Metadata-Version: 2.1
Name: projest
Version: 0.0.5
Summary: A small example package
Home-page: https://github.com/sthhher/projest
Author: Esther Vendrell
Author-email: esvemira@hotmail.com
License: UNKNOWN
Description: # Projest
        
        Made by [Esther Vendrell]
        
        Last updated: July 2019
        
        ## Introduction
        Projest 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/
        
        ## Description
        The 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. 
        In this way we will have functions directly linked to protein like:
        Protein("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. 
        
        ## Functions
        There are 7 functions:
          - get_sequence_aminoacids: in Protein. Returns a list with chain and the sequence of aminoacids (1 letter). From fasta file.
          - get_chain_list: in Protein. Return a list with the differents chains there are.
          - get_aminoacid_list: in Protein. Return a list with the differents aminoacids (3 letters) with the differents sequence numbers classified in chains
          - get_similar_protein: in Protein. Return a dictionary with differents chains and the most similar protein of that chain of the protein.
          - general_dictionary: in Protein. Any return. It inserts in mongodb all the information classified. It is not callable.
          - 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. 
          - 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. 
        
        ## MongoDB
        I could have used an other structure to upload all the information to MongoDB:
        Protein - Model - residue_sequence_number - atom_number - information atom. 
        
        Class Model:
            def __init__(self, model_identifier, model_dict):
                self.model_identifier = model_identifier
                self.chain_list = []
                for chain_identifier, value in model_dict:
                    self.chain_list.append(Chain(chain_identifier))
        
        In this way we obtain information from mongodb. This would be easier but I haven't had enough time since I have realized.
        
        The structure of my project is:
        Portein - Model - residue_name - residue_sequence_number - element_name - atom_name - information atom. 
        
        This 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.
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
