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

Source Code and Data Archive for: "De Novo Design and Computational Validation of a High-Affinity Peptide Inhibitor Targeting the HPV E1-E2 Interface" OverviewThis repository contains the complete computational pipeline, initial structural models, and processed analytical data associated with the manuscript "De Novo Design and Computational Validation of a High-Affinity Peptide Inhibitor Targeting the HPV E1-E2 Interface". It provides all necessary scripts and configurations to fully reproduce the dual-scale molecular dynamics (MD), MM/GBSA thermodynamic scoring, and advanced trajectory analytics utilized to evaluate the de novo generated peptide inhibitors. Repository Organization 00_Generative_Design/: Contains the proprietary AlphaProteo structural generation parameters and the raw amino acid sequences of the generated candidates (sequences.fasta). 01_Structural_Predictions/: Contains the AlphaFold 3 predicted geometries (.cif format) and associated JSON files containing the predicted template metrics (ipTM, PAE) for the candidate binders and the native 1TUE complex. 02_Statistical_Ensemble_MD/: Automated Python scripts to execute the 50-replicate high-throughput screening phase (100 ps/replicate), including GROMACS preparation, NVT/NPT equilibration, production MD, and gmx_MMPBSA thermodynamic scoring. 03_Production_Dynamics_MD/: Scripts for executing the 50 ns continuous triplicate production MD runs for the top candidate binders. 04_Advanced_Trajectory_Analysis/: Post-processing analytics scripts utilizing MDAnalysis, ProDy, NetworkX, and ProLIF to calculate RMSD/Rg/RMSF, Principal Component Analysis (PCA), Dynamic Cross-Correlation Matrices (DCCM), Normal Mode Analysis (NMA) and Dynamic Residue Networks (DRN). 05_MSA_Info/: Raw sequence data and MAFFT execution parameters utilized for the evolutionary conservation analysis of the HPV arginine triad. 06_Tabular_Data/: The complete suite of processed, aggregated tabular data (.csv) extracted from the trajectories. (Note: Due to standard archive size limits, raw GROMACS .xtc trajectory files are omitted. This folder contains all the processed intermediate data required to immediately recreate the figures and statistics in the manuscript without re-running the GPU simulations.) Root Configuration Files README.md: Comprehensive documentation including a step-by-step execution guide to reproduce the pipeline from scratch. environment.yml: A ready-to-use Conda environment file to instantly recreate the Python analytics environment with all required dependencies. LICENSE: MIT License file detailing open-source usage permissions. System Requirements Molecular Dynamics: GROMACS 2025.4, CHARMM36-jul2022 Thermodynamics: gmx_MMPBSA v1.6.4 (AmberTools) Python Analytics: Python 3.9+, MDAnalysis (≥2.6.0), ProDy, NetworkX, ProLIF, pandas, numpy, matplotlib, seaborn UsagePlease refer to the included README.md for exact sequential commands to execute the pipeline.