Prediction of protein long-range contacts using an ensemble of genetic algorithm classifiers with sequence profile centers

Background. Prediction of long-range inter-residue contacts is an important topic in bioinformatics research. It is helpful for determining protein structures, understanding protein foldings, and therefore advancing the annotation of protein functions. Results. In this paper, we propose a novel ensemble of genetic algorithm classifiers (GaCs) to address the long-range contact prediction problem. Our method is based on the key idea called sequence profile centers (SPCs). Each SPC is the average sequence profiles of residue pairs belonging to the same contact class or non-contact class. GaCs train on multiple but different pairs of long-range contact data (positive data) and long-range non-contact data (negative data). The negative data sets, having roughly the same sizes as the positive ones, are constructed by random sampling over the original imbalanced negative data. As a result, about 21.5% long-range contacts are correctly predicted. We also found that the ensemble of GaCs indeed makes an accuracy improvement by around 5.6% over the single GaC. Conclusions. Classifiers with the use of sequence profile centers may advance the long-range contact prediction. In line with this approach, key structural features in proteins would be determined with high efficiency and accuracy. © 2010 Li and Chen; licensee BioMed Central Ltd

Insights into bacterial genome composition through variable target GC content profiling

This study presents a new computational method for guanine (G) and cytosine (C), or GC, content profiling based on the idea of multiple resolution sampling (MRS). The benefit of our new approach over existing techniques follows from its ability to locate significant regions without prior knowledge of the sequence, nor the features being sought. The use of MRS has provided novel insights into bacterial genome composition. Key findings include those that are related to the core composition of bacterial genomes, to the identification of large genomic islands (in Enterobacterial genomes), and to the identification of surface protein determinants in human pathogenic organisms (e.g., Staphylococcus genomes). We observed that bacterial surface binding proteins maintain abnormal GC content, potentially pointing to a viral origin. This study has demonstrated that GC content holds a high informational worth and hints at many underlying evolutionary processes. For online Supplementary Material, see www.liebertonline.com

High functional coherence in k-partite protein cliques of protein interaction networks

We introduce a new topological concept called k-partite protein cliques to study protein interaction (PPI) networks. In particular, we examine functional coherence of proteins in k-partite protein cliques. A k-partite protein clique is a k-partite maximal clique comprising two or more nonoverlapping protein subsets between any two of which full interactions are exhibited. In the detection of PPI&rsquo;s k-partite maximal cliques, we propose to transform PPI networks into induced K-partite graphs with proteins as vertices where edges only exist among the graph&rsquo;s partites. Then, we present a k-partite maximal clique mining (MaCMik) algorithm to enumerate k-partite maximal cliques from K-partite graphs. Our MaCMik algorithm is applied to a yeast PPI network. We observe that there does exist interesting and unusually high functional coherence in k-partite protein cliques&mdash;most proteins in k-partite protein cliques, especially those in the same partites, share the same functions. Therefore, the idea of k-partite protein cliques suggests a novel approach to characterizing PPI networks, and may help function prediction for unknown proteins.<br /

Combining the Silhouette and Skeleton Data for Gait Recognition

Gait recognition, a promising long-distance biometric technology, has aroused intense interest in computer vision. Existing works on gait recognition can be divided into appearance-based methods and model-based methods, which extract features from silhouettes and skeleton data, respectively. However, since appearance-based methods are greatly affected by clothing changing and carrying condition, and model-based methods are limited by the accuracy of pose estimation approaches, gait recognition remains challenging in practical applications. In order to integrate the advantages of such two approaches, a two-branch neural network (NN) is proposed in this paper. Our method contains two branches, namely a CNN-based branch taking silhouettes as input and a GCN-based branch taking skeletons as input. In addition, two new modules are proposed in the GCN-based branch for better gait representation. First, we present a simple yet effective fully connected graph convolution operator to integrate the multi-scale graph convolutions and alleviate the dependence on natural human joint connections. Second, we deploy a multi-dimension attention module named STC-Att to learn spatial, temporal and channel-wise attention simultaneously. We evaluated the proposed two-branch neural network on the CASIA-B dataset. The experimental results show that our method achieves state-of-the-art performance in various conditions.Comment: The paper is under consideration at Computer Vision and Image Understandin

A Modified KNN Algorithm for Activity Recognition in Smart Home

Nowadays, more and more elderly people cannot take care of themselves, and feel uncomfortable in daily activities. Smart home systems can help to improve daily life of elderly people. A smart home can bring residents a more comfortable living environment by recognizing the daily activities automatically. In this paper, in order to improve the accuracy of activity recognition in smart homes, we conduct some improvements in data preprocess and recognition phase, and more importantly, a novel sensor segmentation method and a modified KNN algorithm are proposed. The segmentation algorithm employs segment sensor data into fragments based on predefined activity knowledge, and then the proposed modified KNN algorithm uses center distances as a measure for classification. We also conduct comprehensive experiments, and the results demonstrate that the proposed method outperforms the other classifiers

Mutation of putative N-Linked Glycosylation Sites in Japanese encephalitis Virus Premembrane and Envelope proteins enhances humoral immunity in BALB/C mice after DNA vaccination

Swine are an important host of Japanese encephalitis virus (JEV). The two membrane glycoproteins of JEV, prM and E, each contain a potential N-linked glycosylation site, at positions N15 and N154, respectively. We constructed plasmids that contain the genes encoding wild-type prME (contain the signal of the prM, the prM, and the E coding regions) and three mutant prME proteins, in which the putative N-linked glycosylation sites are mutated individually or in combination, by site-directed mutagenesis. The recombinant plasmids were used as DNA vaccines in mice. Our results indicate that immunizing mice with DNA vaccines that contain the N154A mutation results in elevated levels of interleukin-4 secretion, induces the IgG1 antibody isotype, generates greater titers of anti-JEV antibodies, and shows complete protection against JEV challenge. We conclude that mutation of the putative N-glycosylation site N154 in the E protein of JEV significantly enhances the induced humoral immune response and suggest that this mutant should be further investigated as a potential DNA vaccine against JEV

Serodiagnosis of sheeppox and goatpox using an indirect ELISA based on synthetic peptide targeting for the major antigen P32

<p>Abstract</p> <p>Background</p> <p>Sheeppoxvirus (SPPV), goatpoxvirus (GTPV) and lumpy skin disease virus (LSDV) of cattle belong to the <it>Capripoxvirus </it>genus of the <it>Poxviridae </it>family and can cause significant economic losses in countries where they are endemic. Despite the considerable threat that these viruses pose to livestock production and global trade in sheep, goats, cattle and their products, convenient and effective serodiagnostic tools are not readily available. Toward this goal, two synthetic peptides corresponding to the major antigen P32 were synthesized. These synthetic peptides were then used as antigen to develop an ELISA method to detect anti-SPPV and GTPV antibodies.</p> <p>Results</p> <p>The results indicated that the optimal concentration of coated recombinant antigen was 0.2 μg per well for a serum dilution of 1:10. The ELISA performed favorably when sera from sheep immunized experimentally were tested.</p> <p>Conclusion</p> <p>This assay offers the prospect of synthetic peptide as antigens for indirect ELISA to detect SPPV and GTPV antibody in sheep and goat sera.</p