Computational Prediction and <i>in Vitro</i> Analysis of Potential Physiological Ligands of the Bile Acid Binding Site in Cytochrome <i>c</i> Oxidase

Buhrow, Leann; Hiser, Carrie; Voorst, Jeffrey R. Van; Ferguson‐Miller, Shelagh; Kuhn, Leslie A.

doi:10.1021/bi400674h

Cited by 14 publications

(35 citation statements)

References 83 publications

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“…This demonstrates directly that cholesterol competes with DOC to bind in the steroid site. As previously shown for DOC [20], kinetic analysis of WT RsCcO shows that cholesterol also exhibits inhibitory behavior that is non-competitive with respect to cytochrome c (Supplemental Fig. 5).…”

Section: Resultssupporting

confidence: 74%

“…A ligand binding model involving “upper” and “lower” sites [21] was suggested by crystal structure data and computational analysis [20]. To test for ligand binding in the upper site groove, a mutant was designed to sterically modify the proposed alkyl chain interaction.…”

Section: Resultsmentioning

confidence: 99%

“…Unlike cholesteryl hemisuccinate, a stimulatory carboxylate ligand [20,21], cholesterol inhibited the E101A mutant in 0.01% DDM (Fig. 7).…”

Section: Resultsmentioning

confidence: 99%

“…Although computational analysis [20] and activity titrations (Fig. 7, Section 3.4) suggest that most steroids bind similarly in the K-path entrance of the E101A mutant, detergents seem to bind in the same pocket but in slightly different positions or orientations.…”

Section: Discussionmentioning

confidence: 99%

“…These findings showed conservation of this site from bacteria through to mammals, making a strong case for its physiological significance and the need to further define the ligand interactions at this site. Indeed, docking of cholesterol, porphyrin, retinoic acid and nucleotides into this site was computationally predicted [20]. …”

Section: Introductionmentioning

confidence: 99%

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The K-path entrance in cytochrome c oxidase is defined by mutation of E101 and controlled by an adjacent ligand binding domain

Hiser

Liu

Ferguson‐Miller

2018

Biochimica et Biophysica Acta (BBA) - Bioenergetics

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Three mutant forms of Rhodobacter sphaeroides cytochrome c oxidase (RsCcO) were created to test for multiple K-path entry sites (E101W), the existence of an “upper ligand site” (M350 W), and the nature and binding specificity of the “lower ligand site” (P315W/E101A) in the region of a crystallographically-defined deoxycholate at the K-path entrance. The effects of inhibitory and stimulatory detergents (dodecyl maltoside and Tween20) on these mutants are presented, as well as competition with other ligands, including the potentially physiologically relevant ligands cholesterol and retinoic acid. Ligands are shown to be able to compete with natural lipids to affect the activity of membrane-bound RsCcO. Results point to a single K-path entrance site at E101, with a single ligand binding pocket proximal to the entrance. The affinity of this pocket for amphipathic ligands is enhanced by removal of the E101 carboxyl and blocked by substituting a tryptophan in this area. A new crystal structure of the E101A mutant of RsCcO is presented that illustrates the structural basis of these results, showing that the loss of the E101 carboxyl creates a more hydrophobic groove consistent with altered ligand affinities.

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Section: Resultssupporting

confidence: 74%

Section: Resultsmentioning

confidence: 99%

“…Unlike cholesteryl hemisuccinate, a stimulatory carboxylate ligand [20,21], cholesterol inhibited the E101A mutant in 0.01% DDM (Fig. 7).…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The K-path entrance in cytochrome c oxidase is defined by mutation of E101 and controlled by an adjacent ligand binding domain

Hiser

Liu

Ferguson‐Miller

2018

Biochimica et Biophysica Acta (BBA) - Bioenergetics

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Automated Inference of Chemical Discriminants of Biological Activity

Raschka

Scott

Huertas

et al. 2018

Methods in Molecular Biology

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Ligand-based virtual screening has become a standard technique for the efficient discovery of bioactive small molecules. Following assays to determine the activity of compounds selected by virtual screening, or other approaches in which dozens to thousands of molecules have been tested, machine learning techniques make it straightforward to discover the patterns of chemical groups that correlate with the desired biological activity. Defining the chemical features that generate activity can be used to guide the selection of molecules for subsequent rounds of screening and assaying, as well as help design new, more active molecules for organic synthesis.The quantitative structure-activity relationship machine learning protocols we describe here, using decision trees, random forests, and sequential feature selection, take as input the chemical structure of a single, known active small molecule (e.g., an inhibitor, agonist, or substrate) for comparison with the structure of each tested molecule. Knowledge of the atomic structure of the protein target and its interactions with the active compound are not required. These protocols can be modified and applied to any data set that consists of a series of measured structural, chemical, or other features for each tested molecule, along with the experimentally measured value of the response variable you would like to predict or optimize for your project, for instance, inhibitory activity in a biological assay or ΔG. To illustrate the use of different machine learning algorithms, we step through the analysis of a dataset of inhibitor candidates from virtual screening that were tested recently for their ability to inhibit GPCR-mediated signaling in a vertebrate.

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