On the role of long-range dependencies in learning protein secondary structure

Accuracy of protein secondary structure predictors has been slowly growing during the last decade. Although it is clear that a relatively large fraction of current errors is due to long-range interactions, current predictors are not able to exploit such information. We present a solution based on a generalized bidirectional neural network that learns from sequences and associated interaction graphs to improve secondary structure prediction.