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HPmodel

An ab intio solution for the Prediction of Protein Structure Problem is presented here with Simulated Annealing to find the fold with the optimal score according to an energy function defined by Hydrophobic/Polar (HP) interactions.

Setup

The main.py script runs with the Python Standard Library.

Use uv (recommended) to install dependencies for analysis.py. See setup for more details.

Usage

To run the simulation with the default configuration (N=100, M=20, COOLRATE=1000, PENALTY=-2):

python main.py

To see the arguments for the simulation:

python main.py --help

To run the simulation with a specified configuration:

python main.py -n 10 -m 5 -r 100 -p 1

To run the simulation and analysis with a set of configurations:

bash run.sh

Analysis

For a detailed description of results, see the report.

Data Attributions

This project utilizes experimental and computational protein structure data. Please ensure these sources are properly cited in any resulting publications or derivative works.

  1. Experimental Reference Structure (PDB ID: 1SI4) The reference structure for adult human hemoglobin A2 (R2 state) was obtained from the RCSB Protein Data Bank.
  • Citation: Sen, U., Dasgupta, J., Choudhury, D., Datta, P., Chakrabarti, A., Chakrabarty, S.B., Chakrabarty, A., Dattagupta, J.K. (2004). "Crystal structure of Human hemoglobin A2 (in R2 state) at 2.2 A resolution." Biochemistry, 43(40), 12477-12488.
  • DOI: 10.1021/bi048903i
  • Data DOI: 10.2210/pdb1si4/pdb
  • License: Access to PDB data is provided under CC0 1.0 Universal (Public Domain) terms.
  1. Quark Ab Initio Model Results Comparative results were generated using or derived from the QUARK ab initio protein folding server developed by the Zhang Lab at the University of Michigan.
  • Primary Citation: Xu, D., Zhang, Y. (2012). "Ab initio protein structure assembly using continuous fragments and integrated protein-specific restructuring." Proteins, 80(7), 1715-1735.
  • Secondary Citation (C-QUARK): Zheng, W., et al. (2021). "Improving fragment-based ab initio protein structure assembly using deep learning-assisted contact predictions." Nature Communications, 12, 4993.
  • Web Resource: zhanggroup.org

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protein structure prediction ab initio with simulated annealing

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