Tetrapeptides HWRE, WHRR, and HWRQ identified as potent BACE-1 inhibitors via integrated computational screening
Background
Alzheimer's disease (AD) remains a significant global health challenge, characterized by cognitive decline and the accumulation of amyloid-β (Aβ) plaques. The β-site amyloid precursor protein cleaving enzyme 1 (BACE-1) is a critical therapeutic target because it catalyzes the rate-limiting step in Aβ production. Current AD therapies often fall short, highlighting the need for novel approaches. While small-molecule BACE-1 inhibitors have been extensively studied computationally, large-scale screening and physics-based refinement for peptide scaffolds against this target are underexplored, representing a key gap this research addresses.
Study Design
Researchers developed an integrated machine learning (ML)-to-free energy perturbation (FEP) pipeline to discover tetrapeptide BACE-1 inhibitors. They screened a library of 16,000 tetrapeptides using an XGBoost model, prioritizing four candidates: HWRE, HWER, WHRR, and HWRQ. These candidates underwent structure-based evaluation, including molecular docking to assess binding within the catalytic cleft. Replicate explicit-solvent molecular dynamics (MD) simulations were performed to analyze binding modes, followed by absolute FEP calculations to quantitatively rank their thermodynamic binding affinities.
Results
The XGBoost model successfully prioritized four tetrapeptide candidates for further evaluation. Molecular docking confirmed their favorable positioning within the BACE-1 catalytic cleft. Replicate MD simulations revealed multivalent binding, involving 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, clearly separating three strong predicted binders from a weaker candidate. The three high-affinity peptides (HWRE, WHRR, HWRQ) shared a conserved H/W/R motif, demonstrating persistent dyad anchoring and dense hydrogen-bond networks. HWRE emerged as the top lead across all computational stages.
The three strongest binders (HWRE, WHRR, HWRQ) exhibited predicted binding free energies (ΔGFEP) ranging from -29.45 to -32.00 kcal mol-1, significantly stronger than the weaker candidate HWER (ΔGFEP = -14.78 kcal mol-1). These results provide experimentally testable tetrapeptide candidates.
Key Findings
- An integrated ML-to-FEP pipeline successfully screened 16,000 tetrapeptides for BACE-1 inhibition.
- Four tetrapeptide candidates (HWRE, HWER, WHRR, HWRQ) were prioritized for structure-based evaluation.
- Three peptides (HWRE, WHRR, HWRQ) showed strong predicted BACE-1 binding affinities (ΔGFEP = -29.45 to -32.00 kcal mol-1).
- HWRE was identified as the top lead, exhibiting a conserved H/W/R motif and persistent catalytic dyad anchoring.
- The study establishes a scalable computational framework for peptide-based ligand discovery against aspartyl proteases.
Why It Matters
This study offers a scalable framework for peptide-based ligand discovery, particularly for challenging targets like BACE-1 and other aspartyl proteases. For peptide users and biohackers, this demonstrates the power of computational methods to identify novel peptide scaffolds with high affinity, potentially accelerating the development of new therapeutics for Alzheimer's disease. The identified tetrapeptides (HWRE, WHRR, HWRQ) are compact and amenable to peptidomimetic optimization, suggesting a clear path toward developing more stable and bioavailable drug candidates. While these are computational findings, they provide concrete, experimentally testable leads, moving us closer to a usable protocol for BACE-1 inhibition beyond current small-molecule approaches.
bace-1
alzheimer's-disease
tetrapeptide
peptide-inhibitor
computational-screening
molecular-docking