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<p>Alzheimer’s disease remains a major global health challenge, and β-site amyloid precursor protein cleaving enzyme 1 (BACE-1) represents a critical therapeutic target as it catalyzes the rate-limiting step in amyloid-β production. While computational studies have extensively explored small-molecule BACE-1 inhibitors, large-scale screening combined with physics-based refinement has rarely been applied to peptide scaffolds. Here, we report an integrated machine learning (ML)-to- free energy perturbation (FEP) pipeline, combining XGBoost-based screening, molecular docking, replicate explicit-solvent molecular dynamics (MD), and absolute FEP calculations, as a multi-stage alternative to single-scoring approaches for tetrapeptide lead discovery against BACE-1. Screening a library of 16,000 tetrapeptides with an XGBoost model prioritized four candidates (HWRE, HWER, WHRR, and HWRQ) for structure-based evaluation. Molecular docking confirmed favorable positioning within the catalytic cleft, while replicate MD simulations revealed multivalent binding through hydrogen bonds, salt bridges to the catalytic dyad (Asp32/Asp228), and hydrophobic contacts with conserved pocket residues; interaction-fingerprint and hotspot analyses provided residue-level guidance for optimization. FEP calculations delivered quantitative thermodynamic ranking, separating three strong predicted binders (HWRE, WHRR, HWRQ; <i>ΔG</i><sub>FEP</sub> = − 29.45 to − 32.00&#xa0;kcal mol<sup>−1</sup>) from the weaker candidate HWER (<i>ΔG</i><sub>FEP</sub> = − 14.78&#xa0;kcal mol<sup>−1</sup>). The three high-affinity peptides share a conserved H/W/R motif with persistent dyad anchoring and dense hydrogen-bond networks, a compact scaffold amenable to peptidomimetic optimization, with HWRE as the top lead across all computational stages. This study delivers experimentally testable tetrapeptide candidate inhibitors and establishes a scalable framework for peptide-based ligand discovery against BACE-1 and related aspartyl proteases.</p>

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Tetrapeptide inhibitors of BACE-1 revealed by combined data-driven screening and physics-based free-energy refinement

  • 期刊论文

  • 发表日期：

    2026年7月6日

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  • Trung Hai Nguyen,
  • Hang N. T. Nguyen,
  • Minh Quan Pham,
  • Huong Thi Thu Phung

摘要

Alzheimer’s disease remains a major global health challenge, and β-site amyloid precursor protein cleaving enzyme 1 (BACE-1) represents a critical therapeutic target as it catalyzes the rate-limiting step in amyloid-β production. While computational studies have extensively explored small-molecule BACE-1 inhibitors, large-scale screening combined with physics-based refinement has rarely been applied to peptide scaffolds. Here, we report an integrated machine learning (ML)-to- free energy perturbation (FEP) pipeline, combining XGBoost-based screening, molecular docking, replicate explicit-solvent molecular dynamics (MD), and absolute FEP calculations, as a multi-stage alternative to single-scoring approaches for tetrapeptide lead discovery against BACE-1. Screening a library of 16,000 tetrapeptides with an XGBoost model prioritized four candidates (HWRE, HWER, WHRR, and HWRQ) for structure-based evaluation. Molecular docking confirmed favorable positioning within the catalytic cleft, while replicate MD simulations revealed multivalent binding through hydrogen bonds, salt bridges to the catalytic dyad (Asp32/Asp228), and hydrophobic contacts with conserved pocket residues; interaction-fingerprint and hotspot analyses provided residue-level guidance for optimization. FEP calculations delivered quantitative thermodynamic ranking, separating three strong predicted binders (HWRE, WHRR, HWRQ; ΔGFEP = − 29.45 to − 32.00 kcal mol−1) from the weaker candidate HWER (ΔGFEP = − 14.78 kcal mol−1). The three high-affinity peptides share a conserved H/W/R motif with persistent dyad anchoring and dense hydrogen-bond networks, a compact scaffold amenable to peptidomimetic optimization, with HWRE as the top lead across all computational stages. This study delivers experimentally testable tetrapeptide candidate inhibitors and establishes a scalable framework for peptide-based ligand discovery against BACE-1 and related aspartyl proteases.