Browsing by Subject "Electrostatics"

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    Cellular membranes and lipid-binding domains as attractive targets for drug development
    (Bentham Science Publishers, 2008-08) Sudhahar, C.G.; Haney, R.M.; Xue, Y.; Stahelin, R.V.; Biochemistry and Molecular Biology, School of Medicine
    Interdisciplinary research focused on biological membranes has revealed them as signaling and trafficking platforms for processes fundamental to life. Biomembranes harbor receptors, ion channels, lipid domains, lipid signals, and scaffolding complexes, which function to maintain cellular growth, metabolism, and homeostasis. Moreover, abnormalities in lipid metabolism attributed to genetic changes among other causes are often associated with diseases such as cancer, arthritis and diabetes. Thus, there is a need to comprehensively understand molecular events occurring within and on membranes as a means of grasping disease etiology and identifying viable targets for drug development. A rapidly expanding field in the last decade has centered on understanding membrane recruitment of peripheral proteins. This class of proteins reversibly interacts with specific lipids in a spatial and temporal fashion in crucial biological processes. Typically, recruitment of peripheral proteins to the different cellular sites is mediated by one or more modular lipid-binding domains through specific lipid recognition. Structural, computational, and experimental studies of these lipid-binding domains have demonstrated how they specifically recognize their cognate lipids and achieve subcellular localization. However, the mechanisms by which these modular domains and their host proteins are recruited to and interact with various cell membranes often vary drastically due to differences in lipid affinity, specificity, penetration as well as protein-protein and intramolecular interactions. As there is still a paucity of predictive data for peripheral protein function, these enzymes are often rigorously studied to characterize their lipid-dependent properties. This review summarizes recent progress in our understanding of how peripheral proteins are recruited to biomembranes and highlights avenues to exploit in drug development targeted at cellular membranes and/or lipid-binding proteins.
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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.