- Usman Sumo Friend Tambunan;Arli Aditya Parikesit;Yossy Carolina Unadi;Djati Kerami;
Highly Pathogenic Avian Influenza(HPAI) H5N1 has attracted much attention as a potential pandemic virus in humans, which makes death inevitable in humans. Neuraminidase(NA) has an important role in viral replication. Thus, it is an attractive target when designing anti-influenza virus drug. However, evolving viruses cause some anti-viral drugs to be ineffective, as they show resistance to them. Selection of peptides as drug candidates is important for the peptide-receptor activity and good selectivity. Cyclic bonds in the peptide ligand design aim to improve the stability of the system and remove the obstacles in drug metabolism. The design is based on the polarity of the ligand and amino acid residues in the active site of NA. The results are 4200 cyclic pentapeptides as potential lead compounds. Docking simulations were conducted using MOE 2008.10 and were screened based on the value of the binding energy(?Gbinding). ADME-Tox prediction assay was conducted on the selected ligands.Intra- and inter-molecular interactions, as well as changes in the form of bonds, were tested by molecular dynamics simulations at temperatures of 310 K and 312 K. The results of the docking simulations and toxicity prediction assay show that there are two ligands that have a residual interaction with the target protein: CLDRC and CIWRC. These two ligands have ?Gbindingvalues of –40.5854 and –39.9721 kcal/mol(1 kcal/mol = 4.18 k J/mol). These ligands are prone to be mutagenic and carcinogenic, and they have a good oral bioavailability. The results show that the molecular dynamics of both ligand CLDRC and CIWRC are more feasible at the temperature of 312 K. At the end,both CIWRC and CLDRC ligands can be used as the drug candidates against H5N1 virus.
2015年05期 v.20 431-440页 [查看摘要][在线阅读][下载 444K] [下载次数：48 ] |[网刊下载次数：0 ] |[引用频次：0 ] |[阅读次数：0 ] - Yang Pu;David C.Samuels;Layne T.Watson;Yang Cao;
Insulin secreted by pancreatic islet ˇ-cells is the principal regulating hormone of glucose metabolism.Disruption of insulin secretion may cause glucose to accumulate in the blood, and result in diabetes mellitus.Although deterministic models of the insulin secretion pathway have been developed, the stochastic aspect of this biological pathway has not been explored. The first step in this direction presented here is a hybrid model of the insulin secretion pathway, in which the delayed rectifying KCchannels are treated as stochastic events. This hybrid model can not only reproduce the oscillation dynamics as the deterministic model does, but can also capture stochastic dynamics that the deterministic model does not. To measure the insulin oscillation system behavior, a probability-based measure is proposed and applied to test the effectiveness of a new remedy.
2015年05期 v.20 441-452页 [查看摘要][在线阅读][下载 1034K] [下载次数：19 ] |[网刊下载次数：0 ] |[引用频次：0 ] |[阅读次数：0 ] - Hui Li;Chunmei Liu;Mugizi Robert Rwebangira;Legand Burge;
In proteomics, many methods for the identification of proteins have been developed. However, because of limited known genome sequences, noisy data, incomplete ion sequences, and the accuracy of protein identification,it is challenging to identify peptides using tandem mass spectral data. Noise filtering and removing thus play a key role in accurate peptide identification from tandem mass spectra. In this paper, we employ a Bayesian model to identify proteins based on the prior information of bond cleavages. A Markov Chain Monte Carlo(MCMC)algorithm is used to simulate candidate peptides from the posterior distribution and to estimate the parameters for the Bayesian model. Our simulation and computational experimental results show that the model can identify peptide with a higher accuracy.
2015年05期 v.20 453-459页 [查看摘要][在线阅读][下载 704K] [下载次数：33 ] |[网刊下载次数：0 ] |[引用频次：0 ] |[阅读次数：0 ] - Fatima-Zohra Younsi;Ahmed Bounnekar;Djamila Hamdadou;Omar Boussaid;
This study modeled the spread of an influenza epidemic in the population of Oran, Algeria. We investigated the mathematical epidemic model, SEIR(Susceptible-Exposed-Infected-Removed), through extensive simulations of the effects of social network on epidemic spread in a Small World(SW) network, to understand how an influenza epidemic spreads through a human population. A combined SEIR-SW model was built, to help understand the dynamics of infectious disease in a community, and to identify the main characteristics of epidemic transmission and its evolution over time. The model was also used to examine social network effects to better understand the topological structure of social contact and the impact of its properties. Experiments were conducted to evaluate the combined SEIR-SW model. Simulation results were analyzed to explore how network evolution influences the spread of desease, and statistical tests were applied to validate the model. The model accurately replicated the dynamic behavior of the real influenza epidemic data, confirming that the susceptible size and topological structure of social networks in a human population significantly influence the spread of infectious diseases. Our model can provide health policy decision makers with a better understanding of epidemic spread,allowing them to implement control measures. It also provides an early warning of the emergence of influenza epidemics.
2015年05期 v.20 460-473页 [查看摘要][在线阅读][下载 823K] [下载次数：66 ] |[网刊下载次数：0 ] |[引用频次：9 ] |[阅读次数：0 ] - Yingfeng Wang;Russell L.Malmberg;Liming Cai;
RNA secondary structure has become the most exploitable feature for ab initio detection of non-coding RNA(nc RNA) genes from genome sequences. Previous work has used Minimum Free Energy(MFE) based methods developed to identify nc RNAs by measuring sequence fold stability and certainty. However, these methods yielded variable performances across different nc RNA species. Designing novel reliable structural measures will help to develop effective nc RNA gene finding tools. This paper introduces a new RNA structural measure based on a novel RNA secondary structure ensemble constrained by characteristics of native RNA tertiary structures. The new method makes it possible to achieve a performance leap from the previous structure-based methods. Test results on standard nc RNA datasets(benchmarks) demonstrate that this method can effectively separate most nc RNAs families from genome backgrounds.
2015年05期 v.20 474-483页 [查看摘要][在线阅读][下载 359K] [下载次数：27 ] |[网刊下载次数：0 ] |[引用频次：0 ] |[阅读次数：0 ] - Yao Fu;Gang Chen;Lizhi Fu;Jing Zhang;
Hepatitis C virus(HCV) treatment is on the cutting edge of medicine. Due to the high rate of mutations and low fidelity of HCV replication, resistant strains quickly become dominant in a viral population under the selection pressure of a drug. In this paper, we examined the drug resistance mechanism in the NS5 A region of genotype1 a HCV virus by comparing the sequence data from interferon-ribavirin treated and untreated patients. To find the drug resistance difference, we used innovative Bayesian probability models to detect mutation combinations and inferred detailed interaction structures of these mutations. We aim to provide reference to drug design and mutation mechanism understanding through our work.
2015年05期 v.20 484-490页 [查看摘要][在线阅读][下载 301K] [下载次数：19 ] |[网刊下载次数：0 ] |[引用频次：1 ] |[阅读次数：0 ] - Jiancheng Zhong;Jianxin Wang;Wei Peng;Zhen Zhang;Min Li;
Essential proteins are vital to the survival of a cell. There are various features related to the essentiality of proteins, such as biological and topological features. Many computational methods have been developed to identify essential proteins by using these features. However, it is still a big challenge to design an effective method that is able to select suitable features and integrate them to predict essential proteins. In this work, we first collect 26 features, and use SVM-RFE to select some of them to create a feature space for predicting essential proteins, and then remove the features that share the biological meaning with other features in the feature space according to their Pearson Correlation Coefficients(PCC). The experiments are carried out on S. cerevisiae data. Six features are determined as the best subset of features. To assess the prediction performance of our method, we further compare it with some machine learning methods, such as SVM, Naive Bayes, Bayes Network, and NBTree when inputting the different number of features. The results show that those methods using the 6 features outperform that using other features, which confirms the effectiveness of our feature selection method for essential protein prediction.
2015年05期 v.20 491-499页 [查看摘要][在线阅读][下载 376K] [下载次数：50 ] |[网刊下载次数：0 ] |[引用频次：28 ] |[阅读次数：0 ] - Wei Lan;Jianxin Wang;Min Li;Wei Peng;Fangxiang Wu;
With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a fundamental challenge in human health. A common view is that genes related to a specific or similar disease tend to reside in the same neighbourhood of biomolecular networks. Recently, based on such observations,many methods have been developed to tackle this challenge. In this review, we firstly introduce the concept of disease genes, their properties, and available data for identifying them. Then we review the recent computational approaches for prioritizing candidate disease genes based on Protein-Protein Interaction(PPI) networks and investigate their advantages and disadvantages. Furthermore, some pieces of existing software and network resources are summarized. Finally, we discuss key issues in prioritizing candidate disease genes and point out some future research directions.
2015年05期 v.20 500-512页 [查看摘要][在线阅读][下载 310K] [下载次数：69 ] |[网刊下载次数：0 ] |[引用频次：17 ] |[阅读次数：0 ] - Feng Gu;Xue Wang;
Allele specific expression is essential for cellular programming and development and the diversity of cellular phenotypes. Traditional analysis methods utilize RNA and depend on single nucleotide polymorphisms,thus to suffer from limited amount of materials for analysis. The rapid development of next-generation sequencing technologies provides more comprehensive and powerful approaches to analyze the genomic, epigenetic, and transcriptomic data, and further to detect and measure allele specific expressions. It will potentially enhance the understanding of the allele specific expressions, their complexities, and the effect on biological processes. In this paper, we extensively review the state-of-art enabling technologies and tools to analyze, detect, and measure allele specific expressions, compare their features, and point out the future trend of the methods.
2015年05期 v.20 513-529页 [查看摘要][在线阅读][下载 334K] [下载次数：60 ] |[网刊下载次数：0 ] |[引用频次：0 ] |[阅读次数：0 ] - Wei Hu;
Neoadjuvant chemotherapy for breast cancer patients with large tumor size is a necessary treatment.After this treatment patients who achieve a pathologic Complete Response(p CR) usually have a favorable prognosis than those without. Therefore, p CR is now considered as the best prognosticator for patients with neoadjuvant chemotherapy. However, not all patients can benefit from this treatment. As a result, we need to find a way to predict what kind of patients can induce p CR. Various gene signatures of chemosensitivity in breast cancer have been identified, from which such predictors can be built. Nevertheless, many of them have their prediction accuracy around 80%. As such, identifying gene signatures that could be employed to build high accuracy predictors is a prerequisite for their clinical tests and applications. Furthermore, to elucidate the importance of each individual gene in a signature is another pressing need before such signature could be tested in clinical settings. In this study, Genetic Algorithm(GA) and Sparse Logistic Regression(SLR) along with t-test were employed to identify one signature. It had 28 probe sets selected by GA from the top 65 probe sets that were highly overexpressed between p CR and Residual Disease(RD) and was used to build an SLR predictor of p CR(SLR-28). This predictor tested on a training set(n = 81) and validation set(n = 52) had very precise predictions measured by accuracy,specificity, sensitivity, positive predictive value, and negative predictive value with their corresponding P value all zero. Furthermore, this predictor discovered 12 important genes in the 28 probe set signature. Our findings also demonstrated that the most discriminative genes measured by SLR as a group selected by GA were not necessarily those with the smallest P values by t-test as individual genes, highlighting the ability of GA to capture the interacting genes in p CR prediction as multivariate techniques. Our gene signature produced superior performance over a signature found in one previous study with prediction accuracy 92% vs 76%, demonstrating the potential of GA and SLR in identifying robust gene signatures in chemo response prediction in breast cancer.
2015年05期 v.20 530-536页 [查看摘要][在线阅读][下载 204K] [下载次数：30 ] |[网刊下载次数：0 ] |[引用频次：0 ] |[阅读次数：0 ]