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Protein secondary structure prediction is one of the most important research areas in bioinformatics. In this paper, we propose a two-stage protein secondary structure prediction technique, implemented using neural network models. The first neural network stage of the proposed technique associates the input protein sequence to a bin containing its corresponding homologues. The second stage predicts the secondary structure of the input sequence utilizing a neural prediction model specific to the bin obtained from stage one. The strategy of binning allows for simplified and accurate neural models. This technique is implemented on the RS126 dataset and its prediction accuracy is compared with that of the standard PHD approach.