Abstract6‐methyladenine (6mA) is fairly abundant in nuclear DNA of basal fungi, ciliates and green algae. In these organisms, 6mA is maintained near transcription start sites in ApT context by a parental‐strand instruction dependent maintenance methyltransferase and is positively associated with transcription. In animals and plants, 6mA levels are high only in organellar DNA. The 6mA levels in nuclear DNA are very low. They are attributable to nucleotide salvage and the activity of otherwise mitochondrial METTL4, and may be considered as a (...) price that cells pay for adenine methylation in RNA and/or organellar DNA. Cells minimize this price by sanitizing dNTP pools to limit 6mA incorporation, and by converting 6mA that has been incorporated into DNA back to adenine. Hence, 6mA in nuclear DNA should be described as an epigenetic mark only in basal fungi, ciliates and green algae, but not in animals and plants. (shrink)

The performance of deep Neural Networks (NNs) in the text (ChatGPT) and image (DALL‐E2) domains has attracted worldwide attention. Convolutional NNs (CNNs), Large Language Models (LLMs), Denoising Diffusion Probabilistic Models (DDPMs)/Noise Conditional Score Networks (NCSNs), and Graph NNs (GNNs) have impacted computer vision, language editing and translation, automated conversation, image generation, and social network management. Proteins can be viewed as texts written with the alphabet of amino acids, as images, or as graphs of interacting residues. Each of these perspectives suggests (...) the use of tools from a different area of deep learning for protein structural biology. Here, I review how CNNs, LLMs, DDPMs/NCSNs, and GNNs have led to major advances in protein structure prediction, inverse folding, protein design, and small molecule design. This review is primarily intended as a deep learning primer for practicing experimental structural biologists. However, extensive references to the deep learning literature should also make it relevant to readers who have a background in machine learning, physics or statistics, and an interest in protein structural biology. (shrink)