Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks

TL;DR

This paper introduces a novel deep inception capsule network for protein gamma-turn prediction, significantly improving accuracy over previous methods and marking the first application of capsule networks in bioinformatics.

Contribution

It presents the first bioinformatics application of capsule networks and demonstrates their effectiveness in protein structure prediction tasks.

Findings

Achieved MCC of 0.45 on GT320 benchmark

Outperformed previous best MCC of 0.38

First deep neural network-based gamma-turn prediction method

Abstract

Protein gamma-turn prediction is useful in protein function studies and experimental design. Several methods for gamma-turn prediction have been developed, but the results were unsatisfactory with Matthew correlation coefficients (MCC) around 0.2-0.4. One reason for the low prediction accuracy is the limited capacity of the methods; in particular, the traditional machine-learning methods like SVM may not extract high-level features well to distinguish between turn or non-turn. Hence, it is worthwhile exploring new machine-learning methods for the prediction. A cutting-edge deep neural network, named Capsule Network (CapsuleNet), provides a new opportunity for gamma-turn prediction. Even when the number of input samples is relatively small, the capsules from CapsuleNet are very effective to extract high-level features for classification tasks. Here, we propose a deep inception capsule network for gamma-turn prediction. Its performance on the gamma-turn benchmark GT320 achieved an MCC of 0.45, which significantly outperformed the previous best method with an MCC of 0.38. This is the first gamma-turn prediction method utilizing deep neural networks. Also, to our knowledge, it is the first published bioinformatics application utilizing capsule network, which will provides a useful example for the community.