A Plausible Mechanism for the Mutagenic Activity (Salmonella typhimurium TA100) of MX Compounds: A Formation of CG-CG+-CG Radical Cation by One-electron Reduction

Abstract

Combining our previous QSAR work with recent high-level quantum mechanical calculations, a plausible mechanism for the mutagenic activity of halogenated furanones (so called MX compounds) in Salmonella typhimurium TA100 tester strain is proposed. The mechanism involves one-electron reduction as a key step and it seems reasonable to suggest that the mutagenicity of these direct-acting compounds may be a purely thermodynamic phenomenon, rather than the result of site-specific binding or adduct formation. Overall, the proposed model is consistent with the most experimental findings.     

漫谈有效数字和计算器

介绍了有效数字的概念及其计算规则,介绍了计算器的一般特性及使用技巧,并给出计算实例.     

速度算符和电子的颤动

指出了国外某些量子力学教材中关于速度算符的概念错误和相对论自由电子进行颤动的错误,分析了错误的原因.     

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.