code: def load(cls, path, name):
          
  
          filepath = aux.joinpath(path, name + '.proteindb')
          with zipfile.ZipFile(filepath, 'r', allowZip64=True) as containerZip:
              #Convert the zipfile data into a str object, necessary since
              #containerZip.read() returns a bytes object.
              proteinsString = io.TextIOWrapper(containerZip.open('proteins'),
                                                encoding='utf-8'
                                                ).read()
              peptidesString = io.TextIOWrapper(containerZip.open('peptides'),
                                                encoding='utf-8'
                                                ).read()
              infoString = io.TextIOWrapper(containerZip.open('info'),
                                            encoding='utf-8'
                                            ).read()
          newInstance = cls()
          newInstance.proteins = json.loads(proteinsString,
                                            object_hook=ProteinSequence.jsonHook)
          newInstance.peptides = json.loads(peptidesString,
                                            object_hook=PeptideSequence.jsonHook)
          newInstance.info.update(json.loads(infoString))
          return newInstance
docs: Imports the specified ``proteindb`` file from the hard disk.
  
          :param path: filedirectory of the ``proteindb`` file
          :param name: filename without the file extension ".proteindb"
  
          .. note:: this generates rather large files, which actually take longer
              to import than to newly generate. Maybe saving / loading should be
              limited to the protein database whitout in silico digestion
              information.