NeuroScale: evolutional scale-based protein language models enable prediction of neuropeptides.

Background: Neuropeptides (NPs) are critical signaling molecules involved in various physiological and behavioral processes, including development, metabolism, and memory. They function within both the nervous and endocrine systems and have emerged as promising therapeutic targets for a range of diseases. Despite their significance, the accurate identification of NPs remains a challenge, necessitating the development of more effective computational approaches.

Results: In this study, we introduce NeuroScale, a multi-channel neural network model leveraging evolutionary scale modeling (ESM) for the precise prediction of NPs. By integrating the GoogLeNet framework, NeuroScale effectively captures multi-scale NP features, enabling robust and accurate classification. Extensive benchmarking demonstrates its superior performance, consistently achieving an area under the receiver operating characteristic curve (AUC) exceeding 0.97. Additionally, we systematically analyzed the impact of protein sequence similarity thresholds and multi-scale sequence lengths on model performance, further validating NeuroScale's robustness and generalizability.

Conclusions: NeuroScale represents a significant advancement in neuropeptide prediction, offering both high accuracy and adaptability to diverse sequence characteristics. Its ability to generalize across different sequence similarity thresholds and lengths underscores its potential as a reliable tool for neuropeptide discovery and peptide-based drug development. By providing a scalable and efficient deep learning framework, NeuroScale paves the way for future research in neuropeptide function, disease mechanisms, and therapeutic applications.