Features implemented in the code-base are defined in deeprank2.feature subpackage.

Custom features

Users can add custom features by cloning the repository, creating a new module and placing it in deeprank2.feature subpackage. The custom features can then be used by installing the package in editable mode (see here for more details). We strongly recommend submitting a pull request (PR) to merge the new feature into the official repository.

One requirement for any feature module is to implement an add_features function, as shown below. This will be used in deeprank2.models.query to add the features to the nodes or edges of the graph.

from deeprank2.molstruct.residue import SingleResidueVariant
from deeprank2.utils.graph import Graph

def add_features(
    pdb_path: str,
    graph: Graph,
    single_amino_acid_variant: SingleResidueVariant | None = None

Additionally, the nomenclature of the custom feature should be added in deeprank2.domain.edgestorage or deeprank2.domain.nodestorage, depending on which type of feature it is.

As an example, this is the implementation of the node feature res_type, which represents the one-hot encoding of the amino acid residue and is defined in deeprank2.features.components module:

from deeprank2.domain import nodestorage as Nfeat
from deeprank2.molstruct.atom import Atom
from deeprank2.molstruct.residue import Residue, SingleResidueVariant
from deeprank2.utils.graph import Graph

def add_features(
    pdb_path: str, graph: Graph,
    single_amino_acid_variant: Optional[SingleResidueVariant] = None

    for node in graph.nodes:
        if isinstance(node.id, Residue):
            residue = node.id
        elif isinstance(node.id, Atom):
            atom = node.id
            residue = atom.residue
            raise TypeError(f"Unexpected node type: {type(node.id)}")

        node.features[Nfeat.RESTYPE] = residue.amino_acid.onehot

RESTYPE is the name of the variable assigned to the feature res_type in deeprank2.domain.nodestorage. In order to use the feature from DeepRank2 API, its module needs to be imported and specified during the queries processing:

from deeprank2.features import components

feature_modules = [components]

# Save data into 3D-graphs only
hdf5_paths = queries.process(
    feature_modules = feature_modules)

Then, the feature res_type can be used from the DeepRank2 datasets API:

from deeprank2.dataset import GraphDataset

node_features = ["res_type"]

dataset = GraphDataset(
    hdf5_path = hdf5_paths,
    node_features = node_features

The following is a brief description of the features already implemented in the code-base, for each features’ module.

Default node features

For atomic graphs, when features relate to residues then all atoms of one residue receive the feature value for that residue.

Core properties of atoms and residues: deeprank2.features.components

These features relate to the chemical components (atoms and amino acid residues) of which the graph is composed. Detailed information and descrepancies between sources are described can be found in deeprank2.domain.aminoacidlist.py.

Atom properties:

These features are only used in atomic graphs.

  • atom_type: One-hot encoding of the atomic element. Options are: C, O, N, S, P, H.

  • atom_charge: Atomic charge in Coulomb (float). Taken from deeprank2.domain.forcefield.patch.top.

  • pdb_occupancy: Proportion of structures where the atom was detected at this position (float). In some cases a single atom was detected at different positions, in which case separate structures exist whose occupancies sum to 1. Only the highest occupancy atom is used by deeprank2.

Residue properties:

  • res_type: One-hot encoding of the amino acid residue (size 20).

  • polarity: One-hot encoding of the polarity of the amino acid (options: NONPOLAR, POLAR, NEGATIVE, POSITIVE). Note that sources vary on the polarity for few of the amino acids; see detailed information in deeprank2.domain.aminoacidlist.py.

  • res_size: The number of non-hydrogen atoms in the side chain (int).

  • res_mass: The (average) residue mass in Da (float).

  • res_charge: The charge of the residue (in fully protonated state) in Coulomb (int). Charge is calculated from summing all atoms in the residue, which results in a charge of 0 for all polar and nonpolar residues, +1 for positive residues and -1 for negative residues.

  • res_pI: The isolectric point, i.e. the pH at which the molecule has no net electric charge (float).

  • hb_donors, hb_acceptors: The number of hydrogen bond donor/acceptor atoms in the residue (int). Hydrogen bonds are noncovalent intermolecular interactions formed between an hydrogen atom (partially positively charged) bound to a small, highly electronegative atom (O, N, F) with an unshared electron pair.

Conservation features: deeprank2.features.conservation

These features relate to the conservation state of individual residues.

  • pssm: Position-specific scoring matrix (also known as position weight matrix, PWM) values relative to the residue, is a score of the conservation of the amino acid along all 20 amino acids.

  • info_content: Information content is the difference between the given PSSM for an amino acid and a uniform distribution (float).

  • conservation (only used in SingleResidueVariant queries): Conservation of the wild type amino acid (float). More details required.

  • diff_conservation (only used in SingleResidueVariant queries): Subtraction of wildtype conservation from the variant conservation (float).

Protein context features:

Surface exposure: deeprank2.features.exposure

These features relate to the exposure of residues to the surface, and are computed using biopython. Note that these features can only be calculated per residue and not per atom.

Surface accessibility: deeprank2.features.surfacearea

These features relate to the surface area of the residue, and are computed using freesasa. Note that these features can only be calculated per residue and not per atom.

  • sasa: Solvent-Accessible Surface Area is the surface area (in Å^2) of a biomolecule that is accessible to the solvent (float).

  • bsa: Buried Surface Area is the surface area (in Å^2) that is buried away from the solvent when two or more proteins or subunits associate to form a complex, i.e. it measures the size of the complex interface (float).

Secondary structure: deeprank2.features.secondary_structure

  • sec_struct: One-hot encoding of the DSSP assigned secondary structure of the amino acid, using the three major classes (HELIX, STRAND, COIL). Calculated using DSSP4.

Inter-residue contacts (IRCs): deeprank2.features.irc

These features are only calculated for ProteinProteinInterface queries.

  • irc_total: The number of residues on the other chain that are within a cutoff distance of 5.5 Å (int).

  • irc_nonpolar_nonpolar, irc_nonpolar_polar, irc_nonpolar_negative, irc_nonpolar_positive, irc_polar_polar, irc_polar_negative, irc_polar_positive, irc_negative_negative, irc_positive_positive, irc_negative_positive: As above, but for specific residue polarity pairings.

Default edge features

Contact features: deeprank2.features.contact

These features relate to relationships between individual nodes. For atomic graphs, when features relate to residues then all atoms of one residue receive the feature value for that residue.


  • distance: Interatomic distance between atoms in Å, computed from the xyz atomic coordinates taken from the .pdb file (float). For residue graphs, the the minimum distance between any atom of each residues is used.


These features relate to the structural relationship between nodes.

  • same_chain: Boolean indicating whether the edge connects nodes belonging to the same chain (1) or separate chains (0).

  • same_res: Boolean indicating whether atoms belong to the same residue (1) or separate residues (0). Only used in atomic graphs.

  • covalent: Boolean indicating whether nodes are covalently bound (1) or not (0). Note that covalency is not directly assessed, but any edge with a maximum distance of 2.1 Å is considered covalent.

Nonbond energies:

These features measure nonbond energy potentials between nodes, and are calculated using OPLS forcefield. For residue graphs, the pairwise sum of potentials for all atoms from each residue is used. Note that no distance cutoff is used and the radius of influence is assumed to be infinite, although the potentials tends to 0 at large distance. Also edges are only assigned within a given cutoff radius when graphs are created.

Nonbond energies are set to 0 for any atom pairs (on the same chain) that are within a cutoff radius of 3.6 Å, as these are assumed to be covalent neighbors or linked by no more than 2 covalent bonds (i.e. 1-3 pairs).

  • electrostatic: Electrostatic potential (also known as Coulomb potential) between two nodes, calculated using interatomic distances and charges of each atom (float).

  • vanderwaals: Van der Waals potential (also known as Lennard-Jones potential) between two nodes, calculated using interatomic distance/s and a list of atoms with vanderwaals parameters (deeprank2.domain.forcefield.protein-allhdg5-4_new, float). Atom pairs within a cutoff radius of 4.2 Å (but above 3.6 Å) are assumed to be separated by separated by exactly 2 covalent bonds (i.e. 1-4 pairs) and use a set of lower energy parameters.

Charge and vanderwaals parameters are set to 0 for those atoms that are unknown to the OPLS forcefield, treating such cases as missing values. If this happens for many of the atoms in the PDB file/s provided, depending on the specific dataset it may be worth it to drop the features affected, i.e., electrostatic, vanderwaals, and atom_charge.

  • It may be useful to generate histograms of the processed data to further investigate the distribution of these features’ values before deciding whether to drop them. Refer to the data_generation_xxx.ipynb tutorial files for comprehensive instructions on transforming the data into a Pandas dataframe and generating histograms of the features.