# A Bi-LSTM Based Ensemble Algorithm for Prediction of Protein Secondary Structure
## 1 Introduction  
Currently, the sustained progress of high-throughput genome sequencing methods is providing an exponentially increasing amount of known protein sequences. However, it is practically impossible to do detailed experimental studies for all proteins due to the high cost and low efficiency. As a result, an urgent requirement is to use amino acid sequences to predict the protein structure and function. One important task in such pipelines is to predict the protein secondary structure. The major methods for predicting protein secondary structure can broadly be classified into two categories: Template-based methods and sequence profile-based methods. For the template-based methods, statistical models are frequently used to analyze the probability of specific amino acids appearing in different secondary structure elements [1; 2]. Generally, researchers have to construct the structural template database from known protein structures with certain sequence similarity, and then find alignments between the whole query sequence or its short fragments and sequences in the protein structure template database. The prediction of these methods is ideal only in the case that sequences similar to the query sequence can be found in the template database [3]. Sequence profile based methods not only make use of the sequence profile information, but also benefit from the structure information. The sequence profile typically represented as position specific scoring matrix (PSSM) [4] is constructed based on the multiple sequence alignment between the query sequence and similar sequences. Sequence profile-based methods perform well in the case when a good PSSM is built, since some similar sequences to the query sequence exist in the template database. In other cases,it is difficult to obtain successful results with these methods [5]. As the prediction of protein secondary structure is important for predicting the 3D structure and function of proteins, it is still an active field in bioinformatics and other related fields. The accuracy of three-state prediction increases gradually from <70% to 82%-84% [6; 7; 8]. In recent decades, many machine learning methods, especially support vector machine (SVM) [9], random forest classifier and Markov model [10], have been utilized in the prediction of protein secondary structure. There are some drawbacks with such methods, for example, they cannot deal with sequences with varied lengths which often exist in the training data and cannot capture the long-range dependence among the same protein sequences. In order to solve these problems, various neural network [11] have recently been employed to predict the protein structure. We know that the local contexts, specifically, the neighbors of each amino acid, are critical for the prediction of protein secondary structure. However, the long-range interactions, referring to amino acid residues that are far from each other in their sequence positions but are close in the three-dimensional space, are also vital for the prediction. The Long Short-Term Memory (LSTM) cell proposed by [PERSON] and [PERSON] [12] has the ability to learn both distant and close intra-sequence dependencies. It has been used in many artificial intelligence tasks and has achieved great success in fields such as speech recognition [13], natural language processing [14], and bioinformatics [15]. In this paper, we apply a Bi-LSTM-based ensemble model for the prediction of protein secondary structure. Various classification rules for protein secondary structure may somehow impact the accuracy of the prediction. Based on the Define Secondary Structure of Proteins (DSSP) method [16], each amino acid residue is assigned to one of the three states: H (\(\alpha\)-Helix), E (\(\beta\)-strands), C (random coil). Generally, DSSP provides an eight state assignment of secondary structure denoted by single letter codes: H, T, S, I, G, E, B and C. These states are converted into three classes using the following convention: [H]->H, {E}->E, {B, C, G, I, T, S}->C.