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1. DNA Nanostructures as Catalysts: Double Crossover Tile-Assisted 5′ to 5′ and 3′ to 3′ Chemical Ligation of Oligonucleotides

   https://doi.org/10.1021/acs.bioconjchem.3c00502  

   Bardales, Andrea C; Mills, Joseph R; Kolpashchikov, Dmitry M (January 2024, Bioconjugate Chemistry)

   Accessibility of synthetic oligonucleotides and the success of DNA nanotechnology open a possibility to use DNA nanostructures for building sophisticated enzyme-like catalytic centers. Here we used a double DNA crossover (DX) tile nanostructure to enhance the rate, the yield, and the specificity of 5'-5' ligation of two oligonucleotides with arbitrary sequences. The ligation product was isolated via a simple procedure. The same strategy was applied for the synthesis of 3'-3' linked oligonucleotides, thus introducing a synthetic route to DNA and RNA with a switched orientation that is affordable by a low-resource laboratory. To emphasize the utility of the ligation products, we synthesized a circular structure formed from intramolecular complementarity that we named "an impossible DNA wheel" since it cannot be built from regular DNA strands by enzymatic reactions. Therefore, DX-tile nanostructures can open a route to producing useful chemical products that are unattainable via enzymatic synthesis. This is the first example of the use of DNA nanostructures as a catalyst. This study advocates for further exploration of DNA nanotechnology for building enzyme-like reactive systems. 

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2. DNA Nanostructures as Catalysts: Double Crossover Tile-Assisted 5′ to 5′ and 3′ to 3′ Chemical Ligation of Oligonucleotides

   Bardales A.C, Mills J.R. (January 2024, Bioconjugate chemistry)

   Accessibility of synthetic oligonucleotides and the success of DNA nanotechnology open a possibility to use DNA nanostructures for building sophisticated enzyme-like catalytic centers. Here we used a double DNA crossover (DX) tile nanostructure to enhance the rate, the yield, and the specificity of 5′−5′ ligation of two oligonucleotides with arbitrary sequences. The ligation product was isolated via a simple procedure. The same strategy was applied for the synthesis of 3′−3′ linked oligonucleotides, thus introducing a synthetic route to DNA and RNA with a switched orientation that is affordable by a low- resource laboratory. To emphasize the utility of the ligation products, we synthesized a circular structure formed from intramolecular complementarity that we named “an impossible DNA wheel” since it cannot be built from regular DNA strands by enzymatic reactions. Therefore, DX-tile nanostructures can open a route to producing useful chemical products that are unattainable via enzymatic synthesis. This is the first example of the use of DNA nanostructures as a catalyst. This study advocates for further exploration of DNA nanotechnology for building enzyme-like reactive systems. 

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3. Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products

   https://doi.org/10.1093/bioinformatics/btad089  

   Porokhin, Vladimir; Liu, Li-Ping; Hassoun, Soha (March 2023, Bioinformatics)

   Martelli, Pier Luigi (Ed.)

   Abstract MotivationWhile traditionally utilized for identifying site-specific metabolic activity within a compound to alter its interaction with a metabolizing enzyme, predicting the site-of-metabolism (SOM) is essential in analyzing the promiscuity of enzymes on substrates. The successful prediction of SOMs and the relevant promiscuous products has a wide range of applications that include creating extended metabolic models (EMMs) that account for enzyme promiscuity and the construction of novel heterologous synthesis pathways. There is therefore a need to develop generalized methods that can predict molecular SOMs for a wide range of metabolizing enzymes. ResultsThis article develops a Graph Neural Network (GNN) model for the classification of an atom (or a bond) being an SOM. Our model, GNN-SOM, is trained on enzymatic interactions, available in the KEGG database, that span all enzyme commission numbers. We demonstrate that GNN-SOM consistently outperforms baseline machine learning models, when trained on all enzymes, on Cytochrome P450 (CYP) enzymes, or on non-CYP enzymes. We showcase the utility of GNN-SOM in prioritizing predicted enzymatic products due to enzyme promiscuity for two biological applications: the construction of EMMs and the construction of synthesis pathways. Availability and implementationA python implementation of the trained SOM predictor model can be found at https://github.com/HassounLab/GNN-SOM. Supplementary informationSupplementary data are available at Bioinformatics online. 

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4. Enzyme‐Instructed Self‐Assembly for Cellular Supramolecular Chemistry

   https://doi.org/10.1002/asia.202500920  

   Huang, Yali; Hu, Xingjie; Yang, Zhimou; Xu, Bing (December 2025, Chemistry – An Asian Journal)

   ABSTRACT Enzyme‐instructed self‐assembly (EISA) uses endogenous enzymatic activity to convert soluble precursors into self‐assembling species, enabling the spatiotemporal formation of supramolecular nanostructures directly within cellular environments. Unlike other supramolecular strategies triggered by pH, redox, or light, EISA leverages the inherent spatial localization and dynamic kinetics of enzymes to achieve precise, context‐dependent control over where and when assembly occurs. While previous reviews have summarized EISA's mechanisms and biomedical applications, this perspective positions EISA as a conceptual framework for supramolecular chemical biology—emphasizing its role in mimicking higher‐order protein assemblies and in bridging molecular design with cellular function. We discuss how EISA enables programmable conformational and morphological switching, the creation of growth factor–mimicking assemblies, and the in situ formation of artificial supramolecular architectures inside or around cells. By highlighting EISA as a catalytic strategy for constructing functional supramolecular systems in vivo, this perspective outlines a new direction for integrating enzymatic control with nanoscale self‐organization in cellular supramolecular chemistry, generalizing EISA beyond alkaline phosphatases to programmable multi‐enzyme networks, and thereby advancing adaptive biomaterials, programmable therapeutics, and synthetic cellular machines. 

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5. The role of filamentation in activation and DNA sequence specificity of the sequence-specific endonuclease SgrAI

   https://doi.org/10.1042/BST20220547  

   Lyumkis, Dmitry; Horton, Nancy C. (November 2022, Biochemical Society Transactions)

   Filament formation by metabolic, biosynthetic, and other enzymes has recently come into focus as a mechanism to fine-tune enzyme activity in the cell. Filamentation is key to the function of SgrAI, a sequence-specific DNA endonuclease that has served as a model system to provide some of the deepest insights into the biophysical characteristics of filamentation and its functional consequences. Structure-function analyses reveal that, in the filamentous state, SgrAI stabilizes an activated enzyme conformation that leads to accelerated DNA cleavage activity and expanded DNA sequence specificity. The latter is thought to be mediated by sequence-specific DNA structure, protein–DNA interactions, and a disorder-to-order transition in the protein, which collectively affect the relative stabilities of the inactive, non-filamentous conformation and the active, filamentous conformation of SgrAI bound to DNA. Full global kinetic modeling of the DNA cleavage pathway reveals a slow, rate-limiting, second-order association rate constant for filament assembly, and simulations of in vivo activity predict that filamentation is superior to non-filamenting mechanisms in ensuring rapid activation and sequestration of SgrAI's DNA cleavage activity on phage DNA and away from the host chromosome. In vivo studies demonstrate the critical requirement for accelerated DNA cleavage by SgrAI in its biological role to safeguard the bacterial host. Collectively, these data have advanced our understanding of how filamentation can regulate enzyme structure and function, while the experimental strategies used for SgrAI can be applied to other enzymatic systems to identify novel functional roles for filamentation. 

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