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.

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.

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.

Amyloid-ß/Drug Interactions from Computer Simulations and Cell-Based Assays.

Targeting the early oligomers formed by the amyloid-ß (Aß) peptide of 40 and 42 amino acids is considered one promising therapeutic approach for Alzheimer's disease (AD). In vitro experiments and computer simulations are often used in synergy to reveal the modes of interactions of drugs. In this account, we present our contribution to understanding how small molecules bind to Aß40/Aß42 peptides, based either on extensive coarse-grained and all-atom simulations, or a variety of experimental techniques. We conclude by offering several perspectives on the future of this field to design more efficient drugs.

Properties of protein drug target classes.

Accurate identification of drug targets is a crucial part of any drug development program. We mined the human proteome to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein's sequence, post-translational modifications, secondary structure, germline variants, expression profile and drug target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all G-protein coupled receptors, ion channels, kinases and proteases, as well as proteins that are implicated in cancer. Machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. This was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily those that are directly related to a protein's sequence (e.g. secondary structure). Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins' hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and should therefore be prioritised when building a drug development programme.

Inhibition of protein aggregation and amyloid formation by small molecules.

For decades, drug after drug has failed to slow the progression of Alzheimer's disease in human trials. How compounds reducing fibril formation in vitro and toxicity in transgenic mice and flies bind to the Aß toxic oligomers, is unknown. This account reviews recent drugs mainly targeting Aß, how they were identified and report their successes from in vitro and in vivo experimental studies and their current status in clinical trials. We then focus on recent in vitro and simulation results on how inhibitors interact with Aß monomers and oligomers, highly desirable knowledge for predicting new efficient drugs. We conclude with a perspective on the future of the inhibition of amyloid formation by small molecules.

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.

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.

Design of an N-methylated peptide inhibitor of alpha-synuclein aggregation guided by solid-state NMR.

Many neurodegenerative diseases are associated with the aggregation of misfolded proteins into amyloid oligomers or fibrils that are deposited as pathological lesions within areas of the brain. An attractive therapeutic strategy for preventing or ameliorating amyloid formation is to identify agents that inhibit the onset or propagation of protein aggregation. Here we demonstrate how solid-state nuclear magnetic resonance (ssNMR) may be used to identify key residues within amyloidogenic protein sequences that may be targeted to inhibit the aggregation of the host protein. For alpha-synuclein, the major protein component of Lewy bodies associated with Parkinson's disease, we have used a combination of ssNMR and biochemical data to identify the key region for self-aggregation of the protein as residues 77-82 (VAQKTV). We used our new structural information to design a peptide derived from residues 77 to 82 of alpha-synuclein with an N-methyl group at the C-terminal residue, which was able to disrupt the aggregation of alpha-synuclein. Thus, we have shown how structural data obtained from ssNMR can guide the design of modified peptides for use as amyloid inhibitors, as a primary step toward developing therapeutic compounds for prevention and/or treatment of amyloid diseases.

Peptide inhibitors of beta-amyloid aggregation.

The aggregation of the beta-amyloid peptide (Abeta) into toxic oligomers is probably the key pathogenic event in the onset of Alzheimer's disease, and is therefore a target for the treatment of the disease. A plethora of organic molecules have been shown to inhibit Abeta aggregation and toxicity. Many inhibitors are peptides or peptidomimetics, typically based on the amino acids 16 to 20 of Abeta (KLVFF), which binds to Abeta in isolation. Peptides have been modified by adding bulky groups, charged sequences or polyethylene glycol to their termini, or by N-methylation, replacing a backbone amide by an ester, and by replacing a residue with proline.

Structure and stability of the alpha-helix: lessons for design.

The alpha-helix is the most abundant secondary structure in proteins. We now have an excellent understanding of the rules for helix formation because of experimental studies of helices in isolated peptides and within proteins, examination of helices in crystal structures, computer modeling and simulations, and theoretical work. Here we discuss structural features that are important for designing peptide helices, including amino acid preferences for interior and terminal positions, side chain interactions, disulfide bonding, metal binding, and phosphorylation. The solubility and stability of a potential design can be predicted with helical wheels and helix/coil theory, respectively. The helical content of a peptide is most often quantified by circular dichroism, so its use is discussed in detail.

N-Methylated peptide inhibitors of beta-amyloid aggregation and toxicity. Optimization of the inhibitor structure.

The key pathogenic event in the onset of Alzheimer's disease (AD) is believed to be the aggregation of the beta-amyloid (Abeta) peptide into toxic oligomers. Molecules that interfere with this process may therefore act as therapeutic agents for the treatment of AD. N-Methylated peptides (meptides) are a general class of peptide aggregation inhibitors that act by binding to one face of the aggregating peptide but are unable to hydrogen bond on the other face, because of the N-methyl group replacing a backbone NH group. Here, we optimize the structure of meptide inhibitors of Abeta aggregation, starting with the KLVFF sequence that is known to bind to Abeta. We varied the meptide length, N-methylation sites, acetylation, and amidation of the N and C termini, side-chain identity, and chirality, via five compound libraries. Inhibitor activity was tested by thioflavin T binding, affinity chromatography, electron microscopy, and an 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide toxicity assay. We found that inhibitors should have all d chirality, have a free N terminus but an amidated C terminus, and have large, branched hydrophobic side chains at positions 1-4, while the side chain at position 5 was less important. A single N-methyl group was necessary and sufficient. The most active compound, d-[(chGly)-(Tyr)-(chGly)-(chGly)-(mLeu)]-NH(2), was more active than all previously reported peptide inhibitors. Its related non-N-methylated analogues were insoluble and toxic.

The CXXC motif at the N terminus of an alpha-helical peptide.

An active site containing a CXXC motif is always found in the thiol-disulphide oxidoreductase superfamily. A survey of crystal structures revealed that the CXXC motif had a very high local propensity (26.3 +/- 6.2) for the N termini of alpha-helices. A helical peptide with the sequence CAAC at the N terminus was synthesized to examine the helix-stabilizing capacity of the CXXC motif. Circular dichroism was used to confirm the helical nature of the peptide and study behavior under titration with various species. With DTT, a redox potential of E(o) = -230 mV was measured, indicating that the isolated peptide is reducing in nature and similar to native human thioredoxin. The pK(a) values of the individual Cys residues could not be separated in the titration of the reduced state, giving a single transition with an apparent pK(a) of 6.74 (+/-0.06). In the oxidized state, the N-terminal pK(a) is 5.96 (+/-0.05). Analysis of results with the modified helix-coil theory indicated that the disulfide bond stabilized the alpha-helical structure by 0.5 kcal/mol. Reducing the disulfide destabilizes the helix by 0.9 kcal/mol.

A phosphoserine-lysine salt bridge within an alpha-helical peptide, the strongest alpha-helix side-chain interaction measured to date.

Phosphorylation is ubiquitous in control of protein activity, yet its effects on protein structure are poorly understood. Here we investigate the effect of serine phosphorylation in the interior of an alpha-helix when a salt bridge is present between the phosphate group and a positively charged side chain (in this case lysine) at i,i + 4 spacing. The stabilization of the helix is considerable and can overcome the intrinsically low preference of phosphoserine for the interior of the helix. The effect is pH dependent, as both the lysine and phosphate groups are titratable, and so calculations are given for several charge combinations. These results, with our previous work, highlight the different, context-dependent effects of phosphorylation in the alpha-helix. The interaction between the phosphate(2)(-) group and the lysine side chain is the strongest yet recorded in helix-coil studies. The results are of interest both in de novo design of peptides and in understanding the structural modes of control by phosphorylation.

Pairwise coupling in an Arg-Phe-Met triplet stabilizes alpha-helical peptide via shared rotamer preferences.

The hydrophobic Arg-Phe and Phe-Met side chain interactions stabilize the alpha-helix by -0.29 and -0.59 kcal/mol, respectively, when placed i, i + 4 in an alanine-based peptide. When both interactions are present simultaneously, however, they stabilize the helix by an additional -0.75 kcal/mol, nearly as much as the sum of its parts. We attribute this coupling to a shared rotamer preference, as the central Phe is t in both bonds. The energetic cost of restricting the Phe residue into a t conformation is only paid once in the triplet, rather than twice when the interactions are separate. Coupling is thus demonstrated to have large effects on protein stability.

Improved prediction for N-termini of alpha-helices using empirical information.

The prediction of the secondary structure of proteins from their amino acid sequences remains a key component of many approaches to the protein folding problem. The most abundant form of regular secondary structure in proteins is the alpha-helix, in which specific residue preferences exist at the N-terminal locations. Propensities derived from these observed amino acid frequencies in the Protein Data Bank (PDB) database correlate well with experimental free energies measured for residues at different N-terminal positions in alanine-based peptides. We report a novel method to exploit this data to improve protein secondary structure prediction through identification of the correct N-terminal sequences in alpha-helices, based on existing popular methods for secondary structure prediction. With this algorithm, the number of correctly predicted alpha-helix start positions was improved from 30% to 38%, while the overall prediction accuracy (Q3) remained the same, using cross-validated testing. Although the algorithm was developed and tested on multiple sequence alignment-based secondary structure predictions, it was also able to improve the predictions of start locations by methods that use single sequences to make their predictions. Furthermore, the residue frequencies at N-terminal positions of the improved predictions better reflect those seen at the N-terminal positions of alpha-helices in proteins. This has implications for areas such as comparative modeling, where a more accurate prediction of the N-terminal regions of alpha-helices should benefit attempts to model adjacent loop regions. The algorithm is available as a Web tool, located at http://rocky.bms.umist.ac.uk/elephant.

Cellular kinetics of murine lung: model system to determine basis for radioprotection with keratinocyte growth factor.

PURPOSE: Normal tissue toxicity remains a dose limitation for cancer radiotherapy and chemoradiotherapy. Growth factors offer a novel means of mitigating normal tissue radiotoxicity. In particular, keratinocyte growth factor (rHuKGF), whose proliferative activity is restricted to epithelial cells, holds promise on the basis of the findings of preclinical models of epithelial cytoprotection and the clinical developments to date. We report the radioprotection of murine lung by an increase in tissue cellularity after rHuKGF-induced proliferation. METHODS AND MATERIALS: Flow cytometric and image analysis techniques after bromodeoxyuridine labeling were used to estimate proliferative parameters. Our specialized analytical methods measure not only labeling indexes, but also the durations of S and G(2)+M phases, potential doubling times, and the net cell production rate. Image analysis techniques were used to identify the specific cell types that were proliferating (type II pneumocytes). RESULTS: Lung labeling index control values (0.5%) rose to a maximum (5.5%) at 3 days after intratracheal rHuKGF, returning to normal by Day 7. The potential doubling time fell from 66 days to 4.4 days. The net cell production rate rose from a control value of 1%/d to >15%/d by Day 3. This resulted in a nearly twofold increase in alveolar epithelial cellularity, which remained significantly elevated on Day 7. Saline-treated control animals exhibited no significant changes in the proliferative parameter values or cellularity. On the basis of these data, mice were irradiated, solely to the thorax, with ranges of single doses of 250 kVp X-rays 7 days after either intratracheal administration of 5 mg/kg rHuKGF or phospate-buffered saline. This interval was chosen because the proliferative response of the type II cells was finished but the cellularity of the lung remained increased. Pretreatment with rHuKGF extended the latent period before onset of pneumonitis after all radiation doses. rHuKGF treatment 7 days before thoracic irradiation significantly protected against pneumonitis (median effective dose 13.7 Gy, 95% confidence limit 13.4-14.0) compared with the control pretreatment with phosphate-buffered saline (median effective dose 12.8 Gy, 95% confidence limit 12.6-13.1). CONCLUSION: The data showed that an increase in tissue cellularity, caused by rHuKGF treatment before irradiation, protected the lung from damage due to pneumonitis.

Effect of the N3 residue on the stability of the alpha-helix.

N3 is the third position from the N terminus in the alpha-helix with helical backbone dihedral angles. All 20 amino acids have been placed in the N3 position of a synthetic helical peptide (CH(3)CO-[AAX AAAAKAAAAKAGY]-NH(2)) and the helix content measured by circular dichroism spectroscopy at 273 K. The dependence of peptide helicity on N3 residue identity has been used to determine a free energy scale by analysis with a modified Lifson-Roig helix coil theory that includes a parameter for the N3 energy (n3). The most stabilizing residues at N3 in rank order are Ala, Glu, Met/Ile, Leu, Lys, Ser, Gln, Thr, Tyr, Phe, Asp, His, and Trp. Free energies for the most destabilizing residues (Cys, Gly, Asn, Arg, and Pro) could not be fitted. The results correlate with N1, N2, and helix interior energies and not at all with N-cap preferences. This completes our work on studying the structural and energetic preferences of the amino acids for the N-terminal positions of the alpha-helix. These results can be used to rationally modify protein stability, help design helices, and improve prediction of helix location and stability.