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1. Computational predictions of metal–macrocycle stability constants require accurate treatments of local solvent and pH effects

   https://doi.org/10.1039/D1CP00611H  

   Gentry, Brian M.; Choi, Tae Hoon; Belfield, William S.; Keith, John A. (April 2021, Physical Chemistry Chemical Physics)

   Rational design of molecular chelating agents requires a detailed understanding of physicochemical ligand–metal interactions in solvent phase. Computational quantum chemistry methods should be able to provide this, but computational reports have shown poor accuracy when determining absolute binding constants for many chelating molecules. To understand why, we compare and benchmark static- and dynamics-based computational procedures for a range of monovalent and divalent cations binding to a conventional cryptand molecule: 2.2.2-cryptand ([2.2.2]). The benchmarking comparison shows that dynamics simulations using standard OPLS-AA classical potentials can reasonably predict binding constants for monovalent cations, but these procedures fail for divalent cations. We also consider computationally efficient static procedure using Kohn–Sham density functional theory (DFT) and cluster-continuum modeling that accounts for local microsolvation and pH effects. This approach accurately predicts binding energies for monovalent and divalent cations with an average error of 3.2 kcal mol −1 compared to experiment. This static procedure thus should be useful for future molecular screening efforts, and high absolute errors in the literature may be due to inadequate modeling of local solvent and pH effects. 

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2. What Makes GPCRs from Different Families Bind to the Same Ligand?

   https://doi.org/10.3390/biom12070863  

   Dankwah, Kwabena Owusu; Mohl, Jonathon E.; Begum, Khodeza; Leung, Ming-Ying (July 2022, Biomolecules)

   G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a convincing option to high throughput screening and other experimental approaches during the beginning phases of ligand discovery. In this work, we set out to computationally uncover and understand the binding of a single ligand to GPCRs from several different families. Three-dimensional structural comparisons of the GPCRs that bind to the same ligand revealed local 3D structural similarities and often these regions overlap with locations of binding pockets. These pockets were found to be similar (based on backbone geometry and side-chain orientation using APoc), and they correlate positively with electrostatic properties of the pockets. Moreover, the more similar the pockets, the more likely a ligand binding to the pockets will interact with similar residues, have similar conformations, and produce similar binding affinities across the pockets. These findings can be exploited to improve protein function inference, drug repurposing and drug toxicity prediction, and accelerate the development of new drugs. 

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3. Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions

   https://doi.org/10.1093/bib/bbab476  

   Dhakal, Ashwin; McKay, Cole; Tanner, John J; Cheng, Jianlin (January 2022, Briefings in Bioinformatics)

   Abstract New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein–ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein–ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein–ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein–ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein–ligand interactions. 

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4. Substrate Specific Metal–Ligand Cooperative Binding: Considerations for Weak Intramolecular Lewis Acid/Base Pairs

   https://doi.org/10.1021/acs.inorgchem.1c01382  

   Kiernicki, John J.; Norwine, Emily E.; Zeller, Matthias; Szymczak, Nathaniel K. (January 2021, Inorganic Chemistry)

   null (Ed.)

   Metal–ligand cooperative binding modes were interrogated in a series of zinc bis(thiophenoxide) complexes. A weak B–S binding interaction is observed in solution between the weakly Lewis basic thiophenoxide ligands and an appended trialkylborane. The energy of this binding event is dependent upon the strength of the Lewis acid and its proximity to the zinc thiophenoxide. 

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5. Hemilabile and Redox‐Active Quinone Ligands Unlock sp ³ ‐Rich Couplings in Nickel‐Catalyzed Olefin Carbosulfenylation

   https://doi.org/10.1002/anie.202411870  

   Li, Zi‐Qi; Alturaifi, Turki M; Cao, Yilin; Joannou, Matthew V; Liu, Peng; Engle, Keary M (December 2024, Angewandte Chemie International Edition)

   Abstract A three‐component coupling approach toward structurally complex dialkylsulfides is described via the nickel‐catalyzed 1,2‐carbosulfenylation of unactivated alkenes with organoboron nucleophiles and alkylsulfenamide (N−S) electrophiles. Efficient catalytic turnover is facilitated using a tailored N−S electrophile containing anN‐methyl methanesulfonamide leaving group, allowing catalyst loadings as low as 1 mol %. Regioselectivity is controlled by a collection of monodentate, weakly coordinating native directing groups, including sulfonamides, amides, sulfinamides, phosphoramides, and carbamates. Key to the development of this transformation is the identification of quinones as a family of hemilabile and redox‐active ligands that tune the steric and electronic properties of the metal throughout the catalytic cycle. Density functional theory (DFT) results show that the duroquinone (DQ) ligand adopts different coordination modes in different stages of the Ni‐catalyzed 1,2‐carbosulfenylation‐binding as an η⁶capping ligand to stabilize the precatalyst/resting state and prevent catalyst decomposition, binding as an X‐type redox‐active durosemiquinone radical anion to promote alkene migratory insertion with a less distorted square planar Ni(II) center, and binding as an L‐type ligand to promote N−S oxidative addition at a relatively more electron‐rich Ni(I) center. 

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