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Predicting protein structure from amino acid sequence is one of the main challenges of genomics. Various computational methods have been developed during the last decade to reach this goal. However, the problem of structure prediction remains difficult. Before facing this complex problem, our goal is to focus on the accurate analysis of protein structures at a local level. In our study, we present an approach called "hybrid protein model" (HPM) which uses a training procedure similar to the one of the self-organizing maps. It allows the compression of a non-redundant protein structure databank into a library of overlapping 3D structural fragments. The "hybrid protein model" carries out a multiple alignment of structural fragments. We present in this study an improvement of this strategy by introducing gaps in the local structures, and a sensitivity study of the training according to the control parameters. The library obtained is composed of a finite number of structural classes, each class including fragments sharing similar local structures. These classes are representative of the structural motifs found in the protein structures from the databank. Thus, this library constitutes an efficient tool for determining structural similarities between proteins and especially for predicting the local protein structure from the amino acid sequence.