Protein carbamylation is one of the important post-translational modifications, 
which plays a pivotal role in a number of biological conditions, such as 
diseases, chronic renal failure and atherosclerosis. Therefore, recognition and 
identification of protein carbamylated sites are essential for disease treatment 
and prevention. Yet the mechanism of action of carbamylated lysine sites is 
still not realized. Thus it remains a largely unsolved challenge to uncover it, 
whether experimentally or theoretically. To address this problem, we have 
presented a computational framework for theoretically predicting and analyzing 
carbamylated lysine sites based on both the one-class k-nearest neighbor method 
and two-stage feature selection. The one-class k-nearest neighbor method 
requires no negative samples in training. Experimental results showed that by 
using 280 optimal features the presented method achieved promising performances 
of SN=82.50% for the jackknife test on the training set, and SN=66.67%, 
SP=100.00% and MCC=0.8097 for the independent test on the testing set, 
respectively. Further analysis of the optimal features provided insights into 
the mechanism of action of carbamylated lysine sites. It is anticipated that our 
method could be a potentially useful and essential tool for biologists to 
theoretically investigate carbamylated lysine sites.