Prediction Method for Lysine Acetylation Sites Based on LSTM Network

Abstract

Protein lysine acetylation is an important post- translational modification (PTM) that takes an important part in many cellular processes, such as metabolism, differentiation and replication. However, the current lysine acetylation site identification mainly adopts experimental detection methods which are time-consuming and inefficient. Therefore, several shallow machine learning algorithms were applied for protein lysine acetylation site prediction. However, traditional machine learning algorithms are not as accurate as deep learning algorithms. As a special kind of recurrent neural network (RNN), long short-term memory (LSTM) can not only process sequence data but also solve the vanishing gradient problem. Therefore, this paper proposes a method based on LSTM to predict lysine acetylation sites. In addition, two traditional machine learning algorithms k-nearest neighbors (KNN) and support vector machine (SVM) were adopted for training as comparison. Through experimental verification, the prediction method based on LSTM is superior to the other two based on traditional machine learning algorithms, and the final prediction accuracy of LSTM model can reach up to 81.52%.