A new disease-specific machine learning approach for the prediction of cancer-causing missense variants

High-throughput genotyping and sequencing techniques are rapidly and inexpensively providing large amounts of human genetic variation data. Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability and have been implicated in several human diseases, including cancer. Amino acid mutations resulting from non-synonymous SNPs in coding regions may generate protein functional changes that affect cell proliferation. In this study, we developed a machine learning approach to predict cancer-causing missense variants. We present a Support Vector Machine (SVM) classifier trained on a set of 3163 cancer-causing variants and an equal number of neutral polymorphisms. The method achieve 93% overall accuracy, a correlation coefficient of 0.86, and area under ROC curve of 0.98. When compared with other previously developed algorithms such as SIFT and CHASM our method results in higher prediction accuracy and correlation coefficient in identifying cancer-causing variants.     

Ribonuclease P

The gene coding for the RNA subunit of ribonuclease P (RNase P) is essential in all free-living organisms. The RNA subunit, itself, is an enzyme and, from its evolutionary tree, we can infer that it is a very ancient molecule. The specificity of this enzyme is that it cleaves other RNA molecules at the junction of single-stranded and the 5' end of double-stranded regions of RNA. One can speculate that this molecule was very useful in an ancient world in cleaving long pieces of RNA, which must have contained hairpin regions in it, into shorter molecules with the capability of different functions from the longer parent. Today, the specificity of the enzyme can be used in designing drug therapies.     

Remote thioredoxin recognition using evolutionary conservation and structural dynamics

The thioredoxin family of oxidoreductases plays an important role in redox signaling and control of protein function. Not only are thioredoxins linked to a variety of disorders, but their stable structure has also seen application in protein engineering. Both sequence-based and structure-based tools exist for thioredoxin identification, but remote homolog detection remains a challenge. We developed a thioredoxin predictor using the approach of integrating sequence with structural information. We combined a sequence-based Hidden Markov Model (HMM) with a molecular dynamics enhanced structure-based recognition method (dynamic FEATURE, DF). This hybrid method (HMMDF) has high precision and recall (0.90 and 0.95, respectively) compared with HMM (0.92 and 0.87, respectively) and DF (0.82 and 0.97, respectively). Dynamic FEATURE is sensitive but struggles to resolve closely related protein families, while HMM identifies these evolutionary differences by compromising sensitivity. Our method applied to structural genomics targets makes?a strong prediction of a novel thioredoxin.     

Using multiple microenvironments to find similar ligand-binding sites: application to kinase inhibitor binding

The recognition of cryptic small-molecular binding sites in protein structures is important for understanding off-target side effects and for recognizing potential new indications for existing drugs. Current methods focus on the geometry and detailed chemical interactions within putative binding pockets, but may not recognize distant similarities where dynamics or modified interactions allow one ligand to bind apparently divergent binding pockets. In this paper, we introduce an algorithm that seeks similar microenvironments within two binding sites, and assesses overall binding site similarity by the presence of multiple shared microenvironments. The method has relatively weak geometric requirements (to allow for conformational change or dynamics in both the ligand and the pocket) and uses multiple biophysical and biochemical measures to characterize the microenvironments (to allow for diverse modes of ligand binding). We term the algorithm PocketFEATURE, since it focuses on pockets using the FEATURE system for characterizing microenvironments. We validate PocketFEATURE first by showing that it can better discriminate sites that bind similar ligands from those that do not, and by showing that we can recognize FAD-binding sites on a proteome scale with Area Under the Curve (AUC) of 92%. We then apply PocketFEATURE to evolutionarily distant kinases, for which the method recognizes several proven distant relationships, and predicts unexpected shared ligand binding. Using experimental data from ChEMBL and Ambit, we show that at high significance level, 40 kinase pairs are predicted to share ligands. Some of these pairs offer new opportunities for inhibiting two proteins in a single pathway.     

Characterization and functional activity of murine monoclonal antibodies specific for α1,6-glucan chain of Helicobacter pylori lipopolysaccharide

Background: The outer core region of H. pylori lipopolysaccharide (LPS) contains α1,6‐glucan previously shown to contribute to colonizing efficiency of a mouse stomach. The aim of the present study was to generate monoclonal antibodies (mAbs) specific for α1,6‐glucan and characterize their binding properties and functional activity. Materials and Methods: BALB/c mice were injected intraperitoneally with 10⁸ formalin‐fixed H. pylori O:3 0826::Kan cells 3× over 56 days to achieve significant titer. Anti‐α1,6‐glucan‐producing hybridomas were screened by indirect ELISA using purified H. pylori O:3 0826::Kan LPS. One clone, 1C4F9, was selected for further characterization. The specificities of mAbs were determined by indirect and inhibition ELISA using structurally defined H. pylori LPS and synthetic oligosaccharides, and whole‐cell indirect ELISA (WCE) of clinical isolates. They were further characterized by indirect immunofluorescent (IF) microscopy and their functional activity in vitro determined by serum bactericidal assays against wild‐type and mutant strains of H. pylori. Results: The generated anti‐α1,6‐glucan IgM, 1C4F9, has demonstrated an excellent specificity for the glucan chain containing 5 to 6 α1,6‐linked glucose residues and showed surface accessibility by IF microscopy with H. pylori cells adherent to gastric adenocarcinoma cells monolayers. Of 38 isolates from Chile, 17 strains reacted with antiglucan mAbs in WCE (OD₄₅₀ ≥ 0.2). Bactericidal activity was observed against selective wild‐type and mutant H. pylori strains exhibiting OD₄₅₀ values of ≥0.45 in WCE. Conclusions: Anti‐α1,6‐glucan mAbs could have potential application in typing and surveillance of H. pylori isolates as well as offer insights into structural requirements for the development of LPS‐based vaccine against H. pylori infections.     

Disentangling the relative importance of climate,size and competition on tree growth in Iberian forests: implications for forest management under global change

Most large‐scale multispecies studies of tree growth have been conducted in tropical and cool temperate forests, whereas Mediterranean water‐limited ecosystems have received much less attention. This limits our understanding of how growth of coexisting tree species varies along environmental gradients in these forests, and the implications for species interactions and community assembly under current and future climatic conditions. Here, we quantify the absolute effect and relative importance of climate, tree size and competition as determinants of tree growth patterns in Iberian forests, and explore interspecific differences in the two components of competitive ability (competitive response and effect) along climatic and size gradients. Spatially explicit neighborhood models were developed to predict tree growth for the 15 most abundant Iberian tree species using permanent‐plot data from the Spanish Second and Third National Forest Inventory (IFN). Our neighborhood analyses showed a climatic and size effect on tree growth, but also revealed that competition from neighbors has a comparatively much larger impact on growth in Iberian forests. Moreover, the sensitivity to competition (i.e. competitive response) of target trees varied markedly along climatic gradients causing significant rank reversals in species performance, particularly under xeric conditions. We also found compelling evidence for strong species‐specific competitive effects in these forests. Altogether, these results constitute critical new information which not only furthers our understanding of important theoretical questions about the assembly of Mediterranean forests, but will also be of help in developing new guidelines for adapting forests in this climatic boundary to global change. If we consider the climatic gradients of this study as a surrogate for future climatic conditions, then we should expect absolute growth rates to decrease and sensitivity to competition to increase in most forests of the Iberian Peninsula (in all but the northern Atlantic forests), making these management considerations even more important in the future.     

Biotinylation facilitates the uptake of large peptides by Escherichia coli and other gram-negative bacteria

Gram-negative bacteria such as Escherichia coli can normally only take up small peptides less than 650 Da, or five to six amino acids, in size. We have found that biotinylated peptides up to 31 amino acids in length can be taken up by E. coli and that uptake is dependent on the biotin transporter. Uptake could be competitively inhibited by free biotin or avidin and blocked by the protonophore carbonyl m-chlorophenylhydrazone and was abolished in E. coli mutants that lacked the biotin transporter. Biotinylated peptides could be used to supplement the growth of a biotin auxotroph, and the transported peptides were shown to be localized to the cytoplasm in cell fractionation experiments. The uptake of biotinylated peptides was also demonstrated for two other gram-negative bacteria, Salmonella enterica serovar Typhimurium and Pseudomonas aeruginosa. This finding may make it possible to create new peptide antibiotics that can be used against gram-negative pathogens. Researchers have used various moieties to cause the illicit transport of compounds in bacteria, and this study demonstrates the illicit transport of the largest known compound to date.     

Replication, Variation and Normalisation in Microarray Experiments

INTRODUCTION: Microarray experiments often have complex designs that include sample pooling, biological and technical replication, sample pairing and dye-swapping. This article demonstrates how statistical modelling can illuminate issues in the design and analysis of microarray experiments, and this information can then be used to plan effective studies. METHODS: A very detailed statistical model for microarray data is introduced, to show the possible sources of variation that are present in even the simplest microarray experiments. Based on this model, the efficacy of common experimental designs, normalisation methodologies and analyses is determined. RESULTS: When the cost of the arrays is high compared with the cost of samples, sample pooling and spot replication are shown to be efficient variance reduction methods, whereas technical replication of whole arrays is demonstrated to be very inefficient. Dye-swap designs can use biological replicates rather than technical replicates to improve efficiency and simplify analysis. When the cost of samples is high and technical variation is a major portion of the error, technical replication can be cost effective. Normalisation by centreing on a small number of spots may reduce array effects, but can introduce considerable variation in the results. Centreing using the bulk of spots on the array is less variable. Similarly, normalisation methods based on regression methods can introduce variability. Except for normalisation methods based on spiking controls, all normalisation requires that most genes do not differentially express. Methods based on spatial location and/or intensity also require that the nondifferentially expressing genes are at random with respect to location and intensity. Spotting designs should be carefully done so that spot replicates are widely spaced on the array, and genes with similar expression patterns are not clustered. DISCUSSION: The tools for statistical design of experiments can be applied to microarray experiments to improve both efficiency and validity of the studies. Given the high cost of microarray experiments, the benefits of statistical input prior to running the experiment cannot be over-emphasised.