In Silico Proteome Cleavage Reveals Iterative Digestion Strategy for High Sequence Coverage

In the postgenome era, biologists have sought to measure the complete complement of proteins, termed proteomics. Currently, the most effective method to measure the proteome is with shotgun, or bottom-up, proteomics, in which the proteome is digested into peptides that are identified followed by protein inference. Despite continuous improvements to all steps of the shotgun proteomics workflow, observed proteome coverage is often low; some proteins are identified by a single peptide sequence. Complete proteome sequence coverage would allow comprehensive characterization of RNA splicing variants and all posttranslational modifications, which would drastically improve the accuracy of biological models. There are many reasons for the sequence coverage deficit, but ultimately peptide length determines sequence observability. Peptides that are too short are lost because they match many protein sequences and their true origin is ambiguous. The maximum observable peptide length is determined by several analytical challenges. This paper explores computationally how peptide lengths produced from several common proteome digestion methods limit observable proteome coverage. Iterative proteome cleavage strategies are also explored. These simulations reveal that maximized proteome coverage can be achieved by use of an iterative digestion protocol involving multiple proteases and chemical cleavages that theoretically allow 92.9% proteome coverage.

Application of Hybrid Functional Groups to Predict ATP Binding Proteins

The ATP binding proteins exist as a hybrid of proteins with Walker A motif and universal stress proteins (USPs) having an alternative motif for binding ATP. There is an urgent need to find a reliable and comprehensive hybrid predictor for ATP binding proteins using whole sequence information. In this paper the open source LIBSVM toolbox was used to build a classifier at 10-fold cross-validation. The best hybrid model was the combination of amino acid and dipeptide composition with an accuracy of 84.57% and Mathews Correlation Coefficient (MCC) value of 0.693. This classifier proves to be better than many classical ATP binding protein predictors. The general trend observed is that combinations of descriptors performed better and improved the overall performances of individual descriptors, particularly when combined with amino acid composition. The work developed a comprehensive model for predicting ATP binding proteins irrespective of their functional motifs. This model provides a high probability of success for molecular biologists in predicting and selecting diverse groups of ATP binding proteins irrespective of their functional motifs.

All Phase Resetting Curves Are Bimodal, but Some Are More Bimodal Than Others

Phase resetting curves (PRCs) are phenomenological and quantitative tools that tabulate the transient changes in the firing period of endogenous neural oscillators as a result of external stimuli, for example, presynaptic inputs. A brief current perturbation can produce either a delay (positive phase resetting) or an advance (negative phase resetting) of the subsequent spike, depending on the timing of the stimulus. We showed that any planar neural oscillator has two remarkable points, which we called neutral points, where brief current perturbations produce no phase resetting and where the PRC flips its sign. Since there are only two neutral points, all PRCs of planar neural oscillators are bimodal. The degree of bimodality of a PRC, that is, the ratio between the amplitudes of the delay and advance lobes of a PRC, can be smoothly adjusted when the bifurcation scenario leading to stable oscillatory behavior combines a saddle node of invariant circle (SNIC) and an Andronov-Hopf bifurcation (HB).

Impact of Intravenous Drug Use on HIV/AIDS among Women Prisoners: A Mathematical Modelling Approach

Intravenous drug use and tattooing remain one of the major routes of HIV/AIDS transmission among prisoners. We formulate and analyze a deterministic model for the role of intravenous drug use in HIV/AIDS transmission among women prisoners. With the aid of the Centre Manifold theory, the endemic equilibrium is shown to be locally asymptotically stable when the corresponding reproduction number is greater than unity. Analysis of the reproduction number and numerical simulations suggest that an increase in intravenous drug use among women prisoners as they fail to cope with prison settings fuels the HIV/AIDS epidemic in women prisoners. Failure to control HIV/AIDS among female prisoners may be a time bomb to their communities upon their release. Thus, it may be best to consider free needle/syringe exchange and drug substitution treatment programmes in women prisons as well as considering open prison systems for less serious crimes.

A Reduced Drosophila Model Whose Characteristic Behavior Scales Up

Computational biology seeks to integrate experimental data with predictive mathematical models—testing hypotheses which result from the former through simulations of the latter. Such models should ideally be approachable and accessible to the widest possible community, motivating independent studies. One of the most commonly modeled biological systems involves a gene family critical to segmentation in Drosophila embryogenesis—the segment polarity network (SPN). In this paper, we reduce a celebrated mathematical model of the SPN to improve its accessibility; unlike its predecessor our reduction can be tested swiftly on a widely used platform. By reducing the original model we identify components which are unnecessary; that is, we begin to detect the core of the SPN—those mechanisms that are essentially responsible for its characteristic behavior. Hence characteristic behavior can scale up; we find that any solution of our model (defined as a set of conditions for which characteristic behavior is seen) can be converted into a solution of the original model. The original model is thus made more accessible for independent study through a more approachable reduction which maintains the robustness of its predecessor.

Zebra Finch Glucokinase Containing Two Homologous Halves Is an In Silico Chimera

Chimerical nature of the gene annotated as Zebra finch (Taeniopygia guttata) glucokinase (hexokinase IV) has been proved in this study. N-half of the protein encoded by that gene shows similarity with glucokinase from other vertebrates, while its C-half shows similarity with C-halves of hexokinases II. We mapped 7 new exons coding for N-half of hexokinase II and 4 new exons coding for glucokinase of Zebra finch. Finally, we reconstructed normal genes coding for Zebra finch glucokinase and hexokinase II which are situated in “head-to-tail” orientation on the chromosome 22. Because of the error in gene annotation, exons encoding N-half of normal glucokinase have been fused with exons encoding C-half of normal hexokinase II, even though they are separated from each other by the sequence 98066 nucleotides in length.

Text Mining Perspectives in Microarray Data Mining

Current microarray data mining methods such as clustering, classification, and association analysis heavily rely on statistical and machine learning algorithms for analysis of large sets of gene expression data. In recent years, there has been a growing interest in methods that attempt to discover patterns based on multiple but related data sources. Gene expression data and the corresponding literature data are one such example. This paper suggests a new approach to microarray data mining as a combination of text mining (TM) and information extraction (IE). TM is concerned with identifying patterns in natural language text and IE is concerned with locating specific entities, relations, and facts in text. The present paper surveys the state of the art of data mining methods for microarray data analysis. We show the limitations of current microarray data mining methods and outline how text mining could address these limitations.

Effective Single-Step Posttranscriptional Dynamics Allowing for a Direct Maximum Likelihood Estimation of Transcriptional Activity and the Quantification of Sources of Gene Expression Variability with an Illustration for the Hypoxia and TNFα Regulated Inflammatory Pathway

Data analysis methods for estimating promoter activity from gene reporter data frequently involve the reconstruction of the dynamics of unobserved species and numerical search algorithms for determining optimal model parameters. In contrast, we argue that posttranscriptional dynamics effectively behave like a singlestep stochastic process when gene expression variability is relatively low and, half-lives of the unobserved species are relatively small compared to characteristic observation time scales. In this case, by means of maximum likelihood estimators, for which analytical expressions exist, transcriptional activity of gene promoters can be estimated directly from observed gene reporter data without the need for numerical search algorithms and the reconstruction of unobserved variables. In addition, the model-based data analysis approach yields a single variable that measures the effective strength of the sources that give rise to gene expression variability. The approach is applied to conduct a model-based analysis of the inflammatory pathway under hypoxia condition and stimulation with tumor necrosis factor alpha in HEK293 cells.

Propagation of Nonlinear Pressure Waves in Blood

The propagation of weakly nonlinear pressure waves in a fluid-filled elastic tube has been investigated. The reductive perturbation method has been employed to derive the Korteweg-de Vries equation for small but finite amplitude. The effect of the final inner radius of the tube on the basic properties of the soliton wave was discussed. Moreover, the conditions of stability and the soliton existence via the potential and the corresponding phase portrait were computed. The applicability of the present investigation to flow problems in arteries is discussed.

A Whole Genome Pairwise Comparative and Functional Analysis of Geobacter sulfurreducens PCA

Geobacter species are involved in electricity production, bioremediations, and various environmental friendly activities. Whole genome comparative analyses of Geobacter sulfurreducens PCA, Geobacter bemidjiensis Bem, Geobacter sp. FRC-32, Geobacter lovleyi SZ, Geobacter sp. M21, Geobacter metallireducens GS-15, Geobacter uraniireducens Rf4 have been made to find out similarities and dissimilarities among them. For whole genome comparison of Geobacter species, an in-house tool, Geobacter Comparative Genomics Tool (GCGT) has been developed using BLASTALL program, and these whole genome analyses yielded conserved genes and they are used for functional prediction. The conserved genes identified are about 2184 genes, and these genes are classified into 14 groups based on the pathway information. Functions for 74 hypothetical proteins have been predicted based on the conserved genes. The predicted functions include pilus type proteins, flagellar proteins, ABC transporters, and other proteins which are involved in electron transfer. A phylogenetic tree from 16S rRNA of seven Geobacter species showed that G. sulfurreducens PCA is closely related to G. metallireducens GS-15 and G. lovleyi SZ. For evolutionary study, acetate kinase protein is used, which showed closeness to Pelobacter propionicus, Pelobacter carbinolicus, and Deferribacteraceae family bacterial species. These results will be useful to enhance electricity production by using biotechnological approaches.