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Carbohydrate-active enzymes (CAZymes) can be found in all domains of life and play a crucial role in metabolic and physiological processes. CAZymes often possess a modular structure, comprising not only catalytic domains but also associated domains such as carbohydrate-binding modules (CBMs) and linker domains. By exploring the modular diversity of CAZy families, catalysts with novel properties can be discovered and further insight in their biological functions and evolutionary relationships can be obtained. Here we present the carbohydrate-active enzyme domain analysis tool (CANDy), an assembly of several novel scripts, tools and databases that allows users to analyze the domain architecture of all protein sequences in a given CAZy family. CANDy's usability is shown on glycoside hydrolase family 48, a small yet underexplored family containing multi-domain enzymes. Our analysis reveals the existence of 35 distinct domain assemblies, including eight known architectures, with the remaining assemblies awaiting characterization. Moreover, we substantiate the occurrence of horizontal gene transfer from prokaryotes to insect orthologs and provide evidence for the subsequent removal of auxiliary domains, likely through a gene fission event. CANDy is available at https://github.com/PyEED/CANDy. [ABSTRACT FROM AUTHOR]

Protein lysine methyltransferases (PKMTs) transfer up to three methyl groups to the side chains of lysine residues in proteins and fulfill important regulatory functions by controlling protein stability, localization and protein/protein interactions. The methylation reactions are highly regulated, and aberrant methylation of proteins is associated with several types of diseases including neurologic disorders, cardiovascular diseases, and various types of cancer. This review describes novel insights into the catalytic machinery of various PKMTs achieved by the combined application of biochemical experiments and simulation approaches during the last years, focusing on clinically relevant and well-studied enzymes of this group like DOT1L, SMYD1-3, SET7/9, G9a/GLP, SETD2, SUV420H2, NSD1/2, different MLLs and EZH2. Biochemical experiments have unraveled many mechanistic features of PKMTs concerning their substrate and product specificity, processivity and the effects of somatic mutations observed in PKMTs in cancer cells. Structural data additionally provided information about the substrate recognition, enzyme-substrate complex formation, and allowed for simulations of the substrate peptide interaction and mechanism of PKMTs with atomistic resolution by molecular dynamics and hybrid quantum mechanics/molecular mechanics methods. These simulation technologies uncovered important mechanistic details of the PKMT reaction mechanism including the processes responsible for the deprotonation of the target lysine residue, essential conformational changes of the PKMT upon substrate binding, but also rationalized regulatory principles like PKMT autoinhibition. Further developments are discussed that could bring us closer to a mechanistic understanding of catalysis of this important class of enzymes in the near future. The results described here illustrate the power of the investigation of enzyme mechanisms by the combined application of biochemical experiments and simulation technologies. [ABSTRACT FROM AUTHOR]

EnzymeML is an XML‐based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO‐RK. [ABSTRACT FROM AUTHOR]