Potential of deep representative learning features to interpret the sequence information in proteomics

In molecular biology and proteomics, accurately predicting functions of proteins is a very critical step. However, it sinks a lot of time and resources to detect the protein functions through biological experiments. Therefore, it is necessary to develop an accurate and reliable computational method for this prediction purpose. Since a growing number of deep learning and natural language processing (NLP) models have been developed recently, they hold potential to assist in protein function problems. Therefore, Wang et al. applied them to extract the hidden features of protein sequences and improve the performance of protein function prediction. As a case study, they used their approach to develop a web-server namely prPred-DRLF to predict plant resistance proteins, which play important roles in the detection of pathogen invasion. Cross-validation and independent test results indicate that prPred-DRLF outperformed current state-of-the-art prediction methods on the same datasets. The excellent performance then shows that deep representative learning (using deep learning and NLP) is an accurate and reliable method for protein function prediction. This article is protected by copyright. All rights reserved.