Browsing by Author "Boyd, Donald B."

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    Data Mining and Quantitative Structure-Activity Relationships of Inhibitors for Treating Alzheimer's Disease
    (Office of the Vice Chancellor for Research, 2012-04-13) Nastase, Anthony F.; Boyd, Donald B.
    Amyloid cleaving enzyme-1 (BACE1) is a target of interest for treating patients with Alzheimer’s disease (AD). As of 2007, more than 37 million people worldwide are afflicted with the disease. Incidence of the disease keeps increasing as the population ages and fewer people die of other diseases. ß-Amyloid precursor protein (APP) is a natural protein associated with neurons of the brain. In Alzheimer's disease, APP is cleaved by BACE1 at the beta-site, resulting in short 42 amino acid segments called amyloid-ß (Aß). Aggregation of Aß into plaques results in the death of neurons and is associated with AD. Inhibition of the BACE1 enzyme may prevent Aß formation and prevent the development or progression of AD. Known BACE1 inhibitors are analyzed using computational chemistry techniques, and quantitative structure-activity relationships (QSAR) are developed.
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    Simple Structure-Based Approach for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer's Disease
    (Office of the Vice Chancellor for Research, 2013-04-05) Nastase, Anthony F.; Boyd, Donald B.
    Beta-site amyloid precursor protein cleaving enzyme-1 (BACE1) is a target of interest for treating patients with Alzheimer’s disease (AD). Inhibition of BACE1 may prevent amyloid-ß (Aß) plaque formation and the development or progression of Alzheimer’s disease. Known BACE1 inhibitors were analyzed using computational chemistry and cheminformatics techniques to search for quantitative structure− activity relationships (QSAR). A remarkable relationship was found with only two simple descriptors with a square of the linear correlation coefficient r2 of 0.75. The main descriptor is the number of hydrophobic contacts in the range 4−5 Å between the atoms of the ligand and active site. The other descriptor is the number of short (<2.8 Å) hydrogen bonds. Our approach uses readily available structural data on protein- inhibitor complexes in the Protein Data Bank (PDB) but would be equally applicable to proprietary structural biology data. The findings can aid structure-based design of improved BACE-1 inhibitors. If an inhibitor has less observed activity than predicted by our correlation, the compound should be retested because the first assay may have underestimated the compound’s true activity.
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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.