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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def predict_mhci_binding(job, peptfile, allele, peplen, univ_options, mhci_options):
 """ Predict binding for each peptide in `peptfile` to `allele` using the IEDB mhci binding prediction tool. :param toil.fileStore.FileID peptfile: The input peptide fasta :param str allele: Allele to predict binding against :param str peplen: Length of peptides to process :param dict univ_options: Dict of universal options used by almost all tools :param dict mhci_options: Options specific to mhci binding prediction :return: fsID for file containing the predictions :rtype: toil.fileStore.FileID """

    work_dir = os.getcwd()
    input_files = {
        'peptfile.faa': peptfile}
    input_files = get_files_from_filestore(job, input_files, work_dir, docker=True)
    peptides = read_peptide_file(os.path.join(os.getcwd(), 'peptfile.faa'))
    if not peptides:
        return job.fileStore.writeGlobalFile(job.fileStore.getLocalTempFile())
    parameters = [mhci_options['pred'],
                  allele,
                  peplen,
                  input_files['peptfile.faa']]
    with open('/'.join([work_dir, 'predictions.tsv']), 'w') as predfile:
        docker_call(tool='mhci', tool_parameters=parameters, work_dir=work_dir,
                    dockerhub=univ_options['dockerhub'], outfile=predfile, interactive=True,
                    tool_version=mhci_options['version'])
    output_file = job.fileStore.writeGlobalFile(predfile.name)
    job.fileStore.logToMaster('Ran mhci on %s:%s:%s successfully'
                              % (univ_options['patient'], allele, peplen))
    return output_file