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. Hence, it is worthwhile exploring new 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 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 provide a useful example for the community. Executable and source code can be download at http://dslsrv8.cs.missouri.edu/~cf797/MUFoldGammaTurn/download.html. Protein tertiary structure prediction has been an active research topic since half a century ago 1-3 . Because it is challenging to directly predict the protein tertiary structure from a sequence, it has been divided into some sub-problems, such as protein secondary and super-secondary structure predictions. Protein secondary structures consist of three elements such as alpha-helix, beta-sheet and coil 4 . The coils can be classified into tight turns, bulges and random coil structures 5 . Tight turns can be further classified into alpha-, gamma-, delta-, pi-and beta -turns based on the number of amino acids involved in forming the turns and their features 6 . The tight turns play an important role in forming super-secondary structures and global 3D structure folding. Gamma-turns are the second most commonly found turns (after beta-turns) in proteins. By definition, a gamma-turn contains three consecutive residues (denoted by i, i + 1, i + 2) and a hydrogen bond between the backbone CO i and the backbone NH i+2 (see Fig. 1 ). There are two types of gamma-turns: classic and inverse 7 . Gamma-turns account for 3.4% of total amino acids in proteins 8 . They can be assigned based on protein 3D structures by using PROMOTIF software 9 . There are two types of gamma-turn prediction problems: (1) gamma-turn/ non-gamma-turn prediction 10-12 , and (2) gamma-turn type prediction [13] [14] [15] . The previous methods can be roughly classified into two categories: statistical methods and machine-learning methods. Early predictors 10, 11, 16 built statistical models and machine-learning methods to predict gamma-turns. For example, Garnier et al. 17 , Gibrat et al. 18 , and Chou 13 applied statistical models while Pham et al. 12 employed support vector machine (SVM). The gamma-turn prediction has improved gradually, and the improvement came from both methods and features used. Chou and Blinn 14 applied a residue-coupled model and achieved prediction MCC 0.08. Kaur and Raghava 11 used multiple sequence alignments as the feature input and achieved MCC 0.17. Hu and Li 19 applied SVM and achieved MCC 0.18. Zhu et al. 20 used shape string and position specific scoring matrix (PSSM) from PSIBLAST as inputs and achieved MCC 0.38, which had the best performance prior to this study. The machine-learning methods outperformed statistical methods greatly. However, the gamma-turns prediction performance is still low mainly due to two reasons: (1) gamma-turns are relatively rare in proteins, yielding a small training sample size; and (2) previous machine-learning methods have not fully exploited the relevant features of gamma-turns. The deep-learning framework may provide a more powerful approach for this problem than previous machine-learning techniques, like other deep-learning applications in protein sequence analysis and prediction [21] [22] [23] [24] . Published: xx xx xxxx OPEN www.nature.com/scientificreports/
Fang et al. REF applied a capsule network to the task of protein gamma-turn prediction, rather than to image classification-the first such application of capsule networks in the bioinformatics domain.
Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks
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