ISDTool: A computational model for predicting immunosuppressive domain of HERVs

Human endogenous retroviruses (HERVs) have been found to act as etiological cofactors in several chronic diseases, including cancer, autoimmunity and neurological dysfunction. Immunosuppressive domain (ISD) is a conserved region of transmembrane protein (TM) in envelope gene (env) of retroviruses. In vitro and vivo, evidence has shown that retroviral TM is highly immunosuppressive and a synthetic peptide (CKS-17) that shows homology to ISD inhibits immune function. ISD is probably a potential pathogenic element in HERVs. However, only less than one hundred ISDs of HERVs have been annotated by researchers so far, and universal software for domain prediction could not achieve sufficient accuracy for specific ISD. In this paper, a computational model is proposed to identify ISD in HERVs based on genome sequences only. It has a classification accuracy of 97.9% using Jack-knife test. 117 HERVs families were scanned with the model, 1002 new putative ISDs have been predicted and annotated in the human chromosomes. This model is also applicable to search for ISDs in human T-lymphotropic virus (HTLV), simian T-lymphotropic virus (STLV) and murine leukemia virus (MLV) because of the evolutionary relationship between endogenous and exogenous retroviruses. Furthermore, software named ISDTool has been developed to facilitate the application of the model. Datasets and the software involved in the paper are all available at https://sourceforge.net/projects/isdtool/files/ISDTool-1.0.     

GPCR–drug interactions prediction using random forest with drug-association-matrix-based post-processing procedure

G-protein-coupled receptors (GPCRs) are important targets of modern medicinal drugs. The accurate identification of interactions between GPCRs and drugs is of significant importance for both protein function annotations and drug discovery. In this paper, a new sequence-based predictor called TargetGDrug is designed and implemented for predicting GPCR–drug interactions. In TargetGDrug, the evolutionary feature of GPCR sequence and the wavelet-based molecular fingerprint feature of drug are integrated to form the combined feature of a GPCR–drug pair; then, the combined feature is fed to a trained random forest (RF) classifier to perform initial prediction; finally, a novel drug-association-matrix-based post-processing procedure is applied to reduce potential false positive or false negative of the initial prediction. Experimental results on benchmark datasets demonstrate the efficacy of the proposed method, and an improvement of 15% in the Matthews correlation coefficient (MCC) was observed over independent validation tests when compared with the most recently released sequence-based GPCR–drug interactions predictor. The implemented webserver, together with the datasets used in this study, is freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetGDrug.     

PPM-Dom: A novel method for domain position prediction

Domains are the structural basis of the physiological functions of proteins, and the prediction of which is an advantageous process on the study of protein structure and function. This article proposes a new complete automatic prediction method, PPM-Dom (Domain Position Prediction Method), for predicting the particular positions of domains in a target protein via its atomic coordinate. The presented method integrates complex networks, community division, and fuzzy mean operator (FMO). The whole sequences are divided into potential domain regions by the complex network and community division, and FMO allows the final determination for the domain position. This method will suffice to predict regions that will form a domain structure and those that are unstructured based on completely new atomic coordinate information of the query sequence, and be able to separate different domains in the same query sequence from each other. On evaluating the performance using an independent testing dataset, PPM-Dom reached 91.41% for prediction accuracy, 96.12% for sensitivity and 92.86% for specificity. The tool bag of PPM-Dom is freely available at http://cic.scu.edu.cn/bioinformatics/PPMDom.zip.     

PrAS: Prediction of amidation sites using multiple feature extraction

Amidation plays an important role in a variety of pathological processes and serious diseases like neural dysfunction and hypertension. However, identification of protein amidation sites through traditional experimental methods is time consuming and expensive. In this paper, we proposed a novel predictor for Prediction of Amidation Sites (PrAS), which is the first software package for academic users. The method incorporated four representative feature types, which are position-based features, physicochemical and biochemical properties features, predicted structure-based features and evolutionary information features. A novel feature selection method, positive contribution feature selection was proposed to optimize features. PrAS achieved AUC of 0.96, accuracy of 92.1%, sensitivity of 81.2%, specificity of 94.9% and MCC of 0.76 on the independent test set. PrAS is freely available at https://sourceforge.net/p/praspkg.     

Assessing the similarity of ligand binding conformations with the Contact Mode Score

Structural and computational biologists often need to measure the similarity of ligand binding conformations. The commonly used root-mean-square deviation (RMSD) is not only ligand-size dependent, but also may fail to capture biologically meaningful binding features. To address these issues, we developed the Contact Mode Score (CMS), a new metric to assess the conformational similarity based on intermolecular protein-ligand contacts. The CMS is less dependent on the ligand size and has the ability to include flexible receptors. In order to effectively compare binding poses of non-identical ligands bound to different proteins, we further developed the eXtended Contact Mode Score (XCMS). We believe that CMS and XCMS provide a meaningful assessment of the similarity of ligand binding conformations. CMS and XCMS are freely available at http://brylinski.cct.lsu.edu/content/contact-mode-score and http://geaux-computational-bio.github.io/contact-mode-score/.     

GroupTracker: Video tracking system for multiple animals under severe occlusion

Quantitative analysis of behaviors shown by interacting multiple animals can provide a key for revealing high-order functions of their nervous systems. To resolve these complex behaviors, a video tracking system that preserves individual identity even under severe overlap in positions, i.e., occlusion, is needed. We developed GroupTracker, a multiple animal tracking system that accurately tracks individuals even under severe occlusion. As maximum likelihood estimation of Gaussian mixture model whose components can severely overlap is theoretically an ill-posed problem, we devised an expectation–maximization scheme with additional constraints on the eigenvalues of the covariance matrix of the mixture components. Our system was shown to accurately track multiple medaka (Oryzias latipes) which freely swim around in three dimensions and frequently overlap each other. As an accurate multiple animal tracking system, GroupTracker will contribute to revealing unexplored structures and patterns behind animal interactions. The Java source code of GroupTracker is available at https://sites.google.com/site/fukunagatsu/software/group-tracker.     

Automated prediction of three-way junction topological families in RNA secondary structures

We present an algorithm for automatically predicting the topological family of any RNA three-way junction, given only the information from the secondary structure: the sequence and the Watson–Crick pairings. The parameters of the algorithm have been determined on a data set of 33 three-way junctions whose 3D conformation is known. We applied the algorithm on 53 other junctions and compared the predictions to the real shape of those junctions. We show that the correct answer is selected out of nine possible configurations 64% of the time. Additionally, these results are noticeably improved if homology information is used. The resulting software, Cartaj, is available online and downloadable (with source) at: http://cartaj.lri.fr.     

Using ensemble methods to deal with imbalanced data in predicting protein–protein interactions

In proteins, the number of interacting pairs is usually much smaller than the number of non-interacting ones. So the imbalanced data problem will arise in the field of protein–protein interactions (PPIs) prediction. In this article, we introduce two ensemble methods to solve the imbalanced data problem. These ensemble methods combine the based-cluster under-sampling technique and the fusion classifiers. And then we evaluate the ensemble methods using a dataset from Database of Interacting Proteins (DIP) with 10-fold cross validation. All the prediction models achieve area under the receiver operating characteristic curve (AUC) value about 95%. Our results show that the ensemble classifiers are quite effective in predicting PPIs; we also gain some valuable conclusions on the performance of ensemble methods for PPIs in imbalanced data. The prediction software and all dataset employed in the work can be obtained for free at http://cic.scu.edu.cn/bioinformatics/Ensemble_PPIs/index.html.     

Effect of heating rate and γ-Radiation on the structure of As4SexTe6−x Glasses

The phenomena accompanying the temperature-induced structural changes in five As₄Se_xTe_6–x glasses, withx=1 tox=5, were examined and are discussed. Differential thermal analysis traces of each glass composition at different heating rates from 2 to 50 deg/min were obtained and interpreted. The effect of the Se/Te ratio on the crystallization behaviour is discussed. It is interesting to note that the compositional dependence of the overall behaviour of the crystallization activation energy (E) seems to be similar to that of both the melting point (T_m) and the thermal conductivity () for the investigated glasses. Created structural defects due to gamma-irradiation have some effects on the crystallization process.

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Zusammenfassung Die die temperaturbedingten strukturellen Veränderungen von 5 Glasern der allgemeinen Zusammensetzung As₄Se_xTe_6–x (x=1–5) begleitenden Phänomene wurden untersucht und diskutiert. Die von jedem Glas bei unterschiedlichen Aufheizgeschwindigkeiten zwischen 2 und 50 Grad pro Minute erhaltenen DTA-Kurven werden interpretiert. Der Effekt des Se/Te-Verhältnisses auf das Kristallisationsverhalten wird diskutiert. Von Interesse ist, daß die Abhängigkeit der Aktivierungsenergie (E) der Kristallisation von der Zusammensetzung der des Schmelzpunktes (Tm) und der Wärmeleitfähigkeit (*) der untersuchten Gläser ähnelt. Durch y-Bestrahlung hervorgerufene strukturelle Defekte haben einen gewissen Einfluß auf den Kristallisationsprozeß.

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, As₄Se_x,Te_6–x, c x=1–5. ( 2 50°/) . Se/Te . , , (T _m ) (). , -, .

     

An alternative approach to spectrum base line estimation

We present a new form of merit function which measures agreement between a large number of data and the model function with a particular choice of parameters. We demonstrate the efficiency of the proposed merit function on the common problem of finding the base line of a spectrum. When the base line is expected to be a horizontal straight line, the use of minimization algorithms is not necessary, i.e. the solution is achieved in a small number of steps. We discuss the advantages of the proposed merit function in general, when explicit use of a minimization algorithm is necessary.The hardcopy text is accompanied by an electronic archive, stored on the SAE homepage at http://www1.elsevier.com/homepage/saa/sab/content/lower.htm. The archive contains fully functional demo program with tutorial, examples and Visual Basic source code of the key subroutine.     

Comprehensive comparison of two protein family of P-ATPases (13A1 and 13A3) in insects

The P-type ATPases (P-ATPases) are present in all living cells where they mediate ion transport across membranes on the expense of ATP hydrolysis. Different ions which are transported by these pumps are protons like calcium, sodium, potassium, and heavy metals such as manganese, iron, copper, and zinc. Maintenance of the proper gradients for essential ions across cellular membranes makes P-ATPases crucial for cell survival. In this study, characterization of two families of P-ATPases including P-ATPase 13A1 and P-ATPase 13A3 protein was compared in two different insect species from different orders. According to the conserved motifs found with MEME, nine motifs were shared by insects of 13A1 family but eight in 13A3 family. Seven different insect species from 13A1 and five samples from 13A3 family were selected as the representative samples for functional and structural analyses. The structural and functional analyses were performed with ProtParam, SOPMA, SignalP 4.1, TMHMM 2.0, ProtScale and ProDom tools in the ExPASy database. The tertiary structure of Bombus terrestris as a sample of each family of insects were predicted by the Phyre2 and TM-score servers and their similarities were verified by SuperPose server. The tertiary structures were predicted via the “c3b9bA” model (PDB Accession Code: 3B9B) in P-ATPase 13A1 family and “c2zxeA” model (PDB Accession Code: 2ZXE) in P-ATPase 13A3 family. A phylogenetic tree was constructed with MEGA 6.06 software using the Neighbor-joining method. According to the results, there was a high identity of P-ATPase families so that they should be derived from a common ancestor however they belonged to separate groups. In protein–protein interaction analysis by STRING 10.0, six common enriched pathways of KEGG were identified in B. terrestris in both families. The obtained data provide a background for bioinformatic studies of the function and evolution of other insects and organisms.     

A novel fuzzy set based multifactor dimensionality reduction method for detecting gene–gene interaction

BackgroundGene-gene interaction (GGI) is one of the most popular approaches for finding the missing heritability of common complex traits in genetic association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. In order to identify the best interaction model associated with disease susceptibility, MDR compares all possible genotype combinations in terms of their predictability of disease status from a simple binary high(H) and low(L) risk classification. However, this simple binary classification does not reflect the uncertainty of H/L classification.MethodsWe regard classifying H/L as equivalent to defining the degree of membership of two risk groups H/L. By adopting the fuzzy set theory, we propose Fuzzy MDR which takes into account the uncertainty of H/L classification. Fuzzy MDR allows the possibility of partial membership of H/L through a membership function which transforms the degree of uncertainty into a [0,1] scale. The best genotype combinations can be selected which maximizes a new fuzzy set based accuracy measure.ResultsTwo simulation studies are conducted to compare the power of the proposed Fuzzy MDR with that of MDR. Our results show that Fuzzy MDR has higher power than MDR. We illustrate the proposed Fuzzy MDR by analysing bipolar disorder (BD) trait of the WTCCC dataset to detect GGI associated with BD.ConclusionsWe propose a novel Fuzzy MDR method to detect gene–gene interaction by taking into account the uncertainly of H/L classification and show that it has higher power than MDR. Fuzzy MDR can be easily extended to handle continuous phenotypes as well. The program written in R for the proposed Fuzzy MDR is available at https://statgen.snu.ac.kr/software/FuzzyMDR.     

Connecting Linear Polymers Molecular Structure to Viscoelastic Properties and Melt Flow Index

A modeling pathway and software tool for linking entangled linear polymer molecular properties to linear viscoelasticity and melt index (MI) values is presented. A reptation model links molecular properties to the flow curve, and then, an ANSYS Polyflow model calculates MI values based on the flow curve predicted. The method is thoroughly tested and validated for uni‐ and bimodal, low‐ and high‐density polyethylene grades. An overall accuracy level in the range of 90% on average is exhibited, considering both model prediction steps: (i) molecular weight distribution to flow curve and (ii) flow curve to MI.

     

A computational method for prediction of rSNPs in human genome

Regulatory single nucleotide polymorphisms (rSNPs) in human genomes are thought to be responsible for phenotypic differences, including susceptibility to diseases and treatment outcomes, even they do not change any gene product. However, a genome-wide search for rSNPs has not been properly addressed so far. In this work, a computational method for rSNP identification is proposed. As background SNPs far outnumber rSNPs, an ensemble method is applied to handle imbalanced data, which firstly converts an unbalanced dataset into several balanced ones and then models for every balanced dataset. Two major types of features are extracted, that are sequence based features and allele-specific based features. Then random forest is applied to build the recognition model for each balanced dataset. Finally, ensemble strategies are adopted to combine the result of each model together. We have tested our method on a set of experimentally verified rSNPs, and leave-one-out cross-validation results showed that our method can achieve accuracy with sensitivity of 73.8%, specificity of 71.8% and the area under ROC curve (AUC) is 0.756. In addition, our method is threshold free and doesn’t rely on data of regulatory elements, thus it will have better adaptability when facing different data scenarios. The original data and the source matlab codes involved are available at https://sourceforge.net/projects/rsnpdect/.     

Electrocatalytic Properties and Voltammetric Determination of Melatonin at a Nanostructured Film Electrode

A multi-wall carbon nanotube (MWNT) film coated glassy carbon electrode (GCE) was fabricated, and the electrochemical behavior of melatonin on the MWNT film coated GCE was investigated. The oxidation peak current of melatonin increases significantly and the oxidation peak position shifts positively at the MWNT film modified GCE compared to that at a bare GCE. This indicates that MWNTs feature highly effective catalysis to the electrochemical oxidation of melatonin. A simple and sensitive electroanalytical method was developed for the determination of melatonin. The oxidation peak current is proportional to the concentration of melatonin from 8×10^–8 to 1×10^–5molL^–1. The detection limit is about 2×10^–8molL^–1 for 3min accumulation. The proposed method was demonstrated to work satisfactorily with commercial capsules.     

Liquid chromatography of phenolic compounds in natural water using on-line trace enrichment

Summary Two packing materials, C18 and PLRP-S, are studied for on-line trace enrichment of phenolic compounds in water. Various precolumns of different internal diameter are also tested and the addition of an ion-pair reagent to increase retention and thus, breakthrough volumes of phenolic compounds, is studied. Best results are obtained when a PLRP-S precolumn is coupled on-line with a C18 analytical column and DAD detector. Addition of TBA considerably increases breakthrough volumes. In contrast, when a C18 precolumn is used, breakthrough volumes are lower and it is impossible to determine TCP and PCP, under the experimental conditions used, because of interference of other nonpolar compounds in the samples. The performance of the system is evaluated with river and tap water and the preconcentration of 10 ml of sample in a PLRP-S precolumn involves a linear range between 1 g 1^–1 and 20 l^–1 and limits of determination between 0.5 g l^–1 and 1 g l^–1 are obtained.     

A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data

A major theme in constraint-based modeling is unifying experimental data, such as biochemical information about the reactions that can occur in a system or the composition and localization of enzyme complexes, with high-throughput data including expression data, metabolomics, or DNA sequencing. The desired result is to increase predictive capability and improve our understanding of metabolism. The approach typically employed when only gene (or protein) intensities are available is the creation of tissue-specific models, which reduces the available reactions in an organism model, and does not provide an objective function for the estimation of fluxes. We develop a method, flux assignment with LAD (least absolute deviation) convex objectives and normalization (FALCON), that employs metabolic network reconstructions along with expression data to estimate fluxes. In order to use such a method, accurate measures of enzyme complex abundance are needed, so we first present an algorithm that addresses quantification of complex abundance. Our extensions to prior techniques include the capability to work with large models and significantly improved run-time performance even for smaller models, an improved analysis of enzyme complex formation, the ability to handle large enzyme complex rules that may incorporate multiple isoforms, and either maintained or significantly improved correlation with experimentally measured fluxes. FALCON has been implemented in MATLAB and ATS, and can be downloaded from: https://github.com/bbarker/FALCON. ATS is not required to compile the software, as intermediate C source code is available. FALCON requires use of the COBRA Toolbox, also implemented in MATLAB.     

ProteinLasso: A Lasso regression approach to protein inference problem in shotgun proteomics

Protein inference is an important issue in proteomics research. Its main objective is to select a proper subset of candidate proteins that best explain the observed peptides. Although many methods have been proposed for solving this problem, several issues such as peptide degeneracy and one-hit wonders still remain unsolved. Therefore, the accurate identification of proteins that are truly present in the sample continues to be a challenging task.Based on the concept of peptide detectability, we formulate the protein inference problem as a constrained Lasso regression problem, which can be solved very efficiently through a coordinate descent procedure. The new inference algorithm is named as ProteinLasso, which explores an ensemble learning strategy to address the sparsity parameter selection problem in Lasso model. We test the performance of ProteinLasso on three datasets. As shown in the experimental results, ProteinLasso outperforms those state-of-the-art protein inference algorithms in terms of both identification accuracy and running efficiency. In addition, we show that ProteinLasso is stable under different parameter specifications. The source code of our algorithm is available at: http://sourceforge.net/projects/proteinlasso.     

An ESR and Optical Spectroscopic Study of the Mechanism of Photostimulated Reactions in Hydrogen Cyanide γ-Irradiated at 77 K

Changes in the electronic absorption spectra and ESR spectra in the course of photobleaching of radiolyzed solid HCN with light of different wavelengths (236–600 nm) were studied by ESR and optical spectroscopy. Two bands at 270 and 290 nm in the optical spectrum were attributed to the presence of H₂C=N and HC=NH radicals, respectively (the molar absorption coefficients are k ₂₇₀ 2.7 × 10²l mol^–1cm^–1and k ₂₉₀ 1.5 × 10²l mol^–1cm^–1, respectively). Structureless broad bands with maximums at 313 and 465 nm, which were detected after the exposure of a sample to light with 300 nm, can belong to the cyanide ions (CN^–) and H₂C=N⁺cations (the molar absorption coefficients of the ions are k _ion= (0.4–1.0) × 10²l mol^–1cm^–1). In the photobleaching of -irradiated HCN ( = 236–280 nm), H₂C=N⁺radicals were additionally formed by the photoinduced reaction of electron transfer from the CN^–anion to the H₂C=N⁺cation. The amount of these radicals generated in the course of photobleaching is several times greater than that of the same radicals formed in the radiolysis via hydrogen atom addition to the multiple bond of HCN molecules.