MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

ABSTRACT: Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, 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. 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. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profil

SUBMITTER: Fang C 

PROVIDER: S-EPMC6120586 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature