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1. Substrates (Acyl‐ CoA and Diacylglycerol) Entry and Products ( CoA and Triacylglycerol) Egress Pathways in DGAT1

   https://doi.org/10.1002/jcc.70108  

   Lee, Hwayoung; Im, Wonpil (April 2025, Journal of Computational Chemistry)

   ABSTRACT Diacylglycerol O‐acyltransferase 1 (DGAT1) is an integral membrane protein that uses acyl‐coenzyme A (acyl‐CoA) and diacylglycerol (DAG) to catalyze the formation of triacylglycerides (TAGs). The acyl transfer reaction occurs between the activated carboxylate group of the fatty acid and the free hydroxyl group on the glycerol backbone of DAG. However, how the two substrates enter DGAT1's catalytic reaction chamber and interact with DGAT1 remains elusive. This study aims to explore the structural basis of DGAT1's substrate recognition by investigating each substrate's pathway to the reaction chamber. Using a human DGAT1 cryo‐EM structure in complex with an oleoyl‐CoA substrate, we designed two different all‐atom molecular dynamics (MD) simulation systems: DGAT1^away(both acyl‐CoA and DAG away from the reaction chamber) and DGAT1^bound(acyl‐CoA bound in and DAG away from the reaction chamber). Our DGAT1^awaysimulations reveal that acyl‐CoA approaches the reaction chamber via interactions with positively charged residues in transmembrane helix 7. DGAT1^boundsimulations show DAGs entering into the reaction chamber from the cytosol leaflet. The bound acyl‐CoA's fatty acid lines up with the headgroup of DAG, which appears to be competent to TAG formation. We then converted them into TAG and coenzyme (CoA) and used adaptive biasing force (ABF) simulations to explore the egress pathways of the products. We identify their escape routes, which are aligned with their respective entry pathways. Visualization of the substrate and product pathways and their interactions with DGAT1 is expected to guide future experimental design to better understand DGAT1 structure and function. 

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2. The changes in plant and soil C pools and their C : N stoichiometry control grassland N retention under elevated N inputs

   https://doi.org/10.1002/eap.2517  

   Yang, Sen; Liu, Weixing; Guo, Lulu; Wang, Chengzhang; Deng, Meifeng; Peng, Ziyang; Liu, Lingli (March 2022, Ecological Applications)
3. Open Chemistry, JupyterLab , REST , and quantum chemistry

   https://doi.org/10.1002/qua.26472  

   Hanwell, Marcus D.; Harris, Chris; Genova, Alessandro; Haghighatlari, Mojtaba; El Khatib, Muammar; Avery, Patrick; Hachmann, Johannes; Jong, Wibe Albert (January 2021, International Journal of Quantum Chemistry)

   null (Ed.)
4. Integrated structural model of the palladin–actin complex using XL ‐ MS , docking, NMR , and SAXS

   https://doi.org/10.1002/pro.70122  

   Sargent, Rachel; Liu, David H; Yadav, Rahul; Glennenmeier, Drew; Bradford, Colby; Urbina, Noely; Beck, Moriah R (May 2025, Protein Science)

   Cortajarena, Aitziber L (Ed.)

   Abstract Palladin is an actin‐binding protein that accelerates actin polymerization and is linked to the metastasis of several types of cancer. Previously, three lysine residues in an immunoglobulin‐like domain of palladin have been identified as essential for actin binding. However, it is still unknown where palladin binds to F‐actin. Evidence that palladin binds to the sides of actin filaments to facilitate branching is supported by our previous study showing that palladin was able to compensate for Arp2/3 in the formation ofListeriaactin comet tails. Here, we used chemical crosslinking to covalently link palladin and F‐actin residues based on spatial proximity. Samples were then enzymatically digested, separated by liquid chromatography, and analyzed by tandem mass spectrometry. Peptides containing the crosslinks and specific residues involved were then identified for input to the HADDOCK docking server to model the most likely binding conformation. Small‐angle x‐ray scattering was used to provide further insight into palladin flexibility and the binding interface, and NMR spectra identified potential interactions between palladin's Ig domains. Our final structural model of the F‐actin:palladin complex revealed how palladin interacts with and stabilizes F‐actin at the interface between two actin monomers. Three actin residues that were identified in this study also appear commonly in the actin‐binding interface with other proteins such as myotilin, myosin, and tropomodulin. An accurate structural representation of the complex between palladin and actin extends our understanding of palladin's role in promoting cancer metastasis through the regulation of actin dynamics. 

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5. Toward Robustness across Cosmological Simulation Models I llustris TNG, SIMBA, A strid , and S wift -E agle

   https://doi.org/10.3847/1538-4357/adec78  

   Jo, Yongseok; Genel, Shy; Sengupta, Anirvan; Wandelt, Benjamin; Somerville, Rachel; Villaescusa-Navarro, Francisco (September 2025, The Astrophysical Journal)

   Abstract The rapid advancement of large-scale cosmological simulations has opened new avenues for cosmological and astrophysical research. However, the increasing diversity among cosmological simulation models presents a challenge to therobustness. In this work, we develop the Model-Insensitive ESTimator (Miest), a machine that canrobustlyestimate the cosmological parameters, Ω_mandσ₈, from neural hydrogen maps of simulation models in the Cosmology and Astrophysics with MachinE Learning Simulations project—IllustrisTNG,SIMBA, Astrid, and SWIFT-Eagle. An estimator is consideredrobustif it possesses a consistent predictive power across all simulations, including those used during the training phase. We train our machine using multiple simulation models and ensure that it only extracts common features between the models while disregarding the model-specific features. This allows us to develop a novel model that is capable of accurately estimating parameters across a range of simulation models, without being biased toward any particular model. Upon the investigation of the latent space—a set of summary statistics, we find that the implementation ofrobustnessleads to the blending of latent variables across different models, demonstrating the removal of model-specific features. In comparison to a standard machine lackingrobustness, the average performance of Mieston the unseen simulations during the training phase has been improved by ∼17% for Ω_mand 38% forσ₈. By using a machine learning approach that can extractrobust, yet physical features, we hope to improve our understanding of galaxy formation and evolution in a (subgrid) model-insensitive manner, and ultimately, gain insight into the underlying physical processes responsible forrobustness. 

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