PASTA 2.0: an improved server for protein aggregation prediction

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.

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.

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.

Sequence-Based Prediction of Protein Phase Separation: The Role of Beta-Pairing Propensity

The formation of droplets of bio-molecular condensates through liquid-liquid phase separation (LLPS) of their component proteins is a key factor in the maintenance of cellular homeostasis. Different protein properties were shown to be important in LLPS onset, making it possible to develop predictors, which try to discriminate a positive set of proteins involved in LLPS against a negative set of proteins not involved in LLPS. On the other hand, the redundancy and multivalency of the interactions driving LLPS led to the suggestion that the large conformational entropy associated with non specific side-chain interactions is also a key factor in LLPS. In this work we build a LLPS predictor which combines the ability to form pi-pi interactions, with an unrelated feature, the propensity to stabilize the β-pairing interaction mode. The cross-β structure is formed in the amyloid aggregates, which are involved in degenerative diseases and may be the final thermodynamically stable state of protein condensates. Our results show that the combination of pi-pi and β-pairing propensity yields an improved performance. They also suggest that protein sequences are more likely to be involved in phase separation if the main chain conformational entropy of the β-pairing maintained droplet state is increased. This would stabilize the droplet state against the more ordered amyloid state. Interestingly, the entropic stabilization of the droplet state appears to proceed according to different mechanisms, depending on the fraction of "droplet-driving" proteins present in the positive set.

Sequence patterns and signatures: Computational and experimental discovery of amyloid-forming peptides

Screening amino acid sequence space via experiments to discover peptides that self-assemble into amyloid fibrils is challenging. We have developed a computational peptide assembly design (PepAD) algorithm that enables the discovery of amyloid-forming peptides. Discontinuous molecular dynamics (DMD) simulation with the PRIME20 force field combined with the FoldAmyloid tool is used to examine the fibrilization kinetics of PepAD-generated peptides. PepAD screening of ∼10,000 7-mer peptides resulted in twelve top-scoring peptides with two distinct hydration properties. Our studies revealed that eight of the twelve in silico discovered peptides spontaneously form amyloid fibrils in the DMD simulations and that all eight have at least five residues that the FoldAmyloid tool classifies as being aggregation-prone. Based on these observations, we re-examined the PepAD-generated peptides in the sequence pool returned by PepAD and extracted five sequence patterns as well as associated sequence signatures for the 7-mer amyloid-forming peptides. Experimental results from Fourier transform infrared spectroscopy (FTIR), thioflavin T (ThT) fluorescence, circular dichroism (CD), and transmission electron microscopy (TEM) indicate that all the peptides predicted to assemble in silico assemble into antiparallel β-sheet nanofibers in a concentration-dependent manner. This is the first attempt to use a computational approach to search for amyloid-forming peptides based on customized settings. Our efforts facilitate the identification of β-sheet-based self-assembling peptides, and contribute insights towards answering a fundamental scientific question: "What does it take, sequence-wise, for a peptide to self-assemble?".

Analysis of Sheep and Goat IAPP Provides Insight into IAPP Amyloidogenicity and Cytotoxicity

Human islet amyloid polypeptide (hIAPP) plays a role in glucose regulation but forms pancreatic amyloid deposits in type 2 diabetes, and that process contributes to β-cell dysfunction. Not all species develop diabetes, and not all secrete an IAPP that is amyloidogenic in vitro under normal conditions, a perfect correlation currently exists between both. Studies of IAPPs from such organisms can provide clues about the high amyloidogenicity of hIAPP and can inform the design of soluble analogues of hIAPP. Sheep and goat IAPP are among the most divergent from hIAPP, with 13 and 11 substitutions, respectively, including an unusual Tyr to His substitution at the C-terminus. The properties of sheep and goat IAPP were examined in solution and in the presence of anionic vesicles, resulting in no observed amyloid formation, even at increased concentrations. Furthermore, both peptides are considerably less toxic to cultured β-cells than hIAPP. The effect of the Y37H replacements was studied in the context of hIAPP, as was a Y37R substitution. Buffer- and salt-dependent effects were observed. There was little impact on the time to form amyloid in phosphate-buffered saline; however, a significant deceleration was observed in Tris buffer, and amyloid formation was slower in the absence of added salt. The Y37H substitution had little impact on toxicity, while the Y37R replacement led to a 30% decrease in toxicity compared with that of hIAPP. The implications for the amyloidogenicity of hIAPP and the design of soluble analogues of the human peptide are discussed.

Bioinformatics tools and data resources for assay development of fluid protein biomarkers

Fluid protein biomarkers are important tools in clinical research and health care to support diagnosis and to monitor patients. Especially within the field of dementia, novel biomarkers could address the current challenges of providing an early diagnosis and of selecting trial participants. While the great potential of fluid biomarkers is recognized, their implementation in routine clinical use has been slow. One major obstacle is the often unsuccessful translation of biomarker candidates from explorative high-throughput techniques to sensitive antibody-based immunoassays. In this review, we propose the incorporation of bioinformatics into the workflow of novel immunoassay development to overcome this bottleneck and thus facilitate the development of novel biomarkers towards clinical laboratory practice. Due to the rapid progress within the field of bioinformatics many freely available and easy-to-use tools and data resources exist which can aid the researcher at various stages. Current prediction methods and databases can support the selection of suitable biomarker candidates, as well as the choice of appropriate commercial affinity reagents. Additionally, we examine methods that can determine or predict the epitope - an antibody’s binding region on its antigen - and can help to make an informed choice on the immunogenic peptide used for novel antibody production. Selected use cases for biomarker candidates help illustrate the application and interpretation of the introduced tools.

The Increased Amyloidogenicity of Spike RBD and pH-Dependent Binding to ACE2 May Contribute to the Transmissibility and Pathogenic Properties of SARS-CoV-2 Omicron as Suggested by In Silico Study

SARS-CoV-2 is a rapidly evolving pathogen that has caused a global pandemic characterized by several consecutive waves. Based on epidemiological and NGS data, many different variants of SARS-CoV-2 were described and characterized since the original variant emerged in Wuhan in 2019. Notably, SARS-CoV-2 variants differ in transmissibility and pathogenicity in the human population, although the molecular basis for this difference is still debatable. A significant role is attributed to amino acid changes in the binding surface of the Spike protein to the ACE2 receptor, which may facilitate virus entry into the cell or contribute to immune evasion. We modeled in silico the interaction between Spike RBDs of Wuhan-Hu-1, Delta, and Omicron BA.1 variants and ACE2 at different pHs (pH 5 and pH 7) and showed that the strength of this interaction was higher for the Omicron BA.1 RBD compared to Wuhan-Hu-1 or Delta RBDs and that the effect was more profound at pH 5. This finding is strikingly related to the increased ability of Omicron variants to spread in the population. We also noted that during its spread in the population, SARS-CoV-2 evolved to a more charged, basic composition. We hypothesize that the more basic surface of the Omicron variant may facilitate its spread in the upper respiratory tract but not in the lower respiratory tract, where pH estimates are different. We calculated the amyloidogenic properties of Spike RBDs in different SARS-CoV-2 variants and found eight amyloidogenic regions in the Spike RBDs for each of the variants predicted by the FoldAmyloid program. Although all eight regions were almost identical in the Wuhan to Gamma variants, two of them were significantly longer in both Omicron variants, making the Omicron RBD more amyloidogenic. We discuss how the increased predicted amyloidogenicity of the Omicron variants RBDs may be important for protein stability, influence its interaction with ACE2 and contribute to immune evasion.

The Difference in Structural States between Canonical Proteins and Their Isoforms Established by Proteome-Wide Bioinformatics Analysis

Alternative splicing is an important means of generating the protein diversity necessary for cellular functions. Hence, there is a growing interest in assessing the structural and functional impact of alternative protein isoforms. Typically, experimental studies are used to determine the structures of the canonical proteins ignoring the other isoforms. Therefore, there is still a large gap between abundant sequence information and meager structural data on these isoforms. During the last decade, significant progress has been achieved in the development of bioinformatics tools for structural and functional annotations of proteins. Moreover, the appearance of the AlphaFold program opened up the possibility to model a large number of high-confidence structures of the isoforms. In this study, using state-of-the-art tools, we performed in silico analysis of 58 eukaryotic proteomes. The evaluated structural states included structured domains, intrinsically disordered regions, aggregation-prone regions, and tandem repeats. Among other things, we found that the isoforms have fewer signal peptides, transmembrane regions, or tandem repeat regions in comparison with their canonical counterparts. This could change protein function and/or cellular localization. The AlphaFold modeling demonstrated that frequently isoforms, having differences with the canonical sequences, still can fold in similar structures though with significant structural rearrangements which can lead to changes of their functions. Based on the modeling, we suggested classification of the structural differences between canonical proteins and isoforms. Altogether, we can conclude that a majority of isoforms, similarly to the canonical proteins are under selective pressure for the functional roles.

An additive destabilising effect of compound T60I and V122I substitutions in ATTRv amyloidosis

BACKGROUND

The amyloidogenic transthyretin (TTR) variant, V122I, occurs in 4% of the African American population and frequently presents as a restricted cardiomyopathy. While heterozygosity for TTR V122I predominates, several compound heterozygous cases have been previously described. Herein, we detail features of ATTRv amyloidosis associated with novel compound heterozygous TTR mutation, T60I/V122I and provide evidence supporting the amyloidogenecity of T60I.

METHODS

A 63-year-old African American female presented with atrial fibrillation, congestive heart failure, autonomic and peripheral neuropathy. In vitro studies of TTR T60I and V122I were undertaken to compare the biophysical properties of the proteins.

RESULTS

Congophilic deposits in a rectal biopsy were immunohistochemically positive for TTR. Serum screening by isoelectric focussing revealed two TTR variants in the absence of wild-type protein. DNA sequencing identified compound heterozygous TTR gene mutations, c.239C > T and c.424G > A. Adipose amyloid deposits were composed of both T60I and V122I. While kinetic stabilities of T60I and V122I variants were similar, distinct thermodynamic stabilities and amyloid growth kinetics were observed.

CONCLUSIONS

This report provides clinical and experimental results supporting the amyloidogenic nature of a novel TTR T60I variant. In vitro data indicate that the destabilising effect of individual T60I and V122I variants appears to be additive rather than synergistic.

Astrocytic GABA transporter 1 deficit in novel SLC6A1 variants mediated epilepsy: Connected from protein destabilization to seizures in mice and humans

OBJECTIVE

Mutations in γ-aminobutyric acid (GABA) transporter 1 (GAT-1)-encoding SLC6A1 have been associated with myoclonic atonic epilepsy and other phenotypes. We determined the patho-mechanisms of the mutant GAT-1, in order to identify treatment targets.

METHODS

We conducted whole-exome sequencing of patients with myoclonic atonic epilepsy (MAE) and characterized the seizure phenotypes and EEG patterns. We studied the protein stability and structural changes with homology modeling and machine learning tools. We characterized the function and trafficking of the mutant GAT-1 with 3H radioactive GABA uptake assay and confocal microscopy. We utilized different models including a knockin mouse and human astrocytes derived from induced pluripotent stem cells (iPSCs). We focused on astrocytes because of their direct impact of astrocytic GAT-1 in seizures.

RESULTS

We identified four novel SLC6A1 variants associated with MAE and 2 to 4 Hz spike-wave discharges as a common EEG feature. Machine learning tools predicted that the variant proteins are destabilized. The variant protein had reduced expression and reduced GABA uptake due to endoplasmic reticular retention. The consistent observation was made in cortical and thalamic astrocytes from variant-knockin mice and human iPSC-derived astrocytes. The Slc6a+/A288V mouse, representative of MAE, had increased 5-7 Hz spike-wave discharges and absence seizures.

INTERPRETATION

SLC6A1 variants in various locations of the protein peptides can cause MAE with similar seizure phenotypes and EEG features. Reduced GABA uptake is due to decreased functional GAT-1, which, in thalamic astrocytes, could result in increased extracellular GABA accumulation and enhanced tonic inhibition, leading to seizures and abnormal EEGs.

Human RAD51 Protein Forms Amyloid-like Aggregates In Vitro

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

Identifying the molecular drivers of ALS-implicated missense mutations

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a progressively fatal, neurodegenerative disease associated with both motor and non-motor symptoms, including frontotemporal dementia. Approximately 10% of cases are genetically inherited (familial ALS), while the majority are sporadic. Mutations across a wide range of genes have been associated; however, the underlying molecular effects of these mutations and their relation to phenotypes remain poorly explored.

METHODS

We initially curated an extensive list (n=1343) of missense mutations identified in the clinical literature, which spanned across 111 unique genes. Of these, mutations in genes SOD1, FUS and TDP43 were analysed using in silico biophysical tools, which characterised changes in protein stability, interactions, localisation and function. The effects of pathogenic and non-pathogenic mutations within these genes were statistically compared to highlight underlying molecular drivers.

RESULTS

Compared with previous ALS-dedicated databases, we have curated the most extensive missense mutation database to date and observed a twofold increase in unique implicated genes, and almost a threefold increase in the number of mutations. Our gene-specific analysis identified distinct molecular drivers across the different proteins, where SOD1 mutations primarily reduced protein stability and dimer formation, and those in FUS and TDP-43 were present within disordered regions, suggesting different mechanisms of aggregate formation.

CONCLUSION

Using our three genes as case studies, we identified distinct insights which can drive further research to better understand ALS. The information curated in our database can serve as a resource for similar gene-specific analyses, further improving the current understanding of disease, crucial for the development of treatment strategies.

Protein Design: From the Aspect of Water Solubility and Stability

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

HfaE Is a Component of the Holdfast Anchor Complex That Tethers the Holdfast Adhesin to the Cell Envelope

Bacteria use adhesins to colonize different surfaces and form biofilms. The species of the Caulobacterales order use a polar adhesin called holdfast, composed of polysaccharides, proteins, and DNA, to irreversibly adhere to surfaces. In Caulobacter crescentus, a freshwater Caulobacterales species, the holdfast is anchored at the cell pole via the holdfast anchor (Hfa) proteins HfaA, HfaB, and HfaD. HfaA and HfaD colocalize with holdfast and are thought to form amyloid-like fibers that anchor holdfast to the cell envelope. HfaB, a lipoprotein, is required for the translocation of HfaA and HfaD to the cell surface. Deletion of the anchor proteins leads to a severe defect in adherence resulting from holdfast not being properly attached to the cell and shed into the medium. This phenotype is greater in a ΔhfaB mutant than in a ΔhfaA ΔhfaD double mutant, suggesting that HfaB has other functions besides the translocation of HfaA and HfaD. Here, we identify an additional HfaB-dependent holdfast anchoring protein, HfaE, which is predicted to be a secreted protein. HfaE is highly conserved among Caulobacterales species, with no predicted function. In planktonic culture, hfaE mutants produce holdfasts and rosettes similar to those produced by the wild type. However, holdfasts from hfaE mutants bind to the surface but are unable to anchor cells, similarly to other anchor mutants. We showed that fluorescently tagged HfaE colocalizes with holdfast and that HfaE forms an SDS-resistant high-molecular-weight species consistent with amyloid fiber formation. We propose that HfaE is a novel holdfast anchor protein and that HfaE functions to link holdfast material to the cell envelope. IMPORTANCE For surface attachment and biofilm formation, bacteria produce adhesins that are composed of polysaccharides, proteins, and DNA. Species of the Caulobacterales produce a specialized polar adhesin, holdfast, which is required for permanent attachment to surfaces. In this study, we evaluate the role of a newly identified holdfast anchor protein, HfaE, in holdfast anchoring to the cell surface in two different members of the Caulobacterales with drastically different environments. We show that HfaE plays an important role in adhesion and biofilm formation in the Caulobacterales. Our results provide insights into bacterial adhesins and how they interact with the cell envelope and surfaces.

Comparison of hydrophobicity scales for predicting biophysical properties of antibodies

While antibody-based therapeutics have grown to be one of the major classes of novel medicines, some antibody development candidates face significant challenges regarding expression levels, solubility, as well as stability and aggregation, under physiological and storage conditions. A major determinant of those properties is surface hydrophobicity, which promotes unspecific interactions and has repeatedly proven problematic in the development of novel antibody-based drugs. Multiple computational methods have been devised for in-silico prediction of antibody hydrophobicity, often using hydrophobicity scales to assign values to each amino acid. Those approaches are usually validated by their ability to rank potential therapeutic antibodies in terms of their experimental hydrophobicity. However, there is significant diversity both in the hydrophobicity scales and in the experimental methods, and consequently in the performance of in-silico methods to predict experimental results. In this work, we investigate hydrophobicity of monoclonal antibodies using hydrophobicity scales. We implement several scoring schemes based on the solvent-accessibility and the assigned hydrophobicity values, and compare the different scores and scales based on their ability to predict retention times from hydrophobic interaction chromatography. We provide an overview of the strengths and weaknesses of several commonly employed hydrophobicity scales, thereby improving the understanding of hydrophobicity in antibody development. Furthermore, we test several datasets, both publicly available and proprietary, and find that the diversity of the dataset affects the performance of hydrophobicity scores. We expect that this work will provide valuable guidelines for the optimization of biophysical properties in future drug discovery campaigns.

Membrane fluidity, composition, and charge affect the activity and selectivity of the AMP ascaphin-8

Ascaphins are cationic antimicrobial peptides that have been shown to have potential in the treatment of infectious diseases caused by multidrug-resistant pathogens (MDR). However, to date, their principal molecular target and mechanism of action are unknown. Results from peptide prediction software and molecular dynamics simulations confirmed that ascaphin-8 is an alpha-helical peptide. For the first time, the peptide was described as membranotrophic using biophysical approaches including calcein liposome leakage, Laurdan general polarization, and dynamic light scattering. Ascaphin-8's activity and selectivity were modulated by rearranging the spatial distribution of lysine (Var-K5), aspartic acid (Var-D4) residues, or substitution of phenylalanine with tyrosine (Var-Y). The parental peptide and its variants presented high affinity toward the bacterial membrane model (≤2 μM), but lost activity in sterol-enriched membranes (mammal and fungal models, with cholesterol and ergosterol, respectively). The peptide-induced pore size was estimated to be >20 nm in the bacterial model, with no difference among peptides. The same pattern was observed in membrane fluidity (general polarization) assays, where all peptides reduced membrane fluidity of the bacterial model but not in the models containing sterols. The peptides also showed high activity toward MDR bacteria. Moreover, peptide sensitivity of the artificial membrane models compared with pathogenic bacterial isolates were in good agreement.

NMR Reveals Functionally Relevant Thermally Induced Structural Changes within the Native Ensemble of G-CSF

Structure–function relationships in proteins refer to a trade-off between stability and bioactivity, molded by evolution of the molecule. Identifying which protein amino acid residues jeopardize global or local stability for the benefit of bioactivity would reveal residues pivotal to this structure–function trade-off. Here, we use ¹⁵N–¹H heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectroscopy to probe the microenvironment and dynamics of residues in granulocyte colony-stimulating factor (G-CSF) through thermal perturbation. From this analysis, we identified four residues (G4, A6, T133, and Q134) that we classed as significant to global stability, given that they all experienced large environmental and dynamic changes and were closely correlated to each other in their NMR characteristics. Additionally, we observe that roughly four structural clusters are subject to localized conformational changes or partial unfolding prior to global unfolding at higher temperature. Combining NMR observables with structure relaxation methods reveals that these structural clusters concentrate around loop AB (binding site III inclusive). This loop has been previously implicated in conformational changes that result in an aggregation prone state of G-CSF. Residues H43, V48, and S63 appear to be pivotal to an opening motion of loop AB, a change that is possibly also important for function. Hence, we present here an approach to profiling residues in order to highlight their potential roles in the two vital characteristics of proteins: stability and bioactivity.

Bivalent binding of p14ARF to MDM2 RING and acidic domains inhibits E3 ligase function

ARF tumor suppressor protein is a key regulator of the MDM2-p53 signaling axis. ARF interferes with MDM2-mediated ubiquitination and degradation of p53 by sequestering MDM2 in the nucleolus and preventing MDM2-p53 interaction and nuclear export of p53. Moreover, ARF also directly inhibits MDM2 ubiquitin ligase (E3) activity, but the mechanism remains elusive. Here, we apply nuclear magnetic resonance and biochemical analyses to uncover the mechanism of ARF-mediated inhibition of MDM2 E3 activity. We show that MDM2 acidic and zinc finger domains (AD-ZnF) form a weak intramolecular interaction with the RING domain, where the binding site overlaps with the E2∼ubiquitin binding surface and thereby partially reduces MDM2 E3 activity. Binding of human N-terminal 32 residues of p14ARF to the acidic domain of MDM2 strengthens the AD-ZnF-RING domain interaction. Furthermore, the N-terminal RxFxV motifs of p14ARF participate directly in the MDM2 RING domain interaction. This bivalent binding mode of p14ARF to MDM2 acidic and RING domains restricts E2∼ubiquitin recruitment and massively hinders MDM2 E3 activity. These findings elucidate the mechanism by which ARF inhibits MDM2 E3 activity.