| Summary: | 碩士 === 國立中央大學 === 資訊工程學系 === 107 === Lysine malonylation is one of the newly recognized post-translational modification (PTMs), it is involved in many biological functions, such as cellular regulation, disease processes and carbon fixation. For better understanding the mechanisms of malonylation, identifying malonylation sites is an essential process. Traditionally, their identifications mainly rely on the mass spectrometry and biological experiments, which is time-consuming, labor-intensive and expensive. Recently, some studies have proposed computational approaches to predict malonylation sites in mammalian proteins. However, there has no predictor for malonylation sites in plant proteins. In this study, we developed two deep learning-based frameworks for identifying malonylation sites in mammalian and plant proteins separately. Physicochemical properties, evolutionary information and sequenced-based features were extracted for training the perdition models. We utilized hybrid deep learning models to predict the malonylation sites. The independent testing results for mammalian and plant proteins achieved an area under the receiver operating characteristic curve (AUC) value of 0.943 and 0.772 respectively. Furthermore, the prediction models are freely available as an online server —named Kmalo at http://fdblab.csie.ncu.edu.tw/Kmalo/.
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