A Text Mining and Machine Learning Protocol for Extracting Posttranslational Modifications of Proteins from PubMed: A Special Focus on Glycosylation, Acetylation, Methylation, Hydroxylation, and Ubiquitination

Abstract

Posttranslational modifications (PTMs) of proteins impart a significant role in human cellular functions ranging from localization to signal transduction. Hundreds of PTMs act in a human cell. Among them, only the selected PTMs are well established and documented. PubMed includes thousands of papers on the selected PTMs, and it is a challenge for the biomedical researchers to assimilate useful information manually. Alternatively, text mining approaches and machine learning algorithm automatically extract the relevant information from PubMed. Protein phosphorylation is a well-established PTM and several research works are under way. Many existing systems are there for protein phosphorylation information extraction. A recent approach uses a hybrid approach using text mining and machine learning to extract protein phosphorylation information from PubMed. Some of the other common PTMs that exhibit similar features in terms of entities that are involved in PTM process, that is, the substrate, the enzymes, and the amino acid residues, are glycosylation, acetylation, methylation, hydroxylation, and ubiquitination. This has motivated us to repurpose and extend the text mining protocol and machine learning information extraction methodology developed for protein phosphorylation to these PTMs. In this chapter, the chemistry behind each of the PTMs is briefly outlined and the text mining protocol and machine learning algorithm adaption is explained for the same.