API References#

Important

This project is renamed from pdbx2df. Please go to its documentation for historical features.

PDBx Reader#

PDBx/mmCIF format reading.

Reads a PDBx file into a dict of Pandas DataFrame s. The dict keys are read from the mmCIF category names automatically. For each category, the column names of the corresponding DataFrame are read form the category attributes automatically.

For example:

_audit_conform.dict_name       mmcif_pdbx.dic
_audit_conform.dict_version    5.355
_audit_conform.dict_location   http://mmcif.pdb.org/dictionaries/ascii/mmcif_pdbx.dic

In this category, the category name is _audit_conform, so the returned dict key is _audit_conform. The category attributes are dict_name, dict_version, and dict_location, so the returned dict value as a DataFrame has the exact column names.

moldf.read_pdbx.AF2_MODEL = 4#

For AlphaFold structures, the version to use.

moldf.read_pdbx.read_pdbx(pdbx_file: str | PathLike[str] | None = None, pdb_id: str | None = None, save_pdbx_file: bool = True, pdbx_file_dir: str | PathLike | None = None, category_names: list | None = None, convert_dtype: bool = False) → dict[str, DataFrame][source]#

Reads a .cif file’s categories into a dict of Pandas DataFrame s.

Parameters:

  • pdb_id (optional) – PDB/Uniprot ID. Required if pdbx_file is None. Defaults to None.

  • pdbx_file (optional) – file name for a PDBx/mmCIF file. Used over pdb_id. Defaults to None.

  • category_names (optional) – a list of categories in the mmCIF file format. If None, all is used and all categories will be processed. Defaults to None.

  • save_pdbx_file (optional) – whether to save the fetched PDBx file from RCSB to pdbx_file_dir. Defaults to False.

  • pdbx_file_dir (optional) – directory to save fetched PDBx files. If None but save_pdbx_file is True, ‘./PDBx_files’ is used. Defaults to None.

  • convert_dtype (optional) – whether to convert the data types according to the RCSB mmcif specifications. Defaults to False.

Returns:

A dict of Pandas DataFrame s corresponding to required categories.

Raises:

  • ValueError – if none of pdb_id or pdbx_file is provided, or if pdb_id is given but cannot the PDB file cannot be downloaded from RCSB, or the PDB file is corrupted like no end-line symbol, or some content is irregular.

  • FileNotFoundError – if pdbx_file cannot be found.

PDB Reader#

PDB format reading.

Reads a PDB file, including Chimera compatible ones, into a dict of Pandas DataFrame s.

Atom coordinates and sequences are currently supported by the following category names:

_atom_site: ATOM, HETATM, TER, NUMMDL, MODEL, and ENDMDL lines.

_seq_res: SEQRES lines.

moldf.read_pdb.IMPLEMENTED_PDB_CATS = ['_atom_site', '_seq_res', '_chem_comp']#

PDB categories that are currently implemented.

moldf.read_pdb.ATOM_SITE = ('ATOM', 'HETATM', 'TER')#

_atom_site primary lines.

moldf.read_pdb.NMR_MDL = ('NUMMDL', 'MODEL', 'ENDMDL')#

_atom_site additional lines.

moldf.read_pdb.CHEM_COMP = ('HET   ', 'HETNAM', 'FORMUL', 'HETSYN')#

_chem_comp lines.

moldf.read_pdb.AF2_MODEL = 4#

For AlphaFold structures, the version to use.

moldf.read_pdb.read_pdb(pdb_file: str | PathLike[str] | None = None, pdb_id: str | None = None, category_names: list | None = None, save_pdb_file: bool = True, pdb_file_dir: str | PathLike | None = None, allow_chimera: bool = True, need_ter_lines: bool = True) → dict[str, DataFrame][source]#

Reads a .pdb file’s categories into a dict of Pandas DataFrame s.

Parameters:

  • pdb_id (optional) – PDB/Uniprot ID. Required if pdb_file is None. Defaults to None.

  • pdb_file (optional) – file name for a PDB file. Used over pdb_id. Defaults to None.

  • category_names (optional) –

    a list of categories similar to the mmCIF format. If None, _atom_site is used. To be consistent with the PDBx format, the following category names are used to refer to block(s) in a PDB file and only they are supported:

    1. _atom_site: ATOM, HETATM, and TER lines and possible NUMMDL, MODEL, and ENDMDL lines.

    1. _seq_res: SEQRES lines.

    2. _chem_comp: all lines in CHEM_COMP above. However, only

      compounds included in the ``HET `` lines are extracted which usually means the water molecule is not included.

    Defaults to None.

  • save_pdb_file (optional) – whether to save the fetched PDB file from RCSB to pdb_file_dir. Defaults to True.

  • pdb_file_dir (optional) – directory to save fetched PDB files. If None but save_pdb_file is True, ‘./PDB_files’ is used. Defaults to None.

  • allow_chimera (optional) – whether to allow Chimera-formatted PDB files. Defaults to True.

  • need_ter_lines (optional) – whether to read the TER lines into the DataFrame. Defaults to True.

Returns:

A dict of Pandas DataFrame s corresponding to required categories.

Raises:

  • ValueError – if none of pdb_id or pdbx_file is provided, or if pdb_id is given but cannot the PDB file cannot be downloaded from RCSB,

  • NotImplementedError – if category_names not a subset of allowed names.

  • FileNotFoundError – if pdbx_file cannot be found.

moldf.read_pdb._split_atom_line(line: str, nmr_model: int = -1, allow_chimera: bool = True, is_ter_line: bool = False) → tuple[source]#

Internal function to parse a single line belonging to ATOM, HETATM, or TER lines.

Parameters:

  • line (required) – A ATOM, HETATM, or TER line.

  • nmr_model (optional) – the NMR model number for the line; -1 means not an NMR model. Defaults to -1.

  • allow_chimera (optional) – try to parse as a Chimera-formatted PDB file. Defaults to True

  • is_ter_line (optional) – whether the line starts with TER. Defaults to False.

Returns:

parsed values as a tuple.

MOL2 Reader#

Mol2 format reading.

Read a Tripos .mol2 file into a dictionary of pandas DataFrames. Different categories like ‘ATOM’ and ‘BOND’ are read into different DataFrame objects.

moldf.read_mol2.IMPLEMENTED_MOL2_CATS = ['ATOM', 'MOLECULE', 'BOND', 'HEADER']#

MOL2 categories that are currently implemented.

moldf.read_mol2.ATOM_COL_NAMES = ('atom_id', 'atom_name', 'x', 'y', 'z', 'atom_type', 'subst_id', 'subst_name', 'charge', 'status_bit')#

MOL2 ATOM column names.

moldf.read_mol2.BOND_COL_NAMES = ('bond_id', 'origin_atom_id', 'target_atom_id', 'bond_type', 'status_bit')#

MOL2 BOND column names.

moldf.read_mol2.read_mol2(mol2_file: str | PathLike, category_names: list | None = None) → dict[str, DataFrame][source]#

Reads a .mol2 file’s categories into a dict of Pandas DataFrame s.

Parameters:

  • mol2_file (required) – file name for a PDB file.

  • category_names (optional) – a list of categories as to the .mol2 file format. If None, ['ATOM', 'MOLECULE', 'BOND', 'HEADER'] is used. Defaults to None.

Returns:

A dict of category_name as keys(s) and pd.DataFrame as values.

Raises:

NotImplementedError – if category_names not a subset of ['ATOM', 'MOLECULE', 'BOND', 'HEADER']

moldf.read_mol2._get_header_df(header_lines: list[tuple]) → DataFrame[source]#

Turns the HEADER lines into a Pandas DataFrame. The HEADER lines are those starting with one or multiple # symbols.

Parameters:

header_lines (required) – a list of tuples corresponding to each line’s content. Tuples are generate by splitting the lines by :.

Returns:

Pandas DataFrame of The HEADER category

moldf.read_mol2._get_molecule_df(molecule_lines: list[tuple]) → DataFrame[source]#

Turns the MOLECULE lines into a Pandas DataFrame.

Parameters:

molecule_lines (required) – a list of tuples corresponding to each line’s content.

Returns:

Pandas DataFrame of The MOLECULE category

moldf.read_mol2._set_atom_df_dtypes(data_df: DataFrame) → DataFrame[source]#

Sets the data types for the ATOM category.

Parameters:

data_df (required) – original Pandas DataFrame for the ATOM category with all strings.

Returns:

Pandas DataFrame of The ATOM category

moldf.read_mol2._set_bond_df_dtypes(data_df: DataFrame) → DataFrame[source]#

Sets the data types for the BOND category

Parameters:

data_df (required) – original Pandas DataFrame for the BOND category with all strings.

Returns:

Pandas DataFrame of The BOND category

JCSV Reader#

JCSV format reading.

Reads a JCSV file into a dict of Pandas DataFrame s. It is not limited to any molecular format.

moldf.read_jcsv.read_jcsv(jcsv_file: str | PathLike, category_names: list | None = None) → dict[str, DataFrame][source]#

Reads a JCSV file by name.

Currently no molecular file repository has JCSV files so we can only read from a file name/path.

Parameters:

  • jcsv_file (required) – JCSV file name/path.

  • category_names (optional) – a list of category names. If None, all categories are read. Defaults to None.

Returns:

a dict of Pandas DataFrames for each category.

Raises:

  • TypeError – if category_names is not a list of strings.

  • ValueError – if any of the category_names has double quotes or if the number of items in any line does not match the number of column names in the same category.

moldf.read_jcsv._read_jcsv_by_line(jcsv_file: str | PathLike, category_names: list | None = None) → dict[str, DataFrame][source]#

Reads JCSV file line by line when the file has no meta data to select blocks.

Parameters:

  • jcsv_file (required) – JCSV file name/path.

  • category_names (optional) – a list of category names. If None, all categories are read. Defaults to None. It is passed by the read_jcsv caller, so it is not sanitized here.

Returns:

a dict of Pandas DataFrames for each category.

Raises:

ValueError – if the number of items in any line does not match the number of column names in the same category.

moldf.read_jcsv._count_n_lines(file_name: str | PathLike)[source]#

Gets the number of lines in a file. From https://stackoverflow.com/a/68385697/10094189

Parameters:

file_name (required) – file name or path.

PDBDataFrame Class#

PDBDataFrame as a subclass of Pandas DataFrame.

Several features are added to make PDB data more accessible and selectable:

1. Properties like sequences, heavy_atoms, backbone, and water are directly accessed by . operation.

2. Atom selection by using methods whose names are just the column names plus s (plural form). For example, selecting atoms by names is simply df.atom_names([names]) where atom_name is the column name and atom_names is the selection function. Each selection returns a PDBDataFrame object as well, which means we can chain selections one by one like df.atom_names([names]).residue_numbers([numbers]).

  1. Distance matrix as a @property and @classmethod.

moldf.pdb_dataframe.RESIDUE_CODES = {'ALA': 'A', 'ARG': 'R', 'ASH': 'D', 'ASN': 'N', 'ASP': 'D', 'CYM': 'C', 'CYS': 'C', 'CYX': 'C', 'GLH': 'E', 'GLN': 'Q', 'GLU': 'E', 'GLY': 'G', 'HID': 'H', 'HIE': 'H', 'HIP': 'H', 'HIS': 'H', 'ILE': 'I', 'LEU': 'L', 'LYN': 'K', 'LYS': 'K', 'MET': 'M', 'PHE': 'F', 'PRO': 'P', 'PYL': 'O', 'SEC': 'U', 'SER': 'S', 'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V'}#

dict[str, str], turn 3-, 2-, and 1-letter residue codes to 1-letter codes.

moldf.pdb_dataframe.PDBX_COLS = {'alt_loc': 'label_alt_id', 'atom_name': 'label_atom_id', 'atom_number': 'id', 'b_factor': 'B_iso_or_equiv', 'chain_id': 'label_asym_id', 'charge': 'pdbx_formal_charge', 'element_symbol': 'type_symbol', 'insertion': 'pdbx_PDB_ins_code', 'nmr_model': 'pdbx_PDB_model_num', 'occupancy': 'occupancy', 'record_name': 'group_PDB', 'residue_name': 'label_comp_id', 'residue_number': 'label_seq_id', 'segment_id': 'label_entity_id', 'x_coord': 'Cartn_x', 'y_coord': 'Cartn_y', 'z_coord': 'Cartn_z'}#

dict[str, str], PDB and mmCIF column name dictionary.

class moldf.pdb_dataframe.PDBDataFrame(*args, pdb_format: str | None = None, use_squared_distance: bool = True, use_square_form: bool = False, **kwargs)[source]#

Bases: DataFrame

Pandas DataFrame with extended attributes and methods for PDB data.

It enables Pythonic atom selection methods and convenient . accessing to common PDB structure properties.

Parameters:

  • *args – all pd.DataFrame positional arguments. For example, the _atom_site dataframe returned by reading a PDB file.

  • pdb_format (optional) – PDB format in the underlying provided data. If None, PDB is assumed. Defaults to None.

  • use_squared_distance (optional) – whether to use squared distance when calculating distance matrix. Defaults to True.

  • use_square_form (optional) – whether to use a square matrix for the distance matrix. Defaults to False.

  • **kwargs – all pd.DataFrame acceptable keyword arguments.

Returns:

A PDBDataFrame instance.

Example

>>> from moldf import read_pdb, PDBDataFrame
>>> pdb = read_pdb(pdb_id='1vii')
>>> pdb_df = pdb['_atom_site']
>>> pdb_df = PDBDataFrame(pdb_df)

Warning

This subclass uses a custom __hash__ function for caching some calculations. And thus a custom __eq__ function is also implemented. For other typical DataFrame operations, use those .all(), .any(), .bool() functions to do comparison.

_metadata: list[str] = ['_use_squared_distance', '_use_square_form', '_is_chimera', '_RESIDUE_CODES', '_ELEMENT_MASSES', '_pdb_format']#
property _constructor#

Used when a manipulation result has the same dimensions as the original.

_pdbx_to_pdb(keep_original: bool = False)[source]#

Converts PDBx ‘_atom_site’ DataFrame to PDB format.

Parameters:

keep_original (optional) – whether to keep the original columns in the PDBx ‘_atom_site’ DataFrame. Defaults to False.

property pdb_format: str#

The format of the current PDBDataFrame.

property RESIDUE_CODES: dict[str, str]#

A dict of residue_name as keys and residue_code as values, where residue_code is a 1-character code used in sequences. Settable.

property ELEMENT_MASSES: dict[str, float]#

A dict of element_symbol as keys and element_mass as values, where element_mass is taken from NIST. Settable.

property is_chimera: bool#

Whether the original read-in PDB was Chimera compatible format. The main effect is the residue_name str width is 4 in the Chimera compatible format instead of 3 as in the standard PDB format. Not settable.

property hash_random_state: int#

The random_state used in the __hash__ function. Settable.

property use_squared_distance: bool#

Whether R or R^2 is used in distance matrix calculations. Using R^2 saves computation time. Settable.

property use_square_form: bool#

Whether the distance matrix will be in a square form. Using square form consumes less memory. Settable.

property atoms: Self#

Gets atoms in the ATOM and HETATM entries. In other words, removing ‘TER’ lines.

Returns:

sub PDBDataFrame.

property coords: Self#

Gets the x_coord, y_coord, and z_coord columns only. Use pdb_df.coords.values to get the underlying Numpy array of the coordinates.

property element_set: set#

Gets the set of element symbols.

property bonds: dict#
Gets the list of bonds. Each bond is represented as a pair of

atom_number values.

Raises:

ValueError – if the list of atom_number is not a set.

get_bonds_by_distance(single_radii_set: str | None = None, need_non_covalent: bool = False, non_covalent_cutoff: float = 4.5) → dict[source]#

Gets all the bonds purely by covalent radii constraints.

Parameters:

  • single_radii_set (optional) – radii sets to use. If None, single_C is used as to Cordero (PMID 18478144). Another option is single_PA which refers to Pyykkö’s studies (PMID 19058281;19856342;15832398, and doi:10.1103/PhysRevB.85.024115). Defaults to None.

  • need_non_covalent (optional) – whether non-covalent ‘bonding’ should be included. Defaults to False.

  • non_covalent_cutoff (optional) – distance cutoff for non-covalent ‘bonding’.

Raises:

ValueError – if the list of atom_number is not unique or if the single_radii_set is not valid.

Returns:

a dictionary of bonds with tuple of atom_number as keys and bond types

as values.

get_bonds_by_template() → dict[source]#

Gets covalent bonds based on residue/ligand templates.

property sequences: dict[str, str]#

Gets the sequences for each chain as a dict of chain_id as key(s) and chain_sequence as value(s).

property chain_list: list#

Gets all chain ids as a list.

property residue_list: list[tuple]#

Gets all residues as a list of tuple (chain_id, residue_name, residue_number).

property backbone: Self#

Gets backbone or N+CA+C+O atoms.

Returns:

sub PDBDataFrame

property side_chain: Self#

Gets side chain or NOT N+CA+C+O atoms.

Returns:

sub PDBDataFrame.

property ca_atoms: Self#

Gets the alpha carbon (CA) atoms.

Returns:

sub PDBDataFrame.

property heavy_atoms: Self#

Gets the heavy or NOT hydrogen atoms.

Returns:

sub PDBDataFrame.

property hetero_atoms: Self#

Gets the hetero (HETATM) atoms.

Returns:

sub PDBDataFrame.

property residues: Self#

Gets the residue (ATOM) atoms.

Returns:

sub PDBDataFrame.

property water: Self#

Gets all water atoms.

Returns:

sub PDBDataFrame.

property n_atoms: int#

Gets the number of atoms.

property n_residues: int#

Gets the number of residues.

property n_chains: int#

Gets the number of chains.

property n_segments: int#

Gets the number of segments.

property n_models: int#

Gets the number of models.

property center_of_geometry: ndarray#

Gets the center of geometry as a (3, ) np.ndarray.

property center_of_mass: ndarray#

Gets the center of mass as a (3, ) np.ndarray.

property radius_of_gyration: float#

Gets the radius of gyration

get_masses() → ndarray[source]#

Gets the masses for all atoms in the current dataframe.

property distance_matrix: ndarray#

Gets the distance matrix.

rmsd(other: Self | ndarray | None = None, align: bool = True, weights: list | None = None, selection: Self | list | None = None) → list | float[source]#

Calculates RMSD 1) among sets of coordinates in one PDBDataFrame with multiple nmr_model s or 2) two sets of coordinates in two PDBDataFrames.

Parameters:

  • other (optional) – the other PDBDataFrame or (N, 3) numpy.ndarray to calculate RMSD against. If None, self should contain at least two sets of coordinates (nmr_model has >= 2 unique values). Defaults to None.

  • align (optional) – whether to align the structures before calculating RMSD. If False, the weights and selection keywords are ignored. Defaults to True.

  • weights (optional) – a list of weights for all the atoms in selection to do structure alignment. If None, all coordinates in the selection or self have the same weights. Defaults to None.

  • selection (optional) – a list of atom_number s in self or a PDBDataFrame after the filtering methods. If None, all coordinates in self are used for structure alignment. Defaults to None.

Returns:

RMSD or a list of RMSD’s.

Raises:

  • ValueError – if dimensionalities mismatch among self, other, weights, and selection if they are not None; or atom_number s in self are not unique.

  • TypeError – if other, weights, and selection have unsupported types.

record_names(names: list[str], invert: bool = False) → Self[source]#

Filter by record_name.

Parameters:

  • names (required) – a list of record_name s.

  • invert (optional) – whether to invert the selection. Defaults to False.

Returns:

sub PDBDataFrame

atom_numbers(numbers: list[int] | int, relation: str | None = None, invert: bool = False) → Self[source]#

Filter by atom_number.

Parameters:

  • numbers (required) – one or a list of atom_number s.

  • relation (optional) – atom_number relationship to numbers. If numbers is an integer, it has to be one of <, <=, =, >=, and >. If None, <= is used. Ignored if a list of integers are provided to numbers. Defaults to None.

  • invert (optional) – whether to invert the selection. Defaults to False.

Returns:

sub PDBDataFrame

atom_names(names: list[str], names_2c: list[str] | None = None, invert: bool = False, suppress_warning: bool = False) → Self[source]#

Filter by atom_name.

Parameters:

  • names (required) – a list of atom_name s whose element_symbols have only one character. Atoms in common residues and ligands should be provide here like C, H, O, N, S, P, F.

  • names_2c (optional) – a list of atom_name s whose element_symbols have two characters like ion (FE) and chloride (CL). Defaults to None.

  • invert (optional) – whether to invert the selection. Defaults to False.

  • suppress_warning – whether to suppress the warning message about possible conflicts between names and names_2c. Defaults to False.

Returns:

sub PDBDataFrame

alt_locs(locs: list[str], invert: bool = False) → Self[source]#

Filter by alt_loc.

Parameters:

  • locs (required) – a list of alt_loc s.

  • invert (optional) – whether to invert the selection. Defaults to False.

Returns:

sub PDBDataFrame

residue_names(names: list[str], invert: bool = False) → Self[source]#

Filter by residue_names.

Parameters:

  • names (required) – a list of residue_name s

  • invert (optional) – whether to invert the selection. Defaults to False.

Returns:

sub PDBDataFrame

chain_ids(ids: list[str], invert: bool = False) → Self[source]#

Filter by chain_id.

Parameters:

  • ids (required) – a list of chain_id s.

  • invert (optional) – whether to invert the selection. Defaults to False.

Returns:

sub PDBDataFrame

residue_numbers(numbers: list[int] | int, relation: str | None = None, invert: bool = False) → Self[source]#

Filter by residue_number.

Parameters:

  • numbers (required) – one or a list of residue_number s.

  • relation (optional) – residue_number relationship to numbers. If numbers is an integer, it has to be one of <, <=, =, >=, and >. If None, ‘<=’ is used. Ignored if a list of integers are provided to numbers. Defaults to None.

  • invert (optional) – whether to invert the selection. Defaults to False.

Returns:

sub PDBDataFrame

insertions(codes: list[str], invert: bool = False) → Self[source]#

Filter by insertion.

Parameters:

  • codes (required) – a list of insertion codes.

  • invert (optional) – whether to invert the selection. Defaults to False.

Returns:

sub PDBDataFrame

x_coords(value: float, relation: str | None = None, invert: bool = False, epsilon: float = 0.01) → Self[source]#

Filter by x_coord.

Parameters:

  • value (required) – value to select x_coord s.

  • relation (optional) – x_coord relationship to value. It has to be one of '<', '<=', '=', '>=', and '>'. If None, '<=' is used. Defaults to None.

  • invert (optional) – whether to invert the selection. Defaults to False.

  • epsilon (optional) – atoms abs(x_coord - value) <= epsilon are selected when invert = False. Defaults to 0.01.

Returns:

sub PDBDataFrame

y_coords(value: float, relation: str | None = None, invert: bool = False, epsilon: float = 0.01) → Self[source]#

Filter by y_coord.

Parameters:

  • value (required) – value to select y_coord s.

  • relation (optional) – y_coord relationship to value. It has to be one of '<', '<=', '=', '>=', and '>'. If None, '<=' is used. Defaults to None.

  • invert (optional) – whether to invert the selection. Defaults to False.

  • epsilon (optional) – atoms abs(y_coord - value) <= epsilon are selected when invert = False. Defaults to 0.01.

Returns:

sub PDBDataFrame

z_coords(value: float, relation: str | None = None, invert: bool = False, epsilon: float = 0.01) → Self[source]#

Filter by z_coord.

Parameters:

  • value (required) – value to select z_coord s.

  • relation (optional) – z_coord relationship to value. It has to be one of '<', '<=', '=', '>=', and '>'. If None, '<=' is used. Defaults to None.

  • invert (optional) – whether to invert the selection. Defaults to False.

  • epsilon (optional) – atoms abs(z_coord - value) <= epsilon are selected when invert = False. Defaults to 0.01.

Returns:

sub PDBDataFrame

occupancies(value: float, relation: str | None = None, invert: bool = False, epsilon: float = 0.01) → Self[source]#

Filter by occupancy.

Parameters:

  • value (required) – value to select occupancy s.

  • relation (optional) – occupancy relationship to value. It has to be one of '<', '<=', '=', '>=', and '>'. If None, '<=' is used. Defaults to None.

  • invert (optional) – whether to invert the selection. Defaults to False.

  • epsilon (optional) – atoms abs(occupancy - value) <= epsilon are selected when invert = False. Defaults to 0.01.

Returns:

sub PDBDataFrame

b_factors(value: float, relation: str | None = None, invert: bool = False, epsilon: float = 0.01) → Self[source]#

Filter by b_factor.

Parameters:

  • value (required) – value to select b_factor s.

  • relation (optional) – b_factor relationship to value. It has to be one of '<', '<=', '=', '>=', and '>'. If None, '<=' is used. Defaults to None.

  • invert (optional) – whether to invert the selection. Defaults to False.

  • epsilon (optional) – atoms abs(b_factor - value) <= epsilon are selected when invert = False. Defaults to 0.01.

Returns:

sub PDBDataFrame

segment_ids(ids: list[str], invert: bool = False) → Self[source]#

Filter by segment_id.

Parameters:

  • ids (required) – a list of segment_id s.

  • invert (optional) – whether to invert the selection. Defaults to False.

Returns:

sub PDBDataFrame

element_symbols(symbols: list[str], invert: bool = False) → Self[source]#

Filter by element_symbol.

Parameters:

  • symbols (required) – a list of element_symbol s.

  • invert (optional) – whether to invert the selection. Defaults to False.

Returns:

sub PDBDataFrame

charges(charges: list[str], invert: bool = False) → Self[source]#

Filter by charge.

Parameters:

  • charges (required) – a list of charge s.

  • invert (optional) – whether to invert the selection. Defaults to False.

Returns:

sub PDBDataFrame

Notes: charge is 2-char string in the PDB specifications.

nmr_models(models: list[int] | int, relation: str | None = None, invert: bool = False) → Self[source]#

Filter by nmr_model.

Parameters:

  • models (required) – one or a list of nmr_model ids.

  • relation (optional) – nmr_model relationship to models. If models is an integer, it has to be one of '<', '<=', '=', '>=', and '>'. If None, '<=' is used. Ignored if a list of integers are provided to models. Defaults to None.

  • invert (optional) – whether to invert the selection. Defaults to False.

Returns:

sub PDBDataFrame

distances(other: ndarray | Self | Iterable, cut_off: float = inf, to: str | None = None, invert: bool = False) → Self[source]#

Filter by distance to a reference point or group of atoms.

Parameters:

  • other – the other group’s coordinate(s).

  • cut_off – the distance cutoff to filter.

  • to –

    if other is a group atoms, using which method to determine whether the atoms meet the cut_off distance. If None, COM or center of mass is used if other is PDBDataFrame, and COG or center of geometry is used if other is np.ndarray or Iterable. The following are allowed:

    com, center of mass, center_of_mass:

    use the center of mass for the other.

    cog, center of geometry, center_of_geometry:

    use the center of geometry for the other.

    all:

    whether all the pair-distances meet the cut_off distance criteria.

    any:

    whether any of the pair-distances meets the cut_off distance criteria.

  • invert – whether to invert the selection. Defaults to False.

Returns:

sub PDBDataFrame

_filter_num_col(value: int | float | list[int], num_col_name: str, relation: str | None = None, invert: bool = False, epsilon: float | int = 0.01, suppress_warning: bool = False) → Self[source]#

Generic function to do filter by a numerical column.

Parameters:

  • value (required) – value(s) to select by the column given by the num_col_name input.

  • num_col_name (required) – one of atom_number, residue_number, x_coord, y_coord, or z_coord, occupancy, b_factor, and nmr_model. Note: the charge column is not numerical by PDB format.

  • relation (optional) – x/y/z_coord relationship to value. It has to be one of '<', '<=', '=', '>=', and '>'. If None, '<=' is used. Ignored if a list of integers are provided to value. Defaults to None.

  • invert (optional) – whether to invert the selection. Defaults to False.

  • epsilon (optional) – atoms abs``(``num_col_value - value) <= epsilon are selected when invert = False and relation = '='. Ignored if a list of integers are provided to value. Defaults to 0.01.

  • suppress_warning (optional) – whether to suppress warnings. Defaults to False.

Returns:

sub PDBDataFrame

Raises:

ValueError – if xyz not in [atom_number, residue_number, x_coord, y_coord, or z_coord, occupancy, b_factor, nmr_model] or relation not in ['<', '<=', '=', '>=', '>'] when selecting on float cols.

classmethod get_chain_list(pdb_df: Self) → list[source]#

Gets the list of chain ids given a PDBDataFrame object.

Parameters:

pdb_df (required) – a PDBDataFrame object.

Returns:

a list of chain ids.

Warning

This method relies on the original and standard numbering of the atoms and

residues in the DataFrame. Therefore, any selecting or sorting of the it can lead to wrong results.

classmethod get_residue_list(pdb_df: Self, include_heteros: bool = False) → list[tuple][source]#

Gets the list of residues given a PDBDataFrame object.

Parameters:

  • pdb_df (required) – a PDBDataFrame object.

  • include_heteros (optional) – whether to include hetero ligands. Defaults to False.

Returns:

a list of residues as (chain_id, residue_name, residue_number).

Warning

This method relies on the original and standard numbering of the atoms and

residues in the DataFrame. Therefore, any selecting or sorting of the it can lead to wrong results.

classmethod get_distance_matrix(pdb_df: Self, other_data: Self | tuple | None = None, use_r2: bool = True, square_form: bool = False) → ndarray[source]#

Calculates the distance matrix given a PDBDataFrame object and (optional) reference data.

Parameters:

  • pdb_df (required) – a PDBDataFrame object.

  • other_data (optional) – the coordinates of to calculate the distances against. Defaults to None.

  • use_r2 (optional) – whether to use r^2 or r for distance matrix. Defaults to True.

  • square_form (optional) – whether to output a square form of the density matrix. If two PDBDataFrame``s are different or ``other_data is not a PDBDataFrame, square_form is ignored. Defaults to False.

Returns:

distance matrix (squared or condensed form)

Raises:

ValueError – if other_data is not of PDBDataFrame|tuple|None type or wrong shape if it is a tuple.

PDBx Writer#

PDBx/mmCIF format writing.

Write a dict of Pandas DataFrame back to a PDBx file.

moldf.write_pdbx.write_pdbx(pdbx: dict[str, DataFrame], file_name: str | PathLike | None = None) → None[source]#

Writes a dict of Pandas DataFrame s into a PDBx file.

Parameters:

  • pdbx (required) – a dict of Pandas DataFrame s to write.

  • file_name (optional) – file name to write a PDBx file. If None, moldf_output.cif will be used as the file name. Defaults to None.

Raises:

TypeError – if pdbx is not a valid dict of DataFrame.

PDB Writer#

PDB format writing.

Write a dict of Pandas DataFrame back to a PDB file.

Currently, only the _atom_site category can be written back.

moldf.write_pdb.IMPLEMENTED_PDB_CATS = ['_atom_site']#

PDB categories that are currently implemented.

moldf.write_pdb.write_pdb(pdb: dict[str, DataFrame], file_name: str | PathLike | None = None, allow_chimera: bool = False) → None[source]#

Write a dict of Pandas DataFrame s into a PDB file.

Parameters:

  • pdb (required) – a dict of Pandas DataFrame s to write.

  • file_name (optional) – file name to write a PDB file. If None, moldf_output.pdb will be used as the file name. Defaults to None.

  • allow_chimera (optional) – whether to allow writing to Chimera-formatted PDB files. Defaults to False.

Raises:

  • TypeError – if pdb is not a valid dict of DataFrame.

  • ValueError – if the pdb contains other than supported categories.

MOL2 Writer#

MOL2 format writing.

Write a dict of Pandas DataFrame back to a MOL2 file.

Currently, only the MOLECULE, ATOM, and BOND categories can be written back.

moldf.write_mol2.IMPLEMENTED_MOL2_CATS = ['MOLECULE', 'ATOM', 'BOND', 'HEADER']#

MOL2 categories that are currently implemented.

moldf.write_mol2.write_mol2(mol2: dict[str, DataFrame], file_name: str | PathLike | None = None) → None[source]#

Write a dict of Pandas DataFrame s into a MOL2 file. See https://is.muni.cz/th/fzk5s/dp_jakub_Vana.pdf p19 for column definitions.

Parameters:

  • mol2 (required) – a dict of Pandas DataFrame s to write.

  • file_name (optional) – file name to write a MOL2 file. If None, moldf_output.mol2 will be used as the file name. Defaults to None.

Raises:

  • TypeError – if mol2 is not a valid dict of DataFrame.

  • ValueError – if the mol2 contains other than supported categories.

JCSV Writer#

Write any dict of Pandas DataFrame to JCSV.

moldf.write_jcsv.write_jcsv(data: dict[str, DataFrame], file_name: str | PathLike | None = None, write_meta: bool = True, **kwargs) → None[source]#

Write a dict of Pandas DataFrame s into a JCSV file. See https://github.com/Ruibin-Liu/JCSV for definitions.

Parameters:

  • data (required) – a dict of Pandas DataFrame s to write.

  • file_name (optional) – file name to write a JCSV file. If None, moldf_output.jcsv will be used as the file name if path_or_buf is not specified in **kwargs. Defaults to None.

  • write_meta (optional) – whether to write meta data into the first category. Currently, only the first line number for each category is recorded. Defaults to True.

  • **kwargs – keyword arguments for pd.DataFrame.to_csv. Invalid ones are ignored. Check https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_csv.html

Raises:

  • TypeError – if data is not a valid dict of DataFrame.

  • ValueError – if " in any of the column names.

Covalent Bond#

Gets covalent bonds by covalent radii cutoff or ligand templates.

moldf.covalent_bond.get_covalent_bond_cutoffs(element_symbols: list | set, single_radii_set: str | None = None) → tuple[dict, dict, dict][source]#

Gets the DataFrame for additive covalent radii of relevant elements.

Parameters:

  • element_symbols (required) – a list or set of element symbols whose covalent radii cutoffs are returned.

  • single_radii_set (optional) – radii sets to use. If None, single_C is used as to Cordero (PMID 18478144). Another option is single_PA which refers to Pyykkö’s studies (PMID 19058281;19856342;15832398, and doi:10.1103/PhysRevB.85.024115). Defaults to None.

Raises:

ValueError – if the single_radii_set is not valid.

Returns:

a tuple of the three dictionaries of the distance cutoffs for the pairs of

the queried element symbols: (single_bonds, double_bonds, triple_bonds).

moldf.covalent_bond.get_residue_template(residue_name: str, parent_name: str | None = None, residue_template_file: str | PathLike | None = None, save_template_file: bool = True, template_file_dir: str | PathLike | None = None) → dict[source]#

Gets the intra- covalent bonds of a residue or ligand.

Parameters:

  • residue_name (required) – residue or ligand name to query.

  • parent_name (optional) – parent name of the residue or ligand. This is useful if the residue_name in the PDB file is used by RCSB for a different residue or ligand. If None, it is the same as residue_name. Defaults to None.

  • residue_template_file (optional) – residue or ligand template file name/path. If None, this function queries RCSB by the parent_name. If it’s not a path, this function looks at the current working directory first and then template_file_dir if it is not None. Defaults to None.

  • save_template_file (optional) – whether to save the downloaded (from RCSB) template file. Defaults to True.

  • template_file_dir (optional) – directory to save fetched template files. If None but save_template_file is True, ‘./template_files’ directory is used. Defaults to None.

Returns:

a dictionary of bonds for the residue. The dictionary key format is

(atom_id_1: str, atom_id_2: str), and the dictionary value format is (bond_order: str, is_aromatic: bool, stereo_flag: str)

Raises:

  • ValueError – if a .cif file for the residue/ligand cannot be downloaded from RCSB for any reason if residue_template_file is None. Check the url provided in the error message to download manually if possible.

  • FileNotFoundError – if residue_template_file cannot be found if not None.

Warning

RuntimeWarning: if the template file contains other residue/ligands.

PDBx Line Splitter#

moldf.split_line(line: str, delimiter: str | None = None) → list[source]#

Splits a string line into tokens separated by delimiter s, assuming all ' and " in the start character or following a delimiter are paired to quote a token.

Parameters:

  • line (required) – line as a string

  • delimiter (optional) – delimiter to split the line. If None, ' ' (one space) is used. Defaults to None.

Returns:

A list of tokens