Recognition of chemical classes

Bell, S.E. Nazarov, E.G. Wang, Y.R Rodriguez, J.E. Eiceman, G.A., Neural network recognition of chemical class information in mobility spectra obtained at high temperatures, AwaZ. Chem. 2000, 72, 1192-1198. 

Pattern recognition methods have been successfully applied in about twenty papers to the interpretation of infrared spectra. In these works classifiers for the recognition of chemical classes have been developed for organic compounds. 

A fourth class, proposed by Klaenhammer [21] is rather questionable. This class comprised the complex bacteriocins, composed of protein plus one or more chemical moieties (lipid, carbohydrate) required for activity plantaricin S, leuconocin S, lactocin 27, pediocin SJ-1 [57-61]. The existence of this fourth class was supported by the observation that some bacteriocin activities were destroyed by glycolytic and lipolytic enzymes [60]. However, such bacteriocins have not yet been characterized adequately at the biochemical level and the recognition of this class therefore seems to be premature. The class IIC of the Klaenhammer [21] classification has recently been shown not to exist. 

In the field of chemical sensors, the revolution in software and inexpensive hardware means that not only nonlinear chemical responses can be tolerated, but incomplete selectivity to a variety of chemical species can also be handled. Arrays of imperfectly selective sensors can be used in conjunction with pattern recognition algorithms to sort out classes of chemical compounds and thek concentrations when the latter are mixed together. 

Further experiments focused therefore on [RuCl(en)(r 6-tha)]+ (12) and [RuCl(rj6-p-cym)(en)]+ (22), which represent the two different classes, and their conformational distortion of short oligonucleotide duplexes. Chemical probes demonstrated that the induced distortion extended over at least seven base pairs for [RuCl(rj6-p-cym)(en)]+ (22), whereas the distortion was less extensive for [RuCl(en)(rj6-tha)]+ (12). Isothermal titration calorimetry also showed that the thermodynamic destabilization of the duplex was more pronounced for [RuCl(r 6-p-cym)(en)]+ (22) (89). DNA polymerization was markedly more strongly inhibited by the monofunctional Ru(II) adducts than by monofunctional Pt(II) compounds. The lack of recognition of the DNA monofunctional adducts by HMGB1, an interaction that shields cisplatin-DNA adducts from repair, points to a different mechanism of antitumor activity for the ruthenium-arenes. DNA repair activity by a repair-proficient HeLa cell-free extract (CFE) showed a considerably lower level of damage-induced DNA repair synthesis (about six times) for [RuCl(en)(rj6-tha)] + compared to cisplatin. This enhanced persistence of the adduct is consistent with the higher cytotoxicity of this compound (89). 

Pattern recognition has been applied In many forms to various types of chemical data (1,2). In this paper the use of SIMCA pattern recognition to display data and detect outliers In different types of air pollutant analytical data Is Illustrated. Pattern recognition Is used In the sense of classification of objects Into sets with emphasis on graphical representations of data. Basic assumptions which are Implied In the use of this method are that objects In a class are similar and that the data examined are somehow related to this similarity. 

Since the synthesis of both classes of these MTs has not yet been shown to be specifically regulated by one metal rather than another, any specificity of function may result from molecular recognition in the binding of ions to these polypeptides. It is clear that in M. guttatus, Cu, but not Cd, is bound to certain proteins perhaps because of the different chemical properties of the ions (although as yet transcriptional/translational control of synthesis of these proteins cannot be totally rejected). Furthermore, although both Cu and Cd form PC complexes, it is possible that PC complexes with essential metal ions could be processed differently from those with non-essential metal ions. 

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