About this Research Topic
Biomacromolecular covalent modifications (BCMs) include protein post-translational modifications (PTMs) and nucleic acid modifications. To date, there are 670 types of PTMs, and the most extensively studied PTMs are phosphorylation, ubiquitination and acetylation, which are involved in regulating almost all of biological processes, such as cell cycle, autophagy and metabolism. More than 150 types of RNA modifications and tens of DNA modifications have been discovered, such as N6-methyladenosine (m6A) in messenger RNAs and 5-methylcytosine (5mC) in DNA molecules, which paly crucial roles in controlling gene expression. There is increasing evidence showing that PTMs are related to diseases such as cancer and neurological disorders. RNA modification pathways are also found to be dysregulated in human cancers and epigenomic DNA modifications may provide clues as to why certain diseases and cancer develop with aging.
The modification processes of these different types of biomacromolecules share some common properties. The deposition of chemical modifications (or marks) onto biomacromolecules is catalyzed by specific enzymes named “writers”. The enzymes that remove the modifications are called “erasers”. After recognizing the BCM sites, regulator proteins that produce a cellular response are “readers”.
Identification of these BCM substrates and sites, “writers”, “erasers” and “readers” provide us a better understanding on how cellular activities are dynamically regulated. The development of computational algorithms, pipelines, tools, and databases will support biologists who are exploring BCM regulation, as well as provide potential biomarkers and drug targets for translational medicine.
The aim of this Research Topic is to provide highly valuable computational resources for biologists who are dissecting the biological functions, pathological roles, and/or regulatory mechanisms of protein PTMs, RNA modifications or DNA modifications.
The following areas will be covered, but not limited to:
• New computational methods for identification of BCM substrates and sites
• Useful tools/pipelines for analyzing BCM-related sequence, structure and/or omics data
• Comprehensive databases devoted to the integration of BCM knowledge
• Easy-to-understand tutorials or protocols for using BCM resources
• Systematic applications of BCM resources to make important findings.
• Cutting-edge reviews for certain aspects of BCM resources
The modification processes of these different types of biomacromolecules share some common properties. The deposition of chemical modifications (or marks) onto biomacromolecules is catalyzed by specific enzymes named “writers”. The enzymes that remove the modifications are called “erasers”. After recognizing the BCM sites, regulator proteins that produce a cellular response are “readers”.
Identification of these BCM substrates and sites, “writers”, “erasers” and “readers” provide us a better understanding on how cellular activities are dynamically regulated. The development of computational algorithms, pipelines, tools, and databases will support biologists who are exploring BCM regulation, as well as provide potential biomarkers and drug targets for translational medicine.
The aim of this Research Topic is to provide highly valuable computational resources for biologists who are dissecting the biological functions, pathological roles, and/or regulatory mechanisms of protein PTMs, RNA modifications or DNA modifications.
The following areas will be covered, but not limited to:
• New computational methods for identification of BCM substrates and sites
• Useful tools/pipelines for analyzing BCM-related sequence, structure and/or omics data
• Comprehensive databases devoted to the integration of BCM knowledge
• Easy-to-understand tutorials or protocols for using BCM resources
• Systematic applications of BCM resources to make important findings.
• Cutting-edge reviews for certain aspects of BCM resources
Keywords: Biomacromolecular covalent modification, post-translational modification, DNA modification, RNA modification, machine learning, data integration
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