The rapid evolution of generative and diffusion-based method in artificial intelligence (AI) has transformed de novo peptide and protein design, enabling the creation of sequence–structure–function relationships beyond conventional heuristic exploration. In this work, we developed an AI-guided molecular design pipeline that integrates diffusion-based generative modeling, inverse folding algorithms, and atomistic simulation to design and evaluate novel peptide inhibitors of the LasR quorum-sensing receptor of Pseudomonas aeruginosa. Peptides were generated using multiple complementary AI frameworks, including DiffPepBuilder (a diffusion-based denoising generator), ProteinMPNN (an inverse folding network), RFdiffusion2, Evobind2, and BindCraft (a hybrid sequence–structure generative model). These peptides were then subjected to physicochemical profiling and molecular docking to estimate binding affinities (Kd) and interface complementarity. The top candidates were subjected to 400 ns all-atom molecular dynamics (MD) simulations, performed in triplicate, to assess stability, flexibility, and interaction persistence, and were compared against the experimentally validated peptides benchmark. Post-simulation metrics, including RMSD, RMSF, and radius of gyration (Rg), revealed reproducible but architecture-specific dynamic behaviors, with BindCraft 4 maintaining the most compact and stable configuration, exhibiting minimal fluctuations and consistent binding-site retention. MMGBSA, performed at different time intervals, revealed strong van der Waals and electrostatic stabilization, correlating with sub-micromolar dissociation constants predicted computationally. In contrast, DiffPepBuilder 1–2 exhibited balanced flexibility and moderate stabilization, while ProteinMPNN 2 and the control (Aqs1C) displayed comparatively weaker energetic convergence. Principal Component Analysis (PCA) and Free Energy Landscape (FEL) projections confirmed that BindCraft 4 sampled a single deep energy basin, signifying a thermodynamically optimized conformational state. Collectively, this integrative AI-simulation hybrid computational framework demonstrates the capability of generative diffusion and inverse folding models to design peptides with superior structural stability and binding thermodynamics, offering a rational framework for AI-driven peptide therapeutics targeting Pseudomonas aeruginosa communication networks.
Khan et al. (Sat,) studied this question.