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We tackle the problem of protein secondary structure prediction using acommon task framework. This lead to the introduction of multiple ideas forneural architectures based on state of the art building blocks, used in thistask for the first time. We take a principled machine learning approach, whichprovides genuine, unbiased performance measures, correcting longstanding errorsin the application domain. We focus on the Q8 resolution of secondarystructure, an active area for continuously improving methods. We use anensemble of strong predictors to achieve accuracy of 70.7% (on the CB513 testset using the CB6133filtered training set). These results are statisticallyindistinguishable from those of the top existing predictors. In the spirit ofreproducible research we make our data, models and code available, aiming toset a gold standard for purity of training and testing sets. Such goodpractices lower entry barriers to this domain and facilitate reproducible,extendable research.

High Quality Prediction of Protein Q8 Secondary Structure by Diverse Neural Network Architectures