Identify Bitter Peptides by Using Deep Representation Learning Features

Abstract

A bitter taste often identifies hazardous compounds and it is generally avoided by most animals and humans. Bitterness of hydrolyzed proteins is caused by the presence of bitter peptides. To improve palatability, bitter peptides need to be identified experimentally in a time-consuming and expensive process, before they can be removed or degraded. Here, we report the development of a machine learning prediction method, iBitter-DRLF, which is based on a deep learning pre-trained neural network feature extraction method. It uses three sequence embedding techniques, soft symmetric alignment (SSA), unified representation (UniRep), and bidirectional long short-term memory (BiLSTM). These were initially combined into various machine learning algorithms to build several models. After optimization, the combined features of UniRep and BiLSTM were finally selected, and the model was built in combination with a light gradient boosting machine (LGBM). The results showed that the use of deep representation learning greatly improves the ability of the model to identify bitter peptides, achieving accurate prediction based on peptide sequence data alone. By helping to identify bitter peptides, iBitter-DRLF can help research into improving the palatability of peptide therapeutics and dietary supplements in the future. A webserver is available, too.

IUP-BERT: Identification of Umami Peptides Based on BERT Features

“…Higher dimensions indicated a higher risk of information redundancy, that would result in model overfitting. Feature selection is a good way to solve this problem, which removes redundant and indistinguishable features [ 38 ]. The LGBM feature selection method has been proved to an effective approach for feature selection and was successfully applied for ML-based bio-sequence classification [ 38 , 50 ].…”

“…Feature selection is a good way to solve this problem, which removes redundant and indistinguishable features [ 38 ]. The LGBM feature selection method has been proved to an effective approach for feature selection and was successfully applied for ML-based bio-sequence classification [ 38 , 50 ]. Here, we also used it to find the optimized feature space for umami peptide prediction task.…”

“…Lighting gradient boosting machine (LGBM) [ 38 ] adopts the histogram algorithm. It converts continuous floating-point features into k discrete values, and constructs the histogram with a width of k. Then, the training data are traversed and the cumulative statistics of each discrete value in the histogram are collected.…”

“…Each peptide sequence creates the final embedding matrix by employing a linear layer, , in which L represents the peptide length. In the SSA embedded model, the model was trained and optimized using the SSA strategy [ 37 , 38 ].…”

“…For model performance improvement, the synthetic minority oversampling technique (SMOTE) [ 39 ] was applied first to overcome the data imbalance. To achieve higher prediction accuracy, the pretrained sequence embedding technique SSA or BERT was then combined with five different ML algorithms (KNN, LR, SVM, RF, and light gradient boosting machine (LGBM) [ 38 ]) to build several models. The features of the BERT method combined with the SVM model were finally selected and used to raise the prediction efficacy after optimization.…”