Advances in Bioinformatics最新文献_第5页

Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems. 用多元线性回归重建基因调控网络解决级联误差问题。

Advances in Bioinformatics Pub Date : 2017-01-01 Epub Date: 2017-01-29 DOI: 10.1155/2017/4827171

Faridah Hani Mohamed Salleh, Suhaila Zainudin, Shereena M Arif

{"title":"Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems.","authors":"Faridah Hani Mohamed Salleh, Suhaila Zainudin, Shereena M Arif","doi":"10.1155/2017/4827171","DOIUrl":"https://doi.org/10.1155/2017/4827171","url":null,"abstract":"<p><p>Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.</p>","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2017 ","pages":"4827171"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2017/4827171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34776148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 23

In Silico Analysis of SNPs in PARK2 and PINK1 Genes That Potentially Cause Autosomal Recessive Parkinson Disease 可能导致常染色体隐性帕金森病的PARK2和PINK1基因snp的计算机分析

Advances in Bioinformatics Pub Date : 2016-12-29 DOI: 10.1155/2016/9313746

Y. Bakhit, Mohamed O Ibrahim, M. Amin, Y. Mirghani, M. Hassan

{"title":"In Silico Analysis of SNPs in PARK2 and PINK1 Genes That Potentially Cause Autosomal Recessive Parkinson Disease","authors":"Y. Bakhit, Mohamed O Ibrahim, M. Amin, Y. Mirghani, M. Hassan","doi":"10.1155/2016/9313746","DOIUrl":"https://doi.org/10.1155/2016/9313746","url":null,"abstract":"Introduction. Parkinson's disease (PD) is a common neurodegenerative disorder. Mutations in PINK1 are the second most common agents causing autosomal recessive, early onset PD. We aimed to identify the pathogenic SNPs in PARK2 and PINK1 using in silico prediction software and their effect on the structure, function, and regulation of the proteins. Materials and Methods. We carried out in silico prediction of structural effect of each SNP using different bioinformatics tools to predict substitution influence on protein structure and function. Result. Twenty-one SNPs in PARK2 gene were found to affect transcription factor binding activity. 185 SNPs were found to affect splicing. Ten SNPs were found to affect the miRNA binding site. Two SNPs rs55961220 and rs56092260 affected the structure, function, and stability of Parkin protein. In PINK1 gene only one SNP (rs7349186) was found to affect the structure, function, and stability of the PINK1 protein. Ten SNPs were found to affect the microRNA binding site. Conclusion. Better understanding of Parkinson's disease caused by mutations in PARK2 and PINK1 genes was achieved using in silico prediction. Further studies should be conducted with a special consideration of the ethnic diversity of the different populations.","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/9313746","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64608558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Prediction and In Silico Identification of Novel B-Cells and T-Cells Epitopes in the S1-Spike Glycoprotein of M41 and CR88 (793/B) Infectious Bronchitis Virus Serotypes for Application in Peptide Vaccines 传染性支气管炎病毒M41和CR88 (793/B)血清型S1-Spike糖蛋白新B细胞和t细胞表位的预测和计算机鉴定及其在多肽疫苗中的应用

Advances in Bioinformatics Pub Date : 2016-09-07 DOI: 10.1155/2016/5484972

F. Bande, S. Arshad, M. Bejo, S. Kadkhodaei, A. Omar

{"title":"Prediction and In Silico Identification of Novel B-Cells and T-Cells Epitopes in the S1-Spike Glycoprotein of M41 and CR88 (793/B) Infectious Bronchitis Virus Serotypes for Application in Peptide Vaccines","authors":"F. Bande, S. Arshad, M. Bejo, S. Kadkhodaei, A. Omar","doi":"10.1155/2016/5484972","DOIUrl":"https://doi.org/10.1155/2016/5484972","url":null,"abstract":"Bioinformatic analysis was used to predict antigenic B-cell and T-cell epitopes within the S1 glycoprotein of M41 and CR88 IBV strains. A conserved linear B-cell epitope peptide, YTSNETTDVTS175–185, was identified in M41 IBV strains while three such epitopes types namely, VSNASPNSGGVD279–290, HPKCNFRPENI328–338, and NETNNAGSVSDCTAGT54–69, were predicted in CR88 IBV strains. Analysis of MHCI binding peptides in M41 IBV strains revealed the presence of 15 antigenic peptides out of which 12 were highly conserved in 96–100% of the total M41 strains analysed. Interestingly three of these peptides, GGPITYKVM208, WFNSLSVSI356, and YLADAGLAI472, relatively had high antigenicity index (>1.0). On the other hand, 11 MHCI binding epitope peptides were identified in CR88 IBV strains. Of these, five peptides were found to be highly conserved with a range between 90% and 97%. However, WFNSLSVSL358, SYNISAASV88, and YNISAASVA89 peptides comparably showed high antigenicity scores (>1.0). Combination of antigenic B-cells and T-cells peptides that are conserved across many strains as approach to evoke humoral and CTL immune response will potentially lead to a broad-based vaccine that could reduce the challenges in using live attenuated vaccine technology in the control of IBV infection in poultry.","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/5484972","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64429845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

In Silico Analysis of Gene Expression Network Components Underlying Pigmentation Phenotypes in the Python Identified Evolutionarily Conserved Clusters of Transcription Factor Binding Sites Python色素沉着表型基因表达网络组分的计算机分析鉴定出进化上保守的转录因子结合位点簇

Advances in Bioinformatics Pub Date : 2016-09-06 DOI: 10.1155/2016/1286510

Kristopher J. L. Irizarry, R. Bryden

{"title":"In Silico Analysis of Gene Expression Network Components Underlying Pigmentation Phenotypes in the Python Identified Evolutionarily Conserved Clusters of Transcription Factor Binding Sites","authors":"Kristopher J. L. Irizarry, R. Bryden","doi":"10.1155/2016/1286510","DOIUrl":"https://doi.org/10.1155/2016/1286510","url":null,"abstract":"Color variation provides the opportunity to investigate the genetic basis of evolution and selection. Reptiles are less studied than mammals. Comparative genomics approaches allow for knowledge gained in one species to be leveraged for use in another species. We describe a comparative vertebrate analysis of conserved regulatory modules in pythons aimed at assessing bioinformatics evidence that transcription factors important in mammalian pigmentation phenotypes may also be important in python pigmentation phenotypes. We identified 23 python orthologs of mammalian genes associated with variation in coat color phenotypes for which we assessed the extent of pairwise protein sequence identity between pythons and mouse, dog, horse, cow, chicken, anole lizard, and garter snake. We next identified a set of melanocyte/pigment associated transcription factors (CREB, FOXD3, LEF-1, MITF, POU3F2, and USF-1) that exhibit relatively conserved sequence similarity within their DNA binding regions across species based on orthologous alignments across multiple species. Finally, we identified 27 evolutionarily conserved clusters of transcription factor binding sites within ~200-nucleotide intervals of the 1500-nucleotide upstream regions of AIM1, DCT, MC1R, MITF, MLANA, OA1, PMEL, RAB27A, and TYR from Python bivittatus. Our results provide insight into pigment phenotypes in pythons.","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"97 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/1286510","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64212793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Systematic Bioinformatic Approach for Prediction of Linear B-Cell Epitopes on Dengue E and prM Protein 登革热E和prM蛋白线性b细胞表位预测的系统生物信息学方法

Advances in Bioinformatics Pub Date : 2016-09-01 DOI: 10.1155/2016/1373157

Mahesha N. Nadugala, P. Premaratne, C. Goonasekara

{"title":"Systematic Bioinformatic Approach for Prediction of Linear B-Cell Epitopes on Dengue E and prM Protein","authors":"Mahesha N. Nadugala, P. Premaratne, C. Goonasekara","doi":"10.1155/2016/1373157","DOIUrl":"https://doi.org/10.1155/2016/1373157","url":null,"abstract":"B-cell epitopes on the envelope (E) and premembrane (prM) proteins of dengue virus (DENV) were predicted using bioinformatics tools, BepiPred, Ellipro, and SVMTriP. Predicted epitopes, 32 and 17 for E and prM proteins, respectively, were then characterized for their level of conservations. The epitopes, EP4/E (48–55), epitope number 4 of E protein at amino acids 48–55, EP9/E (165–182), EP11/E (218–233), EP20/E (322–349), EP21/E (326–353), EP23/E (356–365), and EP25/E (380–386), showed a high intraserotype conservancy with very low pan-serotype conservancy, demonstrating a potential target as serotype specific diagnostic markers. EP3 (30–41) located in domain-I and EP26/E (393–409), EP27/E (416–435), EP28/E (417–430) located in the stem region of E protein, and EP8/prM (93–112) from the prM protein have a pan-serotype conservancy higher than 70%. These epitopes indicate a potential use as universal vaccine candidates, subjected to verification of their potential in viral neutralization. EP2/E (16–21), EP5/E (62–123), EP6/E (63–89), EP19/E (310–329), and EP24/E (371–402), which have more than 50% pan-serotype conservancies, were found on E protein regions that are important in host cell attachment. Previous studies further show evidence for some of these epitopes to generate cross-reactive neutralizing antibodies, indicating their importance in antiviral strategies for DENV. This study suggests that bioinformatic approaches are attractive first line of screening for identification of linear B-cell epitopes.","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/1373157","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64219500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Efficacy and Toxicity Assessment of Different Antibody Based Antiangiogenic Drugs by Computational Docking Method 基于计算对接方法的不同抗体型抗血管生成药物的疗效和毒性评价

Advances in Bioinformatics Pub Date : 2016-03-07 DOI: 10.1155/2016/7053712

Sayan Mukherjee, Gopa Chatterjee, M. Ghosh, B. Das, D. Majumder

{"title":"Efficacy and Toxicity Assessment of Different Antibody Based Antiangiogenic Drugs by Computational Docking Method","authors":"Sayan Mukherjee, Gopa Chatterjee, M. Ghosh, B. Das, D. Majumder","doi":"10.1155/2016/7053712","DOIUrl":"https://doi.org/10.1155/2016/7053712","url":null,"abstract":"Bevacizumab and trastuzumab are two antibody based antiangiogenic drugs that are in clinical practice for the treatment of different cancers. Presently applications of these drugs are based on the empirical choice of clinical experts that follow towards population based clinical trials and, hence, their molecular efficacies in terms of quantitative estimates are not being explored. Moreover, different clinical trials with these drugs showed different toxicity symptoms in patients. Here, using molecular docking study, we made an attempt to reveal the molecular rationale regarding their efficacy and off-target toxicity. Though our study reinforces their antiangiogenic potentiality and, among the two, trastuzumab has much higher efficacy; however, this study also reveals that compared to bevacizumab, trastuzumab has higher toxicity effect, specially on the cardiovascular system. This study also reveals the molecular rationale of ocular dysfunction by antiangiogenic drugs. The molecular rationale of toxicity as revealed in this study may help in the judicious choice as well as therapeutic scheduling of these drugs in different cancers.","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/7053712","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64501996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

In Silico Approach for SAR Analysis of the Predicted Model of DEPDC1B: A Novel Target for Oral Cancer 口腔癌新靶点DEPDC1B预测模型的SAR分析方法

Advances in Bioinformatics Pub Date : 2016-02-29 DOI: 10.1155/2016/3136024

Palak Ahuja, Kailash Singh

{"title":"In Silico Approach for SAR Analysis of the Predicted Model of DEPDC1B: A Novel Target for Oral Cancer","authors":"Palak Ahuja, Kailash Singh","doi":"10.1155/2016/3136024","DOIUrl":"https://doi.org/10.1155/2016/3136024","url":null,"abstract":"With the incidence rate of oral carcinogenesis increasing in the Southeast-Asian countries, due to increase in the consumption of tobacco and betel quid as well as infection from human papillomavirus, specifically type 16, it becomes crucial to predict the transition of premalignant lesion to cancerous tissue at an initial stage in order to control the process of oncogenesis. DEPDC1B, downregulated in the presence of E2 protein, was recently found to be overexpressed in oral cancer, which can possibly be explained by the disruption of the E2 open reading frame upon the integration of viral genome into the host genome. DEPDC1B mediates its effect by directly interacting with Rac1 protein, which is known to regulate important cell signaling pathways. Therefore, DEPDC1B can be a potential biomarker as well as a therapeutic target for diagnosing and curing the disease. However, the lack of 3D model of the structure makes the utilization of DEPDC1B as a therapeutic target difficult. The present study focuses on the prediction of a suitable 3D model of the protein as well as the analysis of protein-protein interaction between DEPDC1B and Rac1 protein using PatchDock web server along with the identification of allosteric or regulatory sites of DEPDC1B.","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/3136024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64319549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins 细菌血红蛋白样蛋白预测的支持向量机方法

Advances in Bioinformatics Pub Date : 2016-02-29 DOI: 10.1155/2016/8150784

MuthuKrishnan Selvaraj, Munish Puri, K. Dikshit, Christophe Lefèvre

{"title":"BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins","authors":"MuthuKrishnan Selvaraj, Munish Puri, K. Dikshit, Christophe Lefèvre","doi":"10.1155/2016/8150784","DOIUrl":"https://doi.org/10.1155/2016/8150784","url":null,"abstract":"The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL) proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM) models were developed for predicting HbL proteins based upon amino acid composition (AC), dipeptide composition (DC), hybrid method (AC + DC), and position specific scoring matrix (PSSM). In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM) profiles. The average accuracy, standard deviation (SD), false positive rate (FPR), confusion matrix, and receiver operating characteristic (ROC) were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/8150784","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64552246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm 基于大规模递归神经网络的杜鹃搜索-传粉算法基因调控网络建模

Advances in Bioinformatics Pub Date : 2016-02-16 DOI: 10.1155/2016/5283937

S. Mandal, Abhinandan Khan, Goutam Saha, R. Pal

{"title":"Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm","authors":"S. Mandal, Abhinandan Khan, Goutam Saha, R. Pal","doi":"10.1155/2016/5283937","DOIUrl":"https://doi.org/10.1155/2016/5283937","url":null,"abstract":"The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/5283937","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64420120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Inhibition of Mycobacterium-RmlA by Molecular Modeling, Dynamics Simulation, and Docking 分枝杆菌- rmla的分子模拟、动力学模拟和对接抑制作用

Advances in Bioinformatics Pub Date : 2016-02-14 DOI: 10.1155/2016/9841250

N. Harathi, Madhusudana Pulaganti, C. M. Anuradha, S. K. Chitta

{"title":"Inhibition of Mycobacterium-RmlA by Molecular Modeling, Dynamics Simulation, and Docking","authors":"N. Harathi, Madhusudana Pulaganti, C. M. Anuradha, S. K. Chitta","doi":"10.1155/2016/9841250","DOIUrl":"https://doi.org/10.1155/2016/9841250","url":null,"abstract":"The increasing resistance to anti-tb drugs has enforced strategies for finding new drug targets against Mycobacterium tuberculosis (Mtb). In recent years enzymes associated with the rhamnose pathway in Mtb have attracted attention as drug targets. The present work is on α-D-glucose-1-phosphate thymidylyltransferase (RmlA), the first enzyme involved in the biosynthesis of L-rhamnose, of Mtb cell wall. This study aims to derive a 3D structure of RmlA by using a comparative modeling approach. Structural refinement and energy minimization of the built model have been done with molecular dynamics. The reliability assessment of the built model was carried out with various protein checking tools such as Procheck, Whatif, ProsA, Errat, and Verify 3D. The obtained model investigates the relation between the structure and function. Molecular docking interactions of Mtb-RmlA with modified EMB (ethambutol) ligands and natural substrate have revealed specific key residues Arg13, Lys23, Asn109, and Thr223 which play an important role in ligand binding and selection. Compared to all EMB ligands, EMB-1 has shown better interaction with Mtb-RmlA model. The information thus discussed above will be useful for the rational design of safe and effective inhibitors specific to RmlA enzyme pertaining to the treatment of tuberculosis.","PeriodicalId":39059,"journal":{"name":"Advances in Bioinformatics","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2016/9841250","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64643826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6