Functional impact of cerebral connections

Cerebral networks are complex sets of connections that resemble a ladder-like web of multiple parallel feedforward, lateral, and feedback connections. This static anatomical description has been pivotal in guiding our understanding of signal processing within cerebral networks. However, measures on both magnitude and functional significance of connections are extremely limited. Here, we compare the anatomically defined strengths of a set of cerebral pathways emerging from the visual middle suprasylvian (MS) cortex of the cat with measures of the functional impact the same region has over distant sites. These functional measures were obtained by analyzing the local and distant effects of MS cooling deactivation on deoxyglucose uptake. Relative to major efferent projections from MS cortex that have a strong influence, projections to early visual processing stages have weaker functional influences than predicted from the anatomy. For higher processing stages, the converse holds: projections from MS cortex have stronger functional influence than predicted from the anatomy. We conclude that these and future functional measures, obtained using the same combination of techniques, will furnish fundamental, new information that complements and extends current models of static cerebral networks, and lead to more realistic models of cerebral network function and component interactions.

Peptide design by artificial neural networks and computer-based evolutionary search

A technique for systematic peptide variation by a combination of rational and evolutionary approaches is presented. The design scheme consists of five consecutive steps: (i) identification of a “seed peptide” with a desired activity, (ii) generation of variants selected from a physicochemical space around the seed peptide, (iii) synthesis and testing of this biased library, (iv) modeling of a quantitative sequence-activity relationship by an artificial neural network, and (v) de novo design by a computer-based evolutionary search in sequence space using the trained neural network as the fitness function. This strategy was successfully applied to the identification of novel peptides that fully prevent the positive chronotropic effect of anti-β1-adrenoreceptor autoantibodies from the serum of patients with dilated cardiomyopathy. The seed peptide, comprising 10 residues, was derived by epitope mapping from an extracellular loop of human β1-adrenoreceptor. A set of 90 peptides was synthesized and tested to provide training data for neural network development. De novo design revealed peptides with desired activities that do not match the seed peptide sequence. These results demonstrate that computer-based evolutionary searches can generate novel peptides with substantial biological activity.