ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Network
This addresses the gap between abundant protein sequences and limited known functions for biologists, but it is incremental as it applies existing neural machine translation techniques to a new domain.
The authors tackled protein function prediction by converting it into a language translation problem using a novel protein sequence language and a recurrent neural network-based model, achieving good performance in the CAFA 3 competition and on post-competition data.
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.