In Silico Identification of Protein S-Palmitoylation Sites and Their Involvement in Human Inherited Disease

Abstract

S-Palmitoylation is a key regulatory mechanism controlling protein targeting, localization, stability, and activity. Since increasing evidence shows that its disruption is implicated in many human diseases, the identification of palmitoylation sites is attracting more attention. However, the computational methods that are published so far for this purpose have suffered from a poor balance of sensitivity and specificity; hence, it is difficult to get a good generalized prediction ability on an external validation set, which holds back the further analysis of associations between disruption of palmitoylation and human inherited diseases. In this work, we present a reliable identification method for protein S-palmitoylation sites, called SeqPalm, based on a series of newly composed features from protein sequences and the synthetic minority oversampling technique. With only 16 extracted key features, this approach achieves the most favorable prediction performance up to now with sensitivity, specificity, and Matthew's correlation coefficient values of 95.4%, 96.3%, and 0.917, respectively. Then, all known disease-associated variations are studied by SeqPalm. It is found that 243 potential loss or gain of palmitoylation sites are highly associated with human inherited disease. The analysis presents several potential therapeutic targets for inherited diseases associated with loss or gain of palmitoylation function. There are even biological evidence that are coordinate with our prediction results. Therefore, this work presents a novel approach to discover the molecular basis of pathogenesis associated with abnormal palmitoylation. SeqPalm is now available online at http://lishuyan.lzu.edu.cn/seqpalm , which can not only annotate the palmitoylation sites of proteins but also distinguish loss or gain of palmitoylation sites by protein variations.

MDD-Palm: Identification of protein S-palmitoylation sites with substrate motifs based on maximal dependence decomposition

“…To further demonstrate the effectiveness of our predictive model, the independent test set was utilized to compare the two-layered SVM model with existing prediction tools. Because several predictors are not available online, the comparison was carried out using CSS-Palm 4.0 [16,17], NBA-Palm [18], CKSAAP-Palm [19], WAP-Palm [21], PalmPred [22], and SeqPalm [61] based on an independent testing dataset. As shown in Table 4, this independent testing indicated that most of the predictors have relatively high accuracy; however, they provide an unbalanced prediction performance: higher specificity accompanied with lower sensitivity.…”

An amphipathic α-helix directs palmitoylation of the large intracellular loop of the sodium/calcium exchanger

“…Several palmitoylation prediction tools developed in recent years have attempted to define a palmitoylation “consensus sequence” ( 27 – 29 ). Three recent studies have suggested hydrophobic residues on the N-terminal side and basic residues on the C-terminal side of a particular cysteine will favor its palmitoylation ( 28 , 30 , 31 ). Clearly, this is not the case for NCX1; the N-terminal side of the palmitoylation site is enriched in negatively charged amino acids (9 of the first 20 residues on the N-terminal side are aspartate or glutamate), and the carboxyl side is hydrophobic.…”

Protein post-translational modifications: In silico prediction tools and molecular modeling

“…SwissPalm [212] has been recently introduced, which provides information from the comparison of different palmitoyl-proteomic studies and allows the users to easily search for the protein of interest, determine the predicted S-palmitoylation sites, identify orthologues and compare them across palmitoyl-proteomes. SeqPalm [213] has been recently developed in order to get insights into the correlation between the disruption of palmitoylation sites and diseases. This new computational method allows for the identification of palmitoylation sites based on amino acid compositions, autocorrelation of amino acid physicochemical properties and amino acid position-weighted matrices.…”