Cascading Discriminant and Generative Models for Protein Secondary Structure Prediction

Most of the state-of-the-art methods for protein seconday structure prediction are complex combinations of discriminant models. They apply a local approach of the prediction which is known to induce a limit on the expected prediction accuracy. A priori, the use of generative models should make it possible to overcome this limitation. However, among the numerous hidden Markov models which have been dedicated to this task over more than two decades, none has come close to providing comparable performance. A major reason for this phenomenon is provided by the nature of the relevant information. Indeed, it is well known that irrespective of the model implemented, the prediction should benefit significantly from the availability of evolutionary information. Currently, this knowledge is embedded in position-specific scoring matrices which cannot be processed easily with hidden Markov models. With this observation at hand, the next significant advance should come from making the best of the two approaches, i.e., using a generative model on top of discriminant models. This article introduces the first hybrid architecture of this kind with state-of-the-art performance. The conjunction of the two levels of treatment makes it possible to optimize the recognition rate both at the residue level and at the segment level.