NTxPred2: A large language model for predicting neurotoxic peptides and neurotoxins

Most of the existing methods including our NTxPred, use a single model to predict both neurotoxic peptides and neurotoxic proteins (neurotoxins). In this study, we developed distinct models for predicting neurotoxic peptides and neurotoxins. Our peptide dataset consists of 877 neurotoxic and 877 non-neurotoxic peptides, while our protein dataset includes 775 neurotoxins and 775 non-neurotoxins. Preliminary analysis reveals that certain residues, such as cysteine, are more prevalent in both neurotoxic peptides and proteins, though their abundance differs in magnitude. First, we developed machine learning models using composition and binary profiles, achieving a maximum AUC of 0.97 for peptides and 0.85 for proteins. The performance for proteins improved from an AUC of 0.85 to 0.89 when evolutionary information was incorporated. Next, we built machine learning models using embeddings from protein language models, attaining an AUC of 0.96 for peptides and 0.94 for proteins. We also developed protein language models and achieved an AUC of 0.98 for peptides using esm2-t30 and 0.91 for proteins using esm2-t6. All models were trained and tested using 5-fold cross-validation, and final models were evaluated on an independent dataset not used in training. We further assessed protein models on the peptide dataset and vice versa, highlighting the necessity of separate models. The proposed models outperform existing methods on independent datasets. Our neurotoxicity prediction models will aid in the safety assessment of genetically modified foods and therapeutic proteins by minimizing the need for animal testing. To support the scientific community, we developed a standalone software and web server NTxPred2, for predicting and scanning neurotoxins (https://webs.iiitd.edu.in/raghava/ntxpred2/, https://github.com/raghavagps/ntxpred2/).