Biomedical subjects

Kari Tuppurainen

Publications and source records attributed to Kari Tuppurainen.

10 recordsLinked to original sources

PubMed2005

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.

Carbon Isotopes↗

PubMed2005

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.

Animals↗

PubMed2004

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.

Ligands↗

PubMed2003

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.

Camphanes↗

PubMed2003

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.

Benzofurans↗

PubMed2002

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.

Algorithms↗

PubMedDate not supplied

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.

Quantitative Structure-Activity Relationship↗

PubMedDate not supplied

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.

Algorithms↗

PubMedDate not supplied

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.

Journal Article↗

PubMedDate not supplied

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.

Algorithms↗