DeepSF: deep convolutional neural network for mapping protein sequences to folds

نویسندگان

  • Jie Hou
  • Badri Adhikari
  • Jianlin Cheng
چکیده

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 target 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 sequence into one of 1,195 known folds, which is useful for both fold recognition and the study of sequence-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 maps it to the fold space. We train and test our method on the datasets curated from SCOP1.75, yielding an average classification accuracy of 75.3%. On the independent testing dataset curated from SCOP2.06, the classification accuracy is 73.0%. 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 12.63%-26.32% higher than HHSearch on template-free modeling targets and 3.39%-17.09% 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. Availability The DeepSF server is publicly available at: http://iris.rnet.missouri.edu/DeepSF/. Contact [email protected]. Supplementary information Supplementary data are available at Bioinformatics online.

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DeepSF: deep convolutional neural network for mapping protein sequences to folds Supplementary File

Supplementary File Jie Hou, Badri Adhikari and Jianlin Cheng Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, 65211, USA. 2 Department of Mathematics and Computer Science, University of Missouri-St. Louis, 1 University Blvd. 311 Express Scripts Hall, St. Louis, MO 63121 USA. Informatics Institute, University of Missouri, Columbia, Missouri, ...

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عنوان ژورنال:
  • Bioinformatics

دوره   شماره 

صفحات  -

تاریخ انتشار 2017