Capsule Network for Predicting Zinc Binding Sites in Metalloproteins

Zinc is an important cofactor for various biological functions in plants and animals, which are usually associated with proteins. Zinc also plays an important role in protein structures to which it binds. Hence, it is important to predict the Zinc binding sites in these proteins to better understand the structures and functions of these proteins. Most of the existing tools developed in this domain are structure-based predictors implementing Support Vector Machines on datasets that are more than a decade old. As there is little work done to explore the use of deep learning frameworks in this problem, we propose ZinCaps, a framework based on the capsule network for predicting zinc binding site using sequence-only information on more recently compiled datasets. ZinCaps outperforms previous tools.