PhotoModPlus: A webserver for photosynthetic protein prediction from a genome neighborhood feature

Identification of photosynthetic proteins and their functions is essential for understanding and improving photosynthetic efficiency. We present here a new webserver called PhotoModPlus as a platform to predict photosynthetic proteins via genome neighborhood networks (GNN) and a machine learning method. GNN facilitates users to visualize the overview of the conserved neighboring genes from multiple photosynthetic prokaryotic genomes and provides functional guidance to the query input. We also integrated a newly developed machine learning model for predicting photosynthesis-specific functions based on 24 prokaryotic photosynthesis-related GO terms, namely PhotoModGO, into the webserver. The new model was developed using a multi-label classification approach and genome neighborhood features. The performance of the new model was up to 0.872 of F1 measure, which was better than the sequence-based approaches evaluated by nested five-fold cross-validation. Finally, we demonstrated the applications of the webserver and the new model in the identification of novel photosynthetic proteins. The server was user-friendly designed and compatible with all devices and available at http://bicep.kmutt.ac.th/photomod or http://bicep2.kmutt.ac.th/photomod.