Prediction of lysine crotonylation sites by incorporating the composition of k-spaced amino acid pairs into Chou's general PseAAC.

As one of the most important and common histones post-translational modifications, crotonylation plays a key role in regulating various biological processes. The accurate identification of crotonylation sites is crucial to elucidate the underlying molecular mechanisms of crotonylation. In this study, a novel bioinformatics tool named CKSAAP_CrotSite is developed to predict crotonylation sites. The highlight of CKSAAP_CrotSite is to adopt the composition of k-spaced amino acid pairs as input encoding, and the support vector machine is employed as the classifier. As illustrated by jackknife test, CKSAAP_CrotSite achieves a promising performance with a Sensitivity of 92.45%, a Specificity of 99.17%, an Accuracy of 98.11% and a Matthew's correlation coefficient of 0.9283, which is much better than those of the existing prediction methods. Feature analysis shows that some amino acid pairs such as 'KxG', 'KG' and 'PxP' may play an important role in the prediction of crotonylation sites. The results of analysis and prediction could offer useful information for elucidating the molecular mechanisms of crotonylation and related experimental validations. A user-friendly web-server for CKSAAP_CrotSite is available at 123.206.31.171/CKSAAP_CrotSite/.