Miura Nobuaki, Okuda Shujiro
Division of Bioinformatics, Niigata University Graduate School of Medical and Dental Sciences, 2-5274 Gakkocho-dori, Chuo-ku, Niigata 951-8514, Japan.
Medical AI Center, Niigata University School of Medicine, 2-5274 Gakkocho-dori, Chuo-ku, Niigata 951-8514, Japan.
Comput Struct Biotechnol J. 2023 Jan 16;21:1140-1150. doi: 10.1016/j.csbj.2023.01.015. eCollection 2023.
Metaproteomics is a relatively young field that has only been studied for approximately 15 years. Nevertheless, it has the potential to play a key role in disease research by elucidating the mechanisms of communication between the human host and the microbiome. Although it has been useful in developing an understanding of various diseases, its analytical strategies remain limited to the extended application of proteomics. The sequence databases in metaproteomics must be large because of the presence of thousands of species in a typical sample, which causes problems unique to large databases. In this review, we demonstrate the usefulness of metaproteomics in disease research through examples from several studies. Additionally, we discuss the challenges of applying metaproteomics to conventional proteomics analysis methods and introduce studies that may provide clues to the solutions. We also discuss the need for a standard false discovery rate control method for metaproteomics to replace common target-decoy search approaches in proteomics and a method to ensure the reliability of peptide spectrum match.
宏蛋白质组学是一个相对年轻的领域,仅有大约15年的研究历史。然而,它有潜力通过阐明人类宿主与微生物群之间的通讯机制在疾病研究中发挥关键作用。尽管它在增进对各种疾病的理解方面很有用,但它的分析策略仍然局限于蛋白质组学的扩展应用。由于典型样本中存在数千种物种,宏蛋白质组学中的序列数据库必须很大,这就导致了大型数据库特有的问题。在这篇综述中,我们通过几项研究的实例展示了宏蛋白质组学在疾病研究中的有用性。此外,我们讨论了将宏蛋白质组学应用于传统蛋白质组学分析方法所面临的挑战,并介绍了可能为解决方案提供线索的研究。我们还讨论了宏蛋白质组学需要一种标准的错误发现率控制方法来取代蛋白质组学中常见的目标-诱饵搜索方法,以及一种确保肽谱匹配可靠性的方法。