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Background: Among all the major Post-translational modification, lipid modifications possess special significance due to their widespread functional importance in eukaryotic cells. There exist multiple types of lipid modifications and Palmitoylation, among them, is one of the broader types of modification, having three different types. The N-Palmitoylation is carried out by attachment of palmitic acid to an N-terminal cysteine. Due to the association of N-Palmitoylation with various biological functions and diseases such as Alzheimer’s and other neurodegenerative diseases, its identification is very important. Objective: The in vitro, ex vivo and in vivo identification of Palmitoylation is laborious, time-taking and costly. There is a dire need for an efficient and accurate computational model to help researchers and biologists identify these sites, in an easy manner. Herein, we propose a novel prediction model for the identification of N-Palmitoylation sites in proteins. Method: The proposed prediction model is developed by combining the Chou’s Pseudo Amino Acid Composition (PseAAC) with deep neural networks. We used well-known deep neural networks (DNNs) for both the tasks of learning a feature representation of peptide sequences and developing a prediction model to perform classification. Results: Among different DNNs, Gated Recurrent Unit (GRU) based RNN model showed the highest scores in terms of accuracy, and all other computed measures, and outperforms all the previously reported predictors. Conclusion: The proposed GRU based RNN model can help to identify N-Palmitoylation in a very efficient and accurate manner which can help scientists understand the mechanism of this modification in proteins.

Background: Radioresistance remains a significant obstacle in the treatment of prostate cancer (PCa). The mechanisms underlying the radioresistance in PCa remained to be further investigated. Methods: GSE53902 dataset was used in this study to identify radioresistance-related mRNAs. Protein-protein interaction (PPI) network was constructed based on STRING analysis. DAVID system was used to predict the potential roles of radioresistance-related mRNAs. Results: We screened and re-annotated the GSE53902 dataset to identify radioresistance-related mRNAs. A total of 445 up-regulated and 1036 down-regulated mRNAs were identified in radioresistance PCa cells. Three key PPI networks consisting of 81 proteins were further constructed in PCa. Bioinformatics analysis revealed that these genes were involved in regulating MAP kinase activity, response to hypoxia, regulation of the apoptotic process, mitotic nuclear division, and regulation of mRNA stability. Moreover, we observed that radioresistance-related mRNAs, such as PRC1, RAD54L, PIK3R3, ASB2, FBXO32, LPAR1, RNF14, and UBA7, were dysregulated and correlated to the survival time in PCa. Conclusion: We thought this study would be useful to understand the mechanisms underlying radioresistance of PCa and identify novel prognostic markers for PCa.