pH-Dependent Aggregation in Intrinsically Disordered Proteins Is Determined by Charge and Lipophilicity

An optimized UPLC-MS/MS method for human plasma amyloid-β 42 and 40 measurement and application in Alzheimer's disease diagnosis

Critical events in Alzheimer's disease (AD) involve an imbalance between the production and clearance of amyloid-β (Aβ) peptides from the brain. The ratio of Aβ42 to Aβ40 in plasma was useful for evaluating AD, but quantification is limited by factors including preanalytical analyte loss and insufficient sensitivity. The availability of a targeted UPLC-MS/MS method with adequate analytical sensitivity and accurate values traceable to the SI units is essential for implementing a strategy for assay standardization. A targeted UPLC-MS/MS method for plasma Aβ42 and Aβ40 quantification was developed based on selected characteristic peptides spiked by 15N-labeled Aβ. The calibrator was assigned using an amino acid analysis reference method trace to SI units. UPLC-MS/MS conditions and sample preparation procedures were assessed. 59 plasma samples comparison were used to evaluate immunoassays. Additionally, two clinical cohorts were selected for diagnostic performance evaluation. The LOQ of Aβ42 and Aβ40 is 10 pg mL-1 and 20 pg mL-1, respectively. The linear range was 10-500 pg mL-1 for Aβ42 and 20-1000 pg mL-1 for Aβ40, recoveries between 95.3 % and 108.2 % for Aβ42, 93.2 % and 104.1 % for Aβ40, imprecisions were <7 %. The accuracy of method was validated by analysis of a certified reference material. Clinical cohorts for diagnostic performance evaluation shown that the area under the curve (AUC) for plasma Aβ42 and Aβ42/Aβ40 to differentiate between AD and CN were 0.767 and 0.799, respectively. A robust UPLC-MS/MS method was developed and demonstrated that suitable for a wide range of plasma Aβ42 and Aβ40. Applied to the investigation of clinically discrepant results, this method can act as an arbiter of the concentration of plasma Aβ42 and Aβ40 present.

Aggrescan4D: A comprehensive tool for pH-dependent analysis and engineering of protein aggregation propensity

Aggrescan4D (A4D) is an advanced computational tool designed for predicting protein aggregation, leveraging structural information and the influence of pH. Building upon its predecessor, Aggrescan3D (A3D), A4D has undergone numerous enhancements aimed at assisting the improvement of protein solubility. This manuscript reviews A4D's updated functionalities and explains the fundamental principles behind its pH-dependent calculations. Additionally, it presents an antibody case study to evaluate its performance in comparison with other structure-based predictors. Notably, A4D integrates advanced protein engineering protocols with pH-dependent calculations, enhancing its utility in advising solubility-enhancing mutations. A4D considers the impact of structural flexibility on aggregation propensities, and includes a large set of precalculated predictions. These capabilities should help to open new avenues for both understanding and managing protein aggregation. A4D is accessible through a dedicated web server at https://biocomp.chem.uw.edu.pl/a4d/.

A colloidal model for the equilibrium assembly and liquid-liquid phase separation of the reflectin A1 protein

Reflectin is an intrinsically disordered protein known for its ability to modulate the biophotonic camouflage of cephalopods based on its assembly-induced osmotic properties. Its reversible self-assembly into discrete, size-controlled clusters and condensed droplets are known to depend sensitively on the net protein charge, making reflectin stimuli-responsive to pH, phosphorylation, and electric fields. Despite considerable efforts to characterize this behavior, the detailed physical mechanisms of reflectin's assembly are not yet fully understood. Here, we pursue a coarse-grained molecular understanding of reflectin assembly using a combination of experiments and simulations. We hypothesize that reflectin assembly and phase behavior can be explained from a remarkably simple colloidal model whereby individual protein monomers effectively interact via a short-range attractive and long-range repulsive (SA-LR) pair potential. We parameterize a coarse-grained SA-LR interaction potential for reflectin A1 from small-angle x-ray scattering measurements, and then extend it to a range of pH values using Gouy-Chapman theory to model monomer-monomer electrostatic interactions. The pH-dependent SA-LR interaction is then used in molecular dynamics simulations of reflectin assembly, which successfully capture a number of qualitative features of reflectin, including pH-dependent formation of discrete-sized nanoclusters and liquid-liquid phase separation at high pH, resulting in a putative phase diagram for reflectin. Importantly, we find that at low pH size-controlled reflectin clusters are equilibrium assemblies, which dynamically exchange protein monomers to maintain an equilibrium size distribution. These findings provide a mechanistic understanding of the equilibrium assembly of reflectin, and suggest that colloidal-scale models capture key driving forces and interactions to explain thermodynamic aspects of native reflectin behavior. Furthermore, the success of SA-LR interactions presented in this study demonstrates the potential of a colloidal interpretation of interactions and phenomena in a range of intrinsically disordered proteins.

Fabricating Multiphasic Angiogenic Scaffolds Using Amyloid/Roxadustat-Assisted High-Temperature Protein Printing

Repairing multiphasic defects is cumbersome. This study presents new soft and hard scaffold designs aimed at facilitating the regeneration of multiphasic defects by enhancing angiogenesis and improving cell attachment. Here, the nonimmunogenic, nontoxic, and cost-effective human serum albumin (HSA) fibril (HSA-F) was used to fabricate thermostable (up to 90 °C) and hard printable polymers. Additionally, using a 10.0 mg/mL HSA-F, an innovative hydrogel was synthesized in a mixture with 2.0% chitosan-conjugated arginine, which can gel in a cell-friendly and pH physiological environment (pH 7.4). The presence of HSA-F in both hard and soft scaffolds led to an increase in significant attachment of the scaffolds to the human periodontal ligament fibroblast (PDLF), human umbilical vein endothelial cell (HUVEC), and human osteoblast. Further studies showed that migration (up to 157%), proliferation (up to 400%), and metabolism (up to 210%) of these cells have also improved in the direction of tissue repair. By examining different in vitro and ex ovo experiments, we observed that the final multiphasic scaffold can increase blood vessel density in the process of per-vascularization as well as angiogenesis. By providing a coculture environment including PDLF and HUVEC, important cross-talk between these two cells prevails in the presence of roxadustat drug, a proangiogenic in this study. In vitro and ex ovo results demonstrated significant enhancements in the angiogenic response and cell attachment, indicating the effectiveness of the proposed design. This approach holds promise for the regeneration of complex tissue defects by providing a conducive environment for vascularization and cellular integration, thus promoting tissue healing.

Aggrescan4D: structure-informed analysis of pH-dependent protein aggregation

Protein aggregation is behind the genesis of incurable diseases and imposes constraints on drug discovery and the industrial production and formulation of proteins. Over the years, we have been advancing the Aggresscan3D (A3D) method, aiming to deepen our comprehension of protein aggregation and assist the engineering of protein solubility. Since its inception, A3D has become one of the most popular structure-based aggregation predictors because of its performance, modular functionalities, RESTful service for extensive screenings, and intuitive user interface. Building on this foundation, we introduce Aggrescan4D (A4D), significantly extending A3D's functionality. A4D is aimed at predicting the pH-dependent aggregation of protein structures, and features an evolutionary-informed automatic mutation protocol to engineer protein solubility without compromising structure and stability. It also integrates precalculated results for the nearly 500,000 jobs in the A3D Model Organisms Database and structure retrieval from the AlphaFold database. Globally, A4D constitutes a comprehensive tool for understanding, predicting, and designing solutions for specific protein aggregation challenges. The A4D web server and extensive documentation are available at https://biocomp.chem.uw.edu.pl/a4d/. This website is free and open to all users without a login requirement.

Ophthalmic drug effects on the amyloidogenesis of a transforming growth factor β-induced protein (TGFBIp) peptide fragment

Drugs that can treat one disease may either be detrimental or beneficial toward another due to possible cross-interactions. Therefore, care in choosing a suitable drug for patients with multiple diseases is crucial in successful patient management. This study explores several currently available ophthalmic drugs used to treat common ocular diseases to understand how they can affect the amyloidogenesis of a transforming growth factor β-induced protein (TGFBIp) peptide fragment found in abundance in the corneal protein aggregation deposits of lattice corneal dystrophy (LCD) patients. Results from this study provided supporting evidence that some drugs intended to treat other diseases can enhance or inhibit fibrillar aggregation of TGFBIp peptide, which may have potential implication of affecting the disease progression of LCD by either worsening or ameliorating it. Comparisons of the different properties of ophthalmic compounds explored in this study may also provide some guidance for future design of drugs geared toward the treatment of LCD.

Structural information in therapeutic peptides: Emerging applications in biomedicine

Peptides are attracting a growing interest as therapeutic agents. This trend stems from their cost-effectiveness and reduced immunogenicity, compared to antibodies or recombinant proteins, but also from their ability to dock and interfere with large protein-protein interaction surfaces, and their higher specificity and better biocompatibility relative to organic molecules. Many tools have been developed to understand, predict, and engineer peptide function. However, most state-of-the-art approaches treat peptides only as linear entities and disregard their structural arrangement. Yet, structural details are critical for peptide properties such as solubility, stability, or binding affinities. Recent advances in peptide structure prediction have successfully addressed the scarcity of confidently determined peptide structures. This review will explore different therapeutic and biotechnological applications of peptides and their assemblies, emphasizing the importance of integrating structural information to advance these endeavors effectively.

A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms

Protein aggregation has been associated with aging and different pathologies and represents a bottleneck in the industrial production of biotherapeutics. Numerous past studies performed in Escherichia coli and other model organisms have allowed to dissect the biophysical principles underlying this process. This knowledge fuelled the development of computational tools, such as Aggrescan 3D (A3D) to forecast and re-design protein aggregation. Here, we present the A3D Model Organism Database (A3D-MODB) http://biocomp.chem.uw.edu.pl/A3D2/MODB, a comprehensive resource for the study of structural protein aggregation in the proteomes of 12 key model species spanning distant biological clades. In addition to A3D predictions, this resource incorporates information useful for contextualizing protein aggregation, including membrane protein topology and structural model confidence, as an indirect reporter of protein disorder. The database is openly accessible without any need for registration. We foresee A3D-MOBD evolving into a central hub for conducting comprehensive, multi-species analyses of protein aggregation, fostering the development of protein-based solutions for medical, biotechnological, agricultural and industrial applications.

On-Pathway Oligomer of Human Islet Amyloid Polypeptide Induced and Stabilized by Mechanical Rotation during Magic Angle Spinning Nuclear Magnetic Resonance

Intermediates along the fibrillation pathway are generally considered to be the toxic species responsible for the pathologies of amyloid diseases. However, structural studies of these species have been hampered by heterogeneity and poor stability under standard aqueous conditions. Here, we report a novel methodology for producing stable, on-pathway oligomers of the human type-2 diabetes-associated islet amyloid polypeptide (hIAPP or amylin) using the mechanical forces associated with magic angle spinning (MAS). The species were a heterogeneous mixture of globular and short rod-like species with significant β-sheet content and the capability of seeding hIAPP fibrillation. We used MAS nuclear magnetic resonance to demonstrate that the nature of the species was sensitive to sample conditions, including peptide concentration, ionic strength, and buffer. The methodology should be suitable for studies of other aggregating systems.

Determination of the Structure and Dynamics of the Fuzzy Coat of an Amyloid Fibril of IAPP Using Cryo-Electron Microscopy

In recent years, major advances in cryo-electron microscopy (cryo-EM) have enabled the routine determination of complex biomolecular structures at atomistic resolution. An open challenge for this approach, however, concerns large systems that exhibit continuous dynamics. To address this problem, we developed the metadynamic electron microscopy metainference (MEMMI) method, which incorporates metadynamics, an enhanced conformational sampling approach, into the metainference method of integrative structural biology. MEMMI enables the simultaneous determination of the structure and dynamics of large heterogeneous systems by combining cryo-EM density maps with prior information through molecular dynamics, while at the same time modeling the different sources of error. To illustrate the method, we apply it to elucidate the dynamics of an amyloid fibril of the islet amyloid polypeptide (IAPP). The resulting conformational ensemble provides an accurate description of the structural variability of the disordered region of the amyloid fibril, known as fuzzy coat. The conformational ensemble also reveals that in nearly half of the structural core of this amyloid fibril, the side chains exhibit liquid-like dynamics despite the presence of the highly ordered network backbone of hydrogen bonds characteristic of the cross-β structure of amyloid fibrils.

Sample pH Can Drift during Native Mass Spectrometry Experiments: Results from Ratiometric Fluorescence Imaging

The ability of nanoelectrospray ionization (nanoESI) to generate a continuous flow of charged droplets relies on the electrolytic nature of the process. This electrochemistry can lead to the accumulation of redox products in the sample solution. This consequence can have significant implications for native mass spectrometry (MS), which aims to probe the structures and interactions of biomolecules in solution. Here, ratiometric fluorescence imaging and a pH-sensitive, fluorescent probe are used to quantify changes in solution pH during nanoESI under conditions relevant to native MS. Results show that the extent and rate of change in sample pH depends on several experimental parameters. There is a strong correlation between the extent and rate of change in solution pH and the magnitude of both the nanoESI current and electrolyte concentration. Smaller changes in solution pH are observed during experiments when a negative potential is applied than for those when a positive potential is applied. Finally, we make specific recommendations for designing native MS experiments that control for these effects.

Cooperativity in regulation of membrane protein function: phenomenological analysis of the effects of pH and phospholipids

Interaction between membrane proteins and ligands plays a key role in governing a wide spectrum of cellular processes. These interactions can provide a cooperative-type regulation of protein function. A wide variety of proteins, including enzymes, channels, transporters, and receptors, displays cooperative behavior in their interactions with ligands. Moreover, the ligands involved encompass a vast diversity and include specific molecules or ions that bind to specific binding sites. In this review, our particular focus is on the interaction between integral membrane proteins and ligands that can present multiple "binding sites", such as protons or membrane phospholipids. The study of the interaction that protons or lipids have with membrane proteins often presents challenges for classical mechanistic modeling approaches. In this regard, we show that, like Hill's pioneering work on hemoglobin regulation, phenomenological modeling constitutes a powerful tool for capturing essential features of these systems.

On-Pathway Oligomer of Human Islet Amyloid Polypeptide Induced and Stabilized by Mechanical Rotation During MAS NMR

Intermediates along the fibrillation pathway are generally considered to be the toxic species responsible for the pathologies of amyloid diseases. However, structural studies of these species have been hampered by heterogeneity and poor stability in standard aqueous conditions. Here, we report a novel methodology for producing stable, on-pathway oligomers of the human Type-2 Diabetes-associated islet amyloid polypeptide (hIAPP, or amylin) using the mechanical forces associated with magic angle spinning (MAS). The species were a heterogeneous mixture of globular and short rod-like species with significant beta-sheet content and the capability of seeding hIAPP fibrillation. We used MAS NMR to demonstrate that the nature of the species was sensitive to sample conditions including peptide concentration, ionic strength, and buffer. The methodology should be suitable for studies of other aggregating systems.

Local pH at Nonionic and Zwitterionic Lipid/Water Interfaces Revealed by Heterodyne-Detected Electronic Sum-Frequency Generation: A Unified View to Predict Interfacial pH of Biomembranes

For biomembranes, which are composed of neutral as well as charged lipids, the local pH at lipid/water interfaces is extremely important in their structural formation and functional activity. In our previous study of the charged lipid/water interfaces, we found that the local pH at the interface is governed by the positive or negative sign of the charge of the lipid: i.e., the local pH is dictated by the repulsive or attractive electrostatic interaction between the charged lipid headgroup and the proton. Because of the lack of net charge in the headgroup of the neutral lipid, the factor determining the local pH at neutral lipid/water interfaces is less straightforward, and therefore it is more challenging to predict the local pH. Here we apply heterodyne-detected electronic sum frequency generation (HD-ESFG) spectroscopy to nonionic and zwitterionic lipids to investigate the local pH at the neutral lipid/water interfaces. The obtained results indicate that the local pH at the nonionic lipid/water interface is higher than in bulk water by 0.8 whereas the local pH at the zwitterionic lipid/water interface is lower by 0.6, although the latter is subject to significant uncertainty. The present HD-ESFG study on neutral lipids, combined with the previous study on charged lipids, presents a unified view to consider the local pH at biomembranes based on the balance between the electrostatic interaction and the hydrophobicity provided by the lipid.

Oligomerization of Human Cystatin C—An Amyloidogenic Protein: An Analysis of Small Oligomeric Subspecies

Human cystatin C (HCC), an amyloidogenic protein, forms dimers and higher oligomers (trimers, tetramers and donut like large oligomers) via a domain-swapping mechanism. The aim of this study was the characterization of the HCC oligomeric states observed within the pH range from 2.2 to 10.0 and also in conditions promoting oligomerization. The HCC oligomeric forms obtained in different conditions were characterized using size exclusion chromatography, dynamic light scattering and small-angle X-ray scattering. The marked ability of HCC to form tetramers at low pH (2.3 or 3.0) and dimers at pH 4.0–5.0 was observed. HCC remains monomeric at pH levels above 6.0. Based on the SAXS data, the structure of the HCC tetramer was proposed. Changes in the environment (from acid to neutral) induced a breakdown of the HCC tetramers to dimers. The tetrameric forms of human cystatin C are formed by the association of the dimers without a domain-swapping mechanism. These observations were confirmed by their dissociation to dimers at pH 7.4.

pH Induced Switch in the Conformational Ensemble of Intrinsically Disordered Protein Prothymosin-α and Its Implications for Amyloid Fibril Formation

Aggregation of intrinsically disordered proteins (IDPs) can lead to neurodegenerative diseases. Although there is experimental evidence that acidic pH promotes IDP monomer compaction leading to aggregation, the general mechanism is unclear. We studied the pH effect on the conformational ensemble of prothymosin-α (proTα), which is involved in multiple essential functions, and probed its role in aggregation using computer simulations. We show that compaction in the proTα dimension at low pH is due to the protein's collapse in the intermediate region (E41-D80) rich in glutamic acid residues, enhancing its β-sheet content. We observed by performing dimer simulations that the conformations with high β-sheet content could act as aggregation-prone (N*) states and nucleate the aggregation process. The simulations initiated using N* states form dimers within a microsecond time scale, whereas the non-N* states do not form dimers within this time scale. This study contributes to understanding the general principles of pH-induced IDP aggregation.

Challenges in describing the conformation and dynamics of proteins with ambiguous behavior

Traditionally, our understanding of how proteins operate and how evolution shapes them is based on two main data sources: the overall protein fold and the protein amino acid sequence. However, a significant part of the proteome shows highly dynamic and/or structurally ambiguous behavior, which cannot be correctly represented by the traditional fixed set of static coordinates. Representing such protein behaviors remains challenging and necessarily involves a complex interpretation of conformational states, including probabilistic descriptions. Relating protein dynamics and multiple conformations to their function as well as their physiological context (e.g., post-translational modifications and subcellular localization), therefore, remains elusive for much of the proteome, with studies to investigate the effect of protein dynamics relying heavily on computational models. We here investigate the possibility of delineating three classes of protein conformational behavior: order, disorder, and ambiguity. These definitions are explored based on three different datasets, using interpretable machine learning from a set of features, from AlphaFold2 to sequence-based predictions, to understand the overlap and differences between these datasets. This forms the basis for a discussion on the current limitations in describing the behavior of dynamic and ambiguous proteins.

In-Silico Analysis of pH-Dependent Liquid-Liquid Phase Separation in Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) are essential players in the assembly of biomolecular condensates during liquid–liquid phase separation (LLPS). Disordered regions (IDRs) are significantly exposed to the solvent and, therefore, highly influenced by fluctuations in the microenvironment. Extrinsic factors, such as pH, modify the solubility and disorder state of IDPs, which in turn may impact the formation of liquid condensates. However, little attention has been paid to how the solution pH influences LLPS, despite knowing that this process is context-dependent. Here, we have conducted a large-scale in-silico analysis of pH-dependent solubility and disorder in IDRs known to be involved in LLPS (LLPS-DRs). We found that LLPS-DRs present maximum solubility around physiological pH, where LLPS often occurs, and identified significant differences in solubility and disorder between proteins that can phase-separate by themselves or those that require a partner. We also analyzed the effect of mutations in the resulting solubility profiles of LLPS-DRs and discussed how, as a general trend, LLPS-DRs display physicochemical properties that permit their LLPS at physiologically relevant pHs.

The small aromatic compound SynuClean-D inhibits the aggregation and seeded polymerization of multiple α-synuclein strains

Parkinson’s disease is a neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra, as well as the accumulation of intraneuronal proteinaceous inclusions known as Lewy bodies and Lewy neurites. The major protein component of Lewy inclusions is the intrinsically disordered protein α-synuclein (α-Syn), which can adopt diverse amyloid structures. Different conformational strains of α-Syn have been proposed to be related to the onset of distinct synucleinopathies; however, how specific amyloid fibrils cause distinctive pathological traits is not clear. Here, we generated three different α-Syn amyloid conformations at different pH and salt concentrations and analyzed the activity of SynuClean-D (SC-D), a small aromatic molecule, on these strains. We show that incubation of α-Syn with SC-D reduced the formation of aggregates and the seeded polymerization of α-Syn in all cases. Moreover, we found that SC-D exhibited a general fibril disaggregation activity. Finally, we demonstrate that treatment with SC-D also reduced strain-specific intracellular accumulation of phosphorylated α-Syn inclusions. Taken together, we conclude that SC-D may be a promising hit compound to inhibit polymorphic α-Syn aggregation.

Computational methods to predict protein aggregation

In most cases, protein aggregation stems from the establishment of non-native intermolecular contacts. The formation of insoluble protein aggregates is associated with many human diseases and is a major bottleneck for the industrial production of protein-based therapeutics. Strikingly, fibrillar aggregates are naturally exploited for structural scaffolding or to generate molecular switches and can be artificially engineered to build up multi-functional nanomaterials. Thus, there is a high interest in rationalizing and forecasting protein aggregation. Here, we review the available computational toolbox to predict protein aggregation propensities, identify sequential or structural aggregation-prone regions, evaluate the impact of mutations on aggregation or recognize prion-like domains. We discuss the strengths and limitations of these algorithms and how they can evolve in the next future.

Protein Aggregation and Self Assembly in Health and Disease

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Self-attachment of proteins leading to the formation of highly insoluble protein oligomers and aggregates has become an important focus of research owing to its diverse implications in pathophysiology and diseases. This has become a more frequent phenomenon in most neurological and neurodegenerative diseases as well as in dementia. In recent years such event of protein aggregation has linked to other disease conditions, disorders or adverse health conditions. Interestingly, aggregation of protein also plays role in development, growth or metabolism. Most often physiological proteins are initially bio-synthesised in native or nascent geometrical forms or conformations but later they undergo specific folding pattern and thereby acquire a stable configuration that is biologically relevant and active. It is highly important that these proteins remain in their biologically active configuration in order to exert their functional properties. Any alteration or change to this structural configuration can be detrimental to their specific functions and may cause pathological consequences leading to the onset of diseases or disorders. Several factors such as the action of chaperones, binding partners, physiological metal ions, pH level, temperature, ionic strength, interfacial exposure (solid-liquid, liquid-liquid, gas-liquid), mutation and post translational modification, chemical changes, interaction with small molecules such as lipids, hormones, etc. and solvent environment have been either identified or proposed as important factors in conferring the ultimate status of protein structure and configuration. Among many misfolding protein conformations, self-assembly or aggregation is the most significant. It leads to the formation of highly oligomeric self-aggregates that precipitate and interfere with many biochemical processes with serious pathological consequences. The most common implication of protein aggregation leading to the formation of deposits / plaques of various morphological types is the onset of neurological and neurodegenerative diseases that include Alzheimer’s, Parkinson’s, Huntington, ALS (Amyotrophic Lateral Sclerosis), CJD (Creutzfeldt Jakob Dementia), Prion diseases, Amyloidosis and other forms of dementia. However increasingly studies revealed that protein aggregation may also be associated with other diseases such as cancer, type 2 diabetes, renal, corneal and cardiovascular diseases. Protein aggregation diseases are now considered as part of “Proteinopathy” which refers to conditions where proteins become structurally abnormal or fail to fold into stable normal configurations. In this review, we reflect on various aspects of protein self-aggregation, potential underlying causes, mechanism, role of secondary structures, pathological consequences and possible intervention strategies as reported in published literatures.

Prediction of the Effect of pH on the Aggregation and Conditional Folding of Intrinsically Disordered Proteins with SolupHred and DispHred

Proteins microenvironments modulate their structures. Binding partners, organic molecules, or dissolved ions can alter the protein's compaction, inducing aggregation or order-disorder conformational transitions. Surprisingly, bioinformatic platforms often disregard the protein context in their modeling. In a recent work, we proposed that modeling how pH affects protein net charge and hydrophobicity might allow us to forecast pH-dependent aggregation and conditional disorder in intrinsically disordered proteins (IDPs). As these approaches showed remarkable success in recapitulating the available bibliographical data, we made these prediction methods available for the scientific community as two user-friendly web servers. SolupHred is the first dedicated software to predict pH-dependent aggregation, and DispHred is the first pH-dependent predictor of protein disorder. Here we dissect the features of these two software applications to train and assist scientists in studying pH-dependent conformational changes in IDPs.

Protein Aggregation in the Pathogenesis of Ischemic Stroke

Despite the distinction between ischemic stroke and neurodegenerative disorders, they share numerous pathophysiologies particularly those mediated by inflammation and oxidative stress. Although protein aggregation is considered to be a hallmark of neurodegenerative diseases, the formation of protein aggregates can be also induced within a short time after cerebral ischemia, aggravating cerebral ischemic injury. Protein aggregation uncovers a previously unappreciated molecular overlap between neurodegenerative diseases and ischemic stroke. Unfortunately, compared with neurodegenerative disease, mechanism of protein aggregation after cerebral ischemia and how this can be averted remain unclear. This review highlights current understanding on protein aggregation and its intrinsic role in ischemic stroke.

Condition-dependent structural collapse in the intrinsically disordered N-terminal domain of prion protein

Prion protein is composed of a structure-unsolved N-terminal domain and a globular C-terminal domain. Under limited trypsin digestion, mouse recombinant prion protein can be cleaved into two parts at residue Lys105. Here, we termed these two fragments as the N-domain (sequence 23-105) and the C-domain (sequence 106-230). In this study, the structural properties of the N-domain, the C-domain, and the full-length protein were explored using small-angle X-ray scattering, analytical ultracentrifugation, circular dichroism spectroscopy, and the 8-anilino-1-naphthalenesulfonic acid binding assay. The conformation and size of the prion protein were found to change sensitively under the solvent conditions. The positive residues in the sequence 23-99 of the N-domain were found to be responsible for the enhanced flexibility with the salt concentration reduced below 5 mM. The C-domain containing a hydrophobic patch tends to unfold and aggregate during a salt-induced structural collapse. The N-domain collapsed together with the C-domain at pH 5.2, whereas it collapsed independently at pH 4.2. The positively charged cluster (sequence 100-105) in the N-domain contributed to protecting the exposed hydrophobic surface of the C-domain.

SolupHred: a server to predict the pH-dependent aggregation of intrinsically disordered proteins

SUMMARY

Polypeptides are exposed to changing environmental conditions that modulate their intrinsic aggregation propensities. Intrinsically disordered proteins (IDPs) constitutively expose their aggregation determinants to the solvent, thus being especially sensitive to its fluctuations. However, solvent conditions are often disregarded in computational aggregation predictors. We recently developed a phenomenological model to predict IDPs' solubility as a function of the solution pH, which is based on the assumption that both protein lipophilicity and charge depend on this parameter. The model anticipated solubility changes in different IDPs accurately. In this application note, we present SolupHred, a web-based interface that implements the aforementioned theoretical framework into a predictive tool able to compute IDPs aggregation propensities as a function of pH. SolupHred is the first dedicated software for the prediction of pH-dependent protein aggregation.

AVAILABILITY AND IMPLEMENTATION

The SolupHred web server is freely available for academic users at: https://ppmclab.pythonanywhere.com/SolupHred. It is platform-independent and does not require previous registration.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Variability of Amyloid Propensity in Imperfect Repeats of CsgA Protein of Salmonella enterica and Escherichia coli

CsgA is an aggregating protein from bacterial biofilms, representing a class of functional amyloids. Its amyloid propensity is defined by five fragments (R1–R5) of the sequence, representing non-perfect repeats. Gate-keeper amino acid residues, specific to each fragment, define the fragment’s propensity for self-aggregation and aggregating characteristics of the whole protein. We study the self-aggregation and secondary structures of the repeat fragments of Salmonella enterica and Escherichia coli and comparatively analyze their potential effects on these proteins in a bacterial biofilm. Using bioinformatics predictors, ATR-FTIR and FT-Raman spectroscopy techniques, circular dichroism, and transmission electron microscopy, we confirmed self-aggregation of R1, R3, R5 fragments, as previously reported for Escherichia coli, however, with different temporal characteristics for each species. We also observed aggregation propensities of R4 fragment of Salmonella enterica that is different than that of Escherichia coli. Our studies showed that amyloid structures of CsgA repeats are more easily formed and more durable in Salmonella enterica than those in Escherichia coli.

Quantification of Amyloid-β in Plasma by Simple and Highly Sensitive Immunoaffinity Enrichment and LC-MS/MS Assay

Background

Numerous immunoassays have been developed to quantify amyloid β1-40 (Aβ40) and amyloid β1-42 (Aβ42). Nevertheless, given the low concentration of Aβ and the high levels of interfering factors in plasma, quantification of plasma Aβ is still challenging. To overcome the problems related to the specificity of Aβ immunoassays, this study aimed to develop an immunoaffinity enrichment and LC-MS/MS (IA-MS) assay.

Methods

We developed an IA-MS assay using antibody-labeled magnetic beads for purification and LC-MS/MS for Aβ quantification. To avoid the loss of Aβ due to aggregation in acidic buffer, we used alkaline elution buffer for immunoaffinity enrichment. The concentrations of the Aβs in plasma samples were measured, and the correlation between the plasma and cerebrospinal fluid (CSF) Aβ42/Aβ40 ratio was also evaluated.

Results

The intensities of the Aβ mass peaks were significantly higher with the alkaline elution buffer than with the acidic elution buffer (Aβ40: 3.6-fold, Aβ42: 5.4-fold). This assay exhibited high reproducibility (intra-assay and inter-assay precision, %CV &lt;15), and the working ranges of Aβ40 and Aβ42 were determined to be 21.7 to 692.8 pg/mL and 5.6 to 180.6 pg/mL, respectively. The concentrations of Aβ40 and Aβ42 in plasma were measured by IA-MS, and the plasma Aβ42/Aβ40 ratio was correlated with the CSF Aβ42/Aβ40 ratio (rs = 0.439, P &lt; 0.01).

Conclusions

The IA-MS assay has sufficient analytic performance for measuring endogenous Aβ40 and Aβ42 in plasma. This assay can lead to new lines of clinical discovery related to amyloid pathology.

Assessing SIRAH's Capability to Simulate Intrinsically Disordered Proteins and Peptides

The challenges posed by intrinsically disordered proteins (IDPs) to atomistic and coarse-grained (CG) simulations are boosting efforts to develop and reparametrize current force fields. An assessment of the dynamical behavior of IDPs' and unstructured peptides with the CG SIRAH force field suggests that the current version achieves a fair description of IDPs' conformational flexibility. Moreover, we found a remarkable capability to capture the effect of point mutations in loosely structured peptides.

Functional Amyloids Germinate in Plants

First identified in human disorders, amyloids serve biological functions in bacteria, archaea, fungi, and animals; however, their role in plants has remained unexplored. Recently, Antonets et al. identified a functional amyloid in plants, with a crucial role in seed longevity, confirming that amyloids are universally exploited for organisms' adaptation.

Special Issue: "Inflammation, Oxidative Stress and Protein Aggregation; Any Links?"

Inflammation is a complex immune response that enables survival during infection and maintains tissue homeostasis [...].

Evolutionary Study of Disorder in Protein Sequences

Intrinsically disordered proteins (IDPs) contain regions lacking intrinsic globular structure (intrinsically disordered regions, IDRs). IDPs are present across the tree of life, with great variability of IDR type and frequency even between closely related taxa. To investigate the function of IDRs, we evaluated and compared the distribution of disorder content in 10,695 reference proteomes, confirming its high variability and finding certain correlation along the Euteleostomi (bony vertebrates) lineage to number of cell types. We used the comparison of orthologs to study the function of disorder related to increase in cell types, observing that multiple interacting subunits of protein complexes might gain IDRs in evolution, thus stressing the function of IDRs in modulating protein-protein interactions, particularly in the cell nucleus. Interestingly, the conservation of local compositional biases of IDPs follows residue-type specific patterns, with E- and K-rich regions being evolutionarily stable and Q- and A-rich regions being more dynamic. We provide a framework for targeted evolutionary studies of the emergence of IDRs. We believe that, given the large variability of IDR distributions in different species, studies using this evolutionary perspective are required.

Relevance of Electrostatic Charges in Compactness, Aggregation, and Phase Separation of Intrinsically Disordered Proteins

The abundance of intrinsic disorder in the protein realm and its role in a variety of physiological and pathological cellular events have strengthened the interest of the scientific community in understanding the structural and dynamical properties of intrinsically disordered proteins (IDPs) and regions (IDRs). Attempts at rationalizing the general principles underlying both conformational properties and transitions of IDPs/IDRs must consider the abundance of charged residues (Asp, Glu, Lys, and Arg) that typifies these proteins, rendering them assimilable to polyampholytes or polyelectrolytes. Their conformation strongly depends on both the charge density and distribution along the sequence (i.e., charge decoration) as highlighted by recent experimental and theoretical studies that have introduced novel descriptors. Published experimental data are revisited herein in the frame of this formalism, in a new and possibly unitary perspective. The physicochemical properties most directly affected by charge density and distribution are compaction and solubility, which can be described in a relatively simplified way by tools of polymer physics. Dissecting factors controlling such properties could contribute to better understanding complex biological phenomena, such as fibrillation and phase separation. Furthermore, this knowledge is expected to have enormous practical implications for the design, synthesis, and exploitation of bio-derived materials and the control of natural biological processes.

DispHred: A Server to Predict pH-Dependent Order-Disorder Transitions in Intrinsically Disordered Proteins

The natively unfolded nature of intrinsically disordered proteins (IDPs) relies on several physicochemical principles, of which the balance between a low sequence hydrophobicity and a high net charge appears to be critical. Under this premise, it is well-known that disordered proteins populate a defined region of the charge–hydropathy (C–H) space and that a linear boundary condition is sufficient to distinguish between folded and disordered proteins, an approach widely applied for the prediction of protein disorder. Nevertheless, it is evident that the C–H relation of a protein is not unalterable but can be modulated by factors extrinsic to its sequence. Here, we applied a C–H-based analysis to develop a computational approach that evaluates sequence disorder as a function of pH, assuming that both protein net charge and hydrophobicity are dependent on pH solution. On that basis, we developed DispHred, the first pH-dependent predictor of protein disorder. Despite its simplicity, DispHred displays very high accuracy in identifying pH-induced order/disorder protein transitions. DispHred might be useful for diverse applications, from the analysis of conditionally disordered segments to the synthetic design of disorder tags for biotechnological applications. Importantly, since many disorder predictors use hydrophobicity as an input, the here developed framework can be implemented in other state-of-the-art algorithms.

Computational prediction of protein aggregation: Advances in proteomics, conformation-specific algorithms and biotechnological applications

Protein aggregation is a widespread phenomenon that stems from the establishment of non-native intermolecular contacts resulting in protein precipitation. Despite its deleterious impact on fitness, protein aggregation is a generic property of polypeptide chains, indissociable from protein structure and function. Protein aggregation is behind the onset of neurodegenerative disorders and one of the serious obstacles in the production of protein-based therapeutics. The development of computational tools opened a new avenue to rationalize this phenomenon, enabling prediction of the aggregation propensity of individual proteins as well as proteome-wide analysis. These studies spotted aggregation as a major force driving protein evolution. Actual algorithms work on both protein sequences and structures, some of them accounting also for conformational fluctuations around the native state and the protein microenvironment. This toolbox allows to delineate conformation-specific routines to assist in the identification of aggregation-prone regions and to guide the optimization of more soluble and stable biotherapeutics. Here we review how the advent of predictive tools has change the way we think and address protein aggregation.