Browsing by Subject "Computational chemistry"

Now showing 1 - 3 of 3
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    Chemical Space Overlap with Critical Protein–Protein Interface Residues in Commercial and Specialized Small-Molecule Libraries
    (Wiley, 2018-12-20) Si, Yubing; Xu, David; Bum-Erdene, Khuchtumur; Ghozayel, Mona K.; Yang, Baocheng; Clemons, Paul A.; Meroueh, Samy O.; Biochemistry and Molecular Biology, School of Medicine
    There is growing interest in the use of structure-based virtual screening to identify small molecules that inhibit challenging protein–protein interactions (PPIs). In this study, we investigated how effectively chemical library members docked at the PPI interface mimic the position of critical side-chain residues known as “hot spots”. Three compound collections were considered, a commercially available screening collection (ChemDiv), a collection of diversity-oriented synthesis (DOS) compounds that contains natural-product-like small molecules, and a library constructed using established reactions (the “screenable chemical universe based on intuitive data organization”, SCUBIDOO). Three different tight PPIs for which hot-spot residues have been identified were selected for analysis: uPAR·uPA, TEAD4·Yap1, and CaVα·CaVβ. Analysis of library physicochemical properties was followed by docking to the PPI receptors. A pharmacophore method was used to measure overlap between small-molecule substituents and hot-spot side chains. Fragment-like conformationally restricted small molecules showed better hot-spot overlap for interfaces with well-defined pockets such as uPAR·uPA, whereas better overlap was observed for more complex DOS compounds in interfaces lacking a well-defined binding site such as TEAD4·Yap1. Virtual screening of conformationally restricted compounds targeting uPAR·uPA and TEAD4·Yap1 followed by experimental validation reinforce these findings, as the best hits were fragment-like and had few rotatable bonds for the former, while no hits were identified for the latter. Overall, such studies provide a framework for understanding PPIs in the context of additional chemical matter and new PPI definitions.
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    Structure-based computational studies of protein-ligand interactions
    (2014-12) Wang, Bo; Meroueh, Samy; Pu, Jingzhi; Boyd, Donald B.; Naumann, Christoph A.
    Molecular recognition plays an important role in biological systems. The purpose of this study was to get a better understanding of the process by incorporating computational tools.Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) method and Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) method, the end-point free energy calculations provide the binding free energy the can be used to rank-order protein–ligand structures in virtual screening for compound or target identification. Free energy calculations were performed on a diverse set of 11 proteins bound to 14 small molecules was carried out for. A direct comparison was taken between the calculated free energy and the experimental isothermal titration calorimetry (ITC) data. Four and three systems in MM-GBSA and MM-PBSA calculations, respectively, reproduced the ITC free energy within 1 kcal•mol–1. MM-GBSA exhibited better rank-ordering with a Spearman ρ of 0.68 compared to 0.40 for MM-PBSA with dielectric constant (ε = 1). The rank-ordering performance of MM-PBSA improved with increasing ε (ρ = 0.91 for ε = 10), but the contributions of electrostatics became significantly lower at larger ε level, suggesting that the only nonpolar and entropy components contribute to the improved results. Our previously developed scoring function, Support Vector Regression Knowledge-Based (SVRKB), resulted in excellent rank-ordering (ρ = 0.81) when applied into MD simulations. Filtering MD snapshots by prescoring protein–ligand complexes with a machine learning-based approach (SVMSP) resulted in a significant improvement in the MM-PBSA results (ε = 1) from ρ = 0.40 to ρ = 0.81. Finally, the nonpolar components in the free energy calculations showed strong correlation to the ITC free energy while the electrostatic components did not; the computed entropies did not correlate with the ITC entropy. Explicit-solvent molecular dynamics (MD) simulations offer an opportunity to sample multiple conformational states of a protein-ligand system in molecular recognition. SVMSP is a target-specific rescoring method that combines machine learning with statistical potentials. We evaluate the performance of SVMSP in its ability to enrich chemical libraries docked to MD structures. Seven proteins from the Directory of Useful Decoys (DUD) were involved in the study. We followed an innovative approach by training SVMSP scoring models using MD structures (SVMSPMD). The resulting models remarkably improved enrichment in two cases. We also explored approaches for a prior identification of MD snapshots with high enrichment power from an MD simulation in the absence of active compounds. SVMSP rescoring of protein–compound MD structures was applied for the search of small-molecule inhibitors of the mitochondrial enzyme aldehyde dehydrogenase 2 (ALDH2). Rank-ordering of a commercial library of 50,000 compounds docked to MD optimized structures of ALDH2 led to five small-molecule inhibitors. Four compounds had IC50s below 5 μM. These compounds serve as leads for the design and synthesis of more potent and selective ALDH2 inhibitors.
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    A Theoretical Study of the Benzoylformate Decarboxylase Reaction Mechanism
    (Frontiers Media, 2018-06-26) Planas, Ferran; Sheng, Xiang; McLeish, Michael J.; Himo, Fahmi; Chemistry and Chemical Biology, School of Science
    Density functional theory calculations are used to investigate the detailed reaction mechanism of benzoylformate decarboxylase, a thiamin diphosphate (ThDP)-dependent enzyme that catalyzes the nonoxidative decarboxylation of benzoylformate yielding benzaldehyde and carbon dioxide. A large model of the active site is constructed on the basis of the X-ray structure, and it is used to characterize the involved intermediates and transition states and evaluate their energies. There is generally good agreement between the calculations and available experimental data. The roles of the various active site residues are discussed and the results are compared to mutagenesis experiments. Importantly, the calculations identify off-cycle intermediate species of the ThDP cofactor that can have implications on the kinetics of the reaction.