Abstract
Bacteriophages, which are tremendously important to the ecology and evolution of bacteria, play a key role in the development of genetic engineering. Bacteriophage virion proteins are essential materials of the infectious viral particles and in charge of several of biological functions. The correct identification of bacteriophage virion proteins is of great importance for understanding both life at the molecular level and genetic evolution. However, few computational methods are available for identifying bacteriophage virion proteins. In this paper, we proposed a new method to predict bacteriophage virion proteins using a Multinomial Naïve Bayes classification model based on discrete feature generated from the g-gap feature tree. The accuracy of the proposed model reaches 98.37% with MCC of 96.27% in 10-fold cross-validation. This result suggests that the proposed method can be a useful approach in identifying bacteriophage virion proteins from sequence information. For the convenience of experimental scientists, a web server (PhagePred) that implements the proposed predictor is available, which can be freely accessed on the Internet.
Highlights
A bacteriophage is a virus that is inhabited in bacteria and consists of DNA, RNA, viral proteins, and packaging proteins
The application of bacteriophage virion proteins has wide medical and commercial value, which explains the interest in the identification of novel bacteriophage virion proteins
A Multinomial Naïve Bayes based approach was applied to the prediction of bacteriophage virion proteins by using sequence derived properties
Summary
A bacteriophage is a virus that is inhabited in bacteria and consists of DNA, RNA, viral proteins, and packaging proteins. Bacteriophages play an important role in host bacteria genome evolution. Bacteriophages play an important role in the research of bacterial infections, especially bacterial drug resistant infections [4,5,6]. Bacteriophages infect bacteria by binding to the specific receptors on the surface of the bacterial cell. As fundamental materials of the infectious viral particles, bacteriophage proteins have important biological functions in the interaction between bacteriophage and host bacterial cell. The traditional techniques for protein research, such as Mass spectrometry, have been proved correct but inefficient. It is highly desirable for computational biologists to develop a practical approach that efficiently extracts relevant biological information from sequences to identify the bacteriophage virion proteins
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