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DeepSF: deep convolutional neural network for mapping protein sequences to folds
- Source :
- BCB, Bioinformatics
- Publication Year :
- 2017
-
Abstract
- Motivation Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a tar get protein based on the fold of a template protein with known structure, which cannot explain the relationship between sequence and fold. Only a few methods had been developed to classify protein sequences into a small number of folds due to methodological limitations, which are not generally useful in practice. Results We develop a deep 1D-convolution neural network (DeepSF) to directly classify any protein se quence into one of 1195 known folds, which is useful for both fold recognition and the study of se quence-structure relationship. Different from traditional sequence alignment (comparison) based methods, our method automatically extracts fold-related features from a protein sequence of any length and map it to the fold space. We train and test our method on the datasets curated from SCOP1.75, yielding a classification accuracy of 80.4%. On the independent testing dataset curated from SCOP2.06, the classification accuracy is 77.0%. We compare our method with a top profile profile alignment method - HHSearch on hard template-based and template-free modeling targets of CASP9-12 in terms of fold recognition accuracy. The accuracy of our method is 14.5%-29.1% higher than HHSearch on template-free modeling targets and 4.5%-16.7% higher on hard template-based modeling targets for top 1, 5, and 10 predicted folds. The hidden features extracted from sequence by our method is robust against sequence mutation, insertion, deletion and truncation, and can be used for other protein pattern recognition problems such as protein clustering, comparison and ranking.<br />28 pages, 13 figures
- Subjects :
- Models, Molecular
0301 basic medicine
FOS: Computer and information sciences
Protein Folding
Computer science
02 engineering and technology
medicine.disease_cause
Biochemistry
Convolutional neural network
Machine Learning (cs.LG)
Structural bioinformatics
Protein sequencing
Sequence Analysis, Protein
Protein methods
Mutation
A protein
Fold (geology)
Original Papers
Computer Science Applications
Computational Mathematics
Computational Theory and Mathematics
Protein folding
Target protein
Statistics and Probability
Sequence analysis
0206 medical engineering
Sequence alignment
03 medical and health sciences
medicine
Animals
Humans
Cluster analysis
Molecular Biology
business.industry
Deep learning
Computational Biology
Pattern recognition
Biomolecules (q-bio.BM)
Structural Classification of Proteins database
Protein superfamily
Structural Bioinformatics
Computer Science - Learning
030104 developmental biology
Quantitative Biology - Biomolecules
FOS: Biological sciences
Neural Networks, Computer
Artificial intelligence
Threading (protein sequence)
business
Sequence Alignment
Software
020602 bioinformatics
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- BCB, Bioinformatics
- Accession number :
- edsair.doi.dedup.....11e9f26fcff6cf810254548ab8b992ff