ESM-BBB-Pred: a fine-tuned ESM 2.0 and deep neural networks for the identification of blood-brain barrier peptides.

Abstract

Blood-brain barrier peptides (BBBP) could significantly improve the delivery of drugs to the brain, paving the way for new treatments for central nervous system (CNS) disorders. The primary challenge in treating CNS disorders lies in the difficulty pharmaceutical agent's face in crossing the BBB. Almost 98% of small molecule drugs and nearly all large molecule drugs fail to penetrate the BBB effectively. Thus, identifying these peptides is vital for advancements in healthcare. This study introduces an enhanced intelligent computational model called BBB-PEP- Evolutionary Scale Modeling (ESM), designed to identify BBBP. The relative positions, reverse position and statistical moment-based features have been utilized on the existing benchmark dataset. For classification purpose, six deep classifiers such as fully connected networks, convolutional neural network, simple recurrent neural networks, long short-term memory (LSTM), bidirectional LSTM, and gated recurrent unit have been utilized. In addition to harnessing the effectiveness of the pre-trained model, a protein language model ESM 2.0 has been fine-tuned on a benchmark dataset for BBBP classification. Three tests such as self-consistency, independent set testing, and five-fold cross-validation have been utilized for evaluation purposes with evaluation metrics includes accuracy, specificity, sensitivity, and Matthews correlation coefficient. The fine-tuned model ESM 2.0 has shown superior results as compared to employed classifiers and surpasses the existing benchmark studies. This system will support future research and the scientific community in the computational identification of BBBP.