Prediction of Conotoxin Type Based on Long Short-term Memory Network

Background: Conotoxin is a valuable peptide that targets ion channels and neuronal receptors. The toxin has been proven to be an effective drug for treating a series of diseases, but the process of identifying the type of toxin through traditional wet experiments is very complicated, low efficiency and high cost, but the method of machine learning is used to identify the cono toxin. Training in the process can effectively change this status quo. Methods: A method to predict the type of spiral toxin using the sequence information of the toxin combined with the long-term short-term memory network (LSTM) method model. This method only needs to take the conotoxin peptide sequence as input, and uses the character embedding method in text processing to automatically map the sequence to the feature vector representation, and extract the features for training and prediction. Results: Experimental results show that the correct index of this method on the test set reaches 0.80, and the AUC (area under the ROC curve) value reaches 0.817. For the same test set, the AUC value of the KNN algorithm is 0.641, and the AUC value of the method proposed in this paper is 0.817. Conclusions: The algorithm does not require manual feature extraction and feature reconstruction steps, thereby simplifying the algorithm design, and can use the advantages of the long-term dependence of LSTM according to the characteristics of the cono toxin sequence, so that its classification can be better predicted, and the classification of the cono toxin can be better predicted. The sequence information of spirotoxin combined with the LSTM method can be better than the KNN classification algorithm.