Resnet-Driven In Silico Identification of Lead Peptides from the Venom Gland Transcriptome of Orientothele washanensis Coupled with Molecular Docking and Dynamics Simulation

Abstract

Background: Orientothele washanensis is a venomous spider with considerable ecological and scientific importance. Its venom, characterized by complex composition and ease of collection, serves as a valuable resource for the discovery of natural peptide drugs. Conventional wet-lab screening methods are limited by rigorous experimental conditions, high resource consumption, and long research cycles, which hinder the efficient identification of functional peptides from the venom gland transcriptome of this spider. Methods: To address these technical bottlenecks, this study developed a novel deep learning model named PepPI-DRN for peptide-protein interaction prediction. The model integrated a residual equivariant graph neural network, a residual 1-dimensional convolutional neural network, and a dual-modal attention mechanism by leveraging both sequence and structural features of peptides and proteins. Results: Results on an independent test set indicated that PepPI-DRN achieved the competitive or superior performance compared with state-of-the-art methods on multiple key evaluation metrics. Candidate peptides with high interaction probabilities against targets were obtained from the venom gland transcriptome of Orientothele washanensis. Furthermore, lead peptides with high binding strength and good structural stability were identified from candidate peptides by molecular docking, and molecular dynamics simulation. Conclusions: These results showed that the pipeline with PepPI-DRN, molecular docking and molecular dynamics simulation enabled efficient and reliable identification of lead peptides from the venom gland transcriptome of Orientothele washanensis, providing a robust and effective strategy for the discovery and development of natural peptide drugs from the spider venom.