Lifeglimmer GmbH, Berlin, Germany.
Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, WE, The Netherlands.
Methods Mol Biol. 2023;2643:405-411. doi: 10.1007/978-1-0716-3048-8_29.
Computational approaches are practical when investigating putative peroxisomal proteins and for sub-peroxisomal protein localization in unknown protein sequences. Nowadays, advancements in computational methods and Machine Learning (ML) can be used to hasten the discovery of novel peroxisomal proteins and can be combined with more established computational methodologies. Here, we explain and list some of the most used tools and methodologies for novel peroxisomal protein detection and localization.
计算方法在研究假定的过氧化物酶体蛋白和未知蛋白质序列中的亚过氧化物酶体蛋白定位时非常实用。如今，计算方法和机器学习 (ML) 的进步可用于加速新型过氧化物酶体蛋白的发现，并可与更成熟的计算方法相结合。在这里，我们解释并列出了一些用于新型过氧化物酶体蛋白检测和定位的最常用工具和方法。
