Surface-bound biofilms are the predominant microbial life form in the environment and host organisms. Many biofilms survive and thrive under physical stress from liquid flow in streams, fuel lines, blood, and airways. Strategies for biofilm persistence include shear-dependent adhesion (called catch bonding). In some cases, biofilms are physically strengthened by the formation of cross-β bonds between proteins: the same process that generates amyloids. Cross-β bonds have low dissociation rates. In biofilms, they bind cells to substrates, each other, and the biofilm matrix. Most fungal adhesins include amino acid sequences that can form amyloids. Shear flow activates these adhesins by unfolding pseudo-stable protein domains. The unfolding exposes sequence segments that can form cross-β bonds. These segments interact to form high-avidity adhesin patches on the cell surface. Thus, cross-β bonding is a consequence of flow-induced exposure of the cross-β core sequences. Liquid flow leads to both biofilm establishment through catch bonding and biofilm strengthening through amyloid-like bonds. This shear-dependent induction of biofilm establishment and persistence is a model for many microbial systems.IMPORTANCEThe microbes in biofilms persist in many environments, including industrial and pathological settings. These surface-associated communities show high resistance to antibiotics and microbicides. Biofilms also resist scouring by liquid flow. Amyloid-like cross-β bonds allow the establishment, strengthening, and persistence of many biofilms. This discovery opens a window on the novel use of anti-amyloid strategies to control microbes in biofilms.

Protein misfolding and aggregation play a central role in the progression of neurodegenerative diseases such as Alzheimer's and Parkinson's. These aggregates manifest either as structured amyloid fibrils enriched in β-sheet conformations or as irregular amorphous aggregates with diverse morphologies. Understanding their formation, structure, and behavior is critical for deciphering disease mechanisms and developing targeted diagnostics and therapeutics. This review presents an integrated overview of both conventional and advanced techniques used to detect, distinguish, and structurally characterize these protein aggregates. It covers a range of spectroscopic and spectrometric tools, such as fluorescence, Raman, and mass spectrometry that facilitate aggregate identification. Microscopy methods, including atomic force and electron microscopy, are highlighted for morphological analysis. The review also discusses in situ detection strategies using fluorescent dyes, conformation-specific antibodies, enzymatic reporters, and real-time imaging. Separation methods like centrifugation, electrophoresis, and chromatography are outlined alongside structural analysis tools such as X-ray diffraction. Furthermore, the growing utility of computational approaches and artificial intelligence in predicting aggregation propensities and integrating biological data is emphasized. By critically evaluating each method's capabilities and limitations, this review provides a practical and forward-looking resource for researchers studying the complex landscape of protein aggregation.

The whey protein beta-lactoglobulin (β-lg) is used as a biotemplate for the water-based synthesis of nanostructured and foam-like titania films based on its variation in supramolecular structure when denatured at different pH values. Acting as a matrix, β-lg is mixed with the water-soluble titania precursor Ti(IV) bis(ammonium lactate)dihydroxide (TiBALDh) to promote biotemplated titania precipitation. Since TiBALDh is in chemical equilibrium with anatase titania nanoparticles and Ti(IV)-lactate complexes, and this equilibrium shifts with varying pH, the influence of the pH value on the final film morphology becomes essential. This work investigates this influence for three pH values: pH 7, pH 5, i.e., close to the isoelectric point of β-lg, and pH 2. Spray coating, a method of industrial relevance, is used to fabricate biohybrid β-lg:TiBALDh foam-like films. The obtained films are calcined to combust biotemplate β-lg and achieve nanostructured titania films. To understand the influence of pH on the film morphology, grazing-incidence small-angle and wide-angle X-ray scattering (GISAXS/GIWAXS) and grazing-incidence small-angle neutron scattering (GISANS), in combination with scanning electron microscopy (SEM), are applied to both the biohybrid and biotemplated titania films. With these techniques, information about domain sizes, porosity, and crystal structure is obtained with high statistical significance. Fourier-transform infrared spectroscopy (FTIR) probes the interaction of TiBALDh and β-lg on the molecular level as a function of pH. The results underline pH as a suitable tool for tuning the morphology in biotemplated titania films.

The concept that proteins are selected to fold into a well-defined native state has been effectively addressed within the framework of energy landscapes, underpinning the recent successes of structure prediction tools like AlphaFold. The amyloid fold, however, does not represent a unique minimum for a given single sequence. While the cross-β hydrogen-bonding pattern is common to all amyloids, other aspects of amyloid fiber structures are sensitive not only to the sequence of the aggregating peptides but also to the experimental conditions. This polymorphic nature of amyloid structures challenges structure predictions. In this paper, we use AI to explore the landscape of possible amyloid protofilament structures composed of a single stack of peptides aligned in a parallel, in-register manner. This perspective enables a practical method for predicting protofilament structures of arbitrary sequences: RibbonFold. RibbonFold is adapted from AlphaFold2, incorporating parallel in-register constraints within AlphaFold2's template module, along with an appropriate polymorphism loss function to address the structural diversity of folds. RibbonFold outperforms AlphaFold2/3 on independent test sets, achieving a mean TM-score of 0.5. RibbonFold proves well-suited to study the polymorphic landscapes of widely studied sequences with documented polymorphisms. The resulting landscapes capture these observed polymorphisms effectively. We show that while well-known amyloid-forming sequences exhibit a limited number of plausible polymorphs on their "solubility" landscape, randomly shuffled sequences with the same composition appear to be negatively selected in terms of their relative solubility. RibbonFold is a valuable framework for structurally characterizing amyloid polymorphism landscapes.

Molecular insight into amyloid aggregation is crucial for understanding the details of protein fibril nucleation and growth, which play a significant role in a wide range of proteinopathies. The length and time scales for fibrillization make its computational study an intrinsically multiscale problem, necessitating the use of coarse-grained modeling. A wide variety of coarse-grained models for peptides have been proposed, often parametrized with a combination of top-down and bottom-up approaches. Here, we present a predictive, sequence-transferable bottom-up coarse-grained model, systematically developed using only information from atomistic simulations by applying an extended-ensemble relative entropy minimization technique. The resulting model is capable of accurately recovering conformational properties of peptides constructed from a reduced alphabet of amino acids, of predicting secondary structures of isolated and interacting peptides from their sequences alone, and of simulating aggregation of peptides that have been experimentally characterized as amyloidogenic. Finally, we couple such coarse-grained simulations with a genetic algorithm to characterize the sequence space of the reduced alphabet and identify features of sequences for which ordered fibrillar states are both thermodynamically favorable and kinetically accessible.

Enzymes are critical biological catalysts involved in maintaining the intricate balance of metabolic processes within living organisms. Mutations in enzymes can result in disruptions to their functionality that may lead to a range of diseases. This review focuses on computational studies that investigate the effects of disease-associated mutations in various enzymes. Through molecular dynamics simulations, multiscale calculations, and machine learning approaches, computational studies provide detailed insights into how mutations impact enzyme structure, dynamics, and catalytic activity. This review emphasizes the increasing impact of computational simulations in understanding molecular mechanisms behind enzyme (dis)function by highlighting the application of key computational methodologies to selected enzyme examples, aiding in the prediction of mutation effects and the development of therapeutic strategies.

Protein aggregation is critical to various biological and pathological processes. Besides, it is also an important property in biotherapeutic development. However, experimental methods to profile protein aggregation are costly and labor-intensive, driving the need for more efficient computational alternatives. In this study, we introduce "AggNet," a novel deep learning framework based on the protein language model ESM2 and AlphaFold2, which utilizes physicochemical, evolutionary, and structural information to discriminate amyloid and non-amyloid peptides and identify aggregation-prone regions (APRs) in diverse proteins. Benchmark comparisons show that AggNet outperforms existing methods and achieves state-of-the-art performance on protein aggregation prediction. Also, the predictive ability of AggNet is stable across proteins with different secondary structures. Feature analysis and visualizations prove that the model effectively captures peptides' physicochemical properties effectively, thereby offering enhanced interpretability. Further validation through a case study on MEDI1912 confirms AggNet's practical utility in analyzing protein aggregation and guiding mutation for aggregation mitigation. This study enhances computational tools for predicting protein aggregation and highlights the potential of AggNet in protein engineering. Finally, to improve the accessibility of AggNet, the source code can be accessed at: https://github.com/Hill-Wenka/AggNet.

Advances in solid-state nuclear magnetic resonance (ssNMR) spectroscopy and cryogenic electron microscopy (cryoEM) have revealed the polymorphic nature of the amyloid state of proteins. Given the association of amyloid with protein misfolding disorders, it is important to understand the principles underlying this polymorphism. To address this problem, we combined computational tools to predict the specific regions of the sequence forming the β-spine of amyloid fibrils with the availability of 30, 83 and 24 amyloid structures deposited in the Protein Data Bank (PDB) and Amyloid Atlas (AAt) for the amyloid β (Aβ) peptide, α-synuclein (αS), and the 4R isoforms of tau, associated with Alzheimer's disease, Parkinson's disease, and various tauopathies, respectively. This approach enabled a statistical analysis of sequences forming β-sheet regions in amyloid polymorphs. We computed for any given sequence residue n the fraction of PDB/AAt structures in which that residue adopts a β-sheet conformation (Fβ(n)) to generate an experimental, structure-based profile of Fβ(n) vs n, which represents the β-conformational preference of any residue in the amyloid state. The peaks in the respective Fβ(n) profiles of the three proteins, corresponding to sequence regions adopting more frequently the β-sheet structural core in the various fibrillar structures, align very well with the peaks identified with five predictive algorithms (ZYGGREGATOR, TANGO, PASTA, AGGRESCAN, and WALTZ). These results indicate that, despite amyloid polymorphism, sequence regions most often forming the structural core of amyloid have high hydrophobicity, high intrinsic β-sheet propensity and low electrostatic charge across the sequence, as rationalised and predicted by the algorithms.

Therapeutic antibodies have emerged as a promising treatment option for a wide range of diseases. However, the light chain of antibodies can potentially induce amyloidosis, a condition characterized by protein misfolding and aggregation, posing a significant safety concern. Therefore, it is crucial to assess the amyloidogenic risk of therapeutic antibodies during the early stages of drug development. In this study, we introduce AB-Amy 2.0, a new computational model with enhanced performance for assessing the light chain amyloidogenic risk of therapeutic antibodies. By employing pretrained protein language models (PLMs) embeddings, AB-Amy 2.0 achieves higher accuracy in amyloidogenic risk prediction compared with traditional features offering a crucial tool for early-stage identification of antibodies with low aggregation propensity. The AB-Amy 2.0 was trained on antiBERTy embeddings and utilizes the SVM algorithm, resulting in superior performance metrics. On an independent test dataset, the model achieved high sensitivity, specificity, ACC, MCC and AUC of 93.47%, 89.23%, 91.92%, 0.8261 and 0.9739, respectively. These results highlight the effectiveness and robustness of AB-Amy 2.0 in predicting light chain amyloidogenic risk accurately. To facilitate user-friendly access, we have developed an online web server (http://i.uestc.edu.cn/AB-Amy2) and a command line tool (https://github.com/zzyywww/ABAmy2). These resources enable the broader application of this advanced model and promise to enhance the development of safer therapeutic antibodies.

Amyloidosis is characterized by the abnormal accumulation of misfolded proteins, called amyloid fibrils, leading to diverse clinical manifestations. Recent studies on the amyloidogenesis of SARS-CoV-2 protein segments have raised concerns on their potential link to post-infection neurodegeneration, however, the mechanisms remain unclear. Herein, we investigated the structure, stability, and amyloidogenic propensity of a nine-residue segment (SK9) of the SARS-CoV-2 envelope protein and their impact on neuronal protein α-synuclein (αSyn) aggregation. Specifically, the amino acid sequence of the SK9 wildtype has been modified from a basic and positively charged peptide (SFYVYSRVK), to a nearly neutral and more hydrophobic peptide (SAAVASAVK, labelled as SK9 var1), and to an acidic and negatively charged peptide (SDAVANAVK, labelled as SK9 var2). Our findings reveal that the SK9 wildtype exhibited a pronounced amyloidogenic propensity due to its disordered and unstable nature, while the SK9 variants possessed more ordered and stable structures preventing the amyloid formation. Significantly, the SK9 wildtype demonstrated distinct effect on αSyn aggregation kinetics and aggregate morphology to facilitate the formation of αSyn aggregates with enhanced resistance against enzymatic degradation. This study highlights the potential of modifying short peptide sequences to fine-tune their properties, providing insights into understanding and regulating viral-induced amyloid aggregations.

Protein aggregation is responsible for several degenerative conditions in humans, and it is also a bottleneck in industrial protein production and storage of biotherapeutics. Bioinformatics tools have been developed to predict and redesign protein solubility more efficiently by understanding the underlying principles behind aggregation. As more experimental data become available, dedicated resources for storing, indexing, classifying and consolidating experimental results have emerged. These resources vary in focus, including aggregation-prone regions, 3D patches or protein stretches capable of forming amyloid fibrils. Some of these resources also consider the experimental conditions that cause protein aggregation and how they affect the process. This review article explores how protein aggregation databases have evolved and surveys state-of-the-art resources. We highlight their applications, complementarity and existing limitations. Moreover, we showcase the existing symbiosis between amyloid-related databases and predictive tools. To increase the usefulness of our review, we supplement it with a comprehensive list of present and past amyloid databases: https://biogenies.info/amyloid-database-list/.

Peptide drugs play an essential role in modern therapeutics, but the computational design of these molecules is hindered by several challenges. Traditional methods like molecular docking and molecular dynamics (MD) simulation, as well as recent deep learning approaches, often face limitations related to computational resource demands, complex binding affinity assessments, extensive data requirements, and poor model interpretability. Here, we introduce PepHiRe, an innovative methodology that utilizes the hierarchical structural information in peptide sequences and employs a novel strategy called Ladderpath, rooted in algorithmic information theory, to rapidly generate and enhance the efficiency and clarity of novel peptide design. We applied PepHiRe to develop BH3-like peptide inhibitors targeting myeloid cell leukemia-1, a protein associated with various cancers. By analyzing just eight known bioactive BH3 peptide sequences, PepHiRe effectively derived a hierarchy of subsequences used to create new BH3-like peptides. These peptides underwent screening through MD simulations, leading to the selection of five candidates for synthesis and subsequent in vitro testing. Experimental results demonstrated that these five peptides possess high inhibitory activity, with IC50 values ranging from 28.13 ± 7.93 to 167.42 ± 22.15 nM. Our study explores a white-box model driven technique and a structured screening pipeline for identifying and generating novel peptides with potential bioactivity.

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

Gallibacterium anatis is a member of the Pasteurellaceae family and is an opportunistic pathogen that causes gallibacteriosis in chickens. Stress plays a relevant role in promoting the development of pathogenicity in G. anatis. Epinephrine (E) and norepinephrine (NE) are relevant to stress; however, their effects on G. anatis have not been elucidated. In this work, we evaluated the effects of E and NE on the growth, biofilm formation, expression of adhesins, and proteases of two G. anatis strains, namely, the hemolytic 12656-12 and the nonhemolytic F149T biovars. E (10 μM/mL) and NE (30 and 50 μM/mL) increased the growth of G. anatis 12656-12 by 20 % and 25 %, respectively. E did not affect the growth of F149T, whereas 40 μM/mL NE decreased bacterial growth by 25 %. E and NE at a dose of 30-50 μM/mL upregulated five fibrinogen adhesins in the 12565-12 strain, whereas no effect was observed in the F149T strain. NE increased proteolytic activity in both strains, whereas E diminished proteolytic activity in the 12656-12 strain. E and NE reduced biofilm formation (30 %) and increased Congo red binding (15 %) in both strains. QseBC is the E and NE two-component detection system most common in bacteria. The qseC gene, which is the E and NE receptor in bacteria, was identified in the genomic DNA of the 12565-12 and F149TG. anatis strains via PCR amplification. Our results suggest that QseC can detect host changes in E and NE concentrations and that catecholamines can modulate the expression of several virulence factors in G. anatis.

There is increasing evidence supporting the unique benefits of targeted therapy in treating atherosclerotic disease. Given the complex nature of atherosclerosis development, we proposed a novel strategy for the efficient delivery of rapamycin (RAPA) by targeting both the exposed subendothelial collagen and oxidized low-density lipoprotein (oxLDL) present in plaques. In response, we developed multifunctional peptide (MP) nanoparticles for targeted drug delivery. The ability of MP nanoparticles to load RAPA and target collagen/oxLDL was investigated through molecular dynamics simulations and in vitro experiments. The efficacy of MP nanoparticles in atherosclerosis treatment was assessed via in vivo experiments on ApoE-/- mice. Results indicate that MP nanoparticles have encapsulation and drug loading efficiencies for RAPA of 78.3 % and 43.9 %, respectively. By targeting collagen, MP nanoparticles create steric hindrance that inhibits 77.2 % of platelet adhesion. These nanoparticles can also target oxLDL, delivering RAPA into plaques and significantly reducing macrophage uptake of oxLDL. In vivo experiments showed that MP nanoparticles effectively targeted and accumulated in plaques. Treating mice with MP@RAPA nanoparticles for 10 weeks led to an 81.3 % reduction in the aortic vascular plaque area and decreased concentrations of MCP-1, hs-CRP, MMP-1, P-selectin, IL-1β, and IL-8 inflammatory factors, as well as the optical density of platelet-associated proteins (CD42, CD61, and PECAM-1). These results highlight the promising potential of MP nanoparticles for atherosclerotic disease treatment.

In classical amyloidoses, amyloid fibres form through the nucleation and accretion of protein monomers, with protofibrils and fibrils exhibiting a cross-β motif of parallel or antiparallel β-sheets oriented perpendicular to the fibre direction. These protofibrils and fibrils can intertwine to form mature amyloid fibres. Similar phenomena can occur in blood from individuals with circulating inflammatory molecules (and also some originating from viruses and bacteria). Such pathological clotting can result in an anomalous amyloid form termed fibrinaloid microclots. Previous proteomic analyses of these microclots have shown the presence of non-fibrin(ogen) proteins, suggesting a more complex mechanism than simple entrapment. We thus provide evidence against such a simple entrapment model, noting that clot pores are too large and centrifugation would have removed weakly bound proteins. Instead, we explore whether co-aggregation into amyloid fibres may involve axial (multiple proteins within the same fibril), lateral (single-protein fibrils contributing to a fibre), or both types of integration. Our analysis of proteomic data from fibrinaloid microclots in different diseases shows no significant quantitative overlap with the normal plasma proteome and no correlation between plasma protein abundance and their presence in fibrinaloid microclots. Notably, abundant plasma proteins like α-2-macroglobulin, fibronectin, and transthyretin are absent from microclots, while less abundant proteins such as adiponectin, periostin, and von Willebrand factor are well represented. Using bioinformatic tools, including AmyloGram and AnuPP, we found that proteins entrapped in fibrinaloid microclots exhibit high amyloidogenic tendencies, suggesting their integration as cross-β elements into amyloid structures. This integration likely contributes to the microclots' resistance to proteolysis. Our findings underscore the role of cross-seeding in fibrinaloid microclot formation and highlight the need for further investigation into their structural properties and implications in thrombotic and amyloid diseases. These insights provide a foundation for developing novel diagnostic and therapeutic strategies targeting amyloidogenic cross-seeding in blood clotting disorders.

Insulin and its analogue formulations are the main components in the therapy of insulin-dependent forms of diabetes. Insulin and its analogues can form amyloid-like aggregates during long-term storage and local concentration increases, leading to reduced therapeutic efficacy and potential side effects such as insulin amyloidosis. The aim of this study was to identify and propose new non-toxic inhibitors and preservatives to replace phenol in insulin formulations. The peptide FVNQH and phenol red were studied as promising inhibitors of fibril formation of insulin, lispro, and glargine in vitro using the specific amyloid dye thioflavin T. The peptide FVNQH and phenol red (0.5-1 mg/mL) showed a bacteriostatic effect on the E. coli K-12 strain after 24 h. The fibril-inhibiting and antimicrobial effects of these substances were similar to the effect of phenol at a concentration of 0.5 mg/mL. Thus, the identified inhibitors can potentially replace phenolic components in slowing amyloid aggregation and increase the stability of insulin and its analogues.

Leukodystrophies are rare genetic white matter disorders that have been regarded as mainly occurring in childhood. This perception has been altered in recent years, as a growing number of leukodystrophies have been described as having an onset in adulthood. Still, many adult patients presenting with white matter changes remain without a specific molecular diagnosis. We describe a novel adult onset leukodystrophy in 16 patients from eight families carrying one of four different stop-gain or frameshift dominant variants in the CST3 gene. Clinical and radiological features differ markedly from the previously described Icelandic cerebral amyloid angiopathy found in patients carrying p.Leu68Asn substitution in CST3. The clinical phenotype consists of recurrent episodes of hemiplegic migraine associated with transient unilateral focal deficits and slowly progressing motor symptoms and cognitive decline in mid to older adult ages. In addition, in some cases acute onset clinical deterioration led to a prolonged episode with reduced consciousness and even early death. Radiologically, pathognomonic changes are found at typical predilection sites involving the deep cerebral white matter sparing a periventricular and directly subcortical rim, the middle blade of corpus callosum, posterior limb of the internal capsule, middle cerebellar peduncles, cerebral peduncles and specifically the globus pallidus. Histopathologic characterization in two autopsy cases did not reveal angiopathy, but instead micro- to macrocystic degeneration of the white matter. Astrocytes were activated at early stages and later displayed severe degeneration and loss. In addition, despite the loss of myelin, elevated numbers of partly apoptotic oligodendrocytes were observed. A structural comparison of the variants in CST3 suggests that specific truncations of cystatin C result in an abnormal function, possibly by rendering the protein more prone to aggregation. Future studies are required to confirm the assumed effect on the protein and to determine pathophysiologic downstream events at the cellular level.

The tropomyosin 1 isoform I/C C-terminal domain (Tm1-LC) fibril structure is studied jointly with cryogenic electron microscopy (cryo-EM) and solid state nuclear magnetic resonance (NMR). This study demonstrates the complementary nature of these two structural biology techniques. Chemical shift assignments from solid state NMR are used to determine the secondary structure at the level of individual amino acids, which is faithfully seen in cryo-EM reconstructions. Additionally, solid state NMR demonstrates that the region not observed in the reconstructed cryo-EM density is primarily in a highly mobile random coil conformation rather than adopting multiple rigid conformations. Overall, this study illustrates the benefit of investigations combining cryo-EM and solid state NMR to investigate protein fibril structure.

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

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

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

ARID1B is the most frequently mutated gene in Coffin-Siris syndrome (CSS). To date, the vast majority of causative variants reported in ARID1B are truncating, leading to nonsense-mediated mRNA decay. In the absence of experimental data, only few ARID1B amino acid substitutions have been classified as pathogenic, mainly based on clinical data and their de novo occurrence, while most others are currently interpreted as variants of unknown significance. The present study substantiates the pathogenesis of ARID1B non-truncating/NMD-escaping variants located in the SMARCA4-interacting EHD2 and DNA-binding ARID domains. Overexpression assays in cell lines revealed that the majority of EHD2 variants lead to protein misfolding and formation of cytoplasmic aggresomes surrounded by vimentin cage-like structures and co-localizing with the microtubule organisation center. ARID domain variants exhibited not only aggresomes, but also nuclear aggregates, demonstrating robust pathological effects. Protein levels were not compromised, as shown by quantitative western blot analysis. In silico structural analysis predicted the exposure of amylogenic segments in both domains due to the nearby variants, likely causing this aggregation. Genome-wide transcriptome and methylation analysis in affected individuals revealed expression and methylome patterns consistent with those of the pathogenic haploinsufficiency ARID1B alterations in CSS cases. These results further support pathogenicity and indicate two approaches for disambiguation of such variants in everyday practice. The few affected individuals harbouring EHD2 non-truncating variants described to date exhibit mild CSS clinical traits. In summary, this study paves the way for the re-evaluation of previously unclear ARID1B non-truncating variants and opens a new era in CSS genetic diagnosis.

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

There have been significant advances in the formulation and stabilization of proteins in the liquid state over the past years since our previous review. Our mechanistic understanding of protein-excipient interactions has increased, allowing one to develop formulations in a more rational fashion. The field has moved towards more complex and challenging formulations, such as high concentration formulations to allow for subcutaneous administration and co-formulation. While much of the published work has focused on mAbs, the principles appear to apply to any therapeutic protein, although mAbs clearly have some distinctive features. In this review, we first discuss chemical degradation reactions. This is followed by a section on physical instability issues. Then, more specific topics are addressed: instability induced by interactions with interfaces, predictive methods for physical stability and interplay between chemical and physical instability. The final parts are devoted to discussions how all the above impacts (co-)formulation strategies, in particular for high protein concentration solutions.'

The Tropomyosin 1 isoform I/C C-terminal domain (Tm1-LC) fibril structure is studied jointly with cryogenic electron microscopy (cryo-EM) and solid state nuclear magnetic resonance (NMR). This study demonstrates the complementary nature of these two structural biology techniques. Chemical shift assignments from solid state NMR are used to determine the secondary structure at the level of individual amino acids, which is faithfully seen in cryo-EM reconstructions. Additionally, solid state NMR demonstrates that the region not observed in the reconstructed cryo-EM density is primarily in a highly mobile random coil conformation rather than adopting multiple rigid conformations. Overall, this study illustrates the benefit of investigations combining cryo-EM and solid state NMR to investigate protein fibril structure.

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.

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

We present in this article the PACSAB server, which is designed to provide information about the structural ensemble and interactions of both stable and disordered proteins to researchers in the field of molecular biology. The use of this tool does not require any computational skills as the user just needs to upload the structure of the protein to be studied; the server runs a simulation with the PACSAB model, a highly accurate coarse-grained model that is much more efficient than standard molecular dynamics for the exploration of the conformational space of multiprotein systems. The trajectories generated by the simulations based on this model reveal the propensity of the protein under study for aggregation, identify the residues playing a central role in the aggregation process, and reproduce the whole conformational space of disordered proteins. All of this information is shown and can be downloaded from the web page.

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.

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.

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

The inhibition of platelet adhesion to collagen in exposed vessels represents an innovative approach to the treatment of atherosclerosis and thrombosis. This study aimed to engineer peptide-based nanoparticles that prevent platelet binding to subendothelial collagen by engaging with collagen with high affinity. We examined the interactions between integrin α2/ glycoprotein VI/ von Willebrand factor A3 domain and collagen, as well as between the synthesized peptide nanoparticles and collagen, utilizing molecular dynamics simulations and empirical assays. Our findings indicated that the bond between von Willebrand factor and collagen was more robust. Specifically, the sequences SITTIDV, VDVMQRE, and YLTSEMH in von Willebrand factor were identified as essential for its attachment to collagen. Based on these sequences, three peptide nanoparticles were synthesized (BPa: Capric-GNNQQNYK-SITTIDV, BPb: Capric-GNNQQNYK-VDVMQRE, BPc: Capric-GNNQQNYK-YLTSEMH), each displaying significant affinity towards collagen. Of these, the BPa nanoparticles exhibited the most potent interaction with collagen, leading to a 75% reduction in platelet adhesion.

The scientific community is very interested in protein aggregation because of its involvement in several neurodegenerative diseases and its significance in industry. Remarkably, fibrillar aggregates are utilized naturally for constructing structural scaffolds or creating biological switches and may be intentionally designed to construct versatile nanomaterials. Consequently, there is a significant need to rationalize and predict protein aggregation. Researchers have developed various computational methodologies and algorithms to predict protein aggregation and understand its underlying mechanics. This chapter aims to summarize the significant advancements in computational methods, accessible resources, and prospective developments in the field of in silico research. We assess the existing computational tools for predicting protein aggregation propensities, detecting areas that are prone to sequential and structural aggregation, analyzing the effects of mutations on protein aggregation, or identifying prion-like domains.

Protein self-assembly plays an important role in biological systems, accounting for the formation of mesoscopic structures that can be highly symmetric as in the capsid of viruses or disordered as in molecular condensates or exhibit a one-dimensional fibrillar morphology as in amyloid fibrils. Deposits of the latter in tissues of individuals with degenerative diseases like Alzheimer's and Parkinson's has motivated extensive efforts to understand the sequence of molecular events accounting for their formation. These studies aim to identify on-pathway intermediates that may be the targets for therapeutic intervention. This detailed knowledge of fibril formation remains obscure, in part due to challenges with experimental analyses of these processes. However, important progress is being achieved for short amyloid peptides due to advances in our ability to perform completely unbiased all-atom simulations of the self-assembly process. This perspective discusses recent developments, their implications, and the hurdles that still need to be overcome to further advance the field.

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.

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

The problems associated with the drugs currently used to treat leishmaniasis, including resistance, toxicity, and the high cost of some formulations, call for the urgent identification of new therapeutic agents with novel modes of action. The aggregated protein dye YAT2150 has been found to be a potent antileishmanial compound, with a half-maximal inhibitory concentration (IC50) of approximately 0.5 µM against promastigote and amastigote stages of Leishmania infantum. The encapsulation in liposomes of YAT2150 significantly improved its in vitro IC50 to 0.37 and 0.19 µM in promastigotes and amastigotes, respectively, and increased the half-maximal cytotoxic concentration in human umbilical vein endothelial cells to >50 µM. YAT2150 became strongly fluorescent when binding intracellular protein deposits in Leishmania cells. This fluorescence pattern aligns with the proposed mode of action of this drug in the malaria parasite Plasmodium falciparum, the inhibition of protein aggregation. In Leishmania major, YAT2150 rapidly reduced ATP levels, suggesting an alternative antileishmanial mechanism. To the best of our knowledge, this first-in-class compound is the only one described so far having significant activity against both Plasmodium and Leishmania, thus being a potential drug for the treatment of co-infections of both parasites.

Tauopathies encompass a group of neurodegenerative disorders characterised by diverse tau amyloid fibril structures. The persistence of polymorphism across tauopathies suggests that distinct pathological conditions dictate the adopted polymorph for each disease. However, the extent to which intrinsic structural tendencies of tau amyloid cores contribute to fibril polymorphism remains uncertain. Using a combination of experimental approaches, we here identify a new amyloidogenic motif, PAM4 (Polymorphic Amyloid Motif of Repeat 4), as a significant contributor to tau polymorphism. Calculation of per-residue contributions to the stability of the fibril cores of different pathologic tau structures suggests that PAM4 plays a central role in preserving structural integrity across amyloid polymorphs. Consistent with this, cryo-EM structural analysis of fibrils formed from a synthetic PAM4 peptide shows that the sequence adopts alternative structures that closely correspond to distinct disease-associated tau strains. Furthermore, in-cell experiments revealed that PAM4 deletion hampers the cellular seeding efficiency of tau aggregates extracted from Alzheimer's disease, corticobasal degeneration, and progressive supranuclear palsy patients, underscoring PAM4's pivotal role in these tauopathies. Together, our results highlight the importance of the intrinsic structural propensity of amyloid core segments to determine the structure of tau in cells, and in propagating amyloid structures in disease.

α-, β-, and γ-Synuclein are intrinsically disordered proteins implicated in physiological processes in the nervous system of vertebrates. α-synuclein (αSyn) is the amyloidogenic protein associated with Parkinson's disease and certain other neurodegenerative disorders. Intensive research has focused on the mechanisms that cause αSyn to form amyloid structures, identifying its NAC region as being necessary and sufficient for amyloid assembly. Recent work has shown that a 7-residue sequence (P1) is necessary for αSyn amyloid formation. Although γ-synuclein (γSyn) is 55% identical in sequence to αSyn and its pathological deposits are also observed in association with neurodegenerative conditions, γSyn is resilient to amyloid formation in vitro. Here, we report a rare single nucleotide polymorphism (SNP) in the SNCG gene encoding γSyn, found in two patients with amyotrophic lateral sclerosis (ALS). The SNP results in the substitution of Met38 with Ile in the P1 region of the protein. These individuals also had a second, common and nonpathological, SNP in SNCG resulting in the substitution of Glu110 with Val. In vitro studies demonstrate that the Ile38 variant accelerates amyloid fibril assembly. Contrastingly, Val110 retards fibril assembly and mitigates the effect of Ile38. Substitution of residue 38 with Leu had little effect, while Val retards, and Ala increases the rate of amyloid formation. Ile38 γSyn also results in the formation of γSyn-containing inclusions in cells. The results show how a single point substitution can enhance amyloid formation of γSyn and highlight the P1 region in driving amyloid formation in another synuclein family member.

Several databases collecting amyloidogenic regions have been released to provide information on protein sequences able to form amyloid fibrils. However, most of these resources are built with data from experiments that detect highly hydrophobic stretches located within transiently exposed protein segments. We recently demonstrated that cryptic amyloidogenic regions (CARs) of polar nature have the potential to form amyloid fibrils in vitro. Given the underrepresentation of these types of sequences in current amyloid databases, we developed CARs-DB, the first repository that collects thousands of predicted CARs from intrinsically disordered regions. This protocol chapter describes how to use CARs-DB to search for sequences of interest that might be connected to disease or functional protein-protein interactions. In addition, we provide study cases to illustrate the database's features to users. The CARs-DB is readily accessible at http://carsdb.ppmclab.com/ .

Monoclonal antibody (mAb) solution viscosity in ultra-high concentration formulations is a key developability consideration in mAb development risk mitigation strategies that has implications for downstream processing and patient safety. Predicting viscosity at therapeutically relevant concentrations remains critical, despite the need for large mAb quantities for viscosity measurement being prohibitive. Using a panel of IgG1s, we examined the suitability of viscosity prediction and fitting models at different mAb test concentration regimes. Our findings caution against extrapolation from low concentration measurements, as they lack predictive ability for ultra-high concentration regimes. For the first time, we demonstrate the importance of analyte concentration range selection, and the need for bespoke viscosity model development.

Amyloid fibrils have excellent structural characteristics, such as a high aspect ratio, excellent stiffness, and a wide availability of functional groups on the surface. More studies are now focusing on the formation of amyloid fibrils using food proteins. Protein fibrillation is now becoming recognized as a promising strategy for enhancing the function of food proteins and expanding their range of applications. Wheat gluten is rich in glutamine (Q), hydrophobic amino acids, and the α-helix structure with high β-sheet tendency. These characteristics make it very easy for wheat gluten to form amyloid fibrils. The conditions, formation mechanism, characterization methods, and application of amyloid fibrils formed by wheat gluten are summarized in this review. Further exploration of amyloid fibrils formed by wheat gluten will reveal how they can play a significant role in food, biology, and other fields, especially in medicine.