※ Link:

<1> Databases

1. dbPTM: An integrated resource for protein post-translational modifications (Huang, et al., 2016).

2. SwissPalm: A database on protein S-palmitoylation (Blanc, et al., 2015).

3. HPRD: A centralized platform to visually depict and integrate information pertaining to domain architecture, post-translational modifications, interaction networks and disease association for each protein in the human proteome (Keshava, et al., 2009).

4. UniProt: A comprehensive resource for protein sequence and annotation data (The UniProt Consortium, 2017).

5. PTMD: A database contains the manually curated associations between different PTM types and different diseases (Xu, et al., 2018).

<2> Predictors

1. CSS-Palm 1.0: Palmitoylation site prediction with a clustering and scoring strategy (CSS) (Zhou, et al., 2006).

2. NBA-Palm 1.0: A novel computational method based on Naïve Bayes algorithm for prediction of palmitoylation site (Xue, et al., 2006).

3. CSS-Palm 2.0: An updated software for palmitoylation sites prediction (Ren, et al., 2008).

4. CKSAAP-Palm: Prediction of palmitoylation sites using the composition of k-spaced amino acid pairs (Wang, et al., 2009).

5. PPWMs: A computational predictor using Position Weight Matrices (PWMs) encoding scheme and support vector machine (SVM) for identifying protein palmitoylation sites (Li, et al., 2011).

6. IFS-Palm: Prediction and analysis of protein palmitoylation sites (Hu, et al., 2011).

7. WAP-Palm: The prediction of palmitoylation site locations using a multiple feature extraction method (Shi, et al., 2013).

8. PalmPred: A novel in silico predictor to identify palmitoylation sites from protein sequence information using a support vector machine model (Kumari, et al., 2014).

9. SeqPalm: Sequence information based protein S-palmitoylation sites annotation (Li, et al., 2015).

10. GPS-Lipid: A robust tool for the prediction of multiple lipid modification sites (Xie, et al., 2016).

11. MDD-Palm: Identification of protein S-palmitoylation sites with substrate motifs based on maximal dependence decomposition (Weng, et al., 2017).