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The identification of genes that may be linked to cancer is of great importance for the discovery of new drug targets. The rate at which cancer genes are being found experimentally is slow, however, due to the complexity of the identification and confirmation process, giving a narrow range of therapeutic targets to investigate and develop. One solution to this problem is to use predictive analysis techniques that can accurately identify cancer gene candidates in a timely fashion. Furthermore, the effort in identifying characteristics that are linked to cancer genes is crucial to further our understanding of this disease. These characteristics can be employed in recognising therapeutic drug targets. Here, we investigated whether certain genes’ properties can indicate the likelihood of it to be involved in the initiation or progression of cancer. We found that for cancer, the essentiality scores tend to be higher for cancer genes than for all protein coding human genes. A machine-learning model was developed and we found that essentiality related properties and properties arising from protein-protein interaction networks or evolution are particularly effective in predicting cancer-associated genes. We were also able to identify potential drug targets that have not been previously linked with cancer, but have the characteristics of cancer-related genes.Author SummaryMutations in numerous genes are known to be involved in cancer, yet there are undoubtedly many more to be discovered. We analysed a set of hundreds of cancer genes with the aim of finding out what makes them different from genes not known to be mutated in cancer. In particular, we found that genes that are essential for the survival of an organism are more likely to be involved in cancer. We used the gene properties that we examined to develop an artificial intelligence method that can accurately predict whether a gene is involved in cancer or not. Applying the method gives hundreds of non-cancer genes that resemble cancer genes. New discoveries of cancer genes are likely to be found within this set.

Alzheimer's disease (AD) is characterized by the presence of β-amyloid plaques (Aβ) 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 Aβ in vivo 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.

International audience; 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 (Aβ, 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 over many years.

Supplementary data related to this article can be found at https://doi.org/10.1016/j.abb.2021.108867., Many intrinsically disordered proteins contain Gly-rich regions which are generally assumed to be disordered. Such regions often form biomolecular condensates which play essential roles in organizing cellular processes. However, the bases of their formation and stability are still not completely understood. Based on NMR studies of the Gly-rich H. harveyi "snow flea" antifreeze protein, we recently proposed that Gly-rich sequences, such as the third "RGG" region of Fused in Sarcoma (FUS) protein, may adopt polyproline II helices whose association might stabilize condensates. Here, this hypothesis is tested with a polypeptide corresponding to the third RGG region of FUS. NMR spectroscopy and molecular dynamics simulations suggest that significant populations of polyproline II helix are present. These findings are corroborated in a model peptide Ac-RGGYGGRGGWGGRGGY-NH2, where a peak characteristic of polyproline II helix is observed using CD spectroscopy. Its intensity suggests a polyproline II population of 40%. This result is supported by data from FTIR and NMR spectroscopies. In the latter, NOE correlations are observed between the Tyr and Arg, and Arg and Trp side chain hydrogens, confirming that side chains spaced three residues apart are close in space. Taken together, the data are consistent with a polyproline II helix, which is bent to optimize interactions between guanidinium and aromatic moieties, in equilibrium with a statistical coil ensemble. These results lend credence to the hypothesis that Gly-rich segments of disordered proteins may form polyproline II helices which help stabilize biomolecular condensates., This study was supported by projects: LCF/BQ/PR19/11700003 from “La Caixa Foundation” (ID 100010434) to M.M., BBSRC CASE award with UCB Pharma BB/L014734/1 to AJD, and SAF2016-76678-C2-2-R, 2019AEP121 and BTC-PID2019-109306RB-I00 to DVL from the Spanish Ministry of Economy and Competitivity, the Spanish National Research Council and the Spanish Ministry of Science and Innovation, respectively. The authors acknowledge FCT-Portugal for the PhD studentship attributed to Sara S. Félix (PD/BD/148028/2019). J.O. is a Ramón y Cajal Fellow of the Spanish AEI Ministry of Science and Innovation, and a Leondardo Fellow from the BBVA Foundation (grant number BBM-TRA-0203). NMR experiments were performed in the “Manuel Rico” NMR Laboratory (LMR) of the Spanish National Research Council (CSIC), a node of the Spanish Large-Scale National Facility (ICTS R-LRB). We are grateful to Dr. José Varela Espinosa, Emilia Aporta Sosa and Cristina Quevedo Sierra (MS-CIB/CSIC) for expert technical work with peptide synthesis.

Many intrinsically disordered proteins contain Gly-rich regions which are generally assumed to be disordered. Such regions often form biomolecular condensates which play essential roles in organizing cellular processes. However, the bases of their formation and stability are still not completely understood. Considering NMR studies of the Gly-rich H. harveyi "snow flea" antifreeze protein, we recently proposed that Gly-rich sequences, such as the third "RGG" region of Fused in Sarcoma (FUS) protein, may adopt polyproline II helices whose association might stabilize condensates. Here, this hypothesis is tested with a polypeptide corresponding to the third RGG region of FUS. NMR spectroscopy and molecular dynamics simulations suggest that significant populations of polyproline II helix are present. These findings are corroborated in a model peptide Ac-RGGYGGRGGWGGRGGY-NH2, where a peak characteristic of polyproline II helix is observed using CD spectroscopy. Its intensity suggests a polyproline II population of 40%. This result is supported by data from FTIR and NMR spectroscopies. In the latter, NOE correlations are observed between the Tyr and Arg, and Arg and Trp side chain hydrogens, confirming that side chains spaced three residues apart are close in space. Taken together, the data are consistent with a polyproline II helix, which is bent to optimize interactions between guanidinium and aromatic moieties, in equilibrium with a statistical coil ensemble. In cells, the polyproline II population of these peptides could be augmented by binding profilin protein or SH3, WW or OCRE domains, association with RNA or assembly into polyproline II helical bundles. These results lend credence to the hypothesis that Gly-rich segments of disordered proteins may form polyproline II helices which help stabilize biomolecular condensates.

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.

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. The conformational flexibility of monoribonucleotides can make interpreting their vibrational spectra challenging. Here a combination of classical and ab initio molecular dynamics is used to calculate the contributions of different monoribonucleotide conformers to experimental Raman spectra.

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.

Given the high attrition rates, substantial costs and slow pace of new drug discovery and development, repurposing of 'old' drugs to treat both common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked compounds, with potentially lower overall development costs and shorter development timelines. Various data-driven and experimental approaches have been suggested for the identification of repurposable drug candidates; however, there are also major technological and regulatory challenges that need to be addressed. In this Review, we present approaches used for drug repurposing (also known as drug repositioning), discuss the challenges faced by the repurposing community and recommend innovative ways by which these challenges could be addressed to help realize the full potential of drug repurposing.

During the evolution of multicellular eukaryotes, gene duplication occurs frequently to generate new genes and/or functions. A duplicated gene may have a similar function to its ancestral gene. Therefore, it may be expected that duplicated genes are less likely to be critical for the survival of an organism, since there are multiple copies of the gene rendering each individual copy redundant. In this study, we explored the developmental expression patterns of duplicate gene pairs and the relationship between development co-expression and phenotypes resulting from the knockout of duplicate genes in the mouse. We define genes that generate lethal phenotypes in single gene knockout experiments as essential genes. We found that duplicate gene pairs comprised of two essential genes tend to be expressed at different stages of development, compared to duplicate gene pairs with at least one non-essential member, showing that the timing of developmental expression affects the ability of one paralogue to compensate for the loss of the other. Gene essentiality, developmental expression and gene duplication are thus closely linked.

The dominant model for Alzheimer's disease (AD) is the amyloid cascade hypothesis, in which the accumulation of excess amyloid-β (Aβ) leads to inflammation, excess glutamate and intracellular calcium, oxidative stress, tau hyperphosphorylation and tangle formation, neuronal loss, and ultimately dementia. In a cascade, AD proceeds in a unidirectional fashion, with events only affecting downstream processes. Compelling evidence now exists for the presence of positive feedback loops in AD, however, involving oxidative stress, inflammation, glutamate, calcium, and tau. The pathological state of AD is thus a system of positive feedback loops, leading to amplification of the initial perturbation, rather than a linear cascade. Drugs may therefore be effective by targeting numerous points within the loops, rather than concentrating on upstream processes. Anti-inflammatories and anti-oxidants may be especially valuable, since these processes are involved in many loops and hence would affect numerous processes in AD.

The two hallmarks of Alzheimer’s disease (AD) are the presence of neurofibrillary tangles (NFT) made of aggregates of the hyperphosphorylated tau protein and of amyloid plaques composed of amyloid-β (Aβ) peptides, primarily Aβ1–40 and Aβ1–42. Targeting the production, aggregation, and toxicity of Aβ with small molecule drugs or antibodies is an active area of AD research due to the general acceptance of the amyloid cascade hypothesis, but thus far all drugs targeting Aβ have failed. From a review of the recent literature and our own experience based on in vitro, in silico, and in vivo studies, we present some reasons to explain this repetitive failure.

Essential genes are those that are critical for life. In the specific case of the mouse, they are the set of genes whose deletion means that a mouse is unable to survive after birth. As such, they are the key minimal set of genes needed for all the steps of development to produce an organism capable of life ex utero. We explored a wide range of sequence and functional features to characterise essential (lethal) and non-essential (viable) genes in mice. Experimental data curated manually identified 1301 essential genes and 3451 viable genes. Very many sequence features show highly significant differences between essential and viable mouse genes. Essential genes generally encode complex proteins, with multiple domains and many introns. These genes tend to be: long, highly expressed, old and evolutionarily conserved. These genes tend to encode ligases, transferases, phosphorylated proteins, intracellular proteins, nuclear proteins, and hubs in protein-protein interaction networks. They are involved with regulating protein-protein interactions, gene expression and metabolic processes, cell morphogenesis, cell division, cell proliferation, DNA replication, cell differentiation, DNA repair and transcription, cell differentiation and embryonic development. Viable genes tend to encode: membrane proteins or secreted proteins, and are associated with functions such as cellular communication, apoptosis, behaviour and immune response, as well as housekeeping and tissue specific functions. Viable genes are linked to transport, ion channels, signal transduction, calcium binding and lipid binding, consistent with their location in membranes and involvement with cell-cell communication. From the analysis of the composite features of essential and viable genes, we conclude that essential genes tend to be required for intracellular functions, and viable genes tend to be involved with extracellular functions and cell-cell communication. Knowledge of the features that are over-represented in essential genes allows for a deeper understanding of the functions and processes implemented during mammalian development.

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

Oligomeric forms of β-amyloid (Aβ) 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 Aβ aggregation and toxicity, shown primarily by thioflavin T fluorescence and MTT (Kokkoni, N. et al. (2006) N-Methylated peptide inhibitors of β-amyloid aggregation and toxicity. Optimisation of inhibitor structure. Biochemistry 45, 9906-9918). Here we extensively characterize how SEN304 affects Aβ(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 Aβ(1-42) toxicity, particularly effective at preventing Aβ inhibition of long-term potentiation. It can bind directly to Aβ(1-42), delay β-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 Aβ oligomers. It is therefore a promising lead compound for Alzheimer's disease.

Knowledge of the fold class of a protein is valuable because fold class gives an indication of protein function and evolution. Fold class can be accurately determined from a crystal structure or NMR structure, though these methods are expensive, time-consuming, and inapplicable to all proteins. In contrast, vibrational spectra [infra-red, Raman, or Raman optical activity (ROA)] are rapidly obtained for proteins under wide range of biological molecules under diverse experimental and physiological conditions. Here, we show that the fold class of a protein can be determined from Raman or ROA spectra by converting a spectrum into data of 10 cm−1 bin widths and applying the random forest machine learning algorithm. Spectral data from 605 and 1785 cm−1 were analyzed, as well as the amide I, II, and III regions in isolation and in combination. ROA amide II and III data gave the best performance, with 33 of 44 proteins assigned to one of the correct four top-level structural classification of proteins (SCOP) fold class (all α, all β, α and β, and disordered). The method also shows which spectral regions are most valuable in assigning fold class.

Numerous short peptides have been shown to form beta-sheet amyloid aggregates in vitro. Proteins that contain such sequences are likely to be problematic for a cell, due to their potential to aggregate into toxic structures. We investigated the structures of 30 proteins containing 45 sequences known to form amyloid, to see how the proteins cope with the presence of these potentially toxic sequences, studying secondary structure, hydrogen-bonding, solvent accessible surface area and hydrophobicity. We identified two mechanisms by which proteins avoid aggregation: Firstly, amyloidogenic sequences are often found within helices, despite their inherent preference to form beta structure. Helices may offer a selective advantage, since in order to form amyloid the sequence will presumably have to first unfold and then refold into a beta structure. Secondly, amyloidogenic sequences that are found in beta structure are usually buried within the protein. Surface exposed amyloidogenic sequences are not tolerated in strands, presumably because they lead to protein aggregation via assembly of the amyloidogenic regions. The use of alpha-helices, where amyloidogenic sequences are forced into helix, despite their intrinsic preference for beta structure, is thus a widespread mechanism to avoid protein aggregation.

The aggregation of numerous peptides or proteins has been linked to the onset of disease, including Aβ (amyloid β-peptide) in AD (Alzheimer's disease), asyn (α-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 Aβ 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 Aβ-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.

Collagen sequences frequently deviate from the most thermally stable (Gly-Pro-Hyp)(n) pattern, with many mutations causing osteogenesis imperfecta (or "brittle bone disease"). The effects of collagen mutations have been studied in short peptides. The analysis of this work is problematic, however, as triple-helices fray from their ends, making the coil/triple-helix equilibrium non-two-state. Here, I develop a statistical thermodynamic model to handle this equilibrium that is applicable to peptides that follow the (G-X-Y)(n) pattern, where Gly is present at every third position and where all three chains are identical. Parameters for substitutions at each position are included, as well as a penalty for initiating triple-helix formation. The model is applied to equilibrium experimental data at 37 degrees C to show that the extension of a triple-helix by a three residue unit stabilizes the triple-helix by 0.76 kcal/mol for Gly-Pro-Hyp and 0.33 kcal/mol for Gly-Pro-Pro. The replacement of Hyp by Arg, Asp, or Trp destabilizes the triple-helix by 1.5, 2.4, and 2.9 kcal/mol, respectively, where the substitution is present once in each chain. The model can thus be used to quantitatively interpret data on collagen peptides, giving free energies that can help rationalize mutations that affect collagen stability, and to design new collagen sequences.

The 20 standard amino acids encoded by the Genetic Code were adopted during the RNA World, around 4 billion years ago. This amino acid set could be regarded as a frozen accident, implying that other possible structures could equally well have been chosen to use in proteins. Amino acids were not primarily selected for their ability to support catalysis, since the RNA World already had highly effective cofactors to perform reactions, such as oxidation, reduction and transfer of small molecules. Rather, they were selected to enable the formation of soluble structures with close-packed cores, allowing the presence of ordered binding pockets. Factors to take into account when assessing why a particular amino acid might be used include: its component atoms, functional groups, biosynthetic cost, use in a protein core or on the surface, solubility and stability. Applying these criteria to the 20 standard amino acids, and considering some other simple alternatives that are not used, we find that there are excellent reasons for the selection of every amino acid. Rather than being a frozen accident, the set of amino acids selected appears to be near ideal.

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.

Associations between the protein alpha-synuclein (alpha-syn) and presynaptic vesicles have been implicated in synaptic plasticity and neurotransmitter release and may also affect how the protein aggregates into fibrils found in Lewy bodies, the cellular inclusions associated with neurodegenerative diseases. This work investigated how alpha-syn interacts with model phospholipid membranes and examined what effect protein binding has upon the physical properties of lipid bilayers. Wide line 2H and 31P NMR spectra of phospholipid vesicles revealed that alpha-syn associates with membranes containing lipids with anionic headgroups and can disrupt the integrity of the lipid bilayer, but the protein has little effect on membranes of zwitterionic phosphatidylcholine. A peptide, alpha-syn(10-48), which corresponds to the lysine-rich N-terminal region of alpha-syn, was found to associate with lipid headgroups with a preference for a negative membrane surface charge. Another peptide, alpha-syn(120-140), which corresponds to the glutamate-rich C-terminal region, also associates weakly with lipid headgroups but with a slightly higher affinity for membranes with no net surface charge than for negatively charged membrane surfaces. Binding of alpha-syn(10-48) and alpha-syn(120-140) to the lipid vesicles did not disrupt the lamellar structure of the membranes, but both peptides appeared to induce the lateral segregation of the lipids into clusters of acidic lipid-enriched and acidic lipid-deficient domains. From these findings, it is speculated that the N-terminal and C-terminal domains of full-length alpha-syn might act in concert to organize the membrane components during normal protein function and perhaps play a role in presynaptic vesicle synthesis, maintenance, and fusion.

Methods for predicting protein function from structure are becoming more important as the rate at which structures are solved increases more rapidly than experimental knowledge. As a result, protein structures now frequently lack functional annotations. The majority of methods for predicting protein function are reliant upon identifying a similar protein and transferring its annotations to the query protein. This method fails when a similar protein cannot be identified, or when any similar proteins identified also lack reliable annotations. Here, we describe a method that can assign function from structure without the use of algorithms reliant upon alignments. Using simple attributes that can be calculated from any crystal structure, such as secondary structure content, amino acid propensities, surface properties and ligands, we describe each enzyme in a non-redundant set. The set is split according to Enzyme Classification (EC) number. We combine the predictions of one-class versus one-class support vector machine models to make overall assignments of EC number to an accuracy of 35% with the top-ranked prediction, rising to 60% accuracy with the top two ranks. In doing so we demonstrate the utility of simple structural attributes in protein function prediction and shed light on the link between structure and function. We apply our methods to predict the function of every currently unclassified protein in the Protein Data Bank.

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.

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.

The ability to predict protein function from structure is becoming increasingly important as the number of structures resolved is growing more rapidly than our capacity to study function. Current methods for predicting protein function are mostly reliant on identifying a similar protein of known function. For proteins that are highly dissimilar or are only similar to proteins also lacking functional annotations, these methods fail. Here, we show that protein function can be predicted as enzymatic or not without resorting to alignments. We describe 1178 high-resolution proteins in a structurally non-redundant subset of the Protein Data Bank using simple features such as secondary-structure content, amino acid propensities, surface properties and ligands. The subset is split into two functional groupings, enzymes and non-enzymes. We use the support vector machine-learning algorithm to develop models that are capable of assigning the protein class. Validation of the method shows that the function can be predicted to an accuracy of 77% using 52 features to describe each protein. An adaptive search of possible subsets of features produces a simplified model based on 36 features that predicts at an accuracy of 80%. We compare the method to sequence-based methods that also avoid calculating alignments and predict a recently released set of unrelated proteins. The most useful features for distinguishing enzymes from non-enzymes are secondary-structure content, amino acid frequencies, number of disulphide bonds and size of the largest cleft. This method is applicable to any structure as it does not require the identification of sequence or structural similarity to a protein of known function.

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 Aβ toxic oligomers, is unknown. This account reviews recent drugs mainly targeting Aβ, 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 Aβ 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 self-assembly of the amyloid-β (Aβ) peptide of various amino acid lengths into senile plaques is one hallmark of Alzheimer's disease pathology. In the past decade, many small molecules, including NQTrp, have been identified to reduce aggregation and toxicity. However, due to the heterogeneity of the conformational ensemble of Aβ with drugs, we lack detailed structures of the transient complexes. Following our previous simulation of the monomer of Aβ1-28, here we characterize the equilibrium ensemble of the Aβ1-28 monomer with NQTrp by means of extensive atomistic replica exchange molecular dynamics simulations using a force field known to fold diverse proteins correctly. While the secondary structure content and the intrinsic disorder of the whole peptides are very similar and the lifetimes of the salt-bridges remain constant, the population of β-hairpin is reduced by a factor of 1.5 and the population of α-helix in the region 17–24 is increased by a factor of two upon NQTrp binding. These two factors, which impact the free energy barrier for nucleation, provide a first explanation for the reported reduced Aβ1-40/1-42 aggregation kinetics in the presence of NQTrp. Backbone and side-chain interactions of Aβ with NQTrp may also inhibit Aβ–Aβ contacts. The fraction of free Aβ1-28 monomer is, however, on the order of 20–25% at 17.5 mM, and this shows that the affinity of NQTrp is low and hence its inhibitory activity is not very strong. This inhibitor can be improved to reduce the formation of dimer, a critical step in aggregation and toxicity.

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 the aggregation of the monomeric peptide β-amyloid (Aβ) 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 Aβ 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 Aβ, 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.

Peptide helices in solution form a complex mixture of all helix, all coil or, most frequently, central helices with frayed coil ends. In order to interpret experiments on helical peptides and make theoretical predictions on helices, it is therefore essential to use a helix-coil theory that takes account of this equilibrium. The original Zimm-Bragg and Lifson-Roig helix-coil theories have been greatly extended in the last 10 years to include additional interactions. These include preferences for the N-cap, N1, N2, N3 and C-cap positions, capping motifs, helix dipoles, side chain interactions and 3(10)-helix formation. These have been applied to determine energies for these preferences from experimental data and to predict the helix contents of peptides. This review discusses these newly recognised structural features of helices and how they have been included in helix-coil models.

We analyse for each of 20 amino acids X the statistics of spacings between consecutive occurrences of X within the well-characterized Saccharomyces cerevisiae genome. The occurrences of amino acids may exhibit near random, clustered or smoothed out behaviour, like one-dimensional stochastic processes along the protein chain. If amino acids are distributed randomly within a sequence, then they follow a Poisson process, and a histogram of the number of observations of each gap size would asymptotically follow a negative exponential distribution. The novelty of the present approach lies in the use of differential geometric methods to quantify information on sequencing of amino acids and groups of amino acids, via the sequences of intervals between their occurrences. The differential geometry arises from an information-theoretic distance function on the two-dimensional space of stochastic processes subordinate to gamma distributions-which latter include the random process as a special case. We find that maximum-likelihood estimates of parametric statistics show that all 20 amino acids tend to cluster, some substantially. In other words, the frequencies of short gap lengths tend to be higher and the variance of the gap lengths is greater than expected by chance. This may be because localizing amino acids with the same properties may favour secondary structure formation or transmembrane domains. Gap sizes of 1 or 2 are generally disfavoured, 1 strongly so. The only exceptions to this are Gln and Ser, as a result of poly(Gln) or poly(Ser) sequences. There are preferences for gaps of 4 and 7 that can be attributed to alpha -helices. In particular, a favoured gap of 7 for Leu is found in coiled coils. Our method contributes to the characterization of whole sequences by extracting and quantifying stable stochastic features.

We have investigated the effect of placing phosphoserine at the N-cap, N1, N2, N3, and interior position in alanine-based alpha-helical peptides. Helix contents of each peptide were measured by CD spectroscopy and titrations performed to determine pK(a) values. Data were analyzed with modified Lifson-Roig theory to determine helix-coil parameters (n, n(1), n(2), n(3), and w) and free energy changes for phosphoserine at each helical position. Results are given for a -1 and -2 phosphoserine charge state. Results show that phosphoserine stabilizes at the N-terminal positions by as much as 2.3 kcal.mol(-1), while destabilizes in the helix interior by 1.2 kcal.mol(-1), relative to serine. The rank order of free energies relative to serine at each position is N2N3N1N-capinterior. Moreover, -2 phosphoserine is the most preferred residue known at each of these N-terminal positions. Experimental pK(a) values for the -1 to -2 phosphoserine transition are in the order N2N-capN1N3interior. This order agrees well with electrostatics calculations carried out with phosphoserine at the N-terminal positions and interior positions. Combining these with calculations at the C3, C2, C1, and C-cap positions gives results for phosphoserine along the length of the helix. We see a transition from phosphoserine stabilization at the N-terminus to destabilization at the C-terminus and can explain this in terms of the balance of protein solvation, favorable interactions, and dehydration. These results give insight into the phosphorylatable control of biological systems through positive or negative changes in stability.

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.

A simplistic, yet often used, view of protein stability is that amino acids attract other amino acids with similar polarity, whereas nonpolar and polar side chains repel. Here we show that nonpolar/polar interactions, namely Val or Ile bonding to Lys or Arg in alpha-helices, can in fact be stabilizing. Residues spaced i, i + 4 in alpha-helices are on the same face of the helix, with potential to favorably interact and stabilize the structure. We observe that the nonpolar/polar pairs Ile-Lys, Ile-Arg, and Val-Lys occur in protein helices more often than expected when spaced i, i + 4. Partially helical peptides containing pairs of nonpolar/polar residues were synthesized. Controls with i, i + 5 spacing have the residues on opposite faces of the helix and are less helical than the test peptides with the i, i + 4 interactions. Experimental circular dichroism results were analyzed with helix-coil theory to calculate the free energy for the interactions. All three stabilize the helix with DeltaG between -0.14 and -0.32 kcal x mol(-1). The interactions are hydrophobic with contacts between Val or Ile and the alkyl groups in Arg or Lys. Side chains such as Lys and Arg can thus interact favorably with both polar and nonpolar residues.