Computational prediction of microRNA genes in silkworm genome

MicroRNAs （miRNAs） constitute a novel, extensive class of small RNAs （-21 nucleotides）, and play important gene-regulation roles during growth and development in various organisms. Here we conducted a homology search to identify homologs of previously validated miRNAs from silkworm genome. We identified 24 potential miRNA genes, and gave each of them a name according to the common criteria. Interestingly, we found that a great number of newly identified miRNAs were conserved in silkworm and Drosophila, and family alignment revealed that miRNA families might possess single nucleotide polymorphisms, miRNA gene clusters and possible functions of complement miRNA pairs are discussed.

A novel computational method for protein-protein interaction networks prediction of alpha-synuclein

Alpha-synuclein plays an important role in Parkinson's disease(PD).The current study of alpha-synuclein mainly concentrates at the gene level.However, it is found that the study at the protein level has special significance.Meanwhile, there is free information on the Internet, such as databases and algorithms of protein-protein interactions(PPIs).In this paper, a novel method which integrates distributed heterogeneous data sources and algorithms to predict PPIs for alpha-synuclein in silico is proposed.The PPIs generated by the method take advantage of various experimental data, and indicate new information about PPIs for alpha-synuclein.In the end of this paper, the result illustrates that the method is practical.It is hoped that the prediction result obtained by this method can provide guidance for biological experiments of PPIs for alpha-synuclein to reveal possible mechanisms of PD.

Computational Prediction to Two-Dimensional SnAs

By means of the particle-swarm optimization method and density functional theory calculations, the lowestenergy structure of SnAs is determined to be a bilayer stacking system and the atoms on top of each other are of the same types. Using the hybrid functional of Heyd–Scuseria–Ernzerhof, SnAs is calculated to be a semiconductor with an indirect band gap of 1.71 eV, which decreases to 1.42 eV with the increase of the bi-axial tensile stress up to 2%, corresponding to the ideal value of 1.40 eV for potential photovoltaic applications. Based on the deformation potential theory, the two-dimensional（2 D） SnAs has high electron motilities along x and y directions（1.63 × 103 cm2 V-1s-1）. Our calculated results suggest that SnAs can be viewed as a new type of 2 D materials for applications in optoelectronics and nanoelectronic devices.

Mechanism prediction of Astragalus mongholicus Bunge and Angelica sinensis Diels in treating interstitial lung disease based on network pharmacology and molecular docking

Objective:To investigate the mechanism by which Astragalus mongholicus Bunge(AM),and Angelica sinensis Diels(AS)act in interstitial lung disease(ILD)based on computational prediction.Methods:We screened the ingredients of AM and AS in PubMed,the Web of Science,China National Knowledge Infrastructure(CNKI)Databases,etc.Then obtained the potential effective components.By sharing the same molecular with ILD,we got the possible target genes for ILD treatment and constructed components–targets–disease network with Cytoscape software.The CTD(Comparative Toxicogenomics Database)database was used for GO and KEGG enrichment analysis of these target genes.Results:59 active ingredients that can be druggable were chosen from AM,67 active ingredients were chosen from AS.77 overlapping target genes for AM and ILD and 36 overlapping target genes for AS and ILD were acquired.The hub targets of AM were PTGS2,PTGS1,CDK2,MAOA,ESR1,TOP2A,GSK3B,ESR2,PPARG,NOS2,The hub targets of AS were PTGS2,GABRA1,PTGS1,CHRM1,SLC6A2,ADRA1B,ADRAIA,ADRB2,CHRM3,GABRA2,CHRM2.Quercetin,kaempferol,daidzein,pavilion,7-Hydroxycoumarin,and 5-Hydroxycoumarin were the main active ingredients which have more effective targets.Prediction of the protein-protein interaction network showed PTGS2,GSK3B,PPARG,etc.,were the important predicted targets.The enriched KEGG pathways,including the Immune System,Metabolism of lipids and lipoproteins,Cytokine Signaling in the Immune system,Generic Transcription Pathway,The interleukin pathway,Metabolism of proteins,PI3K-Akt signaling pathway,Metabolic pathways,Innate Immune System,Neuroactive ligand-receptor interaction,Metabolism,GPCR downstream signaling,Amine ligand-binding receptors,Class A/1,Calcium signaling pathway.Molecular docking showed that quercetin,kaempferol,daidzein,pavilion,7-Hydroxycoumarin,5-Hydroxycoumarin had good binding activities with PTGS2 and GSK3B,which mainly mediated PI3K/Akt and other important signaling pathways in the pathogenesis of ILD.Conclusion:The components in AS and AM share some common targets,such as PTGS2.AM and AS may ameliorate ILD through the PI3K-Akt signaling pathway which is mediated by GSK3B.PTGS2,PPARG may also be vital target genes in the treatment of ILD with AM and AS.

Mechanism prediction of Astragalus mongholicus Bunge and Angelica sinensis Diels in treating interstitial lung disease based on network pharmacology and molecular docking

Objective:To investigate the mechanism by which Astragalus mongholicus Bunge(AM),and Angelica sinensis Diels(AS)act in interstitial lung disease(ILD)based on computational prediction.Methods:We screened the ingredients of AM and AS in PubMed,the Web of Science,China National Knowledge Infrastructure(CNKI)Databases,etc.Then obtained the potential effective components.By sharing the same molecular with ILD,we got the possible target genes for ILD treatment and constructed components–targets–disease network with Cytoscape software.The CTD(Comparative Toxicogenomics Database)database was used for GO and KEGG enrichment analysis of these target genes.Results:59 active ingredients that can be druggable were chosen from AM,67 active ingredients were chosen from AS.77 overlapping target genes for AM and ILD and 36 overlapping target genes for AS and ILD were acquired.The hub targets of AM were PTGS2,PTGS1,CDK2,MAOA,ESR1,TOP2A,GSK3B,ESR2,PPARG,NOS2,The hub targets of AS were PTGS2,GABRA1,PTGS1,CHRM1,SLC6A2,ADRA1B,ADRAIA,ADRB2,CHRM3,GABRA2,CHRM2.Quercetin,kaempferol,daidzein,pavilion,7-Hydroxycoumarin,and 5-Hydroxycoumarin were the main active ingredients which have more effective targets.Prediction of the protein-protein interaction network showed PTGS2,GSK3B,PPARG,etc.,were the important predicted targets.The enriched KEGG pathways,including the Immune System,Metabolism of lipids and lipoproteins,Cytokine Signaling in the Immune system,Generic Transcription Pathway,The interleukin pathway,Metabolism of proteins,PI3K-Akt signaling pathway,Metabolic pathways,Innate Immune System,Neuroactive ligand-receptor interaction,Metabolism,GPCR downstream signaling,Amine ligand-binding receptors,Class A/1,Calcium signaling pathway.Molecular docking showed that quercetin,kaempferol,daidzein,pavilion,7-Hydroxycoumarin,5-Hydroxycoumarin had good binding activities with PTGS2 and GSK3B,which mainly mediated PI3K/Akt and other important signaling pathways in the pathogenesis of ILD.Conclusion:The components in AS and AM share some common targets,such as PTGS2.AM and AS may ameliorate ILD through the PI3K-Akt signaling pathway which is mediated by GSK3B.PTGS2,PPARG may also be vital target genes in the treatment of ILD with AM and AS.

Resource pre-allocation algorithms for low-energy task scheduling of cloud computing

In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the redundant, turn on the demanded＂ strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted： a cubic exponential smoothing algorithm based on the conservative control（CESCC） strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching（RA-PM） are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing（RA-ISA） is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems.

Computational Identification of Sweet Wormwood (Artemisia annua) microRNA and Their mRNA Targets

Despite its efficacy against malaria, the relatively low yield （0.01%-0.8%） of artemisinin in Artemisia annua is a serious limitation to the commercialization of the drug. A better understanding of the biosynthetic pathway of artemisinin and its regulation by both exogenous and endogenous factors is essential to improve artemisinin yield. Increasing evidence has shown that microRNAs （miRNAs） play multiple roles in various biological processes. In this study, we used previously known miRNAs from Arabidopsis and rice against expressed sequence tag （EST） database of A. annua to search for potential miRNAs and their targets in A. annua. A total of six potential rniRNAs were predicted, which belong to the miR414 and miR1310 families. Furthermore, eight potential target genes were identified in this species. Among them, seven genes encode proteins that play important roles in artemisinin biosynthesis, including HMG-CoA reductase （HMGR）, amorpha-4,11-diene synthase （ADS）, farnesyl pyrophosphate synthase （FPS） and cytochrome P450. In addition, a gene coding for putative AINTEGUMENTA, which is involved in signal transduction and development, was also predicted as one of the targets. This is the first in silico study to indicate that miRNAs target genes encoding enzymes involved in artemisinin biosynthesis, which may help to understand the miRNA-mediated regulation of artemisinin biosynthesis in A. annua.

Computational analysis and prediction for exons of PAC579 genomic sequence

To isolate the novel genes related to human hepatocellular carcinoma (HCC), we sequenced P1-derived artificial chromosome PAC579 (D17S926 locus) mapped in the minimum LOH (loss of heterozygosity) deletion region of chromosome 17p13.3 in HCC, Four novel genes mapped in this genomic sequence area were isolated and cloned by wet-lab experiments, and the exons of these genes were located. 0-60 kb of this genomic sequence including the genes of interest was scanned with five different computational exon prediction programs as well as four splice site recognition programs. After analyzing and comparing the computationally predicted results with the wet-lab experiment results, some potential exons were predicted in the genomic sequence by using these programs.

A study of miRNAs targets prediction and experimental validation

microRNAs(miRNAs)are 20–24 nucleotide(nt)RNAs that regulate eukaryotic gene expression post-transcriptionally by the degradation or translational inhibition of their target messenger RNAs(mRNAs).To identify miRNA target genes will help a lot by understanding their biological functions.Sophisticated computational approaches for miRNA target prediction,and effective biological techniques for validating these targets now play a central role in elucidating their functions.Owing to the imperfect complementarity of animal miRNAs with their targets,it is difficult to judge the accuracy of the prediction.Complexity of regulation by miRNA-mediated targets at protein and mRNAs levels has made it more challenging to identify the targets.To date,only a few miRNAs targets are confirmed.In this article,we review the methods of miRNA target prediction and the experimental validation for their corresponding mRNA targets in animals.

Identification of miR414 and Expression Analysis of Conserved miRNAs from Stevia rebaudiana

MicroRNAs （miRNAs） usually contain 19-24 nucleotides and have been identified as important eukaryotic gene regulators. Applications of various computational approaches have simplified the task by predicting miRNAs from available sequence data sources. In this study, we identified a conserved miR414 from a computational analysis of EST sequence data available from Stevia rebaudiana. In addition, we also identified six conserved miRNAs namely miR169, miR319, miR414, miR164, miR167 and miR398 using stem-loop RT-PCR analysis. Hence, miR414 was commonly identified using both methods. The expression analysis of these miRNAs documented their roles in growth and development of Stevia. Furthermore, the detected miRNAs were found to target genes involved in plant growth, development, metabolism and signal transducfion. This is the first study reporting these conserved miRNAs and their expression in Stevia.

Characterization of protein-protein interactions between rice viruses and vector insects

Planthoppers are the most notorious rice pests,because they transmit various rice viruses in a persistent-propagative manner.Protein–protein interactions(PPIs)between virus and vector are crucial for virus transmission by vector insects.However,the number of known PPIs for pairs of rice viruses and planthoppers is restricted by low throughput research methods.In this study,we applied DeNovo,a virus-host sequence-based PPI predictor,to predict potential PPIs at a genome-wide scale between three planthoppers and five rice viruses.PPIs were identified at two different confidence thresholds,referred to as low and high modes.The number of PPIs for the five planthopper-virus pairs ranged from 506 to 1985 in the low mode and from 1254 to 4286 in the high mode.After eliminating the“one-too-many”redundant interacting information,the PPIs with unique planthopper proteins were reduced to 343–724 in the low mode and 758–1671 in the high mode.Homologous analysis showed that 11 sets and 31 sets of homologous planthopper proteins were shared by all planthopper-virus interactions in the two modes,indicating that they are potential conserved vector factors essential for transmission of rice viruses.Ten PPIs between small brown planthopper and rice stripe virus(RSV)were verified using glutathione-S-transferase(GST)/His-pull down or co-immunoprecipitation assay.Five of the ten PPIs were proven positive,and three of the five SBPH proteins were confirmed to interact with RSV.The predicted PPIs provide new clues for further studies of the complicated relationship between rice viruses and their vector insects.