Machine learning differentiates enzymatic and non-enzymatic metals in proteins

Abstract

The authors generate the largest structural dataset of enzymatic and non-enzymatic metalloprotein sites to date. They use this dataset to train a decision-tree ensemble machine learning algorithm that allows them to distinguish between catalytic and non-catalytic metal sites. The computational model described here could also be useful for the identification of new enzymatic mechanisms and de novo enzyme design