Photosynthetic protein classification using genome neighborhood-based machine learning feature

Identification of novel photosynthetic proteins is important for understanding and improving photosynthetic efficiency. Synergistically, genomic context such as genome neighborhood can provide additional useful information to identify the photosynthetic proteins. We, therefore, expected that applying the computational approach, particularly machine learning (ML) with the genome neighborhood-based feature should facilitate the photosynthetic function assignment. Our results revealed a functional relationship between photosynthetic genes and their genomic neighbors, indicating the possibility to assign functions from their genome neighborhood profile. Therefore, we created a new method for extracting the patterns based on genome neighborhood network (GNN) and applied for the photosynthetic protein classification using ML algorithms. Random forest (RF) classifier using genome neighborhood-based features achieved the highest accuracy up to 94% in the classification of photosynthetic proteins and also showed better performance (Mathews correlation coefficient = 0.852) than other available tools including the sequence similarity search (0.497) and ML-based method (0.512). Furthermore, we demonstrated the ability of our model to identify novel photosynthetic proteins comparing to the other methods. Our classifier is available at http://bicep.kmutt.ac.th/photomod_standalone, https://bit.ly/2S0I2Ox and DockerHub: https://hub.docker.com/r/asangphukieo/photomod