Pub Date : 2017-01-01Epub Date: 2017-01-29DOI: 10.1155/2017/4827171
Faridah Hani Mohamed Salleh, Suhaila Zainudin, Shereena M Arif
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.
基因调控网络(GRN)重构是通过计算分析从实验数据中识别调控基因相互作用的过程。以往GRN方法性能下降的主要原因之一是对级联基序的预测不准确。级联误差被定义为对级联基序的错误预测，其中间接相互作用被误解为直接相互作用。尽管各种GRN预测方法的研究非常活跃，但对于解决级联误差相关问题的具体方法的讨论仍然缺乏。事实上，过去的研究所做的实验并没有专门针对证明GRN预测方法在避免级联误差发生方面的能力。因此，本研究旨在提出多元线性回归(Multiple Linear Regression, MLR)从基因表达数据中推断GRN，避免将间接相互作用(A→B→C)错误地推断为直接相互作用(A→C)。由于实际实验数据集的观测数量远远少于预测因子的数量，因此，通过现有的提取方法从全局相互作用网络中提取随机子网络，可以消除一些预测因子。此外，本实验还扩展到使用本工作中提出的一种新的实验程序来评估MLR处理级联误差的有效性。实验表明，级联误差的数量非常小。除此之外，Belsley共线性检验证明多重共线性确实对实验中使用的数据集有很大的影响。所有测试子网均获得满意的结果，AUROC值均在0.5以上。
{"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}
Y. Bakhit, Mohamed O Ibrahim, M. Amin, Y. Mirghani, M. Hassan
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.
{"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}
F. Bande, S. Arshad, M. Bejo, S. Kadkhodaei, A. Omar
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.
{"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}
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.
{"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}
Mahesha N. Nadugala, P. Premaratne, C. Goonasekara
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.
{"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}
Sayan Mukherjee, Gopa Chatterjee, M. Ghosh, B. Das, D. Majumder
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.
{"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}
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.
随着东南亚国家口腔癌变发病率的上升，由于烟草和槟榔液消费量的增加以及人乳头瘤病毒(特别是16型)的感染，在早期阶段预测癌前病变向癌组织的转变，以控制癌变过程变得至关重要。在E2蛋白存在下下调的DEPDC1B最近被发现在口腔癌中过表达，这可能与病毒基因组整合到宿主基因组时E2开放阅读框的破坏有关。DEPDC1B通过直接与Rac1蛋白相互作用介导其作用，已知Rac1蛋白调节重要的细胞信号通路。因此，DEPDC1B可能是一种潜在的生物标志物，也是一种诊断和治疗疾病的治疗靶点。然而，由于缺乏该结构的三维模型，使得DEPDC1B作为治疗靶点的利用变得困难。本研究的重点是预测合适的蛋白质3D模型，并利用PatchDock web server分析DEPDC1B与Rac1蛋白之间的蛋白-蛋白相互作用，以及确定DEPDC1B的变抗或调控位点。
{"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}
MuthuKrishnan Selvaraj, Munish Puri, K. Dikshit, Christophe Lefèvre
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.
{"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}
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.
{"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}
N. Harathi, Madhusudana Pulaganti, C. M. Anuradha, S. K. Chitta
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.
随着抗结核药物耐药性的增加，寻找新的抗结核分枝杆菌(Mtb)药物靶点的战略势在必行。近年来，与鼠李糖途径相关的酶作为结核分枝杆菌的药物靶点引起了人们的关注。α- d -葡萄糖-1-磷酸胸苷基转移酶(RmlA)是结核分枝杆菌细胞壁上第一个参与l -鼠李糖生物合成的酶。本研究旨在通过比较建模方法推导出RmlA的三维结构。用分子动力学方法对所建模型进行了结构优化和能量最小化。利用Procheck、Whatif、ProsA、Errat和Verify 3D等多种蛋白质检测工具对构建的模型进行可靠性评估。所得模型考察了结构与功能之间的关系。mmb - rmla与修饰的EMB(乙胺丁醇)配体和天然底物的分子对接作用揭示了在配体结合和选择中起重要作用的特定关键残基Arg13、Lys23、Asn109和Thr223。与所有EMB配体相比，EMB-1与Mtb-RmlA模型表现出更好的相互作用。以上讨论的信息将有助于合理设计安全有效的RmlA酶特异性抑制剂，用于治疗结核病。
{"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}
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