Machine Learning Approaches for Predicting Protein Complex Similarity

Rama: a machine learning approach for ribosomal protein prediction in plants

Ribosomal proteins (RPs) play a fundamental role within all type of cells, as they are major components of ribosomes, which are essential for translation of mRNAs. Furthermore, these proteins are involved in various physiological and pathological processes. The intrinsic biological relevance of RPs motivated advanced studies for the identification of unrevealed RPs. In this work, we propose a new computational method, termed Rama, for the prediction of RPs, based on machine learning techniques, with a particular interest in plants. To perform an effective classification, Rama uses a set of fundamental attributes of the amino acid side chains and applies a two-step procedure to classify proteins with unknown function as RPs. The evaluation of the resultant predictive models showed that Rama could achieve mean sensitivity, precision, and specificity of 0.91, 0.91, and 0.82, respectively. Furthermore, a list of proteins that have no annotation in Phytozome v.10, and are annotated as RPs in Phytozome v.12, were correctly classified by our models. Additional computational experiments have also shown that Rama presents high accuracy to differentiate ribosomal proteins from RNA-binding proteins. Finally, two novel proteins of Arabidopsis thaliana were validated in biological experiments. Rama is freely available at http://inctipp.bioagro.ufv.br:8080/Rama .