Abstract
Lysine succinylation in protein is one type of post-translational modifications (PTMs). Succinylation is associated with some diseases and succinylated sites data just has been found in recent years in experiments. It is highly desired to develop computational methods to identify the candidate proteins and their sites. In view of this, a new predictor called iSuc-PseAAC was proposed by incorporating the peptide position-specific propensity into the general form of pseudo amino acid composition. The accuracy is 79.94%, sensitivity 51.07%, specificity 89.42% and MCC 0.431 in leave-one-out cross validation with support vector machine algorithm. It demonstrated by rigorous leave-one-out on stringent benchmark dataset that the new predictor is quite promising and may become a useful high throughput tool in this area. Meanwhile a user-friendly web-server for iSuc-PseAAC is accessible at http://app.aporc.org/iSuc-PseAAC/ . Users can easily obtain their desired results without the need to understand the complicated mathematical equations presented in this paper just for its integrity.
Highlights
Homologous Non-redundancy consider the following procedures: (a) select or construct a valid benchmark dataset to train and test the predictor; (b) represent the protein or peptide samples with an effective formulation that can truly reflect their intrinsic correlation with the target to be predicted; (c) introduce or develop a powerful algorithm or operation engine to conduct the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated prediction accuracy; (e) establish a user-friendly web-server for the predictor that is accessible to the public
In this study the benchmark dataset was derived from the CPLM7 which was a protein lysine modification database
The corresponding protein sequences were derived from Uniprot database[8]
Summary
Negative consider the following procedures: (a) select or construct a valid benchmark dataset to train and test the predictor; (b) represent the protein or peptide samples with an effective formulation that can truly reflect their intrinsic correlation with the target to be predicted; (c) introduce or develop a powerful algorithm or operation engine to conduct the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated prediction accuracy; (e) establish a user-friendly web-server for the predictor that is accessible to the public. Homologous Non-redundancy consider the following procedures: (a) select or construct a valid benchmark dataset to train and test the predictor; (b) represent the protein or peptide samples with an effective formulation that can truly reflect their intrinsic correlation with the target to be predicted; (c) introduce or develop a powerful algorithm or operation engine to conduct the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated prediction accuracy; (e) establish a user-friendly web-server for the predictor that is accessible to the public.
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