Using word embedding technique to efficiently represent protein sequences for identifying substrate specificities of transporters

Trinh Trung Duong Nguyen, Nguyen Quoc Khanh Le, Quang Thai Ho, Dinh Van Phan, Yu Yen Ou

研究成果: 雜誌貢獻文章同行評審

7 引文 斯高帕斯(Scopus)

摘要

Membrane transport proteins and their substrate specificities play crucial roles in various cellular functions. Identifying the substrate specificities of membrane transport proteins is closely related to protein-target interaction prediction, drug design, membrane recruitment, and dysregulation analysis, thus being an important problem for bioinformatics researchers. In this study, we applied word embedding approach, the main cause for natural language processing breakout in recent years, to protein sequences of transporters. We defined each protein sequence based on the word embeddings and frequencies of its biological words. The protein features were then fed into machine learning models for prediction. We also varied the lengths of protein sequence's constituent biological words to find the optimal length which generated the most discriminative feature set. Compared to four other feature types created from protein sequences, our proposed features can help prediction models yield superior performance. Our best models reach an average area under the curve of 0.96 and 0.99, respectively on the 5-fold cross validation and the independent test. With this result, our study can help biologists identify transporters based on substrate specificities as well as provides a basis for further research that enriches a field of applying natural language processing techniques in bioinformatics.
原文英語
頁(從 - 到)73-81
頁數9
期刊Analytical Biochemistry
577
DOIs
出版狀態已發佈 - 七月 15 2019
對外發佈Yes

ASJC Scopus subject areas

  • Biophysics
  • Biochemistry
  • Molecular Biology
  • Cell Biology

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