Targeting neutrophil elastase in alpha-1 antitrypsin deficiency: a GenAI-based approach for peptide inhibitor discovery

Abstract

Alpha-1 Antitrypsin Deficiency (AATD) is a hereditary disorder primarily affecting the lungs and liver, often resulting in early-onset chronic obstructive pulmonary disease (COPD) due to the unchecked activity of Neutrophil Elastase (NE). Current protein replacement therapies have limited efficacy and accessibility, underscoring the need for alternative therapeutic approaches. In this study, we developed a computational pipeline to design peptide-based inhibitors targeting NE, with the aim of mitigating AATD-associated tissue damage. The workflow began by modeling the AAT–NE complex using available crystal structures (PDB IDs: 1HP7 and 5ABW). Protein-protein docking via ZDOCK confirmed critical interaction sites, notably between Met358 of AAT and the catalytic Ser195 of NE. Structural stability and interaction dynamics were evaluated through molecular dynamics simulations (MDS) in GROMACS, followed by binding free energy calculations using MM-PBSA and MM-GBSA methods. Leveraging these insights, a library of peptide candidates was generated using ProteinMPNN and structurally modeled with ESMFold. Peptides were strategically trimmed near the in-teraction interface and screened for cell-penetrating potential and toxicity. Subsequent docking and MD simulations with NE enabled the identification of stable, high-affinity binders based on binding energies and residue-level interaction profiles. This GenAI-guided pipeline offers a promising route for developing novel peptide therapeutics for AATD by selectively inhibiting NE activity.