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Molecules. 2017 Oct 17;22(10). pii: E1732. doi: 10.3390/molecules22101732.

ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.

Author information

1
Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA. caora@plu.edu.
2
School of Business, Pacific Lutheran University, Tacoma, WA 98447, USA. chanla@plu.edu.
3
Baidu Inc. 1195 Bordeaux Dr, Sunnyvale, CA 94089, USA. miaosunwork@gmail.com.
4
Hiretual Inc., San Jose, CA 95131, USA. stevenjiang@hiretual.com.
5
School of electronic engineering, University of Electronic Science and Technology of China, Chengdu 610051, China. zhangxinchen@uestc.edu.cn.

Abstract

With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction has been a long standing challenge to fill the gap between the huge amount of protein sequences and the known function. In this paper, we propose a novel method to convert the protein function problem into a language translation problem by the new proposed protein sequence language "ProLan" to the protein function language "GOLan", and build a neural machine translation model based on recurrent neural networks to translate "ProLan" language to "GOLan" language. We blindly tested our method by attending the latest third Critical Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the performance of our methods on selected proteins whose function was released after CAFA competition. The good performance on the training and testing datasets demonstrates that our new proposed method is a promising direction for protein function prediction. In summary, we first time propose a method which converts the protein function prediction problem to a language translation problem and applies a neural machine translation model for protein function prediction.

KEYWORDS:

machine learning; neural machine translation; protein function prediction

PMID:
29039790
DOI:
10.3390/molecules22101732
[Indexed for MEDLINE]
Free full text

Conflict of interest statement

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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