Mario Compiani

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In this work we describe a parallel system consisting of feed-forward neural networks supervised by a local genetic algorithm. The system is implemented in a transputer architecture and is used to predict the secondary structures of globular proteins. This method allows a wide search in the parameter space of the neural networks and the determination of(More)
Radial basis function neural networks are trained on a data base comprising 38 globular proteins of well resolved crystallographic structure and the corresponding free energy contributions to the overall protein stability (as computed partially from chrystallographic analysis and partially with multiple regression from experimental thermodynamic data by(More)
A diffusion-collision-like model is proposed for helical proteins with three-state folding dynamics. The model generalizes a previous scheme based on the dynamics of putatively essential parts of the protein (foldons) that was successfully tested on proteins with two-state folding. We show that the extended model, unlike the original one, allows(More)
A data base of minimally frustrated alpha helical segments is defined by filtering a set comprising 822 non redundant proteins, which contain 4783 alpha helical structures. The data base definition is performed using a neural network-based alpha helix predictor, whose outputs are rated according to an entropy criterion. A comparison with the presently(More)
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