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.     

Prediction of amyloidogenic and disordered regions in protein chains

The determination of factors that influence protein conformational changes is very important for the identification of potentially amyloidogenic and disordered regions in polypeptide chains. In our work we introduce a new parameter, mean packing density, to detect both amyloidogenic and disordered regions in a protein sequence. It has been shown that regions with strong expected packing density are responsible for amyloid formation. Our predictions are consistent with known disease-related amyloidogenic regions for eight of 12 amyloid-forming proteins and peptides in which the positions of amyloidogenic regions have been revealed experimentally. Our findings support the concept that the mechanism of amyloid fibril formation is similar for different peptides and proteins. Moreover, we have demonstrated that regions with weak expected packing density are responsible for the appearance of disordered regions. Our method has been tested on datasets of globular proteins and long disordered protein segments, and it shows improved performance over other widely used methods. Thus, we demonstrate that the expected packing density is a useful value with which one can predict both intrinsically disordered and amyloidogenic regions of a protein based on sequence alone. Our results are important for understanding the structural characteristics of protein folding and misfolding.     

Consensus prediction of amyloidogenic determinants in amyloid fibril-forming proteins

We combine the results of three prediction algorithms on a test set of 21 amyloidogenic proteins to predict amyloidogenic determinants. Two prediction algorithms are recently developed prediction algorithms of amyloidogenic stretches in protein sequences, whereas the third is a secondary structure prediction algorithm capable of identifying 'conformational switches' (regions that have both the propensity for alpha-helix and beta-sheet). Surprisingly, the results of prediction agree well and also agree with experimentally investigated amyloidogenic regions. Furthermore, they suggest several previously not identified amino acid stretches as potential amyloidogenic determinants. Most predicted (and experimentally observed) amyloidogenic determinants reside on the protein surface of relevant solved crystal structures. It appears that a consensus prediction algorithm is more objective than individual prediction methods alone.     

The characterization and comparison of amyloidogenic segments and non-amyloidogenic segments shed light on amyloid formation

Amyloid fibrillar aggregates of proteins or peptides are involved in the etiology of several neurodegenerative diseases and represent a major problem in healthcare. Short regions in the protein trigger this aggregation. It is important to understand the basis of such short regions aggregation and amyloidosis for therapeutic intervention. In this study, we describe specific physico-chemical properties of amyloidogenic segments and compare them with non-amyloidogenic segments. First, amyloidogenic segments are characterized by lower values for average net charge, electrostatic potential, solvent accessible surface area and B-factor when compared to the non-amyloidogenic segments of the same proteins. Second, they are enriched in hydrophobic residues and have a tendency to form hydrogen bonds. Thus, amyloidogenic segments have distinct physico-chemical properties that are different from those of non-amyloidogenic segments. Third, and quite unexpectedly, our dynamic simulation studies support the hypothesis that amyloidogenic segments have lower average flexibility than non-amyloidogenic segments. Furthermore, the presence of amyloidogenic segments in disordered proteins does not contradict the observation that amyloidogenic segments are less flexible.     

Search for folding initiation sites from amino acid sequence

A crucial event in protein folding is the formation of a folding nucleus, which is a structured part of the protein chain in the transition state. We demonstrate a correlation between locations of residues involved in the folding nuclei and locations of predicted amyloidogenic regions. The average Phi-values are significantly greater inside amyloidogenic regions than outside them. We have found that fibril formation and normal folding involve many of the same key residues, giving an opportunity to outline the folding initiation site in protein chains. The search for folding initiation sites for apomyoglobin and ribonuclease. A coincides with the predictions made by other approaches.     

Determination of regions involved in amyloid fibril formation for Aβ(1-40) peptide

The studies of amyloid structures and the process of their formation are important problems of biophysics. One of the aspects of such studies is to determine the amyloidogenic regions of a protein chain that form the core of an amyloid fibril. We have theoretically predicted the amyloidogenic regions of the Aβ(1-40) peptide capable of forming an amyloid structure. These regions are from 16 to 21 and from 32 to 36 amino acid residues. In this work, we have attempted to identify these sites experimentally by the method of tandem mass spectrometry. As a result, we show that regions of the Aβ(1-40) peptide from 16 to 22 and from 28 to 40 amino acid residues are resistant to proteases, i.e. they are included in the core of amyloid fibrils. Our results correlate with the results of the theoretical prediction.     

Amyloidogenic determinants are usually not buried

Background  

Amyloidoses are a group of usually fatal diseases, probably caused by protein misfolding and subsequent aggregation into amyloid fibrillar deposits. The mechanisms involved in amyloid fibril formation are largely unknown and are the subject of current, intensive research. In an attempt to identify possible amyloidogenic regions in proteins for further experimental investigation, we have developed and present here a publicly available online tool that utilizes five different and independently published methods, to form a consensus prediction of amyloidogenic regions in proteins, using only protein primary structure data.     

Proteomic analysis of <Emphasis Type="Italic">Escherichia coli</Emphasis> protein fractions resistant to solubilization by ionic detergents

Amyloids are protein fibrils adopting structure of cross-beta spine exhibiting either pathogenic or functionally significant properties. In prokaryotes, there are several groups of functional amyloids; however, all of them were identified by specialized approaches that do not reveal all cellular amyloids. Here, using our previously developed PSIA (Proteomic Screening and Identification of Amyloids) approach, we have conducted a proteomic screening for candidates for novel amyloid-forming proteins in Escherichia coli as one of the most important model organisms and biotechnological objects. As a result, we identified 61 proteins in fractions resistant to treatment with ionic detergents. We found that a fraction of proteins bearing potentially amyloidogenic regions predicted by bioinformatics algorithms was 3-5-fold more abundant among the identified proteins compared to those observed in the entire E. coli proteome. Almost all identified proteins contained potentially amyloidogenic regions, and four of them (BcsC, MukB, YfbK, and YghJ) have asparagineand glutamine-rich regions underlying a crucial feature of many known amyloids. In this study, we demonstrate for the first time that at the proteome level there is a correlation between experimentally demonstrated detergent-resistance of proteins and potentially amyloidogenic regions predicted by bioinformatics approaches. The data obtained enable further comprehensive characterization of entirety of amyloids (or amyloidome) in bacterial cells.     

Are the same or different amino acid residues responsible for correct and incorrect protein folding?

It has been shown for 20 proteins that amino acid residues included into the protein folding nucleus, determined experimentally, are often involved in the theoretically determined amyloidogenic fragments. For 18 proteins, Φ-values indicative of the extent of residue involvement into the folding nucleus are on average higher for amino acid residues within amyloidogenic regions. Amyloidogenic fragments were predicted for 20 proteins by two methods chosen from four on the basis of comparison of prediction of amyloidogenic regions known from experimental data. Since theoretical folding nuclei are detected by the protein three-dimensional structure and amyloidogenic regions by the protein chain primary structure, the detected regularity makes possible predictions of folding nucleation sites on the basis of amino acid sequence.     

An Unstructured Region is Required by GAV Homologue for the Fibrillization of Host Proteins

Accumulating evidence shows that some amyloidogenic proteins contain core sequences, which are critical for their fibrillization. Core sequences of α-synuclein, β-amyloid peptide and prion protein usually reside in their unfolded regions and share a conserved consensus (VGGAVVAGV) designated as GAV homologue. Here we investigate the role of unfolded regions in fibrillization after GAV homologue is attached to the C-terminus or inserted into the loop regions of different host proteins, namely α -Syn_1-65, γ-synuclein, E. coli thioredoxin and immunoglobulin G binding B1 domain of streptococcal protein G. The results imply that an unstructured region is required by GAV homologue for the fibrillization of host proteins. A number of amyloidogenic proteins with core sequences located in unstructured regions are summarized and discussed in details. The finding may provide further insight into the elucidating of the molecular mechanism underlying the fibrillization of α-Syn, Aβ and PrP as well as other amyloidogenic proteins.     

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.     

Characterization of two distinct beta2-microglobulin unfolding intermediates that may lead to amyloid fibrils of different morphology

The self-assembly of beta(2)-microglobulin into fibrils leads to dialysis-related amyloidosis. pH-mediated partial unfolding is required for the formation of the amyloidogenic intermediate that then self-assembles into amyloid fibrils. Two partially folded intermediates of beta(2)-microglobulin have been identified experimentally and linked to the formation of fibrils of distinct morphology, yet it remains difficult to characterize these partially unfolded states at high resolution using experimental approaches. Consequently, we have performed molecular dynamics simulations at neutral and low pH to determine the structures of these partially unfolded amyloidogenic intermediates. In the low-pH simulations, we observed the formation of alpha-sheet structure, which was first proposed by Pauling and Corey. Multiple simulations were performed, and two distinct intermediate state ensembles were identified that may account for the different fibril morphologies. The predominant early unfolding intermediate was nativelike in structure, in agreement with previous NMR studies. The late unfolding intermediate was significantly disordered, but it maintained an extended elongated structure, with hydrophobic clusters and residual alpha-extended chain strands in specific regions of the sequence that map to amyloidogenic peptides. We propose that the formation of alpha-sheet facilitates self-assembly into partially unfolded prefibrillar amyloidogenic intermediates.     

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.     

Hacking the Code of Amyloid Formation: The Amyloid Stretch Hypothesis

Many research efforts in the last years have been directed towards understanding the factors determining protein misfolding and amyloid formation. Protein stability and amino acid composition have been identified as the two major factors in vitro. The research of our group has been focused on understanding the relationship between amino acid sequence and amyloid formation. Our approach has been the design of simple model systems that reproduce the biophysical properties of natural amyloids. An amyloid sequence pattern was extracted that can be used to detect amyloidogenic hexapeptide stretches in proteins. We have added evidence supporting that these amyloidogenic stretches can trigger amyloid formation by nonamyloidogenic proteins. Some experimental results in other amyloid proteins will be analyzed under the conclusions obtained in these studies. Our conclusions together with evidences from other groups suggest that amyloid formation is the result of the interplay between a decrease of protein stability, and the presence of highly amyloidogenic regions in proteins. As many of these results have been obtained in vitro, the challenge for the next years will be to demonstrate their validity in in vivo systems.     

Hacking the Code of Amyloid Formation

Many research efforts in the last years have been directed towards understanding the factors determining protein misfolding and amyloid formation. Protein stability and amino acid composition have been identified as the two major factors in vitro. The research of our group has been focused on understanding the relationship between amino acid sequence and amyloid formation. Our approach has been the design of simple model systems that reproduce the biophysical properties of natural amyloids. An amyloid sequence pattern was extracted that can be used to detect amyloidogenic hexapeptide stretches in proteins. We have added evidence supporting that these amyloidogenic stretches can trigger amyloid formation by non-amyloidogenic proteins. Some experimental results in other amyloid proteins will be analyzed under the conclusions obtained in these studies. Our studies together with evidences from other groups suggest that amyloid formation is the result of the interplay between a decrease of protein stability, and the presence of highly amyloidogenic regions in proteins. As many of these results have been obtained in vitro, the challenge for the next years will be to demonstrate their validity in in vivo systems.     

Rapid assessment of contact-dependent secondary structure propensity: relevance to amyloidogenic sequences

We have previously demonstrated that calculation of contact-dependent secondary structure propensity (CSSP) is highly sensitive in detecting non-native beta-strand propensities in the core sequences of known amyloidogenic proteins. Here we describe a CSSP method based on an artificial neural network that rapidly and accurately quantifies the influence of tertiary contacts (TCs) on secondary structure propensity in local regions of protein sequences. The present method exhibited 72% accuracy in predicting the alternate secondary structure adopted by chameleon sequences located in highly disparate TC regions. Analysis of 1930 nonhomologous protein domains reveals that the alpha-helix and the beta-strand largely share the same sequence context, and that tertiary context is a major determinant of the native conformation. Conversely, it appears that the propensity of random coils for either the alpha-helix or the beta-strand is largely invariant to tertiary effects. The present CSSP method successfully reproduced the amyloidogenic character observed in local regions of the human islet amyloid polypeptide (hIAPP). Furthermore, CSSP profiles were strongly correlated (r = 0.76) with the observed mutational effects on the aggregation rate of acylphosphatase. Taken together, these results provide compelling evidence in support of the present CSSP approach as a sensitive probe useful for analysis of full-length proteins and for detection of core sequences that may trigger amyloid fibril formation. The combined speed and simplicity of the CSSP method lends itself to proteome-wide analysis of the amyloidogenic nature of common proteins.     

Conformational conversion during amyloid formation at atomic resolution

Numerous studies of amyloid assembly have indicated that partially folded protein species are responsible for initiating aggregation. Despite their importance, the structural and dynamic features of amyloidogenic intermediates and the molecular details of how they cause aggregation remain elusive. Here, we use ΔN6, a truncation variant of the naturally amyloidogenic protein β(2)-microglobulin (β(2)m), to determine the solution structure of a nonnative amyloidogenic intermediate at high resolution. The structure of ΔN6 reveals a major repacking of the hydrophobic core to accommodate the nonnative peptidyl-prolyl trans-isomer at Pro32. These structural changes, together with a concomitant pH-dependent enhancement in backbone dynamics on a microsecond-millisecond timescale, give rise to a rare conformer with increased amyloidogenic potential. We further reveal that catalytic amounts of ΔN6 are competent to convert nonamyloidogenic human wild-type β(2)m (Hβ(2)m) into a rare amyloidogenic conformation and provide structural evidence for the mechanism by which this conformational conversion occurs.     

Characterizations of distinct amyloidogenic conformations of the Abeta (1-40) and (1-42) peptides

Major constituents of the amyloid plaques found in the brain of Alzheimer's patients are the 39-43 residue beta-amyloid (Abeta) peptides. Extensive in vitro as well as in vivo biochemical studies have shown that the 40- and 42-residue Abeta peptides play major roles in the neurodegenerative pathology of Alzheimer's disease. Although the two Abeta peptides share common aggregation properties, the 42-residue peptide is more amyloidogenic and more strongly associated with amyloid pathology. Thus, characterizations of the two Abeta peptides are of critical importance in understanding the molecular mechanism of Abeta amyloid formation. In this report, we present combined CD and NMR studies of the monomeric states of the two peptides under both non-amyloidogenic (<5 degrees C) and amyloid-forming conditions (>5 degrees C) at physiological pH. Our CD studies of the Abeta peptides showed that initially unfolded Abeta peptides at low temperature (<5 degrees C) gradually underwent conformational changes to more beta-sheet-like monomeric intermediate states at stronger amyloidogenic conditions (higher temperatures). Detailed residue-specific information on the structural transition was obtained by using NMR spectroscopy. Residues in the N-terminal (3-12) and 20-22 regions underwent conformational changes to more extended structures at the stronger amyloidogenic conditions. Almost identical structural transitions of those residues were observed in the two Abeta peptides, suggesting a similar amyloidogenic intermediate for the two peptides. The 42-residue Abeta (1-42) peptide was, however, more significantly structured at the C-terminal region (39-42), which may lead to the different aggregation propensity of the two peptides.     

Solid‐state NMR reveals differences in the packing arrangements of peptide aggregates derived from the aortic amyloid polypeptide medin

Several polypeptides aggregate into insoluble amyloid fibrils associated with pathologies such as Alzheimer's disease, Parkinson's disease and type 2 diabetes. Understanding the structural and sequential motifs that drive fibrillisation may assist in the discovery and refinement of effective therapies. Here we investigate the effects of three predicted amyloidogenic regions on the structure of aggregates formed by medin, a poorly characterised polypeptide associated with aortic medial amyloidosis. Solid‐state NMR is used to compare the dynamics and sheet packing arrangement of the C‐terminal region encompassing residues F⁴³GSV within full‐length medin (Med_1‐50) and two shorter peptide fragments, Med_30‐50 and Med_42‐49, lacking specific sequences predicted to be amyloidogenic._. Results show that all three peptides have different aggregate morphologies, and Med_30‐50 and Med_1‐50 have different sheet packing arrangements and dynamics to Med_42‐49. These results imply that at least two of the three predicted amyloidogenic regions are required for the formation and elongation of medin fibres observed in the disease state. Copyright © 2011 European Peptide Society and John Wiley & Sons, Ltd.     

A search for amyloidogenic regions in protein chains

A new method was developed for identifying amyloidogenic regions in protein chains. The formation of amyloid fibrils was attributed to protein regions enriched in residues with a high expected packing density. Predictions consistent with experimental findings were obtained for 8 out of 11 amyloid-forming proteins examined.