The architecture of MUFOLD-SS enables effective processing of local and global interactions between amino acids in making accurate prediction. MUFOLD-SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, and HHBlits profile. The input to MUFOLD-SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. In this paper, a new deep neural network architecture, named the Deep inception-inside-inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD-SS. Deep learning offers a new opportunity to significantly improve prediction accuracy. Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions.

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