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

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‐i...

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Bibliographic Details
Published in:Proteins, structure, function, and bioinformatics structure, function, and bioinformatics, 2018-05, Vol.86 (5), p.592-598
Main Authors: Fang, Chao, Shang, Yi, Xu, Dong
Format: Article
Language:English
Subjects:
Amino acid sequence
Amino acids
Artificial neural networks
Chemical properties
deep learning
deep neural networks
Downloading
Machine learning
Neural networks
protein secondary structure
Protein structure
protein structure prediction
Proteins
Secondary structure
Software development tools
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Summary: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 profile, and HHBlits profile. 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. The architecture of MUFOLD‐SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD‐SS outperformed the best existing methods and other deep neural networks significantly. MUFold‐SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html.
ISSN:0887-3585
1097-0134
DOI:10.1002/prot.25487