Biomedical subjects

Denise Mills

Publications and source records attributed to Denise Mills.

10 recordsLinked to original sources

PubMed2006

Altering conserved lipid binding sites in cytochrome c oxidase of Rhodobacter sphaeroides perturbs the interaction between subunits I and III and promotes suicide inactivation of the enzyme.

Subunit III of the three-subunit catalytic core of cytochrome c oxidase (CcO) contains no metal centers, but it does bind two lipids, within a deep cleft, in binding sites conserved from bacteria to humans. Subunit III binds to subunit I, where it prevents the spontaneous suicide inactivation of CcO by decreasing the probability of side reactions at the heme-Cu O2 reduction site in subunit I. Subunit III prevents suicide inactivation by (1) maintaining adequate rates of proton delivery to the heme-Cu active site and (2) stabilizing the structure of the active site during turnover [Mills and Hosler (2005) Biochemistry 44, 4656]. Here, we first show that mutating several individual residues of the conserved lipid binding sites in subunit III disturbs the subunit I-III interface. Then, two lipid binding site mutants were constructed with an affinity tag on subunit III such that the mutant CcOs could be isolated with 100% subunit III. R226A eliminates an ion pair to the phosphate of the outermost lipid of the cleft, while W59A-F86A disrupts interactions with the fatty acid tails of both lipids. Once these mutant CcOs are placed into soybean phospholipid vesicles, where extensive exchange of bacterial for soybean lipids takes place, it is shown that altering the lipid binding sites mimics a major loss of subunit III, even though subunit III is completely retained, in that suicide inactivation becomes much more probable. The rate of proton delivery to the active site remains rapid, ruling out slow proton uptake as the primary reason for increased suicide inactivation upon alteration of the lipid binding sites. We conclude that altering the lipid binding sites of subunit III may promote side reactions leading to suicide inactivation by allowing greater movement to occur in and around the O2 reduction site of subunit I during the catalytic cycle.

Amino Acid Substitution↗

PubMed2006

Quantitative structure-activity relationship (QSAR) studies of quinolone antibacterials against M. fortuitum and M. smegmatis using theoretical molecular descriptors.

The incidence of tuberculosis infections that are resistant to conventional drug therapy has risen steadily in the last decade. Several of the quinolone antibacterials have been examined as inhibitors of M. tuberculosis infection as well as other mycobacterial infections. However, not much has been done to examine specific structure-activity relationships of the quinolone antibacterials against mycobacteria. The present paper describes quantitative structure-activity relationship modeling for a series of antimycobacterial compounds. Most of the antimycobacterial compounds do not have sufficient physicochemical data, and thus predictive methods based on experimental data are of limited use in this situation. Hence, there is a need for the development of quantitative structure-activity relationship (QSAR) models utilizing theoretical molecular descriptors that can be calculated directly from molecular structures. Descriptors associated with chemical structures of N-1 and C-7 substituted quinolone derivatives as well as 8-substituted quinolone derivatives with good antimycobacterial activities against M. fortuitum and M. smegmatis have been evaluated. Ridge regression (RR), Principal component regression (PCR), and partial least squares (PLS) regression were used, comparatively, to develop predictive models for antibacterial activity, based on the activities of the above compounds. The independent variables include topostructural, topochemical and 3-D geometrical indices, which were used in a hierarchical fashion in the model-development process. The predictive ability of the models was assessed by the cross-validated R2. Comparison of the relative effectiveness of the various classes of molecular descriptors in the regression models shows that the easily calculable topological indices explain most of the variance in the data.

Anti-Bacterial Agents↗

PubMed2004

QSAR study using topological indices for inhibition of carbonic anhydrase II by sulfanilamides and Schiff bases.

A QSAR study of two sets of carbonic anhydrase inhibitors is presented using a variety of molecular descriptors including topological indices. The first set consists of 29 benzenesulphonamides, and the second set includes 35 sulphanilamide Schiff bases. Two regression methodologies have been used involving ridge regression and the CODESSA program, and their results are compared with those of previous QSAR studies. Good correlations were found for the former set, and less satisfactory results for the latter set when the number of molecular descriptors is kept below five.

Animals↗

PubMed2003

Usefulness of graphical invariants in quantitative structure-activity correlations of tuberculostatic drugs of the isonicotinic acid hydrazide type.

Quantitative structure-activity relationship (QSAR) studies have been performed for a series of 2-substituted isonicotinic acid hydrazides utilizing theoretical molecular descriptors. 223 topological (topostructural and topochemical) indices along with seven geometrical descriptors were computed for the prediction of antibacterial activity against Mycobacterium tuberculosis. Ridge-regression models assessed by cross-validated R2 have been formulated, and a comparative study on the relative effectiveness of physicochemical vis-à-vis theoretical molecular descriptors performed. The models developed clearly indicate the supremacy of structure-activity over property-activity relationships in the current study and can be used to evaluate the potential tuberculostatic activity of other INH derivatives, real or hypothetical.

Antitubercular Agents↗

PubMed2003

Prediction of human blood: air partition coefficient: a comparison of structure-based and property-based methods.

In recent years, there has been increased interest in the development and use of quantitative structure-activity/property relationship (QSAR/QSPR) models. For the most part, this is due to the fact that experimental data is sparse and obtaining such data is costly, while theoretical structural descriptors can be obtained quickly and inexpensively. In this study, three linear regression methods, viz. principal component regression (PCR), partial least squares (PLS), and ridge regression (RR), were used to develop QSPR models for the estimation of human blood:air partition coefficient (logPblood:air) for a group of 31 diverse low-molecular weight volatile chemicals from their computed molecular descriptors. In general, RR was found to be superior to PCR or PLS. Comparisons were made between models developed using parameters based solely on molecular structure and linear regression (LR) models developed using experimental properties, including saline:air partition coefficient (logPsaline:air) and olive oil:air partition coefficient (logPolive oil:air), as independent variables, indicating that the structure-property correlations are comparable to the property-property correlations. The best models, however, were those that used rat logPblood:air as the independent variable. Haloalkane subgroups were modeled separately for comparative purposes and, although models based on the congeneric compounds were superior, the models developed on the complete set of diverse compounds were of acceptable quality. The structural descriptors were placed into one of three classes based on level of complexity: topostructural (TS), topochemical (TC), or three-dimensional/geometrical (3D). Modeling was performed using the structural descriptor classes both in a hierarchical fashion and separately. The results indicate that highest quality structure-based models, in terms of descriptor classes, were those derived using TC descriptors.

Animals↗

PubMed2002

A mutation in subunit I of cytochrome oxidase from Rhodobacter sphaeroides results in an increase in steady-state activity but completely eliminates proton pumping.

The heme-copper oxidases convert the free energy liberated in the reduction of O(2) to water into a transmembrane proton electrochemical potential (protonmotive force). One of the essential structural elements of the enzyme is the D-channel, which is thought to be the input pathway, both for protons which go to form H(2)O ("chemical protons") and for protons that get translocated across the lipid membrane ("pumped protons"). The D-channel contains a chain of water molecules extending about 25 A from an aspartic acid (D132 in the Rhodobacter sphaeroides oxidase) near the cytoplasmic ("inside") enzyme surface to a glutamic acid (E286) in the protein interior. Mutations in which either of these acidic residues is replaced by their corresponding amides (D132N or E286Q) result in severe inhibition of enzyme activity. In the current work, an asparagine located in the D-channel has been replaced by the corresponding acid (N139 to D; N98 in bovine enzyme) with dramatic consequences. The N139D mutation not only completely eliminates proton pumping but, at the same time, confers a substantial increase (150-300%) in the steady-state cytochrome oxidase activity. The N139D mutant of the R. sphaeroides oxidase was further characterized by examining the rates of individual steps in the catalytic cycle. Under anaerobic conditions, the rate of reduction of heme a(3) in the fully oxidized enzyme, prior to the reaction with O(2), is identical to that of the wild-type oxidase and is not accelerated. However, the rate of reaction of the fully reduced enzyme with O(2) is accelerated by the N139D mutation, as shown by a more rapid F --> O transition. Whereas the rates of formation and decay of the oxygenated intermediates are altered, the nature of the oxygenated intermediates is not perturbed by the N139D mutation.

Amino Acid Substitution↗

PubMed2002

Forespore signaling is necessary for pro-sigmaK processing during Bacillus subtilis sporulation despite the loss of SpoIVFA upon translational arrest.

The sigmaK checkpoint coordinates gene expression in the mother cell with signaling from the forespore during Bacillus subtilis sporulation. The signaling pathway involves SpoIVB, a serine peptidase produced in the forespore, which is believed to cross the innermost membrane surrounding the forespore and activate a complex of proteins, including BofA, SpoIVFA, and SpoIVFB, located in the outermost membrane surrounding the forespore. Activation of the complex allows proteolytic processing of pro-sigmaK, and the resulting sigmaK RNA polymerase transcribes genes in the mother cell. To investigate activation of the pro-sigmaK processing complex, the level of SpoIVFA in extracts of sporulating cells was examined by Western blot analysis. The SpoIVFA level decreased when pro-sigmaK processing began during sporulation. In extracts of a spoIVB mutant defective in forespore signaling, the SpoIVFA level failed to decrease normally and no processing of pro-sigmaK was observed. Although these results are consistent with a model in which SpoIVFA inhibits processing until the SpoIVB-mediated signal is received from the forespore, we discovered that loss of SpoIVFA was insufficient to allow processing under certain conditions, including static incubation of the culture and continued shaking after the addition of inhibitors of oxidative phosphorylation or translation. Under these conditions, loss of SpoIVFA was independent of spoIVB. The inability to process pro-sigmaK under these conditions was not due to loss of SpoIVFB, the putative processing enzyme, or to a requirement for ongoing synthesis of pro-sigmaK. Rather, it was found that the requirements for shaking of the culture, for oxidative phosphorylation, and for translation could be bypassed by mutations that uncouple processing from dependence on forespore signaling. This suggests that ongoing translation is normally required for efficient pro-sigmaK processing because synthesis of the SpoIVB signal protein is needed to activate the processing complex. When translation is blocked, synthesis of SpoIVB ceases, and the processing complex remains inactive despite the loss of SpoIVFA. Taken together, the results suggest that SpoIVB signaling activates the processing complex by performing another function in addition to causing loss of SpoIVFA or by causing loss of SpoIVFA in a different way than when translation is blocked. The results also demonstrate that the processing machinery can function in the absence of translation or an electrochemical gradient across membranes.

Bacillus subtilis↗

PubMedDate not supplied

Prediction of cellular toxicity of halocarbons from computed chemodescriptors: a hierarchical QSAR approach.

A hierarchical quantitative structure-activity relationship (HiQSAR) approach was used to estimate toxicity and genetic toxicity for a set of 55 halocarbons using computed chemodescriptors. The descriptors consisted of topostructural (TS), topochemical (TC), geometrical, semiempirical (AM1) quantum chemical, and ab initio (STO-3G, 6-31G(d), 6-311G, 6-311G(d), and aug-cc-pVTZ) quantum chemical indices. For the two toxicity endpoints investigated, ARR and D(37), the TC indices gave the best cross-validated R(2) values. The 3-D indices also performed either as well as or slightly superior to the TC indices. For the four categories of quantum chemical indices used for the development of predictive models, the AM1 parameters gave the worst performance, and the most advanced ab initio (B3LYP/aug-CC-pVTZ) parameters gave the best results when used alone. This was also the case when the quantum chemical indices were used in the hierarchical QSAR approach for both of the toxicity endpoints, ARR and D(37). The models resulting from HiQSAR are of sufficiently good quality to estimate toxicity of halocarbons from structure.

Aspergillus niger↗

PubMedDate not supplied

Assessing model fit by cross-validation.

When QSAR models are fitted, it is important to validate any fitted model-to check that it is plausible that its predictions will carry over to fresh data not used in the model fitting exercise. There are two standard ways of doing this-using a separate hold-out test sample and the computationally much more burdensome leave-one-out cross-validation in which the entire pool of available compounds is used both to fit the model and to assess its validity. We show by theoretical argument and empiric study of a large QSAR data set that when the available sample size is small-in the dozens or scores rather than the hundreds, holding a portion of it back for testing is wasteful, and that it is much better to use cross-validation, but ensure that this is done properly.

Journal Article↗

PubMedDate not supplied

Quantitative structure-activity relationship modeling of juvenile hormone mimetic compounds for Culex pipiens larvae, with a discussion of descriptor-thinning methods.

Quantitative structure-activity relationship (QSAR) modelers often encounter the problem of multicollinearity owing to the availability of large numbers of computable molecular descriptors. Sparsity of the variables while using descriptors such as atom pairs increases the complexity. Three different predictor-thinning methods, namely, a modified Gram-Schmidt algorithm, a marginal soft thresholding algorithm, and LASSO (least absolute shrinkage and selection operator), were utilized to reduce the number of descriptors prior to developing linear models. Juvenile hormone (JH) activity of 304 compounds on Culex pipiens larvae was taken as the model data set, and predictor trimming of a large number of diverse descriptors comprising 268 global molecular descriptors (topostructural, topochemical, and geometrical), 13 quantum chemical descriptors, and 915 atom pairs (substructural counts) was applied prior to linear regression by the ridge regression method. The data set (N = 304) was split into five calibration data sets of random samples of sizes 60/110/160/210/260, and the remaining 244/194/144/94/44 compounds were used for validations. LASSO was not found to be a very effective method in handling a large set of descriptors because the number of predictors retained could not exceed the number of observations. The results indicated that the modified Gram-Schmidt algorithm could be used to trim the number of predictors in the global molecular descriptor set where collinearity of the descriptors was the major concern. On the contrary, the soft thresholding approach was found to be an effective tool in subset selection from a diverse set of descriptors having both sparsity and multicollinearity, as in the case of the combined set of atom pairs and global molecular descriptors. The final model developed after variable selection was dominated more by atom pairs, which indicated the important structural moieties that affect JH activity of the compounds. The success of the method reiterates the fact that QSAR or quantitative structure-property relationship (QSPR) models can be developed for a diverse set of compounds using properly parametrized and diverse sets of descriptors, of course, with the selection of the appropriate statistical tools.

Animals↗