Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition

Background: Subcellular location prediction of proteins is an important and well-studied problem in bioinformatics. This is a problem of predicting which part in a cell a given protein is transported to, where an amino acid sequence of the protein is given as an input. This problem is becoming more important since information on subcellular location is helpful for annotation of proteins and genes and the number of complete genomes is rapidly increasing. Since existing predictors are based on various heuristics, it is important to develop a simple method with high prediction accuracies. Results: In this paper, we propose a novel and general predicting method by combining techniques for sequence alignment and feature vectors based on amino acid composition. We implemented this method with support vector machines on plant data sets extracted from the TargetP database. Through fivefold cross validation tests, the obtained overall accuracies and average MCC were 0.9096 and 0.8655 respectively. We also applied our method to other datasets including that of WoLF PSORT. Conclusion: Although there is a predictor which uses the information of gene ontology and yields higher accuracy than ours, our accuracies are higher than existing predictors which use only sequence information. Since such information as gene ontology can be obtained only for known proteins, our predictor is considered to be useful for subcellular location prediction of newly-discovered proteins. Furthermore, the idea of combination of alignment and amino acid frequency is novel and general so that it may be applied to other problems in bioinformatics. Our method for plant is also implemented as a web-system and available on http://sunflower.kuicr.kyoto-u.ac.jp/~tamura/slpfa.html webcite

Generalized Holographic Quantum Criticality at Finite Density

We show that the near-extremal solutions of Einstein-Maxwell-Dilaton theories, studied in ArXiv:1005.4690, provide IR quantum critical geometries, by embedding classes of them in higher-dimensional AdS and Lifshitz solutions. This explains the scaling of their thermodynamic functions and their IR transport coefficients, the nature of their spectra, the Gubser bound, and regulates their singularities. We propose that these are the most general quantum critical IR asymptotics at finite density of EMD theories.Comment: v4: Corrected the scaling equation for the conductivity in section 9.

Mining phenotypes for gene function prediction

<p>Abstract</p> <p>Background</p> <p>Health and disease of organisms are reflected in their phenotypes. Often, a genetic component to a disease is discovered only after clearly defining its phenotype. In the past years, many technologies to systematically generate phenotypes in a high-throughput manner, such as RNA interference or gene knock-out, have been developed and used to decipher functions for genes. However, there have been relatively few efforts to make use of phenotype data beyond the single genotype-phenotype relationships.</p> <p>Results</p> <p>We present results on a study where we use a large set of phenotype data – in textual form – to predict gene annotation. To this end, we use text clustering to group genes based on their phenotype descriptions. We show that these clusters correlate well with several indicators for biological coherence in gene groups, such as functional annotations from the Gene Ontology (GO) and protein-protein interactions. We exploit these clusters for predicting gene function by carrying over annotations from well-annotated genes to other, less-characterized genes in the same cluster. For a subset of groups selected by applying objective criteria, we can predict GO-term annotations from the biological process sub-ontology with up to 72.6% precision and 16.7% recall, as evaluated by cross-validation. We manually verified some of these clusters and found them to exhibit high biological coherence, e.g. a group containing all available antennal Drosophila odorant receptors despite inconsistent GO-annotations.</p> <p>Conclusion</p> <p>The intrinsic nature of phenotypes to visibly reflect genetic activity underlines their usefulness in inferring new gene functions. Thus, systematically analyzing these data on a large scale offers many possibilities for inferring functional annotation of genes. We show that text clustering can play an important role in this process.</p

A domain-based approach to predict protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.</p> <p>Results</p> <p>DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.</p> <p>Conclusion</p> <p>We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.</p

Predicting the protein-protein interactions using primary structures with predicted protein surface

<p>Abstract</p> <p>Background</p> <p>Many biological functions involve various protein-protein interactions (PPIs). Elucidating such interactions is crucial for understanding general principles of cellular systems. Previous studies have shown the potential of predicting PPIs based on only sequence information. Compared to approaches that require other auxiliary information, these sequence-based approaches can be applied to a broader range of applications.</p> <p>Results</p> <p>This study presents a novel sequence-based method based on the assumption that protein-protein interactions are more related to amino acids at the surface than those at the core. The present method considers surface information and maintains the advantage of relying on only sequence data by including an accessible surface area (ASA) predictor recently proposed by the authors. This study also reports the experiments conducted to evaluate a) the performance of PPI prediction achieved by including the predicted surface and b) the quality of the predicted surface in comparison with the surface obtained from structures. The experimental results show that surface information helps to predict interacting protein pairs. Furthermore, the prediction performance achieved by using the surface estimated with the ASA predictor is close to that using the surface obtained from protein structures.</p> <p>Conclusion</p> <p>This work presents a sequence-based method that takes into account surface information for predicting PPIs. The proposed procedure of surface identification improves the prediction performance with an <it>F-measure </it>of 5.1%. The extracted surfaces are also valuable in other biomedical applications that require similar information.</p

Host mobility key management in dynamic secure group communication

The key management has a fundamental role in securing group communications taking place over vast and unprotected networks. It is concerned with the distribution and update of the keying materials whenever any changes occur in the group membership. Wireless mobile environments enable members to move freely within the networks, which causes more difficulty to design efficient and scalable key management protocols. This is partly because both member location dynamic and group membership dynamic must be managed concurrently, which may lead to significant rekeying overhead. This paper presents a hierarchical group key management scheme taking the mobility of members into consideration intended for wireless mobile environments. The proposed scheme supports the mobility of members across wireless mobile environments while remaining in the group session with minimum rekeying transmission overhead. Furthermore, the proposed scheme alleviates 1-affect-n phenomenon, single point of failure, and signaling load caused by moving members at the core network. Simulation results shows that the scheme surpasses other existing efforts in terms of communication overhead and affected members. The security requirements studies also show the backward and forward secrecy is preserved in the proposed scheme even though the members move between areas

Salt Dependence of the Tribological Properties of a Surface-Grafted Weak Polycation in Aqueous Solution

The nanoscopic adhesive and frictional behaviour of end-grafted poly[2-(dimethyl amino)ethyl methacrylate] (PDMAEMA) films (brushes) in contact with gold- or PDMAEMA-coated atomic force microscope tips in potassium halide solutions with different concentrations up to 300 mM is a strong function of salt concentration. The conformation of the polymers in the brush layer is sensitive to salt concentration, which leads to large changes in adhesive forces and the contact mechanics at the tip–sample contact, with swollen brushes (which occur at low salt concentrations) yielding large areas of contact and friction–load plots that fit JKR behaviour, while collapsed brushes (which occur at high salt concentrations) yield sliding dominated by ploughing, with conformations in between fitting DMT mechanics. The relative effect of the different anions follows the Hofmeister series, with I − collapsing the brushes more than Br − and Cl − for the same salt concentration

Human embryonic stem cells: preclinical perspectives

Human embryonic stem cells (hESCs) have been extensively discussed in public and scientific communities for their potential in treating diseases and injuries. However, not much has been achieved in turning them into safe therapeutic agents. The hurdles in transforming hESCs to therapies start right with the way these cells are derived and maintained in the laboratory, and goes up-to clinical complications related to need for patient specific cell lines, gender specific aspects, age of the cells, and several post transplantation uncertainties. The different types of cells derived through directed differentiation of hESC and used successfully in animal disease and injury models are described briefly. This review gives a brief outlook on the present and the future of hESC based therapies, and talks about the technological advances required for a safe transition from laboratory to clinic

Using enhanced number and brightness to measure protein oligomerization dynamics in live cells

Protein dimerization and oligomerization are essential to most cellular functions, yet measurement of the size of these oligomers in live cells, especially when their size changes over time and space, remains a challenge. A commonly used approach for studying protein aggregates in cells is number and brightness (N&B), a fluorescence microscopy method that is capable of measuring the apparent average number of molecules and their oligomerization (brightness) in each pixel from a series of fluorescence microscopy images. We have recently expanded this approach in order to allow resampling of the raw data to resolve the statistical weighting of coexisting species within each pixel. This feature makes enhanced N&B (eN&B) optimal for capturing the temporal aspects of protein oligomerization when a distribution of oligomers shifts toward a larger central size over time. In this protocol, we demonstrate the application of eN&B by quantifying receptor clustering dynamics using electron-multiplying charge-coupled device (EMCCD)-based total internal reflection microscopy (TIRF) imaging. TIRF provides a superior signal-to-noise ratio, but we also provide guidelines for implementing eN&B in confocal microscopes. For each time point, eN&B requires the acquisition of 200 frames, and it takes a few seconds up to 2 min to complete a single time point. We provide an eN&B (and standard N&B) MATLAB software package amenable to any standard confocal or TIRF microscope. The software requires a high-RAM computer (64 Gb) to run and includes a photobleaching detrending algorithm, which allows extension of the live imaging for more than an hour