Rab proteins play a crucial role in membrane trafficking and are implicated in various human diseases. Accurate identification of Rab proteins within membrane proteins is of utmost importance for comprehending these diseases and establishing effective drug targets. In this study, we applied a two-dimensional convolutional neural network (CNN) integrated with evolutionary information to discern and identify Rab proteins present within general proteins. Our CNN model exhibited notable performance, achieving a sensitivity of 93.3%, specificity of 98%, accuracy of 96.9%, and a Matthews correlation coefficient (MCC) of 0.91 when tested on an independent dataset. In comparison to previously published methodologies, our approach displayed a substantial 25% improvement in the identification of Rab GTPases. These findings underscore the potential of deep learning techniques for accurately discerning Rab proteins and lay the groundwork for future investigations employing deep learning in the field of bioinformatics.
