Background: Lysine succinylation is one of the reversible protein post-translational modifications
(PTMs), which regulate the structure and function of proteins. It plays a significant role in
various cellular physiologies including some diseases of human as well as many other organisms.
The accurate identification of succinylation site is essential to understand the various biological
functions and drug development.
Methods: In this study, we developed an improved method to predict lysine succinylation sites mapping
on Homo sapiens by the fusion of three encoding schemes such as binary, the composition of kspaced
amino acid pairs (CKSAAP) and amino acid composition (AAC) with the random forest
(RF) classifier. The prediction performance of the proposed random forest (RF) based on the fusion
model in a comparison of other candidates was investigated by using 20-fold cross-validation (CV)
and two independent test datasets were collected from two different sources.
Results: The CV results showed that the proposed predictor achieves the highest scores of sensitivity
(SN) as 0.800, specificity (SP) as 0.902, accuracy (ACC) as 0.919, Mathew correlation coefficient
(MCC) as 0.766 and partial AUC (pAUC) as 0.163 at a false-positive rate (FPR) = 0.10 and area under
the ROC curve (AUC) as 0.958. It achieved the highest performance scores of SN as 0.811, SP
as 0.902, ACC as 0.891, MCC as 0.629 and pAUC as 0.139 and AUC as 0.921 for the independent
test protein set-1 and SN as 0.772, SP as 0.901, ACC as 0.836, MCC as 0.677 and pAUC as 0.141 at
FPR = 0.10 and AUC as 0.923 for the independent test protein set-2. It also outperformed all the
other existing prediction models.
Conclusion: The prediction performances as discussed in this article recommend that the proposed
method might be a useful and encouraging computational resource for lysine succinylation site prediction
in the case of human population.