Prediction of the absolute aggregation rates of amyloidogenic polypeptide chains

Physicochemical determinants of chaperone requirements

We describe a series of stringent relationships between abundance, solubility and chaperone usage of proteins. Based on these relationships, we show that the need of Escherichia coli proteins for the chaperonin GroEL can be predicted with 86% accuracy. Furthermore, from the observation that the abundance and solubility of proteins depend on the physicochemical properties of their amino acid sequences, we demonstrate that the requirement for GroEL can also be predicted directly from the sequences with 90% accuracy. These results indicate that the physicochemical properties of the amino acid sequences represent an essential component of the cellular quality control system that ensures the maintenance of protein homeostasis in living systems.

Quantitative approaches to defining normal and aberrant protein homeostasis

Protein homeostasis refers to the ability of cells to generate and regulate the levels of their constituent proteins in terms of conformations, interactions, concentrations and cellular localisation. We discuss here an approach in which physico-chemical properties of proteins and their environments are used to understand the underlying principles governing this process, which is crucial in all living systems. By adopting the strategy of characterising the origins of specific diseases to inform us about normal biology, we are bringing together methods and concepts from chemistry, physics, engineering, genetics and medicine. In particular, we are using a combination of in vitro, in silico and in vivo approaches to study protein homeostasis through the analysis of the effects that result from its perturbation in a select group of specific proteins, from either amino acid mutations, or changes in concentration and solubility, or interactions with other molecules. By developing a coherent and quantitative description of such phenomena, we are finding that it is possible to shed new light on how the physical and chemical properties of the cellular components can provide an understanding of the normal and aberrant behaviour of living systems. Through such an approach it is possible to provide new insights into the origin and consequences of the failure to maintain homeostasis that is associated with neurodegenerative diseases, in particular, and the phenomenon of ageing, in general, and hence provide a framework for the rational design of therapeutic approaches.

Biophysical characterization of Abeta42 C-terminal fragments: inhibitors of Abeta42 neurotoxicity

A key event in Alzheimer's disease (AD) is age-dependent, brain accumulation of amyloid beta-protein (Abeta) leading to Abeta self-association into neurotoxic oligomers. Previously, we showed that Abeta oligomerization and neurotoxicity could be inhibited by C-terminal fragments (CTFs) of Abeta42. Because these CTFs are highly hydrophobic, we asked if they themselves aggregated and, if so, what parameters regulated their aggregation. To answer these questions, we investigated the dependence of CTF aqueous solubility, aggregation kinetics, and morphology on peptide length and sequence and the correlation between these characteristics and inhibition of Abeta42-induced toxicity. We found that CTFs up to 8 residues long were soluble at concentrations >100 microM and had a low propensity to aggregate. Longer CTFs were soluble at approximately 1-80 microM, and most, but not all, readily formed beta-sheet-rich fibrils. Comparison to Abeta40-derived CTFs showed that the C-terminal dipeptide I41-A42 strongly promoted aggregation. Aggregation propensity correlated with the previously reported tendency to form beta-hairpin conformation but not with inhibition of Abeta42-induced neurotoxicity. The data enhance our understanding of the physical characteristics that affect CTF activity and advance our ability to design, synthesize, and test future generations of inhibitors.

Low-level expression of a folding-incompetent protein in Escherichia coli: search for the molecular determinants of protein aggregation in vivo

Aggregation of peptides and proteins into insoluble amyloid fibrils or related intracellular inclusions is the hallmark of many degenerative diseases, including Alzheimer's disease, Parkinson's disease, and various forms of amyloidosis. In spite of the considerable progress carried out in vitro in elucidating the molecular determinants of the conversion of purified and isolated proteins into amyloid fibrils, very little is known on factors governing this process in the complex environment of living organisms. Taking advantage of increasing evidence that bacterial inclusion bodies consist of amyloid-like aggregates, we have expressed in Escherichia coli both wild type and 21 single-point mutants of the N-terminal domain of the E. coli protein HypF. All variants were expressed as folding-incompetent units in a controlled manner, at low and comparable levels. Their solubilities were measured by quantifying the protein amount contained in the soluble and insoluble fractions by Western blot analysis. A significant negative correlation was found between the solubility of the variants in E. coli and their intrinsic propensity to form amyloid fibrils, predicted using an algorithm previously validated experimentally in vitro on a number of unfolded peptides and proteins, and considering hydrophobicity, beta-sheet propensity, and charge as major sequence determinants of the aggregation process. These findings show that the physicochemical parameters previously recognized to govern amyloid formation by fully or partially unfolded proteins are largely applicable in vivo and pave the way for the molecular exploration of a process as complex as protein aggregation in living organisms.

Amyloidogenic sequences in native protein structures

Numerous short peptides have been shown to form beta-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 beta 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 beta structure. Secondly, amyloidogenic sequences that are found in beta 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 alpha-helices, where amyloidogenic sequences are forced into helix, despite their intrinsic preference for beta structure, is thus a widespread mechanism to avoid protein aggregation.

Conformational properties of unfolded HypF-N

We have measured the intramolecular diffusion rate between distant residues in the aggregation-prone protein HypF-N under various denaturing conditions. Using the method of cysteine quenching of the tryptophan triplet state, we find that intramolecular diffusion remains roughly constant at high concentrations of denaturant (2-6 M GdnHCl) and slows down at low concentrations of denaturant, but the decrease is not uniform throughout the chain. Extrapolation of these measurements to 0 M GdnHCl gives D approximately 10(-7) cm(2) s(-1), about 1 order of magnitude lower than unstructured peptides and at least 2 orders of magnitude higher than well-behaved proteins. This suggests that there is a dynamic range of conformational reorganization within which partially unfolded states are prone to aggregation.

Computational resources for the prediction and analysis of native disorder in proteins

Proteomics attempts to characterise the gene products expressed in a cell or tissue via a range of biophysical techniques including crystallography and NMR and, more relevantly to this volume, chromatography and mass spectrometry. It is becoming increasingly clear that the native states of segments of many of the cellular proteins are not stable, folded structures, and much of the proteome is in an unfolded, disordered state. These proteins and their disordered segments have functionally interesting properties and provide novel challenges for the biophysical techniques that are used to study them. This chapter focuses on computational approaches to predicting such regions and analyzing the functions linked to them, and has implications for protein scientists who wish to study such properties as molecular recognition and post-translational modifications. We also discuss resources where the results of predictions have been collated, making them publicly available to the wider biological community.

Elongation dynamics of amyloid fibrils: a rugged energy landscape picture

Protein amyloid fibrils are a form of linear protein aggregates that are implicated in many neurodegenerative diseases. Here, we study the dynamics of amyloid fibril elongation by performing Langevin dynamic simulations on a coarse-grained model of peptides. Our simulation results suggest that the elongation process is dominated by a series of local minimum due to frustration in monomer-fibril interactions. This rugged energy landscape picture indicates that the amount of recycling of monomers at the fibrils' ends before being fibrilized is substantially reduced in comparison to the conventional two-step elongation model. This picture, along with other predictions discussed, can be tested with current experimental techniques.

Self-assembly of protein amyloids: a competition between amorphous and ordered aggregation

Protein aggregation in the form of amyloid fibrils has important biological and technological implications. Although the self-assembly process is highly efficient, aggregates not in the fibrillar form would also occur and it is important to include these disordered species when discussing the thermodynamic equilibrium behavior of the system. Here, we initiate such a task by considering a mixture of monomeric proteins and the corresponding aggregates in the disordered form (micelles) and in the fibrillar form (amyloid fibrils). Starting with a model on the respective binding free energies for these species, we calculate their concentrations at thermal equilibrium. We then discuss how the incorporation of the disordered structure furthers our understanding on the various amyloid promoting factors observed empirically, and on the kinetics of fibrilization.

Amyloidogenic regions and interaction surfaces overlap in globular proteins related to conformational diseases

Protein aggregation underlies a wide range of human disorders. The polypeptides involved in these pathologies might be intrinsically unstructured or display a defined 3D-structure. Little is known about how globular proteins aggregate into toxic assemblies under physiological conditions, where they display an initially folded conformation. Protein aggregation is, however, always initiated by the establishment of anomalous protein-protein interactions. Therefore, in the present work, we have explored the extent to which protein interaction surfaces and aggregation-prone regions overlap in globular proteins associated with conformational diseases. Computational analysis of the native complexes formed by these proteins shows that aggregation-prone regions do frequently overlap with protein interfaces. The spatial coincidence of interaction sites and aggregating regions suggests that the formation of functional complexes and the aggregation of their individual subunits might compete in the cell. Accordingly, single mutations affecting complex interface or stability usually result in the formation of toxic aggregates. It is suggested that the stabilization of existing interfaces in multimeric proteins or the formation of new complexes in monomeric polypeptides might become effective strategies to prevent disease-linked aggregation of globular proteins.

The aggregation of proteins in tissues is associated with the pathogenesis of more than 40 human diseases. The polypeptides underlying disorders such as Alzheimer's and Parkinson's are devoid of any regular structure, whereas the polypeptides causing familial amyotrophic lateral sclerosis or nonneuropathic systemic amyloidosis correspond to globular proteins. Little is known about the mechanism by which globular proteins under physiological conditions aggregate from their initially folded and soluble conformations. Interestingly, several of these pathogenic proteins display quaternary structure or are bound to other proteins in their physiological context. In the present work, we show that protein-protein interaction surfaces and regions with high aggregation propensity significantly overlap in these polypeptides. This suggests that the formation of native complexes and self-aggregation reactions probably compete in the cell, explaining why point mutations affecting the interface or the stability of the protein complex lead in many cases to the formation of toxic aggregates. This study proposes general strategies to fight against diseases associated with the deposition of globular polypeptides.

Bridging the gap: from protein misfolding to protein misfolding diseases

Protein misfolding and aggregation are pathognomic for a number of the most common age-related degenerative diseases. Great progress has been made in studying protein aggregation in the test tube and also in replicating protein aggregation in vertebrate animal models of these diseases. However, we argue here that the development and effective integration of emerging techniques such as the methods of nanoscience and the use of invertebrate models are now providing powerful new opportunities to advance our current understanding of the fundamental origins of these disorders.

Pathogenic or not? And if so, then how? Studying the effects of missense mutations using bioinformatics methods

Many gene defects are relatively easy to identify experimentally, but obtaining information about the effects of sequence variations and elucidation of the detailed molecular mechanisms of genetic diseases will be among the next major efforts in mutation research. Amino acid substitutions may have diverse effects on protein structure and function; thus, a detailed analysis of the mutations is essential. Experimental study of the molecular effects of mutations is laborious, whereas useful and reliable information about the effects of amino acid substitutions can readily be obtained by theoretical methods. Experimentally defined structures and molecular modeling can be used as a basis for interpretation of the mutations. The effects of missense mutations can be analyzed even when the 3D structure of the protein has not been determined, although structure-based analyses are more reliable. Structural analyses include studies of the contacts between residues, their implication for the stability of the protein, and the effects of the introduced residues. Investigations of steric and stereochemical consequences of substitutions provide insights on the molecular fit of the introduced residue. Mutations that change the electrostatic surface potential of a protein have wide-ranging effects. Analyses of the effects of mutations on interactions with ligands and partners have been performed for elucidation of functional mutations. We have employed numerous methods for predicting the effects of amino acid substitutions. We discuss the applicability of these methods in the analysis of genes, proteins, and diseases to reveal protein structure-function relationships, which is essential to gain insights into disease genotype-phenotype correlations.

Model discrimination and mechanistic interpretation of kinetic data in protein aggregation studies

Given the importance of protein aggregation in amyloid diseases and in the manufacture of protein pharmaceuticals, there has been increased interest in measuring and modeling the kinetics of protein aggregation. Several groups have analyzed aggregation data quantitatively, typically measuring aggregation kinetics by following the loss of protein monomer over time and invoking a nucleated growth mechanism. Such analysis has led to mechanistic conclusions about the size and nature of the nucleus, the aggregation pathway, and/or the physicochemical properties of aggregation-prone proteins. We have examined some of the difficulties that arise when extracting mechanistic meaning from monomer-loss kinetic data. Using literature data on the aggregation of polyglutamine, a mutant beta-clam protein, and protein L, we determined parameter values for 18 different kinetic models. We developed a statistical model discrimination method to analyze protein aggregation data in light of competing mechanisms; a key feature of the method is that it penalizes overparameterization. We show that, for typical monomer-loss kinetic data, multiple models provide equivalent fits, making mechanistic determination impossible. We also define the type and quality of experimental data needed to make more definitive conclusions about the mechanism of aggregation. Specifically, we demonstrate how direct measurement of fibril size provides robust discrimination.

Physicochemical principles that regulate the competition between functional and dysfunctional association of proteins

To maintain protein homeostasis, a variety of quality control mechanisms, such as the unfolded protein response and the heat shock response, enable proteins to fold and to assemble into functional complexes while avoiding the formation of aberrant and potentially harmful aggregates. We show here that a complementary contribution to the regulation of the interactions between proteins is provided by the physicochemical properties of their amino acid sequences. The results of a systematic analysis of the protein-protein complexes in the Protein Data Bank (PDB) show that interface regions are more prone to aggregate than other surface regions, indicating that many of the interactions that promote the formation of functional complexes, including hydrophobic and electrostatic forces, can potentially also cause abnormal intermolecular association. We also show, however, that aggregation-prone interfaces are prevented from triggering uncontrolled assembly by being stabilized into their functional conformations by disulfide bonds and salt bridges. These results indicate that functional and dysfunctional association of proteins are promoted by similar forces but also that they are closely regulated by the presence of specific interactions that stabilize native states.

NetCSSP: web application for predicting chameleon sequences and amyloid fibril formation

The calculation of contact-dependent secondary structure propensity (CSSP) is a unique and sensitive method that detects non-native secondary structure propensities in protein sequences. This method has applications in predicting local conformational change, which typically is observed in core sequences of protein aggregation and amyloid fibril formation. NetCSSP implements the latest version of the CSSP algorithm and provides a Flash chart-based graphic interface that enables an interactive calculation of CSSP values for any user-selected regions in a given protein sequence. This feature also can quantitatively estimate the mutational effect on changes in native or non-native secondary structural propensities in local sequences. In addition, this web tool provides precalculated non-native secondary structure propensities for over 1 400 000 fragments that are seven-residues long, collected from PDB structures. They are searchable for chameleon subsequences that can serve as the core of amyloid fibril formation. The NetCSSP web tool is available at http://cssp2.sookmyung.ac.kr/.

Position-dependent electrostatic protection against protein aggregation

Proteins with a high propensity to aggregate can be largely prevented from doing so with surprisingly small changes to their primary structure. By using a combination of rational design and quantitative measurements of aggregation rates, we show that adding a single charge in specific "gatekeeper" regions is sufficient to change the timescale for amyloid fibril growth from minutes to weeks, thereby dramatically reducing the efficiency of this process.

Identification of amyloidogenic peptide sequences using a coarse-grained physicochemical model

Cross-beta amyloid is implicated in over 20 human diseases. Experiments suggest that specific sequence elements within amyloidogenic proteins play a major role in seeding amyloid formation. Identifying these seeding sequences is important for rationalizing the molecular mechanisms of amyloid formation and for elaborating therapeutic strategies that target amyloid. Theoretical techniques play an important role in facilitating the identification and structural characterization of putative seeding sequences; most amyloid species are not amenable to high resolution experimental structure techniques. In this study we have combined a coarse-grained physicochemical protein model with a highly efficient Monte Carlo sampling technique to identify amyloidogenic sequences in four proteins for which respective experimental peptide fragmentation data exist. Peptide sequences were defined as amyloidogenic if the ensemble structure predicted for three interacting peptides described a stable and regular three-stranded beta-sheet. For such peptides, free energies were calculated to provide a measure of amyloid propensity. The overall agreement between the experimental and predicted data is good, and we correctly identify several self-recognition motifs proposed to define the cross-beta amyloid fibril architectures of two of the proteins. Our results compare very favorably with those obtained using atomistic molecular dynamics methods, though our simulations are 30-40 times faster.

Prediction of amyloid fibril-forming segments based on a support vector machine

BACKGROUND

Amyloid fibrillar aggregates of proteins or polypeptides are known to be associated with many human diseases. Recent studies suggest that short protein regions trigger this aggregation. Thus, identifying these short peptides is critical for understanding diseases and finding potential therapeutic targets.

RESULTS

We propose a method, named Pafig (Prediction of amyloid fibril-forming segments) based on support vector machines, to identify the hexpeptides associated with amyloid fibrillar aggregates. The features of Pafig were obtained by a two-round selection from AAindex. Using a 10-fold cross validation test on Hexpepset dataset, Pafig performed well with regards to overall accuracy of 81% and Matthews correlation coefficient of 0.63. Pafig was used to predict the potential fibril-forming hexpeptides in all of the 64,000,000 hexpeptides. As a result, approximately 5.08% of hexpeptides showed a high aggregation propensity. In the predicted fibril-forming hexpeptides, the amino acids--alanine, phenylalanine, isoleucine, leucine and valine occurred at the higher frequencies and the amino acids--aspartic acid, glutamic acid, histidine, lysine, arginine and praline, appeared with lower frequencies.

CONCLUSION

The performance of Pafig indicates that it is a powerful tool for identifying the hexpeptides associated with fibrillar aggregates and will be useful for large-scale analysis of proteomic data.

Protein aggregation and protein instability govern familial amyotrophic lateral sclerosis patient survival

The nature of the "toxic gain of function" that results from amyotrophic lateral sclerosis (ALS)-, Parkinson-, and Alzheimer-related mutations is a matter of debate. As a result no adequate model of any neurodegenerative disease etiology exists. We demonstrate that two synergistic properties, namely, increased protein aggregation propensity (increased likelihood that an unfolded protein will aggregate) and decreased protein stability (increased likelihood that a protein will unfold), are central to ALS etiology. Taken together these properties account for 69% of the variability in mutant Cu/Zn-superoxide-dismutase-linked familial ALS patient survival times. Aggregation is a concentration-dependent process, and spinal cord motor neurons have higher concentrations of Cu/Zn-superoxide dismutase than the surrounding cells. Protein aggregation therefore is expected to contribute to the selective vulnerability of motor neurons in familial ALS.

Towards quantitative predictions in cell biology using chemical properties of proteins

It has recently been suggested that the concentrations of proteins in the cell are tuned towards their critical values, and that the alteration of this balance often results in misfolding diseases. This concept is intriguing because the in vivo concentrations of proteins are closely regulated by complex cellular processes, while their critical concentrations are primarily determined by the chemical characters of their amino acid sequences. We discuss here how the presence of a link between the upper levels of in vivo concentrations and critical concentrations offers an opportunity to make quantitative predictions in cell biology based on the chemical properties of proteins.

Structural characterization of a neurotoxic threonine-rich peptide corresponding to the human prion protein alpha 2-helical 180-195 segment, and comparison with full-length alpha 2-helix-derived peptides

The 173-195 segment corresponding to the helix 2 of the globular PrP domain is a good candidate to be one of the several 'spots' of intrinsic structural flexibility, which might induce local destabilization and concur to protein transformation, leading to aggregation-prone conformations. Here, we report CD and NMR studies on the alpha2-helix-derived peptide of maximal length (hPrP[180-195]) that is able to exhibit a regular structure different from the prevalently random arrangement of other alpha2-helix-derived peptides. This peptide, which has previously been shown to be affected by buffer composition via the ion charge density dependence typical of Hofmeister effects, corresponds to the C-terminal sequence of the PrP(C) full-length alpha2-helix and includes the highly conserved threonine-rich 188-195 segment. At neutral pH, its conformation is dominated by beta-type contributions, which only very strong environmental modifications are able to modify. On TFE addition, an increase of alpha-helical content can be observed, but a fully helical conformation is only obtained in neat TFE. However, linking of the 173-179 segment, as occurring in wild-type and mutant peptides corresponding to the full-length alpha2-helix, perturbs these intrinsic structural propensities in a manner that depends on whether the environment is water or TFE. Overall, these results confirm that the 180-195 parental region in hPrP(C) makes a strong contribution to the chameleon conformational behavior of the segment corresponding to the full-length alpha2-helix, and could play a role in determining structural rearrangements of the entire globular domain.

Aggregation propensity of the human proteome

Formation of amyloid-like fibrils is involved in numerous human protein deposition diseases, but is also an intrinsic property of polypeptide chains in general. Progress achieved recently now allows the aggregation propensity of proteins to be analyzed over large scales. In this work we used a previously developed predictive algorithm to analyze the propensity of the 34,180 protein sequences of the human proteome to form amyloid-like fibrils. We show that long proteins have, on average, less intense aggregation peaks than short ones. Human proteins involved in protein deposition diseases do not differ extensively from the rest of the proteome, further demonstrating the generality of protein aggregation. We were also able to reproduce some of the results obtained with other algorithms, demonstrating that they do not depend on the type of computational tool employed. For example, proteins with different subcellular localizations were found to have different aggregation propensities, in relation to the various efficiencies of quality control mechanisms. Membrane proteins, intrinsically disordered proteins, and folded proteins were confirmed to have very different aggregation propensities, as a consequence of their different structures and cellular microenvironments. In addition, gatekeeper residues at strategic positions of the sequences were found to protect human proteins from aggregation. The results of these comparative analyses highlight the existence of intimate links between the propensity of proteins to form aggregates with β-structure and their biology. In particular, they emphasize the existence of a negative selection pressure that finely modulates protein sequences in order to adapt their aggregation propensity to their biological context.

Amyloid-like fibrils are insoluble proteinaceous fibrillar aggregates with a characteristic structure (the cross-β core) that form and deposit in more than 40 pathological conditions in humans. These include Alzheimer's disease, Parkinson's disease, type II diabetes, and the spongiform encephalopathies. A number of proteins not involved in any disease can also form amyloid-like fibrils in vitro, suggesting that amyloid fibril formation is an intrinsic property of proteins in general. Recent efforts in understanding the physico-chemical grounds of amyloid fibril formation has led to the development of several algorithms, capable of predicting a number of aggregation-related parameters of a protein directly from its amino acid sequence. In order to study the predicted aggregation behavior of the human proteome, we have run one of these algorithms on the 34,180 human protein sequences. Our results demonstrate that molecular evolution has acted on protein sequences to finely modulate their aggregation propensities, depending on different parameters related to their in vivo environment. Together with cellular control mechanisms, this natural selection protects proteins from aggregation during their lifetime.

Amyloid fibrils: abnormal protein assembly

Amyloid refers to the abnormal fibrous, extracellular, proteinaceous deposits found in organs and tissues. Amyloid is insoluble and is structurally dominated by beta-sheet structure. Unlike other fibrous proteins it does not commonly have a structural, supportive or motility role but is associated with the pathology seen in a range of diseases known as the amyloidoses. These diseases include Alzheimer's, the spongiform encephalopathies and type II diabetes, all of which are progressive disorders with associated high morbidity and mortality. Not surprisingly, research into the physicochemical properties of amyloid and its formation is currently intensely pursued. In this chapter we will highlight the key scientific findings and discuss how the stability of amyloid fibrils impacts on bionanotechnology.

AMYPdb: a database dedicated to amyloid precursor proteins

Background

Misfolding and aggregation of proteins into ordered fibrillar structures is associated with a number of severe pathologies, including Alzheimer's disease, prion diseases, and type II diabetes. The rapid accumulation of knowledge about the sequences and structures of these proteins allows using of in silico methods to investigate the molecular mechanisms of their abnormal conformational changes and assembly. However, such an approach requires the collection of accurate data, which are inconveniently dispersed among several generalist databases.

Results

We therefore created a free online knowledge database (AMYPdb) dedicated to amyloid precursor proteins and we have performed large scale sequence analysis of the included data. Currently, AMYPdb integrates data on 31 families, including 1,705 proteins from nearly 600 organisms. It displays links to more than 2,300 bibliographic references and 1,200 3D-structures. A Wiki system is available to insert data into the database, providing a sharing and collaboration environment. We generated and analyzed 3,621 amino acid sequence patterns, reporting highly specific patterns for each amyloid family, along with patterns likely to be involved in protein misfolding and aggregation.

Conclusion

AMYPdb is a comprehensive online database aiming at the centralization of bioinformatic data regarding all amyloid proteins and their precursors. Our sequence pattern discovery and analysis approach unveiled protein regions of significant interest. AMYPdb is freely accessible [1].

Molecular determinants of the aggregation behavior of alpha- and beta-synuclein

Alpha- and beta-synuclein are closely related proteins, the first of which is associated with deposits formed in neurodegenerative conditions such as Parkinson's disease while the second appears to have no relationship to any such disorders. The aggregation behavior of alpha- and beta-synuclein as well as a series of chimeric variants were compared by exploring the structural transitions that occur in the presence of a widely used lipid mimetic, sodium dodecyl sulfate (SDS). We found that the aggregation rates of all these protein variants are significantly enhanced by low concentrations of SDS. In particular, we inserted the 11-residue sequence of mainly hydrophobic residues from the non-amyloid-beta-component (NAC) region of alpha-synuclein into beta-synuclein and show that the fibril formation rate of this chimeric protein is only weakly altered from that of beta-synuclein. These intrinsic propensities to aggregate are rationalized to a very high degree of accuracy by analysis of the sequences in terms of their associated physicochemical properties. The results begin to reveal that the differences in behavior are primarily associated with a delicate balance between the positions of a range of charged and hydrophobic residues rather than the commonly assumed presence or absence of the highly aggregation-prone region of the NAC region of alpha-synuclein. This conclusion provides new insights into the role of alpha-synuclein in disease and into the factors that regulate the balance between solubility and aggregation of a natively unfolded protein.

Mutations enhance the aggregation propensity of the Alzheimer's A beta peptide

Aggregation of the amyloid beta (A beta) peptide plays a key role in the molecular etiology of Alzheimer's disease. Despite the importance of this process, the relationship between the sequence of A beta and the propensity of the peptide to aggregate has not been fully elucidated. The sequence determinants of aggregation can be revealed by probing the ability of amino acid substitutions (mutations) to increase or decrease aggregation. Numerous mutations that decrease aggregation have been isolated by laboratory-based studies. In contrast, very few mutations that increase aggregation have been reported, and most of these were isolated from rare individuals with early-onset familial Alzheimer's disease. To augment the limited data set of clinically derived mutations, we developed an artificial genetic screen to isolate novel mutations that increase aggregation propensity. The screen relies on the expression of A beta-green fluorescent protein fusion in Escherichia coli. In this fusion, the ability of the green fluorescent protein reporter to fold and fluoresce is inversely correlated with the aggregation propensity of the A beta sequence. Implementation of this screen enabled the isolation of 20 mutant versions of A beta with amino acid substitutions at 17 positions in the 42-residue sequence of A beta. Biophysical studies of synthetic peptides corresponding to sequences isolated by the screen confirm the increased aggregation propensity and amyloidogenic behavior of the mutants. The mutations were isolated using an unbiased screen that makes no assumptions about the sequence determinants of aggregation. Nonetheless, all 16 of the most aggregating mutants contain substitutions that reduce charge and/or increase hydrophobicity. These findings provide compelling evidence supporting the hypothesis that sequence hydrophobicity is a major determinant of A beta aggregation.

Protein abundance profiling of the Escherichia coli cytosol

Background

Knowledge about the abundance of molecular components is an important prerequisite for building quantitative predictive models of cellular behavior. Proteins are central components of these models, since they carry out most of the fundamental processes in the cell. Thus far, protein concentrations have been difficult to measure on a large scale, but proteomic technologies have now advanced to a stage where this information becomes readily accessible.

Results

Here, we describe an experimental scheme to maximize the coverage of proteins identified by mass spectrometry of a complex biological sample. Using a combination of LC-MS/MS approaches with protein and peptide fractionation steps we identified 1103 proteins from the cytosolic fraction of the Escherichia coli strain MC4100. A measure of abundance is presented for each of the identified proteins, based on the recently developed emPAI approach which takes into account the number of sequenced peptides per protein. The values of abundance are within a broad range and accurately reflect independently measured copy numbers per cell.

As expected, the most abundant proteins were those involved in protein synthesis, most notably ribosomal proteins. Proteins involved in energy metabolism as well as those with binding function were also found in high copy number while proteins annotated with the terms metabolism, transcription, transport, and cellular organization were rare. The barrel-sandwich fold was found to be the structural fold with the highest abundance. Highly abundant proteins are predicted to be less prone to aggregation based on their length, pI values, and occurrence patterns of hydrophobic stretches. We also find that abundant proteins tend to be predominantly essential. Additionally we observe a significant correlation between protein and mRNA abundance in E. coli cells.

Conclusion

Abundance measurements for more than 1000 E. coli proteins presented in this work represent the most complete study of protein abundance in a bacterial cell so far. We show significant associations between the abundance of a protein and its properties and functions in the cell. In this way, we provide both data and novel insights into the role of protein concentration in this model organism.

Fibril growth kinetics reveal a region of beta2-microglobulin important for nucleation and elongation of aggregation

Amyloid is a highly ordered form of aggregate comprising long, straight and unbranched proteinaceous fibrils that are formed with characteristic nucleation-dependent kinetics in vitro. Currently, the structural molecular mechanism of fibril nucleation and elongation is poorly understood. Here, we investigate the role of the sequence and structure of the initial monomeric precursor in determining the rates of nucleation and elongation of human β₂-microglobulin (β₂m). We describe the kinetics of seeded and spontaneous (unseeded) fibril growth of wild-type β₂m and 12 variants at pH 2.5, targeting specifically an aromatic-rich region of the polypeptide chain (residues 62–70) that has been predicted to be highly amyloidogenic. The results reveal the importance of aromatic residues in this part of the β₂m sequence in fibril formation under the conditions explored and show that this region of the polypeptide chain is involved in both the nucleation and the elongation phases of fibril formation. Structural analysis of the conformational properties of the unfolded monomer for each variant using NMR relaxation methods revealed that all variants contain significant non-random structure involving two hydrophobic clusters comprising regions 29–51 and 58–79, the extent of which is critically dependent on the sequence. No direct correlation was observed, however, between the extent of non-random structure in the unfolded state and the rates of fibril nucleation and elongation, suggesting that the early stages of aggregation involve significant conformational changes from the initial unfolded state. Together, the data suggest a model for β₂m amyloid formation in which structurally specific interactions involving the highly hydrophobic and aromatic-rich region comprising residues 62–70 provide a complementary interface that is key to the generation of amyloid fibrils for this protein at acidic pH.

Fitting neurological protein aggregation kinetic data via a 2-step, minimal/"Ockham's razor" model: the Finke-Watzky mechanism of nucleation followed by autocatalytic surface growth

The aggregation of proteins has been hypothesized to be an underlying cause of many neurological disorders including Alzheimer's, Parkinson's, and Huntington's diseases; protein aggregation is also important to normal life function in cases such as G to F-actin, glutamate dehydrogenase, and tubulin and flagella formation. For this reason, the underlying mechanism of protein aggregation, and accompanying kinetic models for protein nucleation and growth (growth also being called elongation, polymerization, or fibrillation in the literature), have been investigated for more than 50 years. As a way to concisely present the key prior literature in the protein aggregation area, Table 1 in the main text summarizes 23 papers by 10 groups of authors that provide 5 basic classes of mechanisms for protein aggregation over the period from 1959 to 2007. However, and despite this major prior effort, still lacking are both (i) anything approaching a consensus mechanism (or mechanisms), and (ii) a generally useful, and thus widely used, simplest/"Ockham's razor" kinetic model and associated equations that can be routinely employed to analyze a broader range of protein aggregation kinetic data. Herein we demonstrate that the 1997 Finke-Watzky (F-W) 2-step mechanism of slow continuous nucleation, A --> B (rate constant k1), followed by typically fast, autocatalytic surface growth, A + B --> 2B (rate constant k2), is able to quantitatively account for the kinetic curves from all 14 representative data sets of neurological protein aggregation found by a literature search (the prion literature was largely excluded for the purposes of this study in order provide some limit to the resultant literature that was covered). The F-W model is able to deconvolute the desired nucleation, k1, and growth, k2, rate constants from those 14 data sets obtained by four different physical methods, for three different proteins, and in nine different labs. The fits are generally good, and in many cases excellent, with R2 values >or=0.98 in all cases. As such, this contribution is the current record of the widest set of protein aggregation data best fit by what is also the simplest model offered to date. Also provided is the mathematical connection between the 1997 F-W 2-step mechanism and the 2000 3-step mechanism proposed by Saitô and co-workers. In particular, the kinetic equation for Saitô's 3-step mechanism is shown to be mathematically identical to the earlier, 1997 2-step F-W mechanism under the 3 simplifying assumptions Saitô and co-workers used to derive their kinetic equation. A list of the 3 main caveats/limitations of the F-W kinetic model is provided, followed by the main conclusions from this study as well as some needed future experiments.

Dissociation from the oligomeric state is the rate-limiting step in fibril formation by kappa-casein

Amyloid fibrils are aggregated and precipitated forms of protein in which the protein exists in highly ordered, long, unbranching threadlike formations that are stable and resistant to degradation by proteases. Fibril formation is an ordered process that typically involves the unfolding of a protein to partially folded states that subsequently interact and aggregate through a nucleation-dependent mechanism. Here we report on studies investigating the molecular basis of the inherent propensity of the milk protein, kappa-casein, to form amyloid fibrils. Using reduced and carboxymethylated kappa-casein (RCMkappa-CN), we show that fibril formation is accompanied by a characteristic increase in thioflavin T fluorescence intensity, solution turbidity, and beta-sheet content of the protein. However, the lag phase of RCMkappa-CN fibril formation is independent of protein concentration, and the rate of fibril formation does not increase upon the addition of seeds (preformed fibrils). Therefore, its mechanism of fibril formation differs from the archetypal nucleation-dependent aggregation mechanism. By digestion with trypsin or proteinase K and identification by mass spectrometry, we have determined that the region from Tyr(25) to Lys(86) is incorporated into the core of the fibrils. We suggest that this region, which is predicted to be aggregation-prone, accounts for the amyloidogenic nature of kappa-casein. Based on these data, we propose that fibril formation by RCMkappa-CN occurs through a novel mechanism whereby the rate-limiting step is the dissociation of an amyloidogenic precursor from an oligomeric state rather than the formation of stable nuclei, as has been described for most other fibril-forming systems.

Protein misfolding and disease: from the test tube to the organism

Protein misfolding is the underlying cause of many highly debilitating disorders ranging from Alzheimer's Disease to Cystic Fibrosis. Great strides have been made recently in understanding what causes proteins to misfold, primarily through the use of biophysical and computational techniques that enable systematic and quantitative analysis of the effects of a range of different perturbations in proteins. Correlation of the results of such analyses with observations made in animal models of disease has however been limited by their seemingly irreconcilable differences in methodology and scope. Several recent studies have however begun to overcome this limitation by combining the two approaches. This strategy has made it possible to investigate many of the consequences of protein misfolding in vivo, ranging from disease pathogenesis to epigenetic regulation, in the context of the fundamental physico-chemical principles derived from extensive and highly detailed studies undertaken in vitro.

Non-native protein aggregation kinetics

Experimental kinetics of non-native protein aggregation are of practical importance in that they help dictate viable processing, formulation, and storage conditions for biotechnology products, and appear to play a role in determining the onset of a number of diseases. Fundamentally, aggregation kinetics provide insights into the identity of key intermediates in the process, and quantitative tests of available models of aggregation. Although aggregation kinetics often display seemingly disparate behaviors across different proteins and sample conditions, this review illustrates how many of these can be understood within a general framework that treats aggregation as a multi-stage process, and how most available kinetic models of aggregation can be grouped hierarchically in terms of which stage(s) they include. This provides an aid for workers seeking a mechanistic interpretation of in vitro aggregation kinetics, for discriminating among competing models, and in designing experiments to assess in vitro protein stability. Limitations and the utility of purely kinetic approaches to studying aggregation, clarifications of common misperceptions regarding experimental aggregation kinetics, and some outstanding challenges in the field are briefly discussed.

Core protein-nucleic acid interactions in hepatitis C virus as revealed by Raman and circular dichroism spectroscopy

Molecular interactions required for hepatitis C virus (HCV) assembly are not well known and are poorly understood. The 5' untranslated region (5'UTR) of the RNA genome is highly conserved and has extensive secondary structure, and the highly basic core protein is rich in arginine residues. Using Raman and circular dichroism (CD) spectroscopies, specific interactions have been demonstrated here between the 5'UTR sequence and the core protein that may be important for the specific encapsidation of the viral genome during HCV replication. These interactions can be described as follows: (1) hydrogen bonding of arginine with unpaired guanine and/or with wobble GU base pairs, and arginine-phosphate electrostatic contacts; (2) although the percentage of base pairs in the A-form is maintained in 5'UTR, the HCVc-120 protein is beta-sheet and beta-helix enriched upon formation of protein-5'UTR macromolecular assemblies; (3) protein-5'UTR interactions resulting in protein alpha-helix formation involve guanine bases in duplex segments. The mentioned interactions may represent novel targets for antiviral strategies against this important virus.

Effect of Different Salt Ions on the Propensity of Aggregation and on the Structure of Alzheimer’s Aβ(1-40) Amyloid Fibrils

The formation of amyloid fibrils and other polypeptide aggregates depends strongly on the physico-chemical environment. One such factor affecting aggregation is the presence and concentration of salt ions. We have examined the effects of salt ions on the aggregation propensity of Alzheimer's Abeta(1-40) peptide and on the structure of the dissolved and of the fibrillar peptide. All salts examined promote aggregation strongly. The most pronounced effect is seen within the cationic series, i.e. for MgCl2. Evaluation of different possible explanations suggests that Abeta(1-40) aggregation depends on direct interaction between ions and Abeta(1-40) peptide, and correlates with ion-induced changes of the surface tension. Salts have profound effects on the fibril structure. In the presence of salts, fibrils are associated with smaller diameters, narrower crossover distances and lower amide I maxima. Since Abeta(1-40) aggregation responds to salts in a manner unlike that for other polypeptides, such as glucagon, beta2-microglobulin or alpha-synuclein; these data argue that there is no fully uniform way in which salts affect aggregation of different polypeptide chains. These observations are important for understanding and predicting aggregation on the basis of simple physico-chemical properties.