Python NeighborSearch.search_all方法代码示例

# 需要导入模块: from Bio.PDB import NeighborSearch [as 别名]
# 或者: from Bio.PDB.NeighborSearch import search_all [as 别名]
def calculate_ic(structure, d_cutoff=5.5, selection=None):
    """
    Calculates intermolecular contacts in a parsed structure object.
    """
    atom_list = list(structure.get_atoms())
    ns = NeighborSearch(atom_list)
    all_list = ns.search_all(radius=d_cutoff, level='R')

    if selection:
        _sd = selection_dict
        ic_list = [c for c in all_list if (c[0].parent.id in _sd and c[1].parent.id in _sd)
                    and (_sd[c[0].parent.id] != _sd[c[1].parent.id]) ]
    else:
        ic_list = [c for c in all_list if c[0].parent.id != c[1].parent.id]

    if not ic_list:
        raise ValueError('No contacts found for selection')

    return ic_list

# 需要导入模块: from Bio.PDB import NeighborSearch [as 别名]
# 或者: from Bio.PDB.NeighborSearch import search_all [as 别名]
SCALAR_EXPRESSION %s_exp = exp(%s_s*(%s_currentenergy-%s_o))
SCALAR_EXPRESSION %s_k = %s
SCALAR_EXPRESSION %s_sig = (1-%s_k)+(%s_k/(1+%s_exp))

""") %(structure_id, structurefilename, correspondencefilename, resfilename, structure_id, startenergy, structure_id, structure_id, structure_id, s_value, structure_id, structure_id, structure_id, structure_id, structure_id, k_value, structure_id, structure_id, structure_id, structure_id)
		fitnessfile.write(outstring)

		fitnessstring = fitnessstring + str("*%s_sig") % (structure_id)
		

		nucleosome = structure[0]

		atom_list = Selection.unfold_entities(nucleosome, 'A') # A for atoms
		neighbor_search = NeighborSearch(atom_list)

		contacts_list = neighbor_search.search_all(radius, level = 'R')
		
		repack_residues = []
		for contact in contacts_list:
			res1 = contact[0]
			res2 = contact[1]
			res1id = int(res1.get_id()[1])
			chain1 = res1.get_parent()
			chain1id = chain1.get_id()
			res2id = int(res2.get_id()[1])
			chain2 = res2.get_parent()
			chain2id = chain2.get_id()
			
			res1_in_patch = False
			res2_in_patch = False
			

# 需要导入模块: from Bio.PDB import NeighborSearch [as 别名]
# 或者: from Bio.PDB.NeighborSearch import search_all [as 别名]
    def _build_interface(self, model, id, threshold, rsa_calculation, rsa_threshold, include_waters=False, *chains):
        """
        Return the interface of a model
        """

        self.threshold=threshold

        # Recover chain list from initial unpacking
        chain_list = self.chain_list

        # Unfold atom list
        atom_list = []
        for c in model:
            if c.id in chain_list:
                atom_list.extend(Selection.unfold_entities(c,'A'))

        # Using of NeighborSearch class in order to get the list of all residues at least than
        # the threshold distance of each others
        ns=NeighborSearch(atom_list)
        pairs=ns.search_all(threshold, 'R')

        if not pairs:
            raise ValueError("No atoms found in the interface")        

        # Selection of residues pairs
        # 1. Exclude water contacts
        # 2. Filter same-chain contacts
        # 3. Filter user-defined chain pairs

        uniq_pairs=[]

        for pair in pairs:
             
            pair_resnames = (pair[0].resname, pair[1].resname)
            pair_chains = (pair[0].parent.id, pair[1].parent.id)

            if (not include_waters and 'HOH' in pair_resnames) or (pair_chains[0] == pair_chains[1]):
                continue

            if not (chains and not (pair_chains in chains)):
                uniq_pairs.append(pair)

        # Build the Interface
        # 1. Iterate over the pair list
        # 2. Add residues.

        for resA, resB in uniq_pairs:
            if resA not in self.interface:
                self._add_residue(resA)
            if resB not in self.interface:
                self._add_residue(resB)
                
        # Accessible surface area calculated for each residue
        # if naccess setup on user computer and rsa_calculation
        # argument is TRUE
        if rsa_calculation and os.system('which naccess') == 0:
            rsa_pairs=self._rsa_calculation(model, chain_list, rsa_threshold)
            
        for res in rsa_pairs:
            if res not in self.interface:
                self._add_residue(res)
        self._secondary_structure(model)
        #interface=uniq_pairs
        self.interface.uniq_pairs=uniq_pairs

# 需要导入模块: from Bio.PDB import NeighborSearch [as 别名]
# 或者: from Bio.PDB.NeighborSearch import search_all [as 别名]
    # CONSTRUCT KDTREE
    neighborsearch = NeighborSearch(structure_atoms)

    logging.info('Constructured NeighborSearch.')

    # GET INTERACTIONS
    logging.info('Calculating interactions...')
    for interaction_level in 'ARC':

        if interaction_level in OUTPUTS:

            logging.info('Calculating interactions for {}s...'.format(
                LEVEL_MAP[interaction_level]))

            pairs = neighborsearch.search_all(INTERACTION_THRESHOLD,
                                              level=interaction_level)

            logging.info('Search complete for {}s.'.format(
                LEVEL_MAP[interaction_level]))

            logging.info('Organising interactions for {}s...'.format(
                LEVEL_MAP[interaction_level]))

            interactions = {}

            for entities in pairs:

                entity1, entity2 = entities

                # NO SELFIES
                if (entity1 is entity2) or (entity1 == entity2):