Comprehensive strategy for proton chemical shift prediction: linear prediction with nonlinear corrections.

A fast 3D/4D structure-sensitive procedure was developed and assessed for the chemical shift prediction of protons bonded to sp3carbons, which poses the maybe greatest challenge in the NMR spectral parameter prediction. The LPNC (Linear Prediction with Nonlinear Corrections) approach combines three well-established multivariate methods viz. the principal component regression (PCR), the random forest (RF) algorithm, and the k nearest neighbors (kNN) method. The role of RF is to find nonlinear corrections for the PCR predicted shifts, while kNN is used to take full advantage of similar chemical environments. Two basic molecular models were also compared and discussed: in the MC model the descriptors are computed from an ensemble of the conformers found by conformational search based on Metropolis Monte Carlo (MMC) simulation; in the 4D model the conformational space was further expanded to the fourth dimension (time) by adding molecular dynamics to the MC conformers. An illustrative case study about the application and interpretation of the 4D prediction for a conformationally flexible structure, scopolamine, is described in detail.

Combining NMR ensembles and molecular dynamics simulations provides more realistic models of protein structures in solution and leads to better chemical shift prediction.

While chemical shifts are invaluable for obtaining structural information from proteins, they also offer one of the rare ways to obtain information about protein dynamics. A necessary tool in transforming chemical shifts into structural and dynamic information is chemical shift prediction. In our previous work we developed a method for 4D prediction of protein (1)H chemical shifts in which molecular motions, the 4th dimension, were modeled using molecular dynamics (MD) simulations. Although the approach clearly improved the prediction, the X-ray structures and single NMR conformers used in the model cannot be considered fully realistic models of protein in solution. In this work, NMR ensembles (NMRE) were used to expand the conformational space of proteins (e.g. side chains, flexible loops, termini), followed by MD simulations for each conformer to map the local fluctuations. Compared with the non-dynamic model, the NMRE+MD model gave 6-17% lower root-mean-square (RMS) errors for different backbone nuclei. The improved prediction indicates that NMR ensembles with MD simulations can be used to obtain a more realistic picture of protein structures in solutions and moreover underlines the importance of short and long time-scale dynamics for the prediction. The RMS errors of the NMRE+MD model were 0.24, 0.43, 0.98, 1.03, 1.16 and 2.39 ppm for (1)Hα, (1)HN, (13)Cα, (13)Cß, (13)CO and backbone (15)N chemical shifts, respectively. The model is implemented in the prediction program 4DSPOT, available at http://www.uef.fi/4dspot.

Structure-based classification of active and inactive estrogenic compounds by decision tree, LVQ and kNN methods.

The performance of decision tree (DT), learning vector quantization (LVQ), and k-nearest neighbour (kNN) methods classifying active and inactive estrogenic compounds in terms of their structure activity relationship (SAR) was evaluated. A set of 311 compounds was used for construction of the models, the predictive power of which was verified with separate training and test sets. Principal components derived from molecular descriptors calculated with DRAGON software were used as variables representing the structures of the compounds. Broadly, kNN had the best classification ability and DT the weakest, although the performance of each method was dependent on the group of compounds used for modelling. The best performance was obtained with kNN for the calf estrogen receptor data, averaging 98.3% of correctly classified compounds in the external tests. Overall, the results indicate that all the methods tested are suitable for the SAR classification of estrogenic compounds, producing models with a predictive power ranging from adequate to excellent.

A computationally feasible quantum chemical model for 13C NMR chemical shifts of PCB-derived carboxylic acids.

Two quantum chemical models have been derived for the prediction of 13C NMR chemical shifts of novel PCB acids obtained from PCBs by catalytic carbonylation. 13C isotropic shielding constants were calculated employing the GIAO (gauge-independent atomic orbital) method with density functional theory (DFT). The best results were obtained by cluster calculations, which took the solvent effects into account properly. In this approach, a solvent molecule (acetone) was attached by a hydrogen bond to every hydrogen atom present in a PCB acid, and the geometry of the molecular cluster was optimized employing the AM1 method. For 158 chemical shifts, the cross-validated standard error was 2.8 ppm and the cross-validated correlation coefficient was 0.94.

Correlative motions and memory effects in molecular dynamics simulations of molecules: principal components and rescaled range analysis suggest that the motions of native BPTI are more correlated than those of its mutants.

In this work MD simulations of the native bovine pancreatic trypsin inhibitor (BPTI) and 16 mutants were done in vacuum in order to study memory effects in the mutants using principal component analysis (PCA) and the rescaled range analysis (Hurst exponents). Both PCA and the rescaled range analysis support our previous proposition, based on PCA of lysozyme, that the motions of a native protein are more correlated than those of mutants. The methods are compared, the nature and applications of the rule and the role of the long-range correlations in MD time series (i.e. memory) are discussed in the context of collective motions.

Chemical inhibition of PCDD/F formation in incineration processes.

This review summarises results of our pilot-scale experiments to find suitable inhibitors for preventing the formation of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/F) during waste incineration and to specify the role of the main factors affecting the inhibition process, and is based on doctoral dissertation of Ruokojaärvi (2002). Results of previous experiments reported by other researchers are also presented and compared with ours. The detailed aims of our experiments were (1) to compare the effects of different inhibitors on PCDD/F formation during incineration in a pilot plant, (2) to investigate the role of the particle size distribution of the flue gas on the inhibition of PCDD/Fs, and (3) to find the main parameters affecting PCDD/F inhibition in waste incineration. Prevention of the formation of PCDD/Fs with chemical inhibitors and the effects of different supply points, feed temperatures and process parameters were studied in a pilot scale incinerator (50 kW) using light heating oil and refuse-derived fuel as test fuels. Various concentrations of the gaseous inhibitors (sulfur dioxide, ammonia, dimethylamine and methyl mercaptan) were sprayed into the flue gases after the furnace, in addition to which urea was dissolved in water and injected in at different concentrations. The residence time of the flue gas between the furnace and the PCDD/F sampling point was varied in the tests. In another set of urea tests, urea-water solutions at three concentrations were mixed with the RDF prior to incineration. PCDD/F and chlorophenol concentrations, together with other flue gas parameters (e.g. temperature, O2, CO, CO2 and NO), were analysed in the cooling flue gases. The gaseous and liquid inhibitors both notably reduced PCDD/F concentrations in the flue gas, the reductions achieved with the gaseous inhibitors varying from 50 to 78%, with dimethyl amine the most effective, while that produced with urea was up to 90%. The PCDD/F reductions were greater at increased inhibitor concentrations and with increased residence time of the flue gas between the furnace and the sampling point. PCDD/F concentrations in the particle phase decreased much more markedly than those in the gas phase. The urea inhibitor did not alter the particle size distribution of the PCDD/Fs when the amount of inhibitor was adequate. Chemical inhibitors seem to offer a very promising technique for preventing the formation of PCDD/Fs in waste incineration. The addition of urea to the fuel before combustion proved to be very effective approach and could be a useful technique even in the full-scale incinerators.

NMR and molecular modeling in environmental chemistry: prediction of 13C chemical shifts in selected C10-chloroterpenes employing DFT/GIAO theory.

Accurate predictions of 13C NMR chemical shifts (standard error approximately 1.7 ppm) are achieved for a subset of chlorinated bornanes by empirical scaling of shifts from GIAO calculations with geometries obtained from HF/6-31G* calculations. The optimized molecular geometries were compared with X-ray structures for three of the toxaphene components most frequently detected in environmental samples (Parlar nos. 26, 50 and 62), and the concordance between the experimental and calculated values was found to be satisfactory. Taken overall, the results indicate that theoretical methods hold great promise for rationalizing 13C NMR chemical shifts in organohalogen compounds. However, it appeared that the DFT/GIAO shifts need to be empirically scaled to achieve good numerical agreement with experimental shifts in chlorinated bornanes. Obviously, there is a need to develop new computational methods to describe the large deshielding effects of chlorine atoms properly.

NMR protocol for determination of oxidation susceptibility of serum lipids and application of the protocol to a chocolate study.

A protocol for determination of oxidation susceptibility of serum lipids based on proton nuclear magnetic resonance ((1)H NMR) spectroscopy is presented and compared to the commonly used spectrophotometric method. Even though there are methodological differences between these two methods, the NMR-based oxidation susceptibility correlates well (r(2) = 0.73) with the lag time determined spectrophotometrically. In addition to the oxidizability of serum lipids, the NMR method provides also information about the lipid profile. The NMR oxidation assay was applied to the chocolate study including fasting serum samples (n = 45) from subjects who had consumed white (WC), dark (DC) or high-polyphenol chocolate (HPC) daily for 3 weeks. The oxidation susceptibility of serum lipids decreased in the HPC group, and there was a significant difference between the WC and HPC groups (P = 0.031). According to the random forest analysis, the consumption of the HPC chocolate induced changes to the amounts of HDL, phosphatidylcholine, sphingomyelin, and nervonic, docosahexaenoic and myristic acids. Furthermore, arachidonic, docosahexaenoic, docosapentaenoic and palmitic acids, gamma-glutamyl transferase, hemoglobin, HDL, phosphatidylcholine and choline containing phospholipids explained about 60% of the oxidation susceptibility values. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0323-2) contains supplementary material, which is available to authorized users.

Comparing the performance of FLUFF-BALL to SEAL-CoMFA with a large diverse estrogen data set: from relevant superpositions to solid predictions.

In this work a template-based molecular mechanistic superposition algorithm FLUFF (Flexible Ligand Unified Force Field) and an accompanying local coordinate QSAR method BALL (Boundless Adaptive Localized Ligand) are validated against the benchmark techniques SEAL (Steric and Electrostatic Alignment) and CoMFA (Comparative Molecular Field Analysis) using a large diverse set of 245 xenoestrogens extracted from the EDKB (Endocrine Disruptor Knowledge Base) maintained by NCTR (National Centre for Toxicological Research). The results indicate that FLUFF is capable of generating relevant superpositions not only for BALL but also for CoMFA, as both techniques give predictive QSAR models. When the BALL and CoMFA methods are compared, it is clear that the BALL algorithm met or even exceeded the results of the standard 3D-QSAR method CoMFA using alignments either from the tailor-made superposition technique FLUFF or the reference method SEAL. The FLUFF-BALL method can be easily automated, and it is computationally light, providing thus a good computational "sieve" capable of fast screening of large molecule libraries.

Spectroscopic QSAR methods and self-organizing molecular field analysis for relating molecular structure and estrogenic activity.

The performance of three "spectroscopic" quantitative structure-activity relationship (QSAR) methods (eigenvalue (EVA), electronic eigenvalue (EEVA), and comparative spectra analysis (CoSA)) for relating molecular structure and estrogenic activity are critically evaluated. The methods were tested with respect to the relative binding affinities (RBA) of a diverse set of 36 estrogens previously examined in detail by the comparative molecular field analysis method. The CoSA method with (13)C chemical shifts appears to provide a predictive QSAR model for this data set. EEVA (i.e., molecular orbital energy in this context) is a borderline case, whereas the performances of EVA (i.e., vibrational normal mode) and CoSA with (1)H shifts are substandard and only semiquantitative. The CoSA method with (13)C chemical shifts provides an alternative and supplement to conventional 3D QSAR methods for rationalizing and predicting the estrogenic activity of molecules. If CoSA is to be applied to large data sets, however, it is desirable that the chemical shifts are available from common databases or, alternatively, that they can be estimated with sufficient accuracy using fast prediction schemes. Calculations of NMR chemical shifts by quantum mechanical methods, as in this case study, seem to be too time-consuming at this moment, but the situation is changing rapidly. An inherent shortcoming common to all spectroscopic QSAR methods is that they cannot take the chirality of molecules into account, at least as formulated at present. Moreover, the symmetry of molecules may cause additional problems. There are three pairs of enantiomers and nine symmetric (C(2) or C(2)(v)) molecules present in the data set, so that the predictive ability of full 3D QSAR methods is expected to be better than that of spectroscopic methods. This is demonstrated with SOMFA (self-organizing molecular field analysis). In general, the use of external test sets with randomized data is encouraged as a validation tool in QSAR studies.

FLUFF-BALL, a template-based grid-independent superposition and QSAR technique: validation using a benchmark steroid data set.

The Flexible Ligand Unified Force Field (FLUFF) is a molecular mechanistic superposition algorithm utilizing a template structure, on top of which the ligand(s) are superimposed. FLUFF enables a flexible semiautomatic superimposition in which the ligand and the template are allowed to seek the best common conformation, which can then be used to predict the biological activity by Boundless Adaptive Localized Ligand (BALL). In BALL, the similarity of the electrostatic and van der Waals volumes of the template and ligand is evaluated using the template-based coordinate system which makes the FLUFF-BALL invariant as to the rotations and translations of the global coordinate system. When tested using the CBG (corticosteroid binding globulin) affinities of 31 benchmark steroids, the FLUFF-BALL technique produced results comparable to standard 3D-QSAR methods. Supplementary test calculations were performed with five additional data sets. Due to its high level of automation and high throughput, the FLUFF-BALL is highly suitable for use in drug design and in scanning of large molecular libraries.

Consensus kNN QSAR: a versatile method for predicting the estrogenic activity of organic compounds in silico. A comparative study with five estrogen receptors and a large, diverse set of ligands.

Quantitative structure-activity relationships (QSARs) have proved increasingly useful for predicting the biological activities of molecules (e.g., their binding affinities to different receptors) and can be used in environmental chemistry as a preliminary tool for screening the activities of untested molecules, producing valuable information on which compounds should be tested more thoroughly with experimental affinity assays or in animals. The predictive ability of the consensus kNN QSAR method is corroborated here using a diverse set of 245 compounds, which have been assayed for their relative binding affinities to the estrogen receptor of four species: human (ER alpha and ER beta), calf, mouse, and rat. Leave-one-out cross-validation (LOO-CV) and gamma-randomization tests were applied to the QSAR models for internal validation, and separate training and test sets were used for external validation. The internal predictive abilities of the consensus models for all five data sets were convincing, with cross-validated correlation coefficients (LOO-CV q2 values) varying from 0.69 (human ER beta data) to 0.79 (human ER alpha data). The external predictive abilities were also encouraging, as the predictive r2 scores (pr-r2 values) varied from 0.62 (human ER beta data) to 0.77 (calf and mouse data). The results indicate that consensus kNN QSAR is a feasible method for rapid screening of the estrogenic activity of organic compounds.

Ligand intramolecular motions in ligand-protein interaction: ALPHA, a novel dynamic descriptor and a QSAR study with extended steroid benchmark dataset.

The role of intramolecular motions in ligand-macromolecule interactions has been explored by developing and validating ALPHA, a novel QSAR (quantitative structure-activity relationship) descriptor. It is based on the spectral exponents (alpha), which measure the degree of 1/f alpha noise of coordinate fluctuations in molecular dynamics (MD) simulations. ALPHA is the first truly 'dynamic' QSAR descriptor, i.e., it can be derived directly from an MD trajectory. The performance of ALPHA was tested in detail employing the CBG (corticosteroid binding globulin) affinity of 31 benchmark steroids, supplemented with 11 steroids as an external test set. The only fair (42-50%) correlations of ALPHA with static 3D and electronic descriptors mean that ALPHA forms an independent molecular property. Furthermore, inclusion of ALPHA in the SOMFA/ESP model improves the correlation coefficient from 0.86 to 0.91, and /delta/ave from 0.46 to 0.36 for the benchmark dataset. The predictive ability of ALPHA can be interpreted as indirect evidence of the dynamic contribution to ligand-macromolecule interactions. The physical background of ALPHA is discussed and the importance of molecular motions for biological activity is anticipated.

Perspectives on the formation of polychlorinated dibenzo-p-dioxins and dibenzofurans during municipal solid waste (MSW) incineration and other combustion processes.

The pathways by which polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) are formed and the interactions between their aromatic precursors, in particular chlorophenols (ClPhs), and transition metal catalysts are discussed. A literature survey and data from pilot-scale combustion experiments allow conclusions to be drawn on the relations between ClPhs and PCDD/Fs in municipal waste incineration and other combustion processes. The results suggest that the ClPh pathway is among the most important for the formation of PCDD/Fs.

Evaluation of a novel electronic eigenvalue (EEVA) molecular descriptor for QSAR/QSPR studies: validation using a benchmark steroid data set.

A novel electronic eigenvalue (EEVA) descriptor of molecular structure for use in the derivation of predictive QSAR/QSPR models is described. Like other spectroscopic QSAR/QSPR descriptors, EEVA is also invariant as to the alignment of the structures concerned. Its performance was tested with respect to the CBG (corticosteroid binding globulin) affinity of 31 benchmark steroids. It appeared that the electronic structure of the steroids, i.e., the "spectra" derived from molecular orbital energies, is directly related to the CBG binding affinities. The predictive ability of EEVA is compared to other QSAR approaches, and its performance is discussed in the context of the Hammett equation. The good performance of EEVA is an indication of the essential quantum mechanical nature of QSAR. The EEVA method is a supplement to conventional 3D QSAR methods, which employ fields or surface properties derived from Coulombic and van der Waals interactions.