Incorporating hybrid models into lysine malonylation sites prediction on mammalian and plant proteins.

ABSTRACT: Protein malonylation, a reversible post-translational modification of lysine residues, is associated with various biological functions, such as cellular regulation and pathogenesis. In proteomics, to improve our understanding of the mechanisms of malonylation at the molecular level, the identification of malonylation sites via an efficient methodology is essential. However, experimental identification of malonylated substrates via mass spectrometry is time-consuming, labor-intensive, and expensive. Although numerous methods have been developed to predict malonylation sites in mammalian proteins, the computational resource for identifying plant malonylation sites is very limited. In this study, a hybrid model incorporating multiple convolutional neural networks (CNNs) with physicochemical p

SUBMITTER: Chung CR 

PROVIDER: S-EPMC7324624 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature