Abstract
Lysine malonylation is an important post-translational modification (PTM) in proteins, and has been characterized to be associated with diseases. However, identifying malonyllysine sites still remains to be a great challenge due to the labor-intensive and time-consuming experiments. In view of this situation, the establishment of a useful computational method and the development of an efficient predictor are highly desired. In this study, a predictor Mal-Lys which incorporated residue sequence order information, position-specific amino acid propensity and physicochemical properties was proposed. A feature selection method of minimum Redundancy Maximum Relevance (mRMR) was used to select optimal ones from the whole features. With the leave-one-out validation, the value of the area under the curve (AUC) was calculated as 0.8143, whereas 6-, 8- and 10-fold cross-validations had similar AUC values which showed the robustness of the predictor Mal-Lys. The predictor also showed satisfying performance in the experimental data from the UniProt database. Meanwhile, a user-friendly web-server for Mal-Lys is accessible at http://app.aporc.org/Mal-Lys/.
Highlights
Of the malonylated sites are desirable and mainly depend on mass spectrometry which is expensive and laborious
As a complement for experiments, a computational method for timely and effectively identifying the malonyllysine sites is necessary when facing multitudinous protein sequences generated in the post-genomic age
The detailed processing of the dataset was shown in Methods
Summary
Of the malonylated sites are desirable and mainly depend on mass spectrometry which is expensive and laborious. As a complement for experiments, a computational method for timely and effectively identifying the malonyllysine sites is necessary when facing multitudinous protein sequences generated in the post-genomic age. A new computational method of Mal-Lys which predicts malonyllysine sites from protein primary sequences is proposed. Amino acid position information has been succeeded in PTM prediction and achieved satisfying results[12,13]. Sequence order information (k-grams14), position-specific amino acid propensity and physicochemical properties (AAIndex15) were utilized to construct features. The algorithm of support vector machines (SVMs) was used for training the computational model, whereas the leave-one-out validation and 6-, 8- and 10-fold cross-validations were adopted to evaluate the prediction accuracy and robustness of Mal-Lys. The satisfying performance suggested that Mal-Lys can be a useful tool to identify potential lysine malonylation sites in proteins for further experimental consideration
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