This microreview summarizes recent reports on the preparation of metal complex peptide conjugates by solid‐phase synthesis methods. Procedures for such conjugates are in many cases different from standard solid‐phase peptide synthesis protocols. Specific advantages of general strategies, the synthesis of peptide–ligand conjugates and complexation with excess metal ions on solid support or the incorporation of previously prepared metal complex amino acid derivatives are discussed.(© Wiley‐VCH Verlag GmbH & Co. KGaA, 69451 Weinheim, Germany, 2008)

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International audienceWe report a synthetic route to ansa pyrrole amino acids via olefin ring-closing metathesis of diene precursors in the presence of Grubbs I catalyst. The dienes were prepared by Grignard addition to pyrrole sulfinyl imines. The success of the macrocyclic ring closure depends on the dienes structure and only in the case of the 13-membered compound 28 sufficient material could be isolated by preparative HPLC separation to investigate its structure spectroscopically. As also rationalized by our computations at the B3LYP/6-311+G(d.p)//B3LYP/6-31G(d) level of theory 28 is configurationally stable

We investigate the influence of different kinds of structure on the learning behaviour of a perceptron performing a classification task defined by a teacher rule. The underlying pattern distribution is permitted to have spatial correlations. The prior distribution for the teacher coupling vectors itself is assumed to be nonuniform. Thus classification tasks of quite different difficulty are included. As learning algorithms we discuss Hebbian learning, Gibbs learning, and Bayesian learning with different priors, using methods from statistics and the replica formalism. We find that the Hebb rule is quite sensitive to the structure of the actual learning problem, failing asymptotically in most cases. Contrarily, the behaviour of the more sophisticated methods of Gibbs and Bayes learning is influenced by the spatial correlations only in an intermediate regime of α, where α specifies the size of the training set. Concerning the Bayesian case we show, how enhanced prior knowledge improves the performance.

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