Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis
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Enantioselective C–H annulations enabled by either nickel- or cobalt-electrocatalysed C–H activation for catalyst-controlled chemodivergence
Enantioselective electrocatalysis shows unique potential for the sustainable assembly of enantiomerically enriched molecules. This approach allows electro-oxidative C–H activation to be performed paired to the hydrogen evolution reaction. Recent progress has featured scarce transition metals with limited availability. Here we reveal that the earth-abundant 3d transition metals nickel and cobalt exhibit distinctive performance for enantioselective electrocatalysis with chemodivergent reactivity patterns. Enantioselective desymmetrizations of strained bicyclic alkenes were achieved through C–H annulations. A data-driven optimization of chiral N,O-bidentate salicyloxazoline-type ligands was crucial for enhancing enantioselectivity in nickel electrocatalysis. Notably, in the transition state of the enantio-determining step, secondary weak attractive π–π and CH–π interactions were identified, reflecting the informed adaptations in the ligand design. Detailed mechanistic investigations by experimental and computational studies revealed for the nickel electrocatalysis a C–N bond-forming reductive elimination from nickel(III) and for the cobalt electrocatalysis a C–C bond-forming nucleophilic addition from cobalt(III) as the product-determining steps.
Photo-assisted technologies for environmental remediation
Industrial processes can lead to air and water pollution, particularly from organic contaminants such as toluene and antibiotics, posing threats to human health. Photo-assisted chemical oxidation technologies leverage light energy to mineralize these contaminants. In this Review, we discuss the mechanisms and efficiencies of photo-assisted advanced oxidation processes for wastewater treatment and photothermal technologies for air purification. The integration of solar energy enhances degradation efficiency and reduces energy consumption, enabling more efficient remediation methods. We evaluate the technological aspects of photo-assisted technologies, such as photo-Fenton, photo-persulfate activation, photo-ozonation and photoelectrochemical oxidation, emphasizing their potential for practical applications. Finally, we discuss the challenges in scaling up photo-assisted technologies for specific environmental remediation needs. Photo-assisted technologies have demonstrated effectiveness in environmental remediation, although large-scale applications remain constrained by high costs. Future potential applications of photo-assisted technologies will require that technology selection be tailored to specific pollution scenarios and engineering processes optimized to minimize costs.
Modeling the impact of structure and coverage on the reactivity of realistic heterogeneous catalysts
Adsorbates often cover the surfaces of catalysts densely as they carry out reactions, dynamically altering their structure and reactivity. Understanding adsorbate-induced phenomena and harnessing them in our broader quest for improved catalysts is a substantial challenge that is only beginning to be addressed. Here we chart a path toward a deeper understanding of such phenomena by focusing on emerging in silico modeling methodologies, which will increasingly incorporate machine learning techniques. We first examine how adsorption on catalyst surfaces can lead to local and even global structural changes spanning entire nanoparticles, and how this affects their reactivity. We then evaluate current efforts and the remaining challenges in developing robust and predictive simulations for modeling such behavior. Last, we provide our perspectives in four critical areas—integration of artificial intelligence, building robust catalysis informatics infrastructure, synergism with experimental characterization, and adaptive modeling frameworks—that we believe can help surmount the remaining challenges in rationally designing catalysts in light of these complex phenomena.
Structural basis for intrinsic strand displacement activity of mitochondrial DNA polymerase
Members of the Pol A family of DNA polymerases, found across all domains of life, utilize various strategies for DNA strand separation during replication. In higher eukaryotes, mitochondrial DNA polymerase γ relies on the replicative helicase TWINKLE, whereas the yeast ortholog, Mip1, can unwind DNA independently. Using Mip1 as a model, we present a series of high-resolution cryo-EM structures that capture the process of DNA strand displacement. Our data reveal previously unidentified structural elements that facilitate the unwinding of the downstream DNA duplex. Yeast cells harboring Mip1 variants defective in strand displacement exhibit impaired oxidative phosphorylation and loss of mtDNA, corroborating the structural observations. This study provides a molecular basis for the intrinsic strand displacement activity of Mip1 and illuminates the distinct unwinding mechanisms utilized by Pol A family DNA polymerases.
T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity
T-cell receptor (TCR) structures are currently under-utilised in early-stage drug discovery and repertoire-scale informatics. Here, we leverage a large dataset of solved TCR structures from Immunocore to evaluate the current state-of-the-art for TCR structure prediction, and identify which regions of the TCR remain challenging to model. Through clustering analyses and the training of a TCR-specific model capable of large-scale structure prediction, we find that the alpha chain VJ-recombined loop (CDR3α) is as structurally diverse and correspondingly difficult to predict as the beta chain VDJ-recombined loop (CDR3β). This differentiates TCR variable domain loops from the genetically analogous antibody loops and supports the conjecture that both TCR alpha and beta chains are deterministic of antigen specificity. We hypothesise that the larger number of alpha chain joining genes compared to beta chain joining genes compensates for the lack of a diversity gene segment. We also provide over 1.5M predicted TCR structures to enable repertoire structural analysis and elucidate strategies towards improving the accuracy of future TCR structure predictors. Our observations reinforce the importance of paired TCR sequence information and capture the current state-of-the-art for TCR structure prediction, while our model and 1.5M structure predictions enable the use of structural TCR information at an unprecedented scale.
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