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Software for identifying co-evolutionary sectors in proteins using "Robust Co-evolutional Analysis (RoCA)"

Co-evolution networks of HIV/HCV are modular with direct association to structure and function

Ahmed A. Quadeer, David Morales-Jimenez, and Matthew R. McKay

1. A PC with MATLAB (preferrably v2017a or later) installed on it with the following additional toolboxes:

   • Bioinformatics Toolbox
   • Statistics and Machine Learning Toolbox

2. For running codes related to statistical coupling analysis (SCA), register and download the SCA software fromhttps://ais.swmed.edu/rrlabs/register.htm 

3. For mapping predicted sector residues on crystal structures, download Pymol available athttps://pymol.org/

• Inferring co-evolutionary networks for a protein using RoCA

  • Open MATLAB
  • Run the scriptmain_RoCA.m and provide the MSA matrix as an input

• Reproducing results in the paper for HIV and HCV viral proteins

  • Run the following scripts to generate RoCA (and PCA [Quadeer et al. 2014]) results

    • main_gag.m for HIV Gag
    • main_nef.m for HIV Nef
    • main_ns34a.m for HCV NS3-4A
    • main_ns4b.m for HCV NS4B

  • Run the following scripts to generate SCA results

    • main_gag_sca.m for HIV Gag
    • main_nef_sca.m for HIV Nef
    • main_ns34a_sca.m for HCV NS3-4A
    • main_ns4b_sca.m for HCV NS4B

  • Run the following script (in the GT folder) to compare the performance of RoCA and PCA using binary synthetic data

    • main_GT.m

• To visualizing the step-by-step procedure and the corresponding output

  • Download the html folder
  • Open themain.html file in your browser

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[Quadeer et al. 2014] Quadeer AA, Louie RHY, Shekhar K, Chakraborty AK, Hsing I-M, McKay MR. 2014. Statistical linkage analysis of substitutions in patient-derived sequences of genotype 1a hepatitis C virus non-structural protein 3 exposes targets for immunogen design. J. Virol. 88:7628–44. doi:10.1128/JVI.03812-13.

For any questions or comments, please email atahmedaq@gmail.com.