Beyond Homology Transfer: Deep Learning for Automated Annotation of Proteins

Accurate annotation of protein functions is important for a profound understanding of molecular biology. A large number of proteins remain uncharacterized because of the sparsity of available supporting information. For a large set of uncharacterized proteins, the only type of information available...

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Bibliographic Details
Published in:Journal of grid computing 2019-06, Vol.17 (2), p.225-237
Main Authors: Nauman, Mohammad, Ur Rehman, Hafeez, Politano, Gianfranco, Benso, Alfredo
Format: Article
Language:English
Subjects:
Annotations
Architecture
Computer Science
Deep learning
Feature extraction
Homology
Machine learning
Management of Computing and Information Systems
Model accuracy
Molecular biology
Processor Architectures
Proteins
User Interfaces and Human Computer Interaction
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Summary:Accurate annotation of protein functions is important for a profound understanding of molecular biology. A large number of proteins remain uncharacterized because of the sparsity of available supporting information. For a large set of uncharacterized proteins, the only type of information available is their amino acid sequence. This motivates the need to make sequence based computational techniques that can precisely annotate uncharacterized proteins. In this paper, we propose DeepSeq – a deep learning architecture – that utilizes only the protein sequence information to predict its associated functions. The prediction process does not require handcrafted features; rather, the architecture automatically extracts representations from the input sequence data. Results of our experiments with DeepSeq indicate significant improvements in terms of prediction accuracy when compared with other sequence-based methods. Our deep learning model achieves an overall validation accuracy of 86.72%, with an F1 score of 71.13%. We achieved improved results for protein function prediction problem through DeepSeq, by utilizing sequence only information. Moreover, using the automatically learned features and without any changes to DeepSeq, we successfully solved a different problem i.e. protein function localization, with no human intervention. Finally, we discuss how the same architecture can be used to solve even more complicated problems such as prediction of 2D and 3D structure as well as protein-protein interactions.
ISSN:1570-7873
1572-9184
DOI:10.1007/s10723-018-9450-6