AI-driven de novo design and dynamic mechanistic study of peptide inhibitors targeting the SARS-CoV-2 membrane protein
TANG Qinjie
BAI Zhuohang
YANG Yuning
YANG Xiangmin
YANG Zhiwei
ZHANG Lei
Abstract：Objective To design and screen peptide inhibitors that effectively target the M protein using the artificial intelligence(AI)-assisted computational biology and taking the M protein of the SARS-CoV-2 virus as the target.Methods We employed RFDiffusion,a deep learning-based protein structure generation model,to design peptide backbone scaffolds.Amino acid sequences were then optimized using ProteinMPNN method,and the three-dimensional structure modeling and evaluation were carried out by AlphaFold3.Binding free energies(ΔGbind)were calculated using the MM/PBSA method,and used for the selection of 16 top-affinity peptides.Three representative peptides were further chosen through the cluster analysis for the in-depth characterization,including the analysis of interaction sites,validation of Ramachandran plot,and 1 000 ns molecular dynamics(MD)simulations,in order to assess binding stability and dynamic interaction profiles.Results Three high-affinity peptide candidates were successfully obtained.Ramachandran plot analysis indicated that the dihedral angles of the main chain were all within the allowed regions,and the structure was relatively reasonable.MD simulations revealed the key interaction between the M protein and the designed peptides,mainly involving the hydrogen bonds and salt bridges.Conclusion We have established an integrated AI-driven pipeline for peptide design,covering stages from generative scaffolding,sequence optimization,and structural validation to dynamic binding assessment.Through the prediction of computational simulations,high binding potential peptides targeting the M protein of SARS-CoV-2 are predicted,and the possible binding mechanism is preliminarily elucidated.This will provide theoretical basis and lead structure for subsequent experimental verification and the development of antiviral drugs.
Keywords：SARS-CoV-2 membrane proteinpeptide inhibitorsartificial intelligence-aided drug designmolecular dynamics simulationbinding free energyprotein structure predictionmolecular dockingantiviral drug design
Publication Date：2026-07-31
Online Publishing Date：2026-08-26(First online date of this platform, not the publication date of the document)
Pages：9( 961-968,977 )