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 structurebased 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 sequenceonly information on more recently compiled datasets. ZinCaps outperforms previous tools. Its source codes is freely available for download at https:github.comclemEssienActiveSitePrediction.

Discovering proteinbinding RNA motifs with a generative model of RNA sequences Abstract   Recent advances in highthroughput experimental technologies have generated a huge amount of data on interactions between proteins and nucleic acids. Motivated by the big experimental data, several computational methods have been developed either to predict binding sites in a sequence or to determine if an interaction exists between protein and nucleic acid sequences. However, most of the methods cannot be used to discover new nucleic acid sequences that bind to a target protein because they are classifiers rather than generators. In this paper we propose a generative model for constructing proteinbinding RNA sequences and motifs using a long shortterm memory (LSTM) neural network. Testing the model for several target proteins showed that RNA sequences generated by the model have high binding affinity and specificity for their target proteins and that the proteinbinding motifs derived from the generated RNA sequences are comparable to the motifs from experimentally validated proteinbinding RNA sequences. The results are promising and we believe this approach will help design more efficient in vitro or in vivo experiments by suggesting potential RNA aptamers for a target protein.