Topological maps of protein sequences
A new method based on neural networks to cluster proteins into families is described. The network is trained with the Kohonen unsupervised learning algorithm using matrix pattern representations of the protein sequences as inputs. The components (x,y) of these 20\(\times 20\) matrix patterns are the normalized frequencies of all pairs xy of amino acids in each sequence. We investigate the influence of different learning parameters in the final topological maps obtained with a learning set of ten proteins belonging to three established families. In all cases, except in those where the synaptic vectors remains nearly unchanged during learning, the ten proteins are correctly classified into the expected families. The classification by the trained network of mutated or incomplete sequences of the learned proteins is also analyzed.
scientific article; zbMATH DE number 4215764