目的:研究幽门螺杆菌J99中不同类别蛋白编码靶基因的百分比分布,寻找抑制其增殖的潜在治疗靶点。

Q4 Health Professions
Megha Vaidya, Hetalkumar Panchal
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引用次数: 0

摘要

幽门螺杆菌是人类中最常见的细菌病原体之一,其血清阳性随着年龄和社会经济地位的降低而增加。由于其致病性岛的存在,导致成人和儿童慢性持续性和萎缩性胃炎,通常最终发展为胃和十二指肠溃疡。研究表明,与未感染的个体相比,感染的个体患胃癌和粘膜相关淋巴组织淋巴瘤的风险增加了2至6倍。完整的基因组序列提供了大量潜在的药物靶点。减法研究有望为识别潜在的药物靶点提供一个概念框架,并为理解疾病的生物调控机制提供见解,这在从基因组学信息中寻找新的药物靶点方面发挥着越来越重要的作用。在本文中,我们讨论了用于识别药物靶点的减法研究方法,重点是靶蛋白的建模以及利用计算工具将模型蛋白与可能的配体对接。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
To study percentage distribution of target genes encoding proteins of different classes in Helicobacter pylori strain J99 and identification of potential therapeutic targets to reduce its proliferation.

Helicobacter pylori are one of the most common bacterial pathogens in humans whose seropositivity increases with age and low socio-economic status. Due to presence of its pathogenic-island causes chronic persistent and atrophic gastritis in adults and children that often culminate in development of gastric and duodenal ulcers. Studies indicate that infected individuals have two to sixfold increased risk of developing gastric cancer and mucosal associated lymphoid tissue lymphoma compared to their uninfected counterparts. The complete genome sequences have provided a plethora of potential drug targets. Subtractive study holds the promise of providing a conceptual framework for identification of potential drug targets and providing insights to understand the biological regulatory mechanisms in diseases, which are playing an increasingly important role in searching for novel drug targets from the information contained in genomics. In this paper, we discuss subtractive study approaches for identifying drug targets, with the emphasis on the modelling of target protein and docking of the modelled protein with probable ligand by using computational tools.

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来源期刊
International Journal of Bioinformatics Research and Applications
International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management
CiteScore
0.60
自引率
0.00%
发文量
26
期刊介绍: Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.
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