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Abstract A century or less separates the thermonuclear-powered eruptions of recurrent novae (RNe) in the hydrogen-rich envelopes of massive white dwarfs. The colliding ejecta of successive RN events are predicted to always generate very large (tens of parsecs) super-remnants; only two examples are currently known. T CrB offers an excellent opportunity to test this prediction. As it will almost certainly undergo its next, once in ∼80 yr RN event between 2024 and 2026, we carried out very deep narrowband and continuum imaging to search for the predicted, piled-up ejecta of the past millennia. While nothing is detected in continuum or narrowband [Oiii] images, a ∼30 pc diameter, faint nebulosity surrounding T CrB is clearly present in deep Hα, [Nii], and [Sii] narrowband Condor Array Telescope imagery. We predict that these newly detected nebulosities, as well as the recent ejecta that have not yet reached the super-remnant, are far too optically thin to capture all but a tiny fraction of the photons emitted by RN flashes. We thus predict that fluorescent light echoes willnotbe detectable following the imminent nova flash of T CrB. Dust may be released by the T CrB red giant wind in preeruption outbursts, but we have no reliable estimates of its quantity or geometrical distribution. While we cannot predict the morphology or intensity of dust-induced continuum light echoes following the coming flash, we encourage multiepoch Hubble Space Telescope optical imaging as well as James Webb Space Telescope infrared imaging of T CrB during the year after it erupts. 

Collagen mimetic peptides are composed of triple helices. Triple helical formation frequently utilizes charge pair interactions to direct protein assembly. The design of synthetic triple helices is challenging due to the large number of competing species and the overall fragile nature of collagen mimetics. A successfully designed triple helix incorporates both positive and negative criteria to achieve maximum specificity of the supramolecular assembly. Intrahelical charge pair interactions, particularly those involved in lysine–aspartate and lysine–glutamate pairs, have been especially successful both in driving helix specificity and for subsequent stabilization by covalent capture. Despite this progress, the important sequential and geometric relationships of charged residues in a triple helical context have not been fully explored for either supramolecular assembly or covalent capture stabilization. In this study, we compare the eight canonical axial and lateral charge pairs of lysine and arginine with glutamate and aspartate to their noncanonical, reversed charge pairs. These findings are put into the context of collagen triple helical design and synthesis. 

Prediction of side chain conformations of amino acids in proteins (also termed “packing”) is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have been developed but more accurate ones are still needed. Machine learning (ML) methods have recently become a powerful tool for solving various problems in diverse areas of science, including structural biology. In this study, we evaluate the potential of deep neural networks (DNNs) for prediction of amino acid side chain conformations. We formulate the problem as image-to-image transformation and train a U-net style DNN to solve the problem. We show that our method outperforms other physics-based methods by a significant margin: reconstruction RMSDs for most amino acids are about 20% smaller compared to SCWRL4 and Rosetta Packer with RMSDs for bulky hydrophobic amino acids Phe, Tyr, and Trp being up to 50% smaller.