More Like this

1. Two-Coordinate, Late First-Row Transition Metal Amido Derivatives of the Bulky Ligand -N(SiPr i 3 )Dipp (Dipp = 2,6-diisopropylphenyl): Effects of the Ligand on the Stability of Two-Coordinate Copper(II) Complexes

   https://doi.org/10.1021/acs.inorgchem.9b01159  

   Wagner, Clifton L. ; Tao, Lizhi ; Fettinger, James C. ; Britt, R. David ; Power, Philip P. ( July 2019 , Inorganic Chemistry)
2. Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

   https://doi.org/10.3390/biom13010132  

   Giri, Nabin ; Cheng, Jianlin ( January 2023 , Biomolecules)

   Elucidating protein–ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein–ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein–ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein–ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein–ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein–ligand interactions on cryo-EM data using prior structural information. 

   more » « less
3. Me 6 -TREN/TREN Mixed-Ligand Effect During SET-LRP in the Catalytically Active DMSO Revitalizes TREN into an Excellent Ligand

   https://doi.org/10.1021/acs.biomac.9b01765  

   Maurya, Devendra S. ; Malik, Ayesha ; Feng, Xiaojing ; Bensabeh, Nabil ; Lligadas, Gerard ; Percec, Virgil ( February 2020 , Biomacromolecules)
4. Solution NMR Analysis of Ligand Environment in Quaternary Ammonium-Terminated Self-Assembled Monolayers on Gold Nanoparticles: The Effect of Surface Curvature and Ligand Structure

   https://doi.org/10.1021/jacs.8b11445  

   Wu, Meng ; Vartanian, Ariane M. ; Chong, Gene ; Pandiakumar, Arun Kumar ; Hamers, Robert J. ; Hernandez, Rigoberto ; Murphy, Catherine J. ( February 2019 , Journal of the American Chemical Society)
5. Ligand-coordination effects on the selective hydrogenation of acetylene in single-site Pd-ligand supported catalysts

   https://doi.org/10.1016/j.jcat.2022.06.010  

   Wasim, Eman ; Din, Naseem Ud ; Le, Duy ; Zhou, Xuemei ; Sterbinsky, George E. ; Pape, Michael S. ; Rahman, Talat S. ; Tait, Steven L. ( September 2022 , Journal of Catalysis)