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.     

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.     

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.     

Cooperativity among short amyloid stretches in long amyloidogenic sequences

Amyloid fibrillar aggregates of polypeptides are associated with many neurodegenerative diseases. Short peptide segments in protein sequences may trigger aggregation. Identifying these stretches and examining their behavior in longer protein segments is critical for understanding these diseases and obtaining potential therapies. In this study, we combined machine learning and structure-based energy evaluation to examine and predict amyloidogenic segments. Our feature selection method discovered that windows consisting of long amino acid segments of ~30 residues, instead of the commonly used short hexapeptides, provided the highest accuracy. Weighted contributions of an amino acid at each position in a 27 residue window revealed three cooperative regions of short stretch, resemble the β-strand-turn-β-strand motif in A-βpeptide amyloid and β-solenoid structure of HET-s(218-289) prion (C). Using an in-house energy evaluation algorithm, the interaction energy between two short stretches in long segment is computed and incorporated as an additional feature. The algorithm successfully predicted and classified amyloid segments with an overall accuracy of 75%. Our study revealed that genome-wide amyloid segments are not only dependent on short high propensity stretches, but also on nearby residues.     

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.     

Discordant and chameleon sequences: their distribution and implications for amyloidogenicity

Identification of ambiguous encoding in protein secondary structure is paramount to develop an understanding of key protein segments underlying amyloid diseases. We investigate two types of structurally ambivalent peptides, which were hypothesized in the literature as indicators of amyloidogenic proteins: discordant α-helices and chameleon sequences. Chameleon sequences are peptides discovered experimentally in different secondary-structure types. Discordant α-helices are α-helical stretches with strong β-strand propensity or prediction. To assess the distribution of these features in known protein structures, and their potential role in amyloidogenesis, we analyzed the occurrence of discordant α-helices and chameleon sequences in nonredundant sets of protein domains (n = 4263) and amyloidogenic proteins extracted from the literature (n = 77). Discordant α-helices were identified if discordance was observed between known secondary structures and secondary-structure predictions from the GOR-IV and PSIPRED algorithms. Chameleon sequences were extracted by searching for identical sequence words in α-helices and β-strands. We defined frustrated chameleons and very frustrated chameleons based on varying degrees of total β propensity ≥α propensity. To our knowledge, this is the first study to discern statistical relationships between discordance, chameleons, and amyloidogenicity. We observed varying enrichment levels for some categories of discordant and chameleon sequences in amyloidogenic sequences. Chameleon sequences are also significantly enriched in proteins that have discordant helices, indicating a clear link between both phenomena. We identified the first set of discordant-chameleonic protein segments we predict may be involved in amyloidosis. We present a detailed analysis of discordant and chameleons segments in the family of one of the amyloidogenic proteins, the Prion Protein.     

A search for amyloidogenic regions in protein chain

We suggest a new method to detect amyloidogenic regions in a protein sequence. In the present work it is shown that regions enriched with amino acid residues which have a high expected packing density are responsible for the amyloid formation. Our predictions are consistent with known disease-related amyloidogenic regions for 8 of 11 amyloid-forming proteins and peptides in which positions of amyloidogenic regions have been revealed experimentally.     

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.     

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.     

New strategy for the generation of specific D-peptide amyloid inhibitors

The conversion of a soluble protein into β-sheet-rich oligomeric structures and further fiber formation are critical steps in the pathogenesis of the group of human diseases known as amyloidoses. Drugs that interfere with this process may thus be able to prevent and/or cure these diseases. Recent results have shown that short amino acid stretches can provide most of the driving force needed to trigger amyloid formation of a protein. These evidence suggest that compounds that specifically bind to peptides synthesized upon the sequence of such amyloidogenic protein stretches might also be able to inhibit amyloid formation of the corresponding full-length protein and, likely, amyloid-induced cytotoxicity as well. Here we present a general strategy to obtain d-peptides that specifically interact with protein amyloid stretches. The screening of a d-peptide combinatorial library for inhibitors of an amyloidogenic peptide designed de novo has allowed us to extract a set of empirical rules for the design of d-peptide inhibitors of any six-residue amyloidogenic stretch. d-peptides generated on these bases prevent amyloid formation and disassemble preformed fibrils of different amyloid hexapeptides identified in human amyloid proteins. In addition, they are also specific for their target sequence. The d-peptide designed here for the Alzheimer's Aβ_1-42 peptide not only inhibits and disassembles amyloid material but also reduces Aβ_1-42 amyloid-induced cytotoxicity in cell culture.     

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.     

Genetic and epigenetic control of the efficiency and fidelity of cross‐species prion transmission

Self‐perpetuating amyloid‐based protein isoforms (prions) transmit neurodegenerative diseases in mammals and phenotypic traits in yeast. Although mechanisms that control species specificity of prion transmission are poorly understood, studies of closely related orthologues of yeast prion protein Sup35 demonstrate that cross‐species prion transmission is modulated by both genetic (specific sequence elements) and epigenetic (prion variants, or ‘strains’) factors. Depending on the prion variant, the species barrier could be controlled at the level of either heterologous co‐aggregation or conversion of the aggregate‐associated heterologous protein into a prion polymer. Sequence divergence influences cross‐species transmission of different prion variants in opposing ways. The ability of a heterologous prion domain to either faithfully reproduce or irreversibly switch the variant‐specific prion patterns depends on both sequence divergence and the prion variant. Sequence variations within different modules of prion domains contribute to transmission barriers in different cross‐species combinations. Individual amino acid substitutions within short amyloidogenic stretches drastically alter patterns of cross‐species prion conversion, implicating these stretches as major determinants of species specificity.     

Protein Topology Prediction Algorithms Systematically Investigated in the Yeast Saccharomyces cerevisiae

Membrane proteins perform a variety of functions, all crucially dependent on their orientation in the membrane. However, neither the exact number of transmembrane domains (TMDs) nor the topology of most proteins have been experimentally determined. Due to this, most scientists rely primarily on prediction algorithms to determine topology and TMD assignments. Since these can give contradictory results, single‐algorithm‐based predictions are unreliable. To map the extent of potential misanalysis, the predictions of nine algorithms on the yeast proteome are compared and it is found that they have little agreement when predicting TMD number and termini orientation. To view all predictions in parallel, a webpage called TopologYeast: http://www.weizmann.ac.il/molgen/TopologYeast was created. Each algorithm is compared with experimental data and a poor agreement is found. The analysis suggests that more systematic data on protein topology are required to increase the training sets for prediction algorithms and to have accurate knowledge of membrane protein topology.     

Protein aggregation and amyloid fibril formation prediction software from primary sequence: towards controlling the formation of bacterial inclusion bodies

Proteins might aggregate into ordered or amorphous structures, utilizing relatively short sequence stretches, usually organized in β-sheet-like assemblies. Here, we attempt to list all available software, developed during the last decade or so, for the prediction of such aggregation-prone stretches from protein primary structure, without distinguishing whether these algorithms predict amino acid sequences destined to be involved in ordered fibrillar amyloids or amorphous aggregates. The results of application of four of these programs on 23 proteins related to amyloidoses are compared. Because protein aggregation during protein production in bacterial cell factories has been shown to resemble amyloid formation, the algorithms might become useful tools to improve the solubility of recombinant proteins and for screening therapeutic approaches against amyloidoses under conditions that mimic physiologically relevant environments. One such example is given.     

Identification of amyloid fibril-forming segments based on structure and residue-based statistical potential

MOTIVATION: Experimental evidence suggests that certain short protein segments have stronger amyloidogenic propensities than others. Identification of the fibril-forming segments of proteins is crucial for understanding diseases associated with protein misfolding and for finding favorable targets for therapeutic strategies. RESULT: In this study, we used the microcrystal structure of the NNQQNY peptide from yeast prion protein and residue-based statistical potentials to establish an algorithm to identify the amyloid fibril-forming segment of proteins. Using the same sets of sequences, a comparable prediction performance was obtained from this study to that from 3D profile method based on the physical atomic-level potential ROSETTADESIGN. The predicted results are consistent with experiments for several representative proteins associated with amyloidosis, and also agree with the idea that peptides that can form fibrils may have strong sequence signatures. Application of the residue-based statistical potentials is computationally more efficient than using atomic-level potentials and can be applied in whole proteome analysis to investigate the evolutionary pressure effect or forecast other latent diseases related to amyloid deposits. AVAILABILITY: The fibril prediction program is available at ftp://mdl.ipc.pku.edu.cn/pub/software/pre-amyl/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Prediction of protein secondary structure with a reliability score estimated by local sequence clustering

Most algorithms for protein secondary structure prediction are based on machine learning techniques, e.g. neural networks. Good architectures and learning methods have improved the performance continuously. The introduction of profile methods, e.g. PSI-BLAST, has been a major breakthrough in increasing the prediction accuracy to close to 80%. In this paper, a brute-force algorithm is proposed and the reliability of each prediction is estimated by a z-score based on local sequence clustering. This algorithm is intended to perform well for those secondary structures in a protein whose formation is mainly dominated by the neighboring sequences and short-range interactions. A reliability z-score has been defined to estimate the goodness of a putative cluster found for a query sequence in a database. The database for prediction was constructed by experimentally determined, non-redundant protein structures with <25% sequence homology, a list maintained by PDBSELECT. Our test results have shown that this new algorithm, belonging to what is known as nearest neighbor methods, performed very well within the expectation of previous methods and that the reliability z-score as defined was correlated with the reliability of prediction. This led to the possibility of making very accurate predictions for a few selected residues in a protein with an accuracy measure of Q3 > 80%. The further development of this algorithm, and a nucleation mechanism for protein folding are suggested.     

Inferring function using patterns of native disorder in proteins

Natively unstructured regions are a common feature of eukaryotic proteomes. Between 30% and 60% of proteins are predicted to contain long stretches of disordered residues, and not only have many of these regions been confirmed experimentally, but they have also been found to be essential for protein function. In this study, we directly address the potential contribution of protein disorder in predicting protein function using standard Gene Ontology (GO) categories. Initially we analyse the occurrence of protein disorder in the human proteome and report ontology categories that are enriched in disordered proteins. Pattern analysis of the distributions of disordered regions in human sequences demonstrated that the functions of intrinsically disordered proteins are both length- and position-dependent. These dependencies were then encoded in feature vectors to quantify the contribution of disorder in human protein function prediction using Support Vector Machine classifiers. The prediction accuracies of 26 GO categories relating to signalling and molecular recognition are improved using the disorder features. The most significant improvements were observed for kinase, phosphorylation, growth factor, and helicase categories. Furthermore, we provide predicted GO term assignments using these classifiers for a set of unannotated and orphan human proteins. In this study, the importance of capturing protein disorder information and its value in function prediction is demonstrated. The GO category classifiers generated can be used to provide more reliable predictions and further insights into the behaviour of orphan and unannotated proteins.     

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.     

Prediction of Protein Function Improving Sequence Remote Alignment Search by a Fuzzy Logic Algorithm

The functional annotation of the new protein sequences represents a major drawback for genomic science. The best way to suggest the function of a protein from its sequence is by finding a related one for which biological information is available. Current alignment algorithms display a list of protein sequence stretches presenting significant similarity to different protein targets, ordered by their respective mathematical scores. However, statistical and biological significance do not always coincide, therefore, the rearrangement of the program output according to more biological characteristics than the mathematical scoring would help functional annotation. A new method that predicts the putative function for the protein integrating the results from the PSI-BLAST program and a fuzzy logic algorithm is described. Several protein sequence characteristics have been checked in their ability to rearrange a PSI-BLAST profile according more to their biological functions. Four of them: amino acid content, matched segment length and hydropathic and flexibility profiles positively contributed, upon being integrated by a fuzzy logic algorithm into a program, BYPASS, to the accurate prediction of the function of a protein from its sequence. Antonio Gómez and Juan Cedano contributed equally to this work.