How do thermophilic proteins resist aggregation?

Aggregation is an ancient threat that must be overcome by proteins from all organisms to maintain their native functional states. This is essential for the maintenance of metabolic flux and viability of their cellular machineries. Here, we compare the aggregation-resistance strategies adapted by the thermophilic proteins and their mesophilic homologs using a dataset of 373 protein families. Like their mesophilic homologs, the thermophilic protein sequences also contain potential aggregation prone regions (APRs), capable of forming cross-β motif and amyloid-like fibrils. Tetrapeptide and hexapeptide amyloid-like fibril forming sequence patterns and experimentally proven amyloid-like fibril forming peptide sequences were also detected in the thermophilic proteins. Both the thermophilic and the mesophilic proteins use similar strategies to resist aggregation. However, the thermophilic proteins show superior utilization of these strategies. The thermophilic protein monomers show greater ability to "stow away" the APRs in the hydrophobic cores to protect them from solvent exposure. The thermophilic proteins are also better at gatekeeping the APRs by surrounding them with charged residues (Asp, Glu, Lys, and Arg) and Pro to a greater extent. While thermophilic and mesophilic proteins in our dataset are highly homologous and show strong overall sequence conservation, the APRs are not conserved between the homologs. These findings indicate that evolution is working to avoid amyloidogenic regions in proteins. Our results are also consistent with the observation that thermophilic cells often accumulate small molecule osmolytes capable of stabilizing their proteins and other macromolecules. This study has important implications for rational design and formulation of therapeutic proteins and antibodies.     

On the Role of Aggregation Prone Regions in Protein Evolution,Stability, and Enzymatic Catalysis: Insights from Diverse Analyses

The various roles that aggregation prone regions (APRs) are capable of playing in proteins are investigated here via comprehensive analyses of multiple non-redundant datasets containing randomly generated amino acid sequences, monomeric proteins, intrinsically disordered proteins (IDPs) and catalytic residues. Results from this study indicate that the aggregation propensities of monomeric protein sequences have been minimized compared to random sequences with uniform and natural amino acid compositions, as observed by a lower average aggregation propensity and fewer APRs that are shorter in length and more often punctuated by gate-keeper residues. However, evidence for evolutionary selective pressure to disrupt these sequence regions among homologous proteins is inconsistent. APRs are less conserved than average sequence identity among closely related homologues (≥80% sequence identity with a parent) but APRs are more conserved than average sequence identity among homologues that have at least 50% sequence identity with a parent. Structural analyses of APRs indicate that APRs are three times more likely to contain ordered versus disordered residues and that APRs frequently contribute more towards stabilizing proteins than equal length segments from the same protein. Catalytic residues and APRs were also found to be in structural contact significantly more often than expected by random chance. Our findings suggest that proteins have evolved by optimizing their risk of aggregation for cellular environments by both minimizing aggregation prone regions and by conserving those that are important for folding and function. In many cases, these sequence optimizations are insufficient to develop recombinant proteins into commercial products. Rational design strategies aimed at improving protein solubility for biotechnological purposes should carefully evaluate the contributions made by candidate APRs, targeted for disruption, towards protein structure and activity.     

Detecting hidden sequence propensity for amyloid fibril formation

The preponderance of evidence implicates protein misfolding in many unrelated human diseases. In all cases, normal correctly folded proteins transform from their proper native structure into an abnormal beta-rich structure known as amyloid fibril. Here we introduce a computational algorithm to detect nonnative (hidden) sequence propensity for amyloid fibril formation. Analyzing sequence-structure relationships in terms of tertiary contact (TC), we find that the hidden beta-strand propensity of a query local sequence can be quantitatively estimated from the secondary structure preferences of template sequences of known secondary structure found in regions of high TC. The present method correctly pinpoints the minimal peptide fragment shown experimentally as the likely local mediator of amyloid fibril formation in beta-amyloid peptide, islet amyloid polypeptide (hIAPP), alpha-synuclein, and human acetylcholinesterase (AChE). It also found previously unrecognized beta-strand propensities in the prototypical helical protein myoglobin that has been reported as amyloidogenic. Analysis of 2358 nonhomologous protein domains provides compelling evidence that most proteins contain sequences with significant hidden beta-strand propensity. The present method may find utility in many medically relevant applications, such as the engineering of protein sequences and the discovery of therapeutic agents that specifically target these sequences for the prevention and treatment of amyloid diseases.     

Amino acid sequence determinants and molecular chaperones in amyloid fibril formation

Amyloid consists of cross-β-sheet fibrils and is associated with about 25 human diseases, including several neurodegenerative diseases, systemic and localized amyloidoses and type II diabetes mellitus. Amyloid-forming proteins differ in structures and sequences, and it is to a large extent unknown what makes them convert from their native conformations into amyloid. In this review, current understanding of amino acid sequence determinants and the effects of molecular chaperones on amyloid formation are discussed. Studies of the nonpolar, transmembrane surfactant protein C (SP-C) have revealed amino acid sequence features that determine its amyloid fibril formation, features that are also found in the amyloid β-peptide in Alzheimer’s disease and the prion protein. Moreover, a proprotein chaperone domain (CTC_Brichos) that prevents amyloid-like aggregation during proSP-C biosynthesis can prevent fibril formation also of other amyloidogenic proteins.     

Is it possible to predict amyloidogenic regions from sequence alone?

Identification of potentially amyloidogenic regions in polypeptide chains is very important because the amyloid fibril formation can be induced in most normal proteins. In our work we suggest a new method to detect amyloidogenic regions in protein sequence. It is based on the assumption that packing is tight inside an amyloid and therefore regions which could potentially pack well would have a tendency to form amyloids. This means that the regions with strong expected packing of residues would be responsible for the amyloid formation. We use this property to identify potentially amyloidogenic regions in proteins basing on their amino acid sequences only. Our predictions are consistent with known disease-related amyloidogenic regions for 8 of 11 amyloid-forming proteins and peptides in which the positions of amyloidogenic regions have been revealed experimentally. Predictions of the regions which are responsible for the formation of amyloid fibrils in proteins unrelated to disease have been also done.     

Polyglutamine expansion diseases: More than simple repeats

Polyglutamine (polyQ) repeat-containing proteins are widespread in the human proteome but only nine of them are associated with highly incapacitating neurodegenerative disorders. The genetic expansion of the polyQ tract in disease-related proteins triggers a series of events resulting in neurodegeneration. The polyQ tract plays the leading role in the aggregation mechanism, but other elements modulate the aggregation propensity in the context of the full-length proteins, as implied by variations in the length of the polyQ tract required to trigger the onset of a given polyQ disease. Intrinsic features such as the presence of aggregation-prone regions (APRs) outside the polyQ segments and polyQ-flanking sequences, which synergistically participate in the aggregation process, are emerging for several disease-related proteins. The inherent polymorphic structure of polyQ stretches places the polyQ proteins in a central position in protein–protein interaction networks, where interacting partners may additionally shield APRs or reshape the aggregation course. Expansion of the polyQ tract perturbs the cellular homeostasis and contributes to neuronal failure by modulating protein–protein interactions and enhancing toxic oligomerization. Post-translational modifications further regulate self-assembly either by directly altering the intrinsic aggregation propensity of polyQ proteins, by modulating their interaction with different macromolecules or by modifying their withdrawal by the cell quality control machinery. Here we review the recent data on the multifaceted aggregation pathways of disease-related polyQ proteins, focusing on ataxin-3, the protein mutated in Machado-Joseph disease. Further mechanistic understanding of this network of events is crucial for the development of effective therapies for polyQ diseases.     

A Simple Lattice Model That Captures Protein Folding,Aggregation and Amyloid Formation

The ability of many proteins to convert from their functional soluble state to amyloid fibrils can be attributed to inter-molecular beta strand formation. Such amyloid formation is associated with neurodegenerative disorders like Alzheimer''s and Parkinson''s. Molecular modelling can play a key role in providing insight into the factors that make proteins prone to fibril formation. However, fully atomistic models are computationally too expensive to capture the length and time scales associated with fibril formation. As the ability to form fibrils is the rule rather than the exception, much insight can be gained from the study of coarse-grained models that capture the key generic features associated with amyloid formation. Here we present a simple lattice model that can capture both protein folding and beta strand formation. Unlike standard lattice models, this model explicitly incorporates the formation of hydrogen bonds and the directionality of side chains. The simplicity of our model makes it computationally feasible to investigate the interplay between folding, amorphous aggregation and fibril formation, and maintains the capability of classic lattice models to simulate protein folding with high specificity. In our model, the folded proteins contain structures that resemble naturally occurring beta-sheets, with alternating polar and hydrophobic amino acids. Moreover, fibrils with intermolecular cross-beta strand conformations can be formed spontaneously out of multiple short hydrophobic peptide sequences. Both the formation of hydrogen bonds in folded structures and in fibrils is strongly dependent on the amino acid sequence, indicating that hydrogen-bonding interactions alone are not strong enough to initiate the formation of beta sheets. This result agrees with experimental observations that beta sheet and amyloid formation is strongly sequence dependent, with hydrophobic sequences being more prone to form such structures. Our model should open the way to a systematic study of the interplay between the factors that lead to amyloid formation.     

Investigation of the interactions between aptamer and misfolded proteins: From monomer and oligomer to fibril by single‐molecule force spectroscopy

Increasing knowledge on the understanding interactions of aptamer with misfolded proteins (including monomer, oligomer, and amyloid fibril) is crucial for development of aggregation inhibitors and diagnosis of amyloid diseases. Herein, the interactions of lysozyme monomer–, oligomer‐, and amyloid fibril–aptamer were investigated using single‐molecule force spectroscopy. The results revealed that the aptamer screened against lysozyme monomer could also bind to oligomer and amyloid fibril, in spite of the recognition at a lower binding probability. It may be attributed to the inherent structural differences of misfolded proteins and the flexible conformation of aptamer. In addition, dynamic force spectra showed that there were similar dissociation paths in the dissociation process of lysozyme monomer–, oligomer‐, and amyloid fibril–aptamer complexes. It showed that the dissociation only passed 1 energy barrier from the binding state to the detachment. However, the dynamic parameters suggested that the oligomer‐ and amyloid fibril–aptamer were more stable than lysozyme monomer–aptamer. The phenomena may result from the exposure of aptamer‐recognized sequences on the surface and the electrostatic interactions. This work demonstrated that single‐molecule force spectroscopy could be a powerful tool to study the binding behavior of the aptamer with misfolded proteins at single‐molecule level, providing abundant information for researches and comprehensive applications of aptamer probes in diagnosis of amyloid diseases.     

Insights into Stabilizing Forces in Amyloid Fibrils of Differing Sizes from Polarizable Molecular Dynamics Simulations

Pathological aggregation of amyloid-forming proteins is a hallmark of a number of human diseases, including Alzheimer's, type 2 diabetes, Parkinson's, and more. Despite having very different primary amino acid sequences, these amyloid proteins form similar supramolecular, fibril structures that are highly resilient to physical and chemical denaturation. To better understand the structural stability of disease-related amyloids and to gain a greater understanding of factors that stabilize functional amyloid assemblies, insights into tertiary and quaternary interactions are needed. We performed molecular dynamics simulations on human tau, amyloid-β, and islet amyloid polypeptide fibrils to determine key physicochemical properties that give rise to their unique characteristics and fibril structures. These simulations are the first of their kind in employing a polarizable force field to explore properties of local electric fields on dipole properties and other electrostatic forces that contribute to amyloid stability. Across these different amyloid fibrils, we focused on how the underlying forces stabilize fibrils to elucidate the driving forces behind the protein aggregation. The polarizable model allows for an investigation of how side-chain dipole moments, properties of structured water molecules in the fibril core, and the local environment around salt bridges contribute to the formation of interfaces essential for fibril stability. By systematically studying three amyloidogenic proteins of various fibril sizes for key structural properties and stabilizing forces, we shed light on properties of amyloid structures related to both diseased and functional states at the atomistic level.     

Understanding and controlling amyloid aggregation with chirality

Amyloid aggregation and human disease are inextricably linked. Examples include Alzheimer disease, Parkinson disease, and type II diabetes. While seminal advances on the mechanistic understanding of these diseases have been made over the last decades, controlling amyloid fibril formation still represents a challenge, and it is a subject of active research. In this regard, chiral modifications have increasingly been proved to offer a particularly well-suited approach toward accessing to previously unknown aggregation pathways and to provide with novel insights on the biological mechanisms of action of amyloidogenic peptides and proteins. Here, we summarize recent advances on how the use of mirror-image peptides/proteins and d-amino acid incorporations have helped modulate amyloid aggregation, offered new mechanistic tools to study cellular interactions, and allowed us to identify key positions within the peptide/protein sequence that influence amyloid fibril growth and toxicity.     

Usage of a dataset of NMR resolved protein structures to test aggregation versus solubility prediction algorithms

There has been an increased interest in computational methods for amyloid and (or) aggregate prediction, due to the prevalence of these aggregates in numerous diseases and their recently discovered functional importance. To evaluate these methods, several datasets have been compiled. Typically, aggregation‐prone regions of proteins, which form aggregates or amyloids in vivo, are more than 15 residues long and intrinsically disordered. However, the number of such experimentally established amyloid forming and non‐forming sequences are limited, not exceeding one hundred entries in existing databases. In this work, we parsed all available NMR‐resolved protein structures from the PDB and assembled a new, sevenfold larger, dataset of unfolded sequences, soluble at high concentrations. We proposed to use these sequences as a negative set for evaluating methods for predicting aggregation in vivo. We also present the results of benchmarking cutting edge tools for the prediction of aggregation versus solubility propensity.     

Amyloid fibril formation in vitro from halophilic metal binding protein: Its high solubility and reversibility minimized formation of amorphous protein aggregations

Halophilic proteins are characterized by high net negative charges and relatively small fraction of hydrophobic amino acids, rendering them aggregation resistant. These properties are also shared by histidine‐rich metal binding protein (HP) from moderate halophile, Chromohalobacter salexigens, used in this study. Here, we examined how halophilic proteins form amyloid fibrils in vitro. His‐tagged HP, incubated at pH 2.0 and 58°C, readily formed amyloid fibrils, as observed by thioflavin fluorescence, CD spectra, and transmission or atomic force microscopies. Under these low‐pH harsh conditions, however, His‐HP was promptly hydrolyzed to smaller peptides most likely responsible for rapid formation of amyloid fibril. Three major acid‐hydrolyzed peptides were isolated from fibrils and turned out to readily form fibrils. The synthetic peptides predicted to form fibrils in these peptide sequences by Waltz software also formed fibrils. Amyloid fibril was also readily formed from full‐length His‐HP when incubated with 10–20% 2,2,2‐trifluoroethanol at pH 7.8 and 25°C without peptide bond cleavage.     

AmylPepPred: Amyloidogenic Peptide Prediction tool

We present an efficient computational architecture designed using supervised machine learning model to predict amyloid fibril forming protein segments, named AmylPepPred. The proposed prediction model is based on bio-physio-chemical properties of primary sequences and auto-correlation function of their amino acid indices. AmylPepPred provides a user friendly web interface for the researchers to easily observe the fibril forming and non-fibril forming hexmers in a given protein sequence. We expect that this stratagem will be highly encouraging in discovering fibril forming regions in proteins thereby benefit in finding therapeutic agents that specifically aim these sequences for the inhibition and cure of amyloid illnesses.

Availability

AmylPepPred is available freely for academic use at www.zoommicro.in/amylpeppred     

A molecular dynamics study of the interaction of D-peptide amyloid inhibitors with their target sequence reveals a potential inhibitory pharmacophore conformation

The self-assembly of soluble proteins and peptides into β-sheet-rich oligomeric structures and insoluble fibrils is a hallmark of a large number of human diseases known as amyloid diseases. Drugs that are able to interfere with these processes may be able to prevent and/or cure these diseases. Experimental difficulties in the characterization of the intermediates involved in the amyloid formation process have seriously hampered the application of rational drug design approaches to the inhibition of amyloid formation and growth. Recently, short model peptide systems have proved useful in understanding the relationship between amino acid sequence and amyloid formation using both experimental and theoretical approaches. Moreover, short d-peptide sequences have been shown to specifically interfere with those short amyloid stretches in proteins, blocking oligomer formation or disassembling mature fibrils. With the aim of rationalizing which interactions drive the binding of inhibitors to nascent β-sheet oligomers, in this study, we have carried out extensive molecular dynamics simulations of the interaction of selected d-peptide sequences with oligomers of the target model sequence STVIIE. Structural analysis of the simulations helped to identify the molecular determinants of an inhibitory core whose conformational and physicochemical properties are actually shared by nonpeptidic small-molecule inhibitors of amyloidogenesis. Selection of one of these small molecules and experimental validation against our model system proved that it was indeed an effective inhibitor of fibril formation by the STVIIE sequence, supporting theoretical predictions. We propose that the inhibitory determinants derived from this work be used as structural templates in the development of pharmacophore models for the identification of novel nonpeptidic inhibitors of aggregation.     

Prediction of amyloid aggregation in vivo

Many human diseases owe their pathology, to some degree, to the erroneous conversion of proteins from their soluble state into fibrillar, β-structured aggregates, often referred to as amyloid fibrils. Neurodegenerative diseases, such as Alzheimer and spongiform encephalopathies, as well as type 2 diabetes and both localized and systemic amyloidosis, are among the conditions that are associated with the formation of amyloid fibrils. Several mathematical tools can rationalize and even predict important parameters of amyloid fibril formation. It is not clear, however, whether such algorithms have predictive powers for in vivo systems, in which protein aggregation is affected by the presence of other biological factors. In this review, we briefly describe the existing algorithms and use them to predict the effects of mutations on the aggregation of specific proteins, for which in vivo experimental data are available. The comparison between the theoretical predictions and the experimental data obtained in vivo is shown for each algorithm and experimental data set, and statistically significant correlations are found in most cases.     

Effect of introducing a short amyloidogenic sequence from the Aβ peptide at the N‐terminus of 18‐residue amphipathic helical peptides

Fibril formation is the hallmark of pathogenesis in Alzheimer's disease and other amyloid disorders caused by conformational alterations leading to the aggregation of soluble monomers. Aβ40 self‐associates to form amyloid fibrils. Its central seven‐residue segment KLVFFAE (Aβ16–22), which is thought to be crucial for fibril formation of the full‐length peptide, forms fibrils even in isolation. Context‐dependent induction of amyloid formation by such sequences in peptides, which otherwise do not have that propensity, is of considerable interest. We have examined the effect of introducing the Aβ16–22 sequence at the N‐terminus of two amphipathic helical 18‐residue peptides Ac‐WYSEMKRNVQRLERAIEE‐am and Ac‐KQLIRFLKRLDRNLWGLA‐am, which have high average hydrophobic moment <μH> values but have net charges of 0 and +4, respectively, at neutral pH. Upon incubation in aqueous buffer, fibril‐like aggregates were discernible by transmission electron microscopy for the peptide with only 0 net charge, which also displayed ThT binding and β‐structure. Although both the sequences have been derived from amphipathic helical segments in globular proteins and possess high average hydrophobic moments, the +4 charge peptide lacks the ability to form fibrils, while the peptide with 0 charge has the tendency to form fibrillar structures. Variation in the net charge and the presence of several glutamic acids in the sequence of the peptide with net charge 0 appear to favor the formation of fibrils when the Aβ16–22 sequence is attached at the N‐terminus. Copyright © 2011 European Peptide Society and John Wiley & Sons, Ltd.     

Amyloidogenic sequences in native protein structures

Numerous short peptides have been shown to form β‐sheet amyloid aggregates in vitro. Proteins that contain such sequences are likely to be problematic for a cell, due to their potential to aggregate into toxic structures. We investigated the structures of 30 proteins containing 45 sequences known to form amyloid, to see how the proteins cope with the presence of these potentially toxic sequences, studying secondary structure, hydrogen‐bonding, solvent accessible surface area and hydrophobicity. We identified two mechanisms by which proteins avoid aggregation: Firstly, amyloidogenic sequences are often found within helices, despite their inherent preference to form β structure. Helices may offer a selective advantage, since in order to form amyloid the sequence will presumably have to first unfold and then refold into a β structure. Secondly, amyloidogenic sequences that are found in β structure are usually buried within the protein. Surface exposed amyloidogenic sequences are not tolerated in strands, presumably because they lead to protein aggregation via assembly of the amyloidogenic regions. The use of α‐helices, where amyloidogenic sequences are forced into helix, despite their intrinsic preference for β structure, is thus a widespread mechanism to avoid protein aggregation.     

Motif mining: an assessment and perspective for amyloid fibril prediction tool

Amyloid fibril forming regions in protein sequences are associated with a number of diseases. Experimental evidences compel in favor of the hypothesis that short motif regions are responsible for its amyloidogenic behavior. Thus, identifying these short peptides is critical in understanding the cause of diseases associated with aggregation of proteins and developing sequencetargeted anti-aggregation drugs. Owing to the constraints of wet lab molecular techniques for the identification of amyloid fibril forming targets, computational methods are implemented to offer better and affordable in silico predictions. The present study takes into consideration an assessment and perspective of the recent tools available for predicting a peptide status: amyloidogenic or non-amyloidogenic. To the best of our knowledge, the existing review articles on amyloidogenic prediction tools have not touched upon their effectiveness in terms of true positive rates or false positive rates. In this work, we compare few tools such as Aggrescan, Amylpred and FoldAmyloid to evaluate the performance of their predictability based on the experimentally proved data in terms of specificity, sensitivity, Matthews Correlation Coefficient and Balanced accuracy. As evident from the results, a significant reduction of sensitivity associated with a gain in specificity is noted in all the tools considered under the present study.     

Expanding the Repertoire of Amyloid Polymorphs by Co-polymerization of Related Protein Precursors

Amyloid fibrils can be generated from proteins with diverse sequences and folds. Although amyloid fibrils assembled in vitro commonly involve a single protein precursor, fibrils formed in vivo can contain more than one protein sequence. How fibril structure and stability differ in fibrils composed of single proteins (homopolymeric fibrils) from those generated by co-polymerization of more than one protein sequence (heteropolymeric fibrils) is poorly understood. Here we compare the structure and stability of homo and heteropolymeric fibrils formed from human β₂-microglobulin and its truncated variant ΔN6. We use an array of approaches (limited proteolysis, magic angle spinning NMR, Fourier transform infrared spectroscopy, and fluorescence) combined with measurements of thermodynamic stability to characterize the different fibril types. The results reveal fibrils with different structural properties, different side-chain packing, and strikingly different stabilities. These findings demonstrate how co-polymerization of related precursor sequences can expand the repertoire of structural and thermodynamic polymorphism in amyloid fibrils to an extent that is greater than that obtained by polymerization of a single precursor alone.     

Nucleation-dependent Aggregation Kinetics of Yeast Sup35 Fragment GNNQQNY

An N-terminal hepta-peptide sequence of yeast prion protein Sup35 with the sequence GNNQQNY is widely used as a model system for amyloid fibril formation. In this study, we used a reproducible solubilisation protocol that allows the generation of a homogenous monomeric solution of GNNQQNY to uncover the molecular details of its self-assembly mechanism. The aggregation kinetics data show that the GNNQQNY sequence follows nucleation-dependent aggregation kinetics with a critical nucleus of size ~7 monomers and that the efficiency of nucleation were found to be inversely related to the reaction temperature. The nucleus reduces the thermodynamic energy barrier by acting as a template for further self-assembly and results in highly ordered amyloid fibrils. The fibers grown at different temperatures showed similar Thioflavin T fluorescence, Congo-red binding and β-sheet rich structures displaying a characteristic cross-β diffraction pattern. These aggregates also share morphological and structural identity with those reported earlier. The mature GNNQQNY fibers did not exert significant oxidative stress or cytotoxicity upon incubating with differentiated SHSY5Y cells. To our knowledge, this is the first study to experimentally validate previous nucleus size predictions based on theoretical and molecular dynamics simulations. These findings provide the basis for understanding the kinetics and thermodynamics of amyloid nucleation and elongation of amyloidogenic proteins/peptides associated with many systemic and neurodegenerative diseases.