Multi-dimensional deep learning drives efficient discovery of novel neuroprotective peptides from walnut protein isolates

Neurodegenerative diseases, such as Alzheimer's and Parkinson's, are multi-factor induced neurological disorders that require management from multiple pathologies. The peptides from natural proteins with diverse physiological activity can be candidates as multifunctional neuroprotective agents. However, traditional methods for screening neuroprotective peptides are not only time-consuming and laborious but also poorly accurate, which makes it difficult to effectively obtain the needed peptides. In this case, a multi-dimensional deep learning model called MiCNN-LSTM was proposed to screen for multifunctional neuroprotective peptides. Compared to other multi-dimensional algorithms, MiCNN-LSTM reached a higher accuracy value of 0.850. The MiCNN-LSTM was used to acquire candidate peptides from walnut protein hydrolysis. Following molecular docking, behavioral and biochemical index experimental validation eventually found 4 hexapeptides (EYVTLK, VFPTER, EPEVLR and ELEWER) demonstrating excellent multifunctional neuroprotective properties. Therein, EPEVLR performed the best and can be investigated in depth as a multifunctional neuroprotective agent. This strategy will greatly improve the efficiency of screening multifunctional bioactive peptides, and it will be beneficial for the development of food functional peptides.

Automated summary

Neurodegenerative diseases require management from multiple pathologies, and peptides from natural proteins can be potential candidates for multifunctional neuroprotective agents. Traditional methods for screening neuroprotective peptides are time-consuming and inaccurate, but a multi-dimensional deep learning model called MiCNN-LSTM showed higher accuracy. MiCNN-LSTM was used to screen candidate peptides from walnut protein hydrolysis, and 4 hexapeptides (EYVTLK, VFPTER, EPEVLR, and ELEWER) with excellent neuroprotective properties were discovered. Among them, EPEVLR performed the best and can be further investigated as a multifunctional neuroprotective agent. This strategy will significantly improve the efficiency of screening bioactive peptides and benefit the development of functional food peptides.