Atom transfer radical polymerization
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Biomimetic 1,2-amino migration via photoredox catalysis
Synthetic organic chemists continually draw inspiration from biocatalytic processes to innovate synthetic methodologies beyond existing catalytic platforms. Within this context, although 1,2-amino migration represents a viable biochemical process, it remains underutilized within the synthetic organic chemistry community. Here we present a biomimetic 1,2-amino migration accomplished through the synergistic combination of biocatalytic mechanism and photoredox catalysis. This platform enables the modular synthesis of γ-substituted β-amino acids by utilizing abundant α-amino-acid derivatives and readily available organic molecules as coupling partners. This mild method features excellent substrate and functionality compatibility, affording a diverse range of γ-substituted β-amino acids (more than 80 examples) without the need for laborious multistep synthesis. Mechanistic studies, supported by both experimental observations and theoretical analysis, indicate that the 1,2-amino migration mechanism involves radical addition to α-vinyl-aldimine ester, 3-exo–trig cyclization and a subsequent rearrangement process. We anticipate that this transformation will serve as a versatile platform for the highly efficient construction of unnatural γ-substituted β-amino acids.
Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data
Machine learning offers great promise for fast and accurate binding affinity predictions. However, current models lack robust evaluation and fail on tasks encountered in (hit-to-) lead optimisation, such as ranking the binding affinity of a congeneric series of ligands, thereby limiting their application in drug discovery. Here, we address these issues by first introducing a novel attention-based graph neural network model called AEV-PLIG (atomic environment vector–protein ligand interaction graph). Second, we introduce a new and more realistic out-of-distribution test set called the OOD Test. We benchmark our model on this set, CASF-2016, and a test set used for free energy perturbation (FEP) calculations, that not only highlights the competitive performance of AEV-PLIG, but provides a realistic assessment of machine learning models with rigorous physics-based approaches. Moreover, we demonstrate how leveraging augmented data (generated using template-based modelling or molecular docking) can significantly improve binding affinity prediction correlation and ranking on the FEP benchmark (weighted mean PCC and Kendall’s τ increases from 0.41 and 0.26 to 0.59 and 0.42). These strategies together are closing the performance gap with FEP calculations (FEP+ achieves weighted mean PCC and Kendall’s τ of 0.68 and 0.49 on the FEP benchmark) while being ~400,000 times faster.
The design space of E(3)-equivariant atom-centred interatomic potentials
Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time. Here we construct a mathematical framework that unifies these models: atomic cluster expansion is extended and recast as one layer of a multi-layer architecture, while the linearized version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in this unified design space. An ablation study of NequIP, via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, sheds some light on which design choices are critical to achieving high accuracy. A much-simplified version of NequIP, which we call BOTnet (for body-ordered tensor network), has an interpretable architecture and maintains its accuracy on benchmark datasets.
Multiple DNA damages induced by water radiolysis demonstrated using a dynamic Monte Carlo code
Multiple DNA damage resulting from different nearby ionizations of water molecules is an important process of the initial step of radiobiological effects. Several important characteristics of the damaged DNA site such as the critical size and types of chemical lesions are not well-known. We investigated this long-term issue by developing a dynamic Monte Carlo code for the chemical process. The reaction probabilities and the spatial distribution of lesions were theoretically solved as a function of the spur radius and distance between DNA and the initial ionisation position. From our previous reported results, we suggest that a hydroxyl radical and a hydrated electron from a single spur can concomitantly react within a 10 base pairs DNA to induce a multiple DNA damage site comprising a DNA single-strand break and reductive nucleobase damage; however, the reaction probability is 0.4% or less. Once this combination arises, it may result in a DNA double-strand break (DSB). DSBs are difficult to repair, which may lead to cell death or misrepair, and could lead to point mutations in the genome.
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