Glutamic acid is an alpha-amino acid used by all living beings in protein 
biosynthesis. One of the important glutamic acid modifications is 
post-translationally modified 4-carboxyglutamate. It has a significant role in 
blood coagulation. 4-carboxyglumates are required for the binding of calcium 
ions. On the contrary, this modification can also cause different diseases such 
as bone resorption, osteoporosis, papilloma, and plaque atherosclerosis. 
Considering its importance, it is necessary to predict the occurrence of 
glutamic acid carboxylation in amino acid stretches. As there is no 
computational based prediction model available to identify 4-carboxyglutamate 
modification, this study is, therefore, designed to predict 4-carboxyglutamate 
sites with a less computational cost. A machine learning model is devised with a 
Multilayered Perceptron (MLP) classifier using Chou's 5-step rule. It may help 
in learning statistical moments and based on this learning, the prediction is to 
be made accurately either it is 4-carboxyglutamate residue site or detected 
residue site having no 4-carboxyglutamate. Prediction accuracy of the proposed 
model is 94% using an independent set test, while obtained prediction accuracy 
is 99% by self-consistency tests.