Dataset Information

NBA-Palm: prediction of palmitoylation site implemented in Naive Bayes algorithm.

ABSTRACT: BACKGROUND: Protein palmitoylation, an essential and reversible post-translational modification (PTM), has been implicated in cellular dynamics and plasticity. Although numerous experimental studies have been performed to explore the molecular mechanisms underlying palmitoylation processes, the intrinsic feature of substrate specificity has remained elusive. Thus, computational approaches for palmitoylation prediction are much desirable for further experimental design. RESULTS: In this work, we present NBA-Palm, a novel computational method based on Naïve 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. Two more algorithms, RBF network and support vector machine (SVM), also have been employed and compared with NBA-Palm. CONCLUSION: Taken together, our analyses demonstrate that NBA-Palm is a useful computational program that provides insights for further experimentation. The accuracy of NBA-Palm is comparable with our previously described tool CSS-Palm. The NBA-Palm is freely accessible from: http://www.bioinfo.tsinghua.edu.cn/NBA-Palm.

SUBMITTER: Xue Y

PROVIDER: S-EPMC1624852 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

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NBA-Palm: prediction of palmitoylation site implemented in Naïve Bayes algorithm.

Xue Yu Y Chen Hu H Jin Changjiang C Sun Zhirong Z Yao Xuebiao X

BMC bioinformatics 20061017

<h4>Background</h4>Protein palmitoylation, an essential and reversible post-translational modification (PTM), has been implicated in cellular dynamics and plasticity. Although numerous experimental studies have been performed to explore the molecular mechanisms underlying palmitoylation processes, the intrinsic feature of substrate specificity has remained elusive. Thus, computational approaches for palmitoylation prediction are much desirable for further experimental design.<h4>Results</h4>In t ...[more]

PMID: 17044919

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Project description:Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naïve Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader) were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that the proposed DBPPred can be an alternative perspective predictor for large-scale determination of DNA-binding proteins.

Dataset Information

NBA-Palm: prediction of palmitoylation site implemented in Naive Bayes algorithm.

Publications

NBA-Palm: prediction of palmitoylation site implemented in Naïve Bayes algorithm.

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