Thermophilic proteins have great potential to be utilized as biocatalysts in biotechnology. Machine learning algorithms are gaining increasing use in identifying such enzymes, reducing or even eliminating the need for experimental studies. While most previously used machine learning methods were based on manually designed features, we developed BertThermo, a model using Bidirectional Encoder Representations from Transformers (BERT), as an automatic feature extraction tool. This method combines a variety of machine learning algorithms and feature engineering methods, while relying on single-feature encoding based on the protein sequence alone for model input. BertThermo achieved an accuracy of 96.97% and 97.51% in 5-fold cross-validation and in independent testing, respectively, identifying thermophilic proteins more reliably than any previously described predictive algorithm. Additionally, BertThermo was tested by a balanced dataset, an imbalanced dataset and a dataset with homology sequences, and the results show that BertThermo was with the best robustness as comparied with state-of-the-art methods. The source code of BertThermo is available.

Umami peptides enhance the umami taste of food and have good food processing properties, nutritional value, and numerous potential applications. Wet testing for the identification of umami peptides is a time-consuming and expensive process. Here, we report the iUmami-DRLF that uses a logistic regression (LR) method solely based on the deep learning pre-trained neural network feature extraction method, unified representation (UniRep based on multiplicative LSTM), for feature extraction from the peptide sequences. The findings demonstrate that deep learning representation learning significantly enhanced the capability of models in identifying umami peptides and predictive precision solely based on peptide sequence information. The newly validated taste sequences were also used to test the iUmami-DRLF and other predictors, and the result indicates that the iUmami-DRLF has better robustness and accuracy and remains valid at higher probability thresholds. The iUmami-DRLF method can aid further studies on enhancing the umami flavor of food for satisfying the need for an umami-flavored diet.