参与更多结构域类型的蛋白质往往更重要。

Q4 Health Professions
Lu Chen, Yingjiao Cheng, Min Li, Jianxin Wang
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引用次数: 4

摘要

研究必需蛋白质对于理解细胞生命、药物设计和其他实际目的具有重要价值。在目前的大多数研究中,必需蛋白质通常是在具有不同拓扑特征的蛋白质-蛋白质相互作用(PPI)网络中挖掘的。在这项研究中,我们从一个新的角度研究了什么样的蛋白质是必不可少的。研究表明,蛋白质的重要性与蛋白质结构域有关,蛋白质结构域是蛋白质的功能、结构和进化单位。具有较多结构域类型(NDT)的蛋白质往往是必需的。对22个物种的分析表明,通过无损检测鉴定出的必需蛋白要比随机鉴定出的多得多。结构特征的考虑使我们减少了对网络数据的依赖,从而使我们能够在网络数据不完整和/或不一致的情况下研究更多物种的蛋白质必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Proteins involved in more domain types tend to be more essential.

Investigation of essential proteins is significantly valuable for understanding of cellular life, drug design and other practical purposes. In most of current studies, essential proteins are generally mined in protein-protein interaction (PPI) networks with diverse topology features. In this study, we investigate what kind of proteins is inclined to be essential from a new perspective. The investigation implies that protein essentiality is correlated with protein domains, which are functional, structural and evolutionary units of proteins. Proteins with a larger Number of Domain Types (NDT) tend to be essential. The analyses on 22 species show that essential proteins identified by NDT are much more than those identified by ten random identifications. The consideration of the structural feature makes us less dependent on network data and thus enables us to investigate protein essentiality of more species with incomplete and/or inconsistent network data.

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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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