Journal
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
Volume 86, Issue 5, Pages 592-598Publisher
WILEY
DOI: 10.1002/prot.25487
Keywords
deep learning; deep neural networks; protein secondary structure; protein structure prediction
Categories
Funding
- NIH [R01GM100701]
- NSF [CNS1429294]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1429294] Funding Source: National Science Foundation
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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 .
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