral polyneuropathy. In accordance with previous findings, the phenotype resembled within these families but did not correlate with the genotype. To gain insights into the effect of the variants at the molecular level, we analysed the structure of lysozyme and performed comparative computational predictions on aggregation propensity and conformational stability. Our study supports that decreased conformational stability is a key factor for lysozyme variants to be prone to aggregation. In summary, ALys amyloidosis is a very rare, but still heterogeneous disease that can manifest at an early age. Our newly identified lysozyme variant is associated with nephropathy and peripheral polyneuropathy. Further research is needed to understand its pathogenesis and to enable the development of new treatments.