Protein sequence classification using natural language processing techniques

Abstract

Proteins are essential to numerous biological functions, with their sequences determining their roles within organisms. Traditional methods for determining protein function are time-consuming and labor-intensive. This study addresses the increasing demand for precise, effective, and automated protein sequence classification methods by employing natural language processing (NLP) techniques on a dataset comprising 75 target protein classes. We explored various machine learning and deep learning models, including K-Nearest Neighbors (KNN), Multinomial Naive Bayes, Logistic Regression, Multi-Layer Perceptron (MLP), Decision Tree, Random Forest, XGBoost, Voting and Stacking classifiers, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and transformer models (BertForSequenceClassification, DistilBERT, and ProtBert). Experiments were conducted using amino acid ranges of 1-4 grams for machine learning models and different sequence lengths for CNN and LSTM models. The KNN algorithm performed best on tri-gram data with 70.0% accuracy and a macro F1 score of 63.0%. The Voting classifier achieved best performance with 74.0% accuracy and an F1 score of 65.0%, while the Stacking classifier reached 75.0% accuracy and an F1 score of 64.0%. ProtBert demonstrated the highest performance among transformer models, with a accuracy 76.0% and F1 score 61.0% which is same for all three transformer models. Advanced NLP techniques, particularly ensemble methods and transformer models, show great potential in protein classification. Our results demonstrate that ensemble methods, particularly Voting Soft classifiers, achieved superior results, highlighting the importance of sufficient training data and addressing sequence similarity across different classes. Author summaryProteins are vital to numerous biological functions, with their sequences determining their roles within organisms. Traditional methods of protein function determination are time-consuming. This study proposes a system utilizing machine learning, deep learning, and natural language processing (NLP) techniques for protein sequence classification. We explore various models, including K-Nearest Neighbors, Multinomial Naive Bayes, Logistic Regression, Multilayer Perceptron, Decision Tree Classifier, Random Forest Classifier, XGBoost, ensemble methods (Voting and Stacking classifier), LSTM, CNN, and transformer models (BertForSequenceClassification, DistilBERT, ProtBert). Our results demonstrate that ensemble methods, particularly Voting Soft classifiers, achieve superior performance, highlighting the potential of advanced NLP techniques in protein classification.