Palmitoylation site detection software tools | Post-translational modification data analysis
Identification of palmitoylated proteins with their sites is the foundation for understanding molecular mechanisms and regulatory roles of palmitoylation. Contrasting to the labor-intensive and time-consuming experimental approaches, in silico prediction of palmitoylation sites has attracted much attention as a popular strategy.
A computer program for palmitoylation site prediction. By comparison with previous versions, the performance of CSS-Palm 4.0 was greatly improved. Finally, the standalone version of CSS-Palm 4.0 was implemented in Java SE 6 with high speed. The CSS-Palm 4.0 could predict out potential palmitoylation sites for ~1,000 proteins (with an average length of ~1000aa) within two minutes.
A predictor to identify palmitoylation sites from protein sequence information using a support vector machine model. The best performance of PalmPred was obtained by incorporating sequence conservation features of peptide of window size 11 using a leave-one-out approach. It helped in achieving an accuracy of 91.98%, sensitivity of 79.23%, specificity of 94.30%, and Matthews Correlation Coefficient of 0.71.
A tool for predicting palmitoylation sites based on protein sequence. For a sequence segment in a given protein, the encoding scheme based on the composition of k-spaced amino acid pairs (CKSAAP) is introduced, and then the support vector machine is used as the predictor. The proposed prediction model CKSAAP-Palm outperforms the existing method CSS-Palm2.0 on both cross-validation experiments and some independent testing data sets. These results imply that our CKSAAP-Palm is able to predict more potential palmitoylation sites and increases research productivity in palmitoylation sites discovery.
A computational method based on naive Bayes algorithm for prediction of palmitoylation site. The training data is curated from scientific literature (PubMed) and includes 245 palmitoylated sites from 105 distinct proteins after redundancy elimination. The proper window length for a potential palmitoylated peptide is optimized as six. To evaluate the prediction performance of NBA-Palm, 3-fold cross-validation, 8-fold cross-validation and Jack-Knife validation have been carried out. Prediction accuracies reach 85.79% for 3-fold cross-validation, 86.72% for 8-fold cross-validation and 86.74% for Jack-Knife validation.
Represents a reliable identification method for protein S-palmitoylation sites which is based on a series of composed features from protein sequence and the synthetic minority oversampling technique. The SeqPalm interface allows to study all types of disease associated variations. With this tool, users are able to discover the molecular basis of pathogenesis associated with abnormal palmitoylation, annotate the palmitoylation sites of proteins and distinguish loss or gain of palmitoylation sites by protein variations.
A web tool for identifying protein palmitoylation sites based on weight amino acid composition, auto-correlation functions and position specific scoring matrix profiles. WAP-Palm achieves a Matthews correlation coefficient of 72.26%. The results obtained from both the cross-validation and independent tests suggest that the WAP-Palm model might facilitate the identification and annotation of protein palmitoylation locations.
A software tool for predicting palmitoylation sites based on the amino acid sequence features. The core of IFS-Palm is that the amino acid factors, conservation, disorder feature as well as specific features of palmitoylation site were used to represent the sequence fragments, and that the incremental feature selection (IFS) method was employed to select the predictor with the best performance. IFS-Palm achieved an accuracy of 90.65% in Jackknife cross-validation test.