Back

De Novo Design and Computational Validation of a High-Affinity Peptide Inhibitor Targeting the HPV E1-E2 Interface

Fletcher, S.; Biswas-Fiss, E. E.; Biswas, S. B.

2026-06-04 bioinformatics
10.64898/2026.06.01.729313 bioRxiv
Show 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.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
Frontiers in Immunology
638 papers in training set
Top 0.9%
12.1%
2
Journal of Chemical Information and Modeling
238 papers in training set
Top 0.5%
11.2%
3
Briefings in Bioinformatics
354 papers in training set
Top 1%
6.9%
4
Protein Science
246 papers in training set
Top 0.8%
4.9%
5
Scientific Reports
3612 papers in training set
Top 28%
3.6%
6
PLOS ONE
5266 papers in training set
Top 35%
3.6%
7
International Journal of Molecular Sciences
494 papers in training set
Top 3%
3.3%
8
mAbs
32 papers in training set
Top 0.2%
3.3%
9
International Journal of Biological Macromolecules
76 papers in training set
Top 0.5%
3.2%
50% of probability mass above
10
Communications Biology
993 papers in training set
Top 7%
2.8%
11
Computational and Structural Biotechnology Journal
242 papers in training set
Top 2%
2.5%
12
Nucleic Acids Research
1281 papers in training set
Top 7%
2.4%
13
Molecular Therapy Nucleic Acids
39 papers in training set
Top 0.3%
2.4%
14
Communications Chemistry
48 papers in training set
Top 0.5%
1.8%
15
Journal of Medicinal Chemistry
77 papers in training set
Top 0.6%
1.5%
16
Nature Communications
5641 papers in training set
Top 49%
1.4%
17
Advanced Science
286 papers in training set
Top 6%
1.4%
18
Proteins: Structure, Function, and Bioinformatics
88 papers in training set
Top 0.9%
1.1%
19
Viruses
332 papers in training set
Top 4%
1.0%
20
Journal of Biological Chemistry
690 papers in training set
Top 8%
0.9%
21
PLOS Pathogens
820 papers in training set
Top 9%
0.9%
22
International Journal of Biological Sciences
10 papers in training set
Top 0.1%
0.9%
23
Journal of Chemical Theory and Computation
140 papers in training set
Top 1%
0.9%
24
Frontiers in Microbiology
427 papers in training set
Top 8%
0.9%
25
Biochemistry
148 papers in training set
Top 2%
0.9%
26
Journal of Molecular Biology
232 papers in training set
Top 4%
0.9%
27
Structure
193 papers in training set
Top 2%
0.9%
28
Journal of Molecular Graphics and Modelling
17 papers in training set
Top 0.4%
0.6%
29
ACS Omega
105 papers in training set
Top 4%
0.6%
30
Cell Chemical Biology
94 papers in training set
Top 2%
0.6%