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        "author": "Esther Vendrell",
        "author_email": "esvemira@hotmail.com",
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            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python :: 3"
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        "description": "# Projest\n\nMade by [Esther Vendrell]\n\nLast updated: July 2019\n\n## Introduction\nProjest is my first program. Consist in introduce the name of a protein and obtain information about that as the most similar protein, the differents models it has or the atoms. All the information you can obtain comes from: https://www.rcsb.org/\n\n## Description\nThe main file is protein.py. In spite of main functions are in extract_atoms_information.py - load protein pdb or load protein fasta - the rest of functions are in protein. \nIn this way we will have functions directly linked to protein like:\nProtein(\"2ki5\").get_similar_protein() -> We will obtain fasta file, pdb file, all the information of protein in mongodb (in the init) and the similar protein of each chain. \n\n## Functions\nThere are 7 functions:\n  - get_sequence_aminoacids: in Protein. Returns a list with chain and the sequence of aminoacids (1 letter). From fasta file.\n  - get_chain_list: in Protein. Return a list with the differents chains there are.\n  - get_aminoacid_list: in Protein. Return a list with the differents aminoacids (3 letters) with the differents sequence numbers classified in chains\n  - get_similar_protein: in Protein. Return a dictionary with differents chains and the most similar protein of that chain of the protein.\n  - general_dictionary: in Protein. Any return. It inserts in mongodb all the information classified. It is not callable.\n  - load_protein_pdb: in extract_atoms_information. Returns the pdb file of the protein. Download the pdb file of the protein in the same directory of the files. \n  - load_protein_fasta: in extract_atoms_information. Returns the fasta file of the protein.Download the fasta file of the protein in the same directory of the files. \n\n## MongoDB\nI could have used an other structure to upload all the information to MongoDB:\nProtein - Model - residue_sequence_number - atom_number - information atom. \n\nClass Model:\n    def __init__(self, model_identifier, model_dict):\n        self.model_identifier = model_identifier\n        self.chain_list = []\n        for chain_identifier, value in model_dict:\n            self.chain_list.append(Chain(chain_identifier))\n\nIn this way we obtain information from mongodb. This would be easier but I haven't had enough time since I have realized.\n\nThe structure of my project is:\nPortein - Model - residue_name - residue_sequence_number - element_name - atom_name - information atom. \n\nThis is more easy to understand the information we are getting and classified it. This is why I have used this structure. Visually is better but for obtaining this information internally it is worse.\n\n\n\n",
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        "summary": "A small example package",
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