Bacterial dioxygenase- and monooxygenase-catalysed sulfoxidation of benzo[b]thiophenes

Asymmetric heteroatom oxidation of benzo[b]thiophenes to yield the corresponding sulfoxides was catalysed by toluene dioxygenase (TDO), naphthalene dioxygenase (NDO) and styrene monooxygenase (SMO) enzymes present in P. putida mutant and E. coli recombinant whole cells. TDO-catalysed oxidation yielded the relatively unstable benzo[b] thiophene sulfoxide; its dimerization, followed by dehydrogenation, resulted in the isolation of stable tetracyclic sulfoxides as minor products with cis-dihydrodiols being the dominant metabolites. SMO mainly catalysed the formation of enantioenriched benzo[b] thiophene sulfoxide and 2-methyl benzo[b] thiophene sulfoxides which racemized at ambient temperature. The barriers to pyramidal sulfur inversion of 2- and 3-methyl benzo[b] thiophene sulfoxide metabolites, obtained using TDO and NDO as biocatalysts, were found to be ca.: 25-27 kcal mol(-1). The absolute configurations of the benzo[b] thiophene sulfoxides were determined by ECD spectroscopy, X-ray crystallography and stereochemical correlation. A site-directed mutant E. coli strain containing an engineered form of NDO, was found to change the regioselectivity toward preferential oxidation of the thiophene ring rather than the benzene ring.

Improved conjugate gradient implementation for least squares support vector machines

As a promising method for pattern recognition and function estimation, least squares support vector machines (LS-SVM) express the training in terms of solving a linear system instead of a quadratic programming problem as for conventional support vector machines (SVM). In this paper, by using the information provided by the equality constraint, we transform the minimization problem with a single equality constraint in LS-SVM into an unconstrained minimization problem, then propose reduced formulations for LS-SVM. By introducing this transformation, the times of using conjugate gradient (CG) method, which is a greatly time-consuming step in obtaining the numerical solution, are reduced to one instead of two as proposed by Suykens et al. (1999). The comparison on computational speed of our method with the CG method proposed by Suykens et al. and the first order and second order SMO methods on several benchmark data sets shows a reduction of training time by up to 44%. (C) 2011 Elsevier B.V. All rights reserved.