Pub Date : 2018-10-30DOI: 10.4172/0974-276X-C2-114
pMarek Cieplakp
{"title":"Structural changes in proteins at fluid-fluid interfaces","authors":"pMarek Cieplakp","doi":"10.4172/0974-276X-C2-114","DOIUrl":"https://doi.org/10.4172/0974-276X-C2-114","url":null,"abstract":"","PeriodicalId":73911,"journal":{"name":"Journal of proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70916126","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}
Pub Date : 2018-10-01DOI: 10.4172/0974-276X-C1-111
pSatoru Miyanop
{"title":"Computational challenges for breaking complexity of cancer from genomes to networks","authors":"pSatoru Miyanop","doi":"10.4172/0974-276X-C1-111","DOIUrl":"https://doi.org/10.4172/0974-276X-C1-111","url":null,"abstract":"","PeriodicalId":73911,"journal":{"name":"Journal of proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70916042","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}
Bioinformatics and sequence mining are the application and development of data mining techniques to solve problems by comprehending biological data. Sequence analysis is the most primitive operation in sequence mining techniques. Modern sequence mining research is specialized in analyzing sequential patterns which are relevant and distinct from one another and utilizing retrieved sequences similarity and distance between different protein sequences can be analyzed. Diabetic retinopathy is the major cause of blindness in individuals mostly adults with diabetes and is it is the common problem of diabetes mellitus across the world. Various research analyses stated that there are many proteins which are found to take part in diabetic retinopathy. In this paper, we have evaluated certain proteins which are closely related with diabetic retinopathy with the help of multiple alignment tool viz. Clustal Omega and obtained a phylogenetic tree of 28 protein sequences gathered from National Center for Biotechnology Information (NCBI). In this work data mining technique called sequence mining plays a significant role in providing phylogram obtained with Neighbor-Joining algorithm. From the phylogenetic tree it was recognized that cortistatin, vitamin-D receptor and somatostatin proteins has close connection with diabetic retinopathy. Molecular docking studies have also been performed which is the most extensively used method for the calculation of protein-ligand interactions. In silico docking studies indicated that four inhibitory compounds i.e. Quercetin, Kaempferol, Naringenin and Melicitrin interact with aldose reductase which also found to have role in diabetic retinopathy. Outcomes infer that techniques intended to standardize cortistatin, vitamin-D receptor and somatostatin activities be of huge advantage and provide benefit in inhibiting diabetic retinopathy.
{"title":"Identification of Protein Biomarkers for Diabetic Retinopathy using Sequence Mining Techniques","authors":"Ratnagiri Devarapu, G. Murali, H. Thota","doi":"10.4172/jpb.1000472","DOIUrl":"https://doi.org/10.4172/jpb.1000472","url":null,"abstract":"Bioinformatics and sequence mining are the application and development of data mining techniques to solve problems by comprehending biological data. Sequence analysis is the most primitive operation in sequence mining techniques. Modern sequence mining research is specialized in analyzing sequential patterns which are relevant and distinct from one another and utilizing retrieved sequences similarity and distance between different protein sequences can be analyzed. Diabetic retinopathy is the major cause of blindness in individuals mostly adults with diabetes and is it is the common problem of diabetes mellitus across the world. Various research analyses stated that there are many proteins which are found to take part in diabetic retinopathy. In this paper, we have evaluated certain proteins which are closely related with diabetic retinopathy with the help of multiple alignment tool viz. Clustal Omega and obtained a phylogenetic tree of 28 protein sequences gathered from National Center for Biotechnology Information (NCBI). In this work data mining technique called sequence mining plays a significant role in providing phylogram obtained with Neighbor-Joining algorithm. From the phylogenetic tree it was recognized that cortistatin, vitamin-D receptor and somatostatin proteins has close connection with diabetic retinopathy. Molecular docking studies have also been performed which is the most extensively used method for the calculation of protein-ligand interactions. In silico docking studies indicated that four inhibitory compounds i.e. Quercetin, Kaempferol, Naringenin and Melicitrin interact with aldose reductase which also found to have role in diabetic retinopathy. Outcomes infer that techniques intended to standardize cortistatin, vitamin-D receptor and somatostatin activities be of huge advantage and provide benefit in inhibiting diabetic retinopathy.","PeriodicalId":73911,"journal":{"name":"Journal of proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42642209","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}
P53 is a globular protein with distinct domains and a key tumor suppressor that functions through transcriptional transactivation, repression and protein-protein interactions. Numerous studies have implicated protein-protein interactions between p53 and a multitude of cellular proteins with a variety of known functions. Because of these interactions, and the many gene expression regulations, a multitude of potential mechanisms and their relationship to tumor suppression have been proposed. It is desirable to test these interactions in an in vitro setting to demonstrate that any identified interaction is direct. Due to the difficulties associated with purifying recombinant full-length p53, many studies have utilized the p53 DNA binding domain to test for direct protein interactions with p53. The DNA binding domain of p53 is structured, folds independently and dictates the stability of the full-length protein. Therefore, it is reasonable to perform in vitro experiments with this isolated domain. However, we demonstrate that if a HIStag is present on the interacting partner when testing for an interaction with p53, this can lead to detection of an artefactual protein-protein interaction raising the possibility of false positive results. Furthermore, the presence of the HIS-tag promotes aggregation and precipitation of the p53 DNA binding domain.
{"title":"P53 and HIS-tag Binding","authors":"Lindsey Barron, A. Bishop","doi":"10.4172/JPB.1000467","DOIUrl":"https://doi.org/10.4172/JPB.1000467","url":null,"abstract":"P53 is a globular protein with distinct domains and a key tumor suppressor that functions through transcriptional transactivation, repression and protein-protein interactions. Numerous studies have implicated protein-protein interactions between p53 and a multitude of cellular proteins with a variety of known functions. Because of these interactions, and the many gene expression regulations, a multitude of potential mechanisms and their relationship to tumor suppression have been proposed. It is desirable to test these interactions in an in vitro setting to demonstrate that any identified interaction is direct. Due to the difficulties associated with purifying recombinant full-length p53, many studies have utilized the p53 DNA binding domain to test for direct protein interactions with p53. The DNA binding domain of p53 is structured, folds independently and dictates the stability of the full-length protein. Therefore, it is reasonable to perform in vitro experiments with this isolated domain. However, we demonstrate that if a HIStag is present on the interacting partner when testing for an interaction with p53, this can lead to detection of an artefactual protein-protein interaction raising the possibility of false positive results. Furthermore, the presence of the HIS-tag promotes aggregation and precipitation of the p53 DNA binding domain.","PeriodicalId":73911,"journal":{"name":"Journal of proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/JPB.1000467","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46217654","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}
Pub Date : 2018-03-17DOI: 10.4172/0974-276X-C1-104
Ruth Ololade Amiola, A. Ademakinwa, Z. A. Ayinla, Esther Nkechi Ezima, F. Agboola
Abstract Background β-Cyanoalanine synthase plays essential roles in germinating seeds, such as in cyanide homeostasis. Methods β-Cyanoalanine synthase was isolated from sorghum seeds, purified using chromatographic techniques and its biochemical and catalytic properties were determined. Results The purified enzyme had a yield of 61.74% and specific activity of 577.50 nmol H2S/min/mg of protein. The apparent and subunit molecular weight for purified β-cyanoalanine synthase were 58.26±2.41 kDa and 63.4 kDa, respectively. The kinetic parameters with sodium cyanide as substrate were 0.67±0.08 mM, 17.60±0.50 nmol H2S/mL/min, 2.97×10−1 s−1 and 4.43×102 M−1 s−1 for KM, Vmax, kcat and kcat/KM, respectively. With L-cysteine as substrate, the kinetic parameters were 2.64±0.37 mM, 63.41±4.04 nmol H2S/mL/min, 10.71×10−1 s−1 and 4.06×102 M−1 s−1 for KM, Vmax, kcat and kcat/KM, respectively. The optimum temperature and pH for activity were 35°C and 8.5, respectively. The enzyme retained more than half of its activity at 40°C. Inhibitors such as HgCl2, EDTA, glycine and iodoacetamide reduced enzyme activity. Conclusion The biochemical properties of β-cyanoalanine synthase in germinating sorghum seeds highlights its roles in maintaining cyanide homeostasis.
{"title":"Purification and biochemical characterization of a β-cyanoalanine synthase expressed in germinating seeds of Sorghum bicolor (L.) moench","authors":"Ruth Ololade Amiola, A. Ademakinwa, Z. A. Ayinla, Esther Nkechi Ezima, F. Agboola","doi":"10.4172/0974-276X-C1-104","DOIUrl":"https://doi.org/10.4172/0974-276X-C1-104","url":null,"abstract":"Abstract Background β-Cyanoalanine synthase plays essential roles in germinating seeds, such as in cyanide homeostasis. Methods β-Cyanoalanine synthase was isolated from sorghum seeds, purified using chromatographic techniques and its biochemical and catalytic properties were determined. Results The purified enzyme had a yield of 61.74% and specific activity of 577.50 nmol H2S/min/mg of protein. The apparent and subunit molecular weight for purified β-cyanoalanine synthase were 58.26±2.41 kDa and 63.4 kDa, respectively. The kinetic parameters with sodium cyanide as substrate were 0.67±0.08 mM, 17.60±0.50 nmol H2S/mL/min, 2.97×10−1 s−1 and 4.43×102 M−1 s−1 for KM, Vmax, kcat and kcat/KM, respectively. With L-cysteine as substrate, the kinetic parameters were 2.64±0.37 mM, 63.41±4.04 nmol H2S/mL/min, 10.71×10−1 s−1 and 4.06×102 M−1 s−1 for KM, Vmax, kcat and kcat/KM, respectively. The optimum temperature and pH for activity were 35°C and 8.5, respectively. The enzyme retained more than half of its activity at 40°C. Inhibitors such as HgCl2, EDTA, glycine and iodoacetamide reduced enzyme activity. Conclusion The biochemical properties of β-cyanoalanine synthase in germinating sorghum seeds highlights its roles in maintaining cyanide homeostasis.","PeriodicalId":73911,"journal":{"name":"Journal of proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70915686","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}
Joana F Tavares, Filipe Assis-Santos, Manuel A. S. Santos
Protein biosynthesis is a highly accurate biological process essential for life. Amino acid misincorporation errors (mistranslation) normally occur at low levels, but can increase sharply upon amino acid starvation, exposure to drugs, oxidative stress and other physiological perturbations. These processes disrupt protein function and are normally regarded as being deleterious, however, recent work has shown that they can also be regulated to produce advantageous phenotypes in both prokaryotes and eukaryotes. The biology of such unexpected adaptive mistranslation is poorly understood due to technical difficulties in the identification and quantification of amino acid misincorporations. In this mini-review, we describe proteome scale methodologies involving the use of mass-spectrometry and bioinformatics tools to directly detect and quantify mistranslation events and also indirect functional methods that permit sensitive, flexible and low-cost analysis of site specific amino acid variation.
{"title":"Proteomics Analysis for Amino Acid Misincorporation Detection: Mini Review","authors":"Joana F Tavares, Filipe Assis-Santos, Manuel A. S. Santos","doi":"10.4172/jpb.1000464","DOIUrl":"https://doi.org/10.4172/jpb.1000464","url":null,"abstract":"Protein biosynthesis is a highly accurate biological process essential for life. Amino acid misincorporation errors (mistranslation) normally occur at low levels, but can increase sharply upon amino acid starvation, exposure to drugs, oxidative stress and other physiological perturbations. These processes disrupt protein function and are normally regarded as being deleterious, however, recent work has shown that they can also be regulated to produce advantageous phenotypes in both prokaryotes and eukaryotes. The biology of such unexpected adaptive mistranslation is poorly understood due to technical difficulties in the identification and quantification of amino acid misincorporations. In this mini-review, we describe proteome scale methodologies involving the use of mass-spectrometry and bioinformatics tools to directly detect and quantify mistranslation events and also indirect functional methods that permit sensitive, flexible and low-cost analysis of site specific amino acid variation.","PeriodicalId":73911,"journal":{"name":"Journal of proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47195477","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}
Statement of the Problem: Drug discovery is a lengthy process, taking on average 12 years for the drugs to reach the market –but as Sir James Black OM once said “the best way to discover a new drug is to start with the old one”. As result, this will drive to Drug repositioning concept. Drug Repurposing and repositioning is Finding a new clinical use for an approved drug. There are many factors that can be used to predict new target disease i.e., protein-protein interaction, chemical structure, gene expression and functional genomics, Phenotype and side effect, genetic variation and Machine learning. Protein-protein interaction PPI is Physical contacts with molecular docking between proteins that occur in a cell or in a living organism in vivo. There is Two Alternative Approaches PPI “Binary: yeast two hybrid (Y2H) and co-complex: (TAP-MS)”. Drug Repositioning System, is a system built based on protein-protein Binary interaction to predict new targets for the approved drugs. The system curate the data sets for human PPI, Drugs and diseases from well-known online sources (PPI from HRPD, drugs from DrugBank, Diseases from DisGeNET), Drug Repositioning System relates the 3 data sets based on genes name. Drug Repositioning Network System consisting of two interfaces: backend system where the curated data sets stored based on rational database and using Big Data tools, and frontend web interface where the end users can use many search engines to search inside the system for diseases, genes and drugs to predict and find new targets for the approved drugs based on protein interactions, from the web interface the user can make analysis based on his search result and build network between the genes, diseases and drugs and generate statistics to be able to answer his question. There are many Questions that can be answered by Drug Repositioning System and generate statistics: for example the main question is can we find new indications for existing approved drugs. Drug similarity: from the Drug Repositioning System we able to measure the percentage of drugs similarity between any pair genes interaction based on the number of shared drugs between them to rate the level of drug repositioning strength and then use the ROC analysis.
问题陈述:药物发现是一个漫长的过程，药物进入市场平均需要12年的时间——但正如詹姆斯•布莱克爵士(Sir James Black OM)曾经说过的那样，“发现新药的最好方法是从旧药物开始”。因此，这将推动药物概念的重新定位。药物再利用和重新定位是为已批准的药物寻找新的临床用途。有许多因素可以用来预测新的目标疾病，如蛋白质-蛋白质相互作用、化学结构、基因表达和功能基因组学、表型和副作用、遗传变异和机器学习。蛋白质-蛋白质相互作用(PPI)是指在细胞或生物体内发生的蛋白质之间的分子对接的物理接触。有两种替代方法PPI“二元:酵母双杂交(Y2H)和共络合物(TAP-MS)”。药物重新定位系统是一种基于蛋白质-蛋白质二元相互作用来预测已获批药物新靶点的系统。该系统从知名的在线资源(PPI来自HRPD, Drugs来自DrugBank, diseases来自DisGeNET)中整理了人类PPI、药物和疾病的数据集，药物重新定位系统根据基因名称将3个数据集关联起来。药物再定位网络系统由两个接口组成:后端系统是基于理性数据库并使用大数据工具存储的整理数据集，前端web界面是终端用户可以使用多个搜索引擎在系统内部搜索疾病、基因和药物，根据蛋白质相互作用预测和发现已批准药物的新靶点，从web界面用户可以根据搜索结果进行分析并建立基因之间的网络;疾病和药物，并产生统计数据来回答他的问题。药物重新定位系统可以回答许多问题并产生统计数据:例如，主要问题是我们是否可以为现有已批准的药物找到新的适应症。药物相似度:从药物重新定位系统中，我们能够基于它们之间共享药物的数量来测量任何一对基因相互作用之间药物相似度的百分比，以评估药物重新定位强度的水平，然后使用ROC分析。
{"title":"Drug Repositioning Network System Using the Power of Network Analysis and Machine Learning to Predict new Indications for the Approved Drugs “Drug Repositioning and Rate the Level of Drug Similarity","authors":"Sherief El Rweney","doi":"10.4172/JPB.1000463","DOIUrl":"https://doi.org/10.4172/JPB.1000463","url":null,"abstract":"Statement of the Problem: Drug discovery is a lengthy process, taking on average 12 years for the drugs to reach the market –but as Sir James Black OM once said “the best way to discover a new drug is to start with the old one”. As result, this will drive to Drug repositioning concept. Drug Repurposing and repositioning is Finding a new clinical use for an approved drug. There are many factors that can be used to predict new target disease i.e., protein-protein interaction, chemical structure, gene expression and functional genomics, Phenotype and side effect, genetic variation and Machine learning. Protein-protein interaction PPI is Physical contacts with molecular docking between proteins that occur in a cell or in a living organism in vivo. There is Two Alternative Approaches PPI “Binary: yeast two hybrid (Y2H) and co-complex: (TAP-MS)”. Drug Repositioning System, is a system built based on protein-protein Binary interaction to predict new targets for the approved drugs. The system curate the data sets for human PPI, Drugs and diseases from well-known online sources (PPI from HRPD, drugs from DrugBank, Diseases from DisGeNET), Drug Repositioning System relates the 3 data sets based on genes name. Drug Repositioning Network System consisting of two interfaces: backend system where the curated data sets stored based on rational database and using Big Data tools, and frontend web interface where the end users can use many search engines to search inside the system for diseases, genes and drugs to predict and find new targets for the approved drugs based on protein interactions, from the web interface the user can make analysis based on his search result and build network between the genes, diseases and drugs and generate statistics to be able to answer his question. There are many Questions that can be answered by Drug Repositioning System and generate statistics: for example the main question is can we find new indications for existing approved drugs. Drug similarity: from the Drug Repositioning System we able to measure the percentage of drugs similarity between any pair genes interaction based on the number of shared drugs between them to rate the level of drug repositioning strength and then use the ROC analysis.","PeriodicalId":73911,"journal":{"name":"Journal of proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/JPB.1000463","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48638347","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}
Sara Aiman, Muhammad Shehroz, Mehwish Munir, Sahib Gul, M. Shah, Asifullah Khan
The gram negative bacteria species of Pseudomonas putida (P. putida) are important for heterologous expression of diverse biosynthetic pathways and numerous secondary metabolites biosynthesis. The genes code for such secondary metabolites biosynthetic proteins are organized in microbial genomes as clusters to bring the concerted expression of entire biosynthetic machinery. The complete and whole genome sequences of more than fifty different strains available in public DNA sequences databases provide an excellent opportunity to investigate the genetically encoded secondary metabolites potential of ecologically diverse P. putida strains. We implement the advance bioinformatics resources to annotate the so far available P. putida strains genomes for biosynthetic gene clusters (BGCs) and underlie secondary metabolites chemical scaffolds. The P. puida strains are found to harbor genomic signatures coding the molecular machinery for diverse secondary metabolites biosynthesis. The corresponding BGCs of these metabolites are found to be uniquely distributed across different P. putida strains speculate their role toward strain's ecological competency acquirement. The chemoinformatics dereplication and DrugBank database searching revealed the chemical mimicry of one putative metabolite with 2, 3, Dihydroxybenzoylserine, that mediates an antibiotic iron depletion along with human neutrophil lipocalin during innate immune response.
{"title":"Species-Wide Genome Mining of Pseudomonas putida for Potential Secondary Metabolites and Drug-Like Natural Products Characterization","authors":"Sara Aiman, Muhammad Shehroz, Mehwish Munir, Sahib Gul, M. Shah, Asifullah Khan","doi":"10.4172/JPB.1000460","DOIUrl":"https://doi.org/10.4172/JPB.1000460","url":null,"abstract":"The gram negative bacteria species of Pseudomonas putida (P. putida) are important for heterologous expression of diverse biosynthetic pathways and numerous secondary metabolites biosynthesis. The genes code for such secondary metabolites biosynthetic proteins are organized in microbial genomes as clusters to bring the concerted expression of entire biosynthetic machinery. The complete and whole genome sequences of more than fifty different strains available in public DNA sequences databases provide an excellent opportunity to investigate the genetically encoded secondary metabolites potential of ecologically diverse P. putida strains. We implement the advance bioinformatics resources to annotate the so far available P. putida strains genomes for biosynthetic gene clusters (BGCs) and underlie secondary metabolites chemical scaffolds. The P. puida strains are found to harbor genomic signatures coding the molecular machinery for diverse secondary metabolites biosynthesis. The corresponding BGCs of these metabolites are found to be uniquely distributed across different P. putida strains speculate their role toward strain's ecological competency acquirement. The chemoinformatics dereplication and DrugBank database searching revealed the chemical mimicry of one putative metabolite with 2, 3, Dihydroxybenzoylserine, that mediates an antibiotic iron depletion along with human neutrophil lipocalin during innate immune response.","PeriodicalId":73911,"journal":{"name":"Journal of proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/JPB.1000460","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44519148","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}
Pub Date : 2018-01-01Epub Date: 2018-06-26DOI: 10.4172/jpb.1000477
Lixn Dong, Hongmei Ren
Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Early detection of CRC can significantly reduce this mortality rate. Unfortunately, recommended screening modalities, including colonoscopy, are hampered by poor patient acceptance, low sensitivity and high cost. Recent studies have demonstrated that colorectal oncogenesis is a multistep event resulting from the accumulation of a variety of genetic and epigenetic changes in colon epithelial cells, which can be reflected by epigenetic alterations in blood. DNA methylation is the most extensively studied dysregulated epigenetic mechanism in CRC. In this review, we focus on current knowledge on DNA methylation as potential blood-based biomarkers for early detection of CRC.
{"title":"Blood-based DNA Methylation Biomarkers for Early Detection of Colorectal Cancer.","authors":"Lixn Dong, Hongmei Ren","doi":"10.4172/jpb.1000477","DOIUrl":"https://doi.org/10.4172/jpb.1000477","url":null,"abstract":"<p><p>Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Early detection of CRC can significantly reduce this mortality rate. Unfortunately, recommended screening modalities, including colonoscopy, are hampered by poor patient acceptance, low sensitivity and high cost. Recent studies have demonstrated that colorectal oncogenesis is a multistep event resulting from the accumulation of a variety of genetic and epigenetic changes in colon epithelial cells, which can be reflected by epigenetic alterations in blood. DNA methylation is the most extensively studied dysregulated epigenetic mechanism in CRC. In this review, we focus on current knowledge on DNA methylation as potential blood-based biomarkers for early detection of CRC.</p>","PeriodicalId":73911,"journal":{"name":"Journal of proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/jpb.1000477","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36332074","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}
Pub Date : 2018-01-01DOI: 10.4172/0974-276X-C3-117
pAnnette G BeckSickingerp
{"title":"Structural insights in the binding mode of neuropeptide Y at G protein coupled receptors and consequences for drug development","authors":"pAnnette G BeckSickingerp","doi":"10.4172/0974-276X-C3-117","DOIUrl":"https://doi.org/10.4172/0974-276X-C3-117","url":null,"abstract":"","PeriodicalId":73911,"journal":{"name":"Journal of proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70916187","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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