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A tool for predicting the effects of missense mutations on protein stability changes upon missense mutation using protein sequence only. PROST uses colab AlhpaFold2 for the prediction of pdb struture from FASTA sequence.

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PROST

A tool for predicting the effects of missense mutations on protein stability changes upon missense mutation using protein sequence only. PROST uses colab AlhpaFold2 for the prediction of pdb struture from FASTA sequence.

Screen Shot 2022-06-26 at 6 33 29 pm

Requirements: Listed separately as requirementsPy2*.txt and requirementsPy3*.txt for virtual environments. Install ColabFold on your PC from https://github.com/YoshitakaMo/localcolabfold

Installation of Anaconda3 is prefered

  1. create python3 virtual environment and fulfil (install packages) requirements3.txt

  2. create python2 virtual environment and fulfil (install packages) requirementspy2.txt [Required for running run_list_spd33.sh on a new sequence file]. Activate python2 in spd33_run_list.sh [Required for running on a new sequence]

    Download the following databases and unzip

    i) uniref50 (https://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref50) [make this ready for blast by using the following command]

    	makeblastdb -in uniref50.fasta -dbtype prot -out uniref50
    

    ii) uniclust30_2018_08 (http://wwwuser.gwdg.de/~compbiol/uniclust/2018_08/uniclust30_2018_08_hhsuite.tar.gz)

    iii) uniprot20_2016_02 (https://ftp.uniprot.org/pub/databases/uniprot/previous_releases/release-2016_02/uniref/uniref2016_02.tar.gz)

    2.1) Check and rectify paths to DATABASES in Mutation_pred.py and spd33_run_list.sh

3)Activate your colabfold-conda environment correctly in run_list_alphafold2.sh

  1. Activate python3 virtual environment and run the python script (Mutation_pred.py):

    Command-line arguments:

     {-file,--file}	protein sequence (FASTA format)
    
     {-mutation, --mutation}	missence mutation (example: A 12 W or GLN 10 ALA)
    
     {-mutlist, --mutlist, --ml, --mutation-list}	list of mutations
    
     {-outdir, --outdir, --out-dir}	directory name for results
    
     {-out-file, --out-file} Name for the result output file
    
     {-h, --help}	command-line summary
    

Single mutation

python Mutation_pred.py -file fasta.txt -mutation wild-residue position mutant-residue  -outdir(optional) Result -out-file (optional) mutation_result

List of mutations

python Mutation_pred.py -file fasta.txt -mutlist Mut_list.txt -outdir(optional) Result -out-file(optional) mut_list_Result
  1. Example:

    1. python Mutation_pred.py -file Input/Frataxin.txt -mutlist Input/Frataxin_mut.txt -outdir Result

    2. python Mutation_pred.py -file Input/Frataxin.txt -mutation D 21 G -outdir Result -out-file D21G_result

**Internal files will be stored inside Gen_Files folder.

Citation:

Iqbal, Shahid, Fang Ge, Fuyi Li, Tatsuya Akutsu, Yuanting Zheng, Robin B. Gasser, Dong-Jun Yu, Geoffrey I. Webb, and Jiangning Song. "PROST: AlphaFold2-aware sequence-based predictor to estimate protein stability changes upon missense mutations." Journal of Chemical Information and Modeling 62, no. 17 (2022): 4270-4282.

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A tool for predicting the effects of missense mutations on protein stability changes upon missense mutation using protein sequence only. PROST uses colab AlhpaFold2 for the prediction of pdb struture from FASTA sequence.

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