Evolution and neural networks - protein secondary structure prediction above 71% accuracy

Some 30,000 protein sequences are known. For 1,000 the structure is experimentally solved. Another 4,000 can be modeled by homology. For the remaining 25,000 sequences, the tertiary structure (3D) cannot be predicted generally from the sequence. A reduction of the problem is the projection of 3D structure onto a one-dimensional string of secondary structure assignments. Predictions in three states rate between 36% (random) and 88% (homology modelling) accuracy. Here, we present an improvement of a neural network system using information about evolutionary conservation. The method achieves a sustained overall accuracy of 71.4%. A test on 45 new proteins confirms the estimated accuracy. Of practical importance is the definition of a reliability index at each residue position: e.g. about 40% of the predicted residues have an expected accuracy of 88%. The method has been made publicly available by an automatic e-mail server.