  Motivation: Protein secondary structure prediction can provide important
information for protein 3D structure prediction and protein functions. Deep
learning, which has been successfully applied to various research fields such
as image classification and voice recognition, provides a new opportunity to
significantly improve the secondary structure prediction accuracy. Although
several deep-learning methods have been developed for secondary structure
prediction, there is room for improvement. MUFold-SS was developed to address
these issues. Results: Here, a very deep neural network, the deep
inception-inside-inception networks (Deep3I), is proposed for protein secondary
structure prediction and a software tool was implemented using this network.
This network takes two inputs: a protein sequence and a profile generated by
PSI-BLAST. The output is the predicted eight states (Q8) or three states (Q3)
of secondary structures. The proposed Deep3I not only achieves the
state-of-the-art performance but also runs faster than other tools. Deep3I
achieves Q3 82.8% and Q8 71.1% accuracies on the CB513 benchmark.
