Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer's Disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis.

Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aß, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease (AD), Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.

Raman Spectroscopy to Monitor Post-Translational Modifications and Degradation in Monoclonal Antibody Therapeutics.

Monoclonal antibodies (mAbs) represent a rapidly expanding market for biotherapeutics. Structural changes in the mAb can lead to unwanted immunogenicity, reduced efficacy, and loss of material during production. The pharmaceutical sector requires new protein characterization tools that are fast, applicable in situ and to the manufacturing process. Raman has been highlighted as a technique to suit this application as it is information-rich, minimally invasive, insensitive to water background and requires little to no sample preparation. This study investigates the applicability of Raman to detect Post-Translational Modifications (PTMs) and degradation seen in mAbs. IgG4 molecules have been incubated under a range of conditions known to result in degradation of the therapeutic including varied pH, temperature, agitation, photo, and chemical stresses. Aggregation was measured using size-exclusion chromatography, and PTM levels were calculated using peptide mapping. By combining principal component analysis (PCA) with Raman spectroscopy and circular dichroism (CD) spectroscopy structural analysis we were able to separate proteins based on PTMs and degradation. Furthermore, by identifying key bands that lead to the PCA separation we could correlate spectral peaks to specific PTMs. In particular, we have identified a peak which exhibits a shift in samples with higher levels of Trp oxidation. Through separation of IgG4 aggregates, by size, we have shown a linear correlation between peak wavenumbers of specific functional groups and the amount of aggregate present. We therefore demonstrate the capability for Raman spectroscopy to be used as an analytical tool to measure degradation and PTMs in-line with therapeutic production.

Quantification of protein glycation using vibrational spectroscopy.

Glycation is a protein modification prevalent in the progression of diseases such as Diabetes and Alzheimer's, as well as a byproduct of therapeutic protein expression, notably for monoclonal antibodies (mAbs). Quantification of glycated protein is thus advantageous in both assessing the advancement of disease diagnosis and for quality control of protein therapeutics. Vibrational spectroscopy has been highlighted as a technique that can easily be modified for rapid analysis of the glycation state of proteins, and requires minimal sample preparation. Glycated samples of lysozyme and albumin were synthesised by incubation with 0.5 M glucose for 30 days. Here we show that both FTIR-ATR and Raman spectroscopy are able to distinguish between glycated and non-glycated proteins. Principal component analysis (PCA) was used to show separation between control and glycated samples. Loadings plots found specific peaks that accounted for the variation - notably a peak at 1027 cm-1 for FTIR-ATR. In Raman spectroscopy, PCA emphasised peaks at 1040 cm-1 and 1121 cm-1. Therefore, both FTIR-ATR and Raman spectroscopy found changes in peak intensities and wavenumbers within the sugar C-O/C-C/C-N region (1200-800 cm-1). For quantification of the level of glycation of lysozyme, partial least squares regression (PLSR), with statistical validation, was employed to analyse Raman spectra from solution samples containing 0-100% glycated lysozyme, generating a robust model with R2 of 0.99. We therefore show the scope and potential of Raman spectroscopy as a high throughput quantification method for glycated proteins in solution that could be applied in disease diagnostics, as well as therapeutic protein quality control.

Effect of IAPP on the proteome of cultured Rin-5F cells.

BACKGROUND: Islet amyloid polypeptide (IAPP) or amylin deposits can be found in the islets of type 2 diabetes patients. The peptide is suggested to be involved in the etiology of the disease through formation of amyloid deposits and destruction of ß islet cells, though the underlying molecular events leading from IAPP deposition to ß cell death are still largely unknown. RESULTS: We used OFFGEL™ proteomics to study how IAPP exposure affects the proteome of rat pancreatic insulinoma Rin-5F cells. The OFFGEL™ methodology is highly effective at generating quantitative data on hundreds of proteins affected by IAPP, with its accuracy confirmed by In Cell Western and Quantitative Real Time PCR results. Combining data on individual proteins identifies pathways and protein complexes affected by IAPP. IAPP disrupts protein synthesis and degradation, and induces oxidative stress. It causes decreases in protein transport and localization. IAPP disrupts the regulation of ubiquitin-dependent protein degradation and increases catabolic processes. IAPP causes decreases in protein transport and localization, and affects the cytoskeleton, DNA repair and oxidative stress. CONCLUSIONS: Results are consistent with a model where IAPP aggregates overwhelm the ability of a cell to degrade proteins via the ubiquitin system. Ultimately this leads to apoptosis. IAPP aggregates may be also toxic to the cell by causing oxidative stress, leading to DNA damage or by decreasing protein transport. The reversal of any of these effects, perhaps by targeting proteins which alter in response to IAPP, may be beneficial for type II diabetes.

Determination of Protein Secondary Structure from Infrared Spectra Using Partial Least-Squares Regression.

Infrared (IR) spectra contain substantial information about protein structure. This has previously most often been exploited by using known band assignments. Here, we convert spectral intensities in bins within Amide I and II regions to vectors and apply machine learning methods to determine protein secondary structure. Partial least squares was performed on spectra of 90 proteins in H2O. After preprocessing and removal of outliers, 84 proteins were used for this work. Standard normal variate and second-derivative preprocessing methods on the combined Amide I and II data generally gave the best performance, with root-mean-square values for prediction of ∼12% for α-helix, ∼7% for ß-sheet, 7% for antiparallel ß-sheet, and ∼8% for other conformations. Analysis of Fourier transform infrared (FTIR) spectra of 16 proteins in D2O showed that secondary structure determination was slightly poorer than in H2O. Interval partial least squares was used to identify the critical regions within spectra for secondary structure prediction and showed that the sides of bands were most valuable, rather than their peak maxima. In conclusion, we have shown that multivariate analysis of protein FTIR spectra can give α-helix, ß-sheet, other, and antiparallel ß-sheet contents with good accuracy, comparable to that of circular dichroism, which is widely used for this purpose.

Secondary Structure and Glycosylation of Mucus Glycoproteins by Raman Spectroscopies.

The major structural components of protective mucus hydrogels on mucosal surfaces are the secreted polymeric gel-forming mucins. The very high molecular weight and extensive O-glycosylation of gel-forming mucins, which are key to their viscoelastic properties, create problems when studying mucins using conventional biochemical/structural techniques. Thus, key structural information, such as the secondary structure of the various mucin subdomains, and glycosylation patterns along individual molecules, remains to be elucidated. Here, we utilized Raman spectroscopy, Raman optical activity (ROA), circular dichroism (CD), and tip-enhanced Raman spectroscopy (TERS) to study the structure of the secreted polymeric gel-forming mucin MUC5B. ROA indicated that the protein backbone of MUC5B is dominated by unordered conformation, which was found to originate from the heavily glycosylated central mucin domain by isolation of MUC5B O-glycan-rich regions. In sharp contrast, recombinant proteins of the N-terminal region of MUC5B (D1-D2-D'-D3 domains, NT5B), C-terminal region of MUC5B (D4-B-C-CK domains, CT5B) and the Cys-domain (within the central mucin domain of MUC5B) were found to be dominated by the ß-sheet. Using these findings, we employed TERS, which combines the chemical specificity of Raman spectroscopy with the spatial resolution of atomic force microscopy to study the secondary structure along 90 nm of an individual MUC5B molecule. Interestingly, the molecule was found to contain a large amount of α-helix/unordered structures and many signatures of glycosylation, pointing to a highly O-glycosylated region on the mucin.

Combined experimental and simulation studies suggest a revised mode of action of the anti-Alzheimer disease drug NQ-Trp.

Inhibition of the aggregation of the monomeric peptide ß-amyloid (Aß) into oligomers is a widely studied therapeutic approach in Alzheimer's disease (AD). Many small molecules have been reported to work in this way, including 1,4-naphthoquinon-2-yl-L-tryptophan (NQ-Trp). NQ-Trp has been reported to inhibit aggregation, to rescue cells from Aß toxicity, and showed complete phenotypic recovery in an in vivo AD model. In this work we investigated its molecular mechanism by using a combined approach of experimental and theoretical studies, and obtained converging results. NQ-Trp is a relatively weak inhibitor and the fluorescence data obtained by employing the fluorophore widely used to monitor aggregation into fibrils can be misinterpreted due to the inner filter effect. Simulations and NMR experiments showed that NQ-Trp has no specific "binding site"-type interaction with mono- and dimeric Aß, which could explain its low inhibitory efficiency. This suggests that the reported anti-AD activity of NQ-Trp-type molecules in in vivo models has to involve another mechanism. This study has revealed the potential pitfalls in the development of aggregation inhibitors for amyloidogenic peptides, which are of general interest for all the molecules studied in the context of inhibiting the formation of toxic aggregates.

Essentiality, protein-protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning.

The distinctive nature of cancer as a disease prompts an exploration of the special characteristics the genes implicated in cancer exhibit. The identification of cancer-associated genes and their characteristics is crucial to further our understanding of this disease and enhanced likelihood of therapeutic drug targets success. However, the rate at which cancer genes are being identified experimentally is slow. Applying predictive analysis techniques, through the building of accurate machine learning models, is potentially a useful approach in enhancing the identification rate of these genes and their characteristics. Here, we investigated gene essentiality scores and found that they tend to be higher for cancer-associated genes compared to other protein-coding human genes. We built a dataset of extended gene properties linked to essentiality and used it to train a machine-learning model; this model reached 89% accuracy and > 0.85 for the Area Under Curve (AUC). The model showed that essentiality, evolutionary-related properties, and properties arising from protein-protein interaction networks are particularly effective in predicting cancer-associated genes. We were able to use the model to identify potential candidate genes that have not been previously linked to cancer. Prioritising genes that score highly by our methods could aid scientists in their cancer genes research.

Local control of a disorder-order transition in 4E-BP1 underpins regulation of translation via eIF4E.

The molecular mechanism underpinning regulation of eukaryotic translation initiation factor eIF4E by 4E-BP1 has remained unclear. We use isothermal calorimetry, circular dichroism, NMR, and computational modeling to analyze how the structure of the eIF4E-binding domain of 4E-BP1 determines its affinity for the dorsal face of eIF4E and thus the ability of this regulator to act as a competitive inhibitor. This work identifies the key role of solvent-facing amino acids in 4E-BP1 that are not directly engaged in interactions with eIF4E. These amino acid residues influence the propensity of the natively unfolded binding motif to fold into a conformation, including a stretch of α-helix, that is required for tight binding to eIF4E. In so doing, they contribute to a free energy landscape for 4E-BP1 folding that is poised so that phosphorylation of S65 at the C-terminal end of the helical region can modulate the propensity of folding, and thus regulate the overall free energy of 4E-BP1 binding to eIF4E, over a physiologically significant range. Thus, phosphorylation acts as an intramolecular structural modulator that biases the free energy landscape for the disorder-order transition of 4E-BP1 by destabilizing the α-helix to favor the unfolded form that cannot bind eIF4E. This type of order-disorder regulatory mechanism is likely to be relevant to other intermolecular regulatory phenomena in the cell.

Predicting congenital renal tract malformation genes using machine learning.

Congenital renal tract malformations (RTMs) are the major cause of severe kidney failure in children. Studies to date have identified defined genetic causes for only a minority of human RTMs. While some RTMs may be caused by poorly defined environmental perturbations affecting organogenesis, it is likely that numerous causative genetic variants have yet to be identified. Unfortunately, the speed of discovering further genetic causes for RTMs is limited by challenges in prioritising candidate genes harbouring sequence variants. Here, we exploited the computer-based artificial intelligence methodology of supervised machine learning to identify genes with a high probability of being involved in renal development. These genes, when mutated, are promising candidates for causing RTMs. With this methodology, the machine learning classifier determines which attributes are common to renal development genes and identifies genes possessing these attributes. Here we report the validation of an RTM gene classifier and provide predictions of the RTM association status for all protein-coding genes in the mouse genome. Overall, our predictions, whilst not definitive, can inform the prioritisation of genes when evaluating patient sequence data for genetic diagnosis. This knowledge of renal developmental genes will accelerate the processes of reaching a genetic diagnosis for patients born with RTMs.

The N-methylated peptide SEN304 powerfully inhibits Aß(1-42) toxicity by perturbing oligomer formation.

Oligomeric forms of ß-amyloid (Aß) have potent neurotoxic activity and are the primary cause of neuronal injury and cell death in Alzheimer's disease (AD). Compounds that perturb oligomer formation or structure may therefore be therapeutic for AD. We previously reported that d-[(chGly)-(Tyr)-(chGly)-(chGly)-(mLeu)]-NH(2) (SEN304) is able to inhibit Aß aggregation and toxicity, shown primarily by thioflavin T fluorescence and MTT (Kokkoni, N. et al. (2006) N-Methylated peptide inhibitors of ß-amyloid aggregation and toxicity. Optimisation of inhibitor structure. Biochemistry 45, 9906-9918). Here we extensively characterize how SEN304 affects Aß(1-42) aggregation and toxicity, using biophysical assays (thioflavin T, circular dichroism, SDS-PAGE, size exclusion chromatography, surface plasmon resonance, traveling wave ion mobility mass spectrometry, electron microscopy, ELISA), toxicity assays in cell culture (MTT and lactate dehydrogenase in human SH-SHY5Y cells, mouse neuronal cell death and synaptophysin) and long-term potentiation in a rat hippocampal brain slice. These data, with dose response curves, show that SEN304 is a powerful inhibitor of Aß(1-42) toxicity, particularly effective at preventing Aß inhibition of long-term potentiation. It can bind directly to Aß(1-42), delay ß-sheet formation and promote aggregation of toxic oligomers into a nontoxic form, with a different morphology that cannot bind thioflavin T. SEN304 appears to work by inducing aggregation, and hence removal, of Aß oligomers. It is therefore a promising lead compound for Alzheimer's disease.

Viral Involvement in Alzheimer's Disease.

Alzheimer's disease (AD) is characterized by the presence of ß-amyloid plaques (Aß) and neurofibrillary tangles (NFTs) in the brain. The prevalence of the disease is increasing and is expected to reach 141 million cases by 2050. Despite the risk factors associated with the disease, there is no known causative agent for AD. Clinical trials with many drugs have failed over the years, and no therapeutic has been approved for AD. There is increasing evidence that pathogens are found in the brains of AD patients and controls, such as human herpes simplex virus-1 (HSV-1). Given the lack of a human model, the route for pathogen entry into the brain remains open for scrutiny and may include entry via a disturbed blood-brain barrier or the olfactory nasal route. Many factors can contribute to the pathogenicity of HSV-1, such as the ability of HSV-1 to remain latent, tau protein phosphorylation, increased accumulation of Aß invivo and in vitro, and repeated cycle of reactivation if immunocompromised. Intriguingly, valacyclovir, a widely used drug for the treatment of HSV-1 and HSV-2 infection, has shown patient improvement in cognition compared to controls in AD clinical studies. We discuss the potential role of HSV-1 in AD pathogenesis and argue for further studies to investigate this relationship.

Properties and identification of antibiotic drug targets.

BACKGROUND: We analysed 48 non-redundant antibiotic target proteins from all bacteria, 22 antibiotic target proteins from E. coli only and 4243 non-drug targets from E. coli to identify differences in their properties and to predict new potential drug targets. RESULTS: When compared to non-targets, bacterial antibiotic targets tend to be long, have high beta-sheet and low alpha-helix contents, are polar, are found in the cytoplasm rather than in membranes, and are usually enzymes, with ligases particularly favoured. Sequence features were used to build a support vector machine model for E. coli proteins, allowing the assignment of any sequence to the drug target or non-target classes, with an accuracy in the training set of 94%. We identified 319 proteins (7%) in the non-target set that have target-like properties, many of which have unknown function. 63 of these proteins have significant and undesirable similarity to a human protein, leaving 256 target like proteins that are not present in humans. CONCLUSIONS: We suggest that antibiotic discovery programs would be more likely to succeed if new targets are chosen from this set of target like proteins or their homologues. In particular, 64 are essential genes where the cell is not able to recover from a random insertion disruption.

Accurate determination of protein secondary structure content from Raman and Raman optical activity spectra.

Raman spectroscopy measures molecular vibrations triggered by the inelastic scattering of light, while Raman optical activity (ROA) measures a small difference in the Raman scattering from chiral molecules using circularly polarized light. We used Raman or ROA spectra to determine the secondary structure contents (helix, sheet, or other) of proteins. Forty-four ROA and 24 Raman protein spectra were converted into mean intensities within 10 cm(-1) width bins. The partial least squares algorithm with 5-fold cross-validation was used to construct models to give secondary structure contents from spectral data. The optimized algorithm gives highly accurate secondary structure contents, with R(2) and rmsd values of 0.99, and 0.6-1.7%, respectively, for second derivative Raman data when comparing predicted to experimental data. Using ROA data from 620 to 1850 cm(-1) is almost as accurate. Analysis of amide I, II, and III and backbone spectral regions reveals the importance of each of these regions for secondary structure assignment. Raman and ROA may be the methods of choice for rapid measurement of protein secondary structure contents, since they have unprecedented accuracy.

Properties and identification of human protein drug targets.

MOTIVATION: We analysed 148 human drug target proteins and 3573 non-drug targets to identify differences in their properties and to predict new potential drug targets. RESULTS: Drug targets are rare in organelles; they are more likely to be enzymes, particularly oxidoreductases, transferases or lyases and not ligases; they are involved in binding, signalling and communication; they are secreted; and have long lifetimes, shown by lack of PEST signals and the presence of N-glycosylation. This can be summarized into eight key properties that are desirable in a human drug target, namely: high hydrophobicity, high length, SignalP motif present, no PEST motif, more than two N-glycosylated amino acids, not more than one O-glycosylated Ser, low pI and membrane location. The sequence features were used as inputs to a support vector machine (SVM), allowing the assignment of any sequence to the drug target or non-target classes with an accuracy in the training set of 96%. We identified 668 proteins (23%) in the non-target set that have target-like properties. We suggest that drug discovery programmes would be more likely to succeed if new targets are chosen from this set or their homologues.

Deconvolution of conformational exchange from Raman spectra of aqueous RNA nucleosides.

Ribonucleic acids (RNAs) are key to the central dogma of molecular biology. While Raman spectroscopy holds great potential for studying RNA conformational dynamics, current computational Raman prediction and assignment methods are limited in terms of system size and inclusion of conformational exchange. Here, a framework is presented that predicts Raman spectra using mixtures of sub-spectra corresponding to major conformers calculated using classical and ab initio molecular dynamics. Experimental optimization allowed purines and pyrimidines to be characterized as predominantly syn and anti, respectively, and ribose into exchange between equivalent south and north populations. These measurements are in excellent agreement with Raman spectroscopy of ribonucleosides, and previous experimental and computational results. This framework provides a measure of ribonucleoside solution populations and conformational exchange in RNA subunits. It complements other experimental techniques and could be extended to other molecules, such as proteins and carbohydrates, enabling biological insights and providing a new analytical tool.

SitesIdentify: a protein functional site prediction tool.

BACKGROUND: The rate of protein structures being deposited in the Protein Data Bank surpasses the capacity to experimentally characterise them and therefore computational methods to analyse these structures have become increasingly important. Identifying the region of the protein most likely to be involved in function is useful in order to gain information about its potential role. There are many available approaches to predict functional site, but many are not made available via a publicly-accessible application. RESULTS: Here we present a functional site prediction tool (SitesIdentify), based on combining sequence conservation information with geometry-based cleft identification, that is freely available via a web-server. We have shown that SitesIdentify compares favourably to other functional site prediction tools in a comparison of seven methods on a non-redundant set of 237 enzymes with annotated active sites. CONCLUSION: SitesIdentify is able to produce comparable accuracy in predicting functional sites to its closest available counterpart, but in addition achieves improved accuracy for proteins with few characterised homologues. SitesIdentify is available via a webserver at http://www.manchester.ac.uk/bioinformatics/sitesidentify/

Inhibitors of protein aggregation and toxicity.

The aggregation of numerous peptides or proteins has been linked to the onset of disease, including Abeta (amyloid beta-peptide) in AD (Alzheimer's disease), asyn (alpha-synuclein) in Parkinson's disease and amylin in Type 2 diabetes. Diverse amyloidogenic proteins can often be cut down to an SRE (self-recognition element) of as few as five residues that retains the ability to aggregate. SREs can be used as a starting point for aggregation inhibitors. In particular, N-methylated SREs can bind to a target on one side, but have hydrogen-bonding blocked on their methylated face, interfering with further assembly. We applied this strategy to develop Abeta toxicity inhibitors. Our compounds, and a range of compounds from the literature, were compared under the same conditions, using biophysical and toxicity assays. Two N-methylated D-peptide inhibitors with unnatural side chains were the most effective and can reverse Abeta-induced inhibition of LTP (long-term potentiation) at concentrations as low as 10 nM. An SRE in asyn (VAQKTV) was identified using solid-state NMR. When VAQKTV was N-methylated, it was able to disrupt asyn aggregation. N-methylated derivatives of the SRE of amylin are also able to inhibit amylin aggregation.

The effects of alpha-synuclein on phospholipid vesicle integrity: a study using 31P NMR and electron microscopy.

Associations between the 140 amino acid protein alpha-synuclein (asyn) and presynaptic vesicles may play a role in maintaining synaptic plasticity and neurotransmitter release. These physiological processes may involve disruption and fusion of vesicles, arising from interactions between specific regions of asyn, including the highly basic N-terminal domain, and the surface of vesicles. This work investigates whether asyn affects the integrity of model unilamellar vesicles of varying size and phospholipid composition, by monitoring paramagnetic Mn(2+)-induced broadening of peaks in the (31)P nuclear magnetic resonance spectrum of the lipid head groups. It is shown that asyn increases the permeability to Mn(2+) of both large (200 nm diameter) and small (50 nm diameter) vesicles composed of zwitterionic phosphatidylcholine and anionic phosphatidylglycerol at protein/lipid molar ratios as low as 1:2000. Further experiments on peptides corresponding to sequences in the N-terminal (10-48), C-terminal (120-140) and central hydrophobic (71-82) regions of asyn suggest that single regions of the protein are capable of permeabilizing the vesicles to varying extents. Electron micrographs of the vesicles after addition of asyn indicate that the enhanced permeability is coupled to large-scale disruption or fusion of the vesicles. These results indicate that asyn is able to permeabilize phospholipid vesicles at low relative concentrations, dependent upon the properties of the vesicles. This could have implications for asyn playing a role in vesicle synthesis, maintenance and fusion within synapses.