Computational investigations of selected enzymes from two iron and α-ketoglutarate-dependent families
文献情報
Madison B. Berger, Alice R. Walker, Erik Antonio Vázquez-Montelongo, G. Andrés Cisneros
DNA alkylation is used as the key epigenetic mark in eukaryotes, however, most alkylation in DNA can result in deleterious effects. Therefore, this process needs to be tightly regulated. The enzymes of the AlkB and Ten-Eleven Translocation (TET) families are members of the Fe and alpha-ketoglutarate-dependent superfamily of enzymes that are tasked with dealkylating DNA and RNA in cells. Members of these families span all species and are an integral part of transcriptional regulation. While both families catalyze oxidative dealkylation of various bases, each has specific preference for alkylated base type as well as distinct catalytic mechanisms. This perspective aims to provide an overview of computational work carried out to investigate several members of these enzyme families including AlkB, ALKB Homolog 2, ALKB Homolog 3 and Ten-Eleven Translocate 2. Insights into structural details, mutagenesis studies, reaction path analysis, electronic structure features in the active site, and substrate preferences are presented and discussed.
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.












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