We have recently described a method based on artificial neural networks to cluster protein sequences into families. The network was trained with Kohonen's unsupervised learning algorithm using, as inputs, the matrix patterns derived from the dipeptide composition of the proteins. We present here a large-scale application of that method to classify the 1,758 human protein sequences stored in the SwissProt database (release 19.0), whose lengths are greater than 50 amino acids. In the final 2-dimensional topologically ordered map of 15 X 15 neurons, proteins belonging to known families were associated with the same neuron or with neighboring ones. Also, as an attempt to reduce the time-consuming learning procedure, we compared 2 learning protocols: one of 500 epochs (100 SUN CPU-hours [CPU-h]), and another one of 30 epochs (6.7 CPU-h). A further reduction of learningcomputing time, by a factor of about 3.3, with similar protein clustering results, was achieved using a matrix of 11 x 11 components to represent the sequences. Although network training is time consuming, the classification of a new protein in the final ordered map is very fast (14.6 CPU-seconds). We also show a comparison between the artificial neural network approach and conventional methods of biosequence analysis.

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A l'aide des concepts de la théorie des chaînes de Markov il a été possible de construire un modèle d'homopolymérisation. Ce modèle dépend de six paramètres et décrit une homopolymérisation en masse en y incluant les mécanismes de terminaison des chaînes (couplage et disproportion) ainsi que le transfert sur monomère. Cette modélisation, suivant les valeurs des paramètres, est capable de représenter des polymérisations pouvant avoir des indices de polydispersité supérieurs à deux, et saffranchit ainsi des contraintes habituelles.

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