bioRxivBioinformaticsPreprint
De Novo Design and Computational Validation of a High-Affinity Peptide Inhibitor Targeting the HPV E1-E2 Interface
Abstract
The oncogenic progression of high-risk Human Papillomavirus (HPV) strains relies on the cooperative interaction between the E1 replicative helicase and the E2 origin-binding protein to initiate viral DNA amplification. Disrupting this protein-protein interaction represents a promising, yet clinically unrealized, therapeutic paradigm for treating established HPV infections prior to malignant transformation. This study presents a comprehensive computational pipeline for the de novo design and evaluation of peptide inhibitors targeting the HPV E1-E2 interface, specifically a conserved arginine triad on the solvent-exposed surface of the E1 helicase. AlphaProteo was used for sequence discovery, and AlphaFold 3 for complex structural prediction, generating a candidate library that was subsequently subjected to dual-scale Molecular Dynamics (MD) simulations and MM/GBSA thermodynamic validation using GROMACS. Binder 8 emerged as the lead candidate, yielding a predicted binding free energy of -59.1 {+/-} 0.7 kcal/mol -- a statistically significant improvement over the native E1-E2 baseline (Welchs t-test, p = 8.14e-19; Cohens d = 2.21). As an implicit solvent method, MM/GBSA overestimates absolute affinities; reported values reflect effective binding enthalpy and should be interpreted as relative rankings. Per-residue energy decomposition confirms binding is anchored through multi-point interactions with the arginine triad. Physicochemical profiling via CSM-Toxin and AlgPred 2.0 confirms zero predicted toxicity and non-allergenic properties for Binder 8. Sequence alignment across 183 oncogenic Alpha-papillomavirus genotypes demonstrates near-universal conservation of the targeted triad, supporting Binder 8 as a candidate scaffold for broad-spectrum antiviral development. These findings provide a computationally validated blueprint for future in vitro validation via Bio-layer interferometry.
- organism: human
- assay: proteomics
- research focus: protein design
- approach: deep learning
- approach: statistical model
- Citations
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