Protein Fold Recognition with Representation Learning and Long Short-Term Memory

• Tsubaki Masashi

  Graduate School of Information Science, Nara Institute of Science and Technology

• Shimbo Masashi

  Graduate School of Information Science, Nara Institute of Science and Technology

• Matsumoto Yuji

  Graduate School of Information Science, Nara Institute of Science and Technology

抄録

Predicting the 3D structure of a protein from its amino acid sequence is an important challenge in bioinformatics. Since directly predicting the 3D structure is hard to achieve, classifying a protein into one of the “folds”, which are pre-defined structural labels in protein databases such as SCOP and CATH, is generally used as an intermediate step to determine the 3D structure. This classification task is called protein fold recognition (PFR), and much research has addressed the problem of either (i) feature extractions from amino acid sequences or (ii) classification methods of the protein folds. In this paper, we propose a new approach for PFR with (i) learning feature representations with unsupervised methods from a large protein database instead of manual feature selection and using external tools. (ii) learning deep neural architectures, recurrent neural networks (RNNs) with long short-term memory (LSTM) units, and re-training the representations instead of fixing the extracted features. On a benchmark dataset, our approach outperforms existing methods that use various physicochemical features.

収録刊行物

• IPSJ Transactions on Bioinformatics

  IPSJ Transactions on Bioinformatics 10 (0), 2-8, 2017

  一般社団法人 情報処理学会

キーワード

• protein fold recognition
• deep learning
• representation learning
• recurrent neural network
• long short-term memory

詳細情報

• CRID: 1390001205294469504
• NII論文ID: 130005292357
• DOI: 10.2197/ipsjtbio.10.2
• ISSN: 18826679
• Web Site: https://www.jstage.jst.go.jp/article/ipsjtbio/10/0/10_2/_pdf
• 本文言語コード: en
• データソース種別:
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