Deep3BPP: Identification of blood-brain barrier penetrating peptides using word embedding feature extraction method and CNN-LSTM

To prevent different chemicals from entering the brain, the blood-brain barrier penetrating peptide (3BPP) acts as a vital barrier between the bloodstream and the central nervous system (CNS). This barrier significantly hinders the treatment of neurological and CNS disorders. 3BPP can get beyond this barrier, making it easier to enter the brain and essential for treating CNS and neurological diseases and disorders. Computational techniques are being explored because traditional laboratory tests for 3BPP identification are costly and time-consuming. In this work, we introduced a novel technique for 3BPP prediction with a hybrid deep learning model. Our proposed model, Deep3BPP, leverages the LSA, a word embedding method for peptide sequence extraction, and integrates CNN with LSTM (CNN-LSTM) for the final prediction model. Deep3BPP performance metrics show a remarkable accuracy of 97.42%, a Kappa value of 0.9257, and an MCC of 0.9362. These findings indicate a more efficient and cost-effective method of identifying 3BPP, which has important implications for researchers in the pharmaceutical and medical industries. Thus, this work offers insightful information that can advance both scientific research and the well-being of people overall.