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The following open-source and academic software and computational tools[1] were used at different stages of the workflow, including sequence analysis, structural modeling, molecular docking, molecular dynamics simulations, and post-simulation analysis.

All tools and software were downloaded[2] and used as required at different stages of the study.

Bioinformatics and Scripting

Python was used for scripting and automation tasks, including peptide generation, FASTA handling, and sequence-based analyses using the Biopython library.
NetMHCpan was used to predict peptide binding affinities to HLA-B27:05.
Sequence similarity analysis was performed using BLAST and pairwise alignment methods implemented through Bio.Align.

Molecular Modeling and Docking

Structural modeling and docking of peptide–HLA complexes were performed using AlphaFold-Multimer and HADDOCK.
Visualization of structural models and trajectories was carried out using PyMOL and VMD.

Molecular Dynamics Simulation

System preparation was carried out using AmberTools 23, including protonation state assignment and force-field parameterization.
Molecular dynamics simulations, trajectory correction, and post-simulation analyses were performed using GROMACS 2024.

Binding Free Energy Calculations

MM-GBSA binding free energy calculations were performed using this tool.

Supporting Tools

Footnotes

  1. All software used in this study is open-source or freely available for academic use.

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  2. All available Download links for the used tools are available in the project GitHub repository (software.md)).

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References
  1. GitHub, Inc. (2024). GitHub.
  2. Executable Books Community. (2024). Jupyter Book.
Protein–Peptide Molecular Mimicry Study
Introduction
Protein–Peptide Molecular Mimicry Study
Methods