Bio Files: Read and write common biology file formats

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This Rust and Python library contains functionality to load and save data in common biology file formats. It operates on data structures that are specific to each file format; you will need to convert to and from the structures used by your application. The API docs, and examples below are sufficient to get started.

Note: Install the pip version with pip install biology-files due to a name conflict.

Supported formats:

• mmCIF (Protein atom, residue, chain, and related data like secondary structure)
• mmCIF (structure factors / 2fo-fc: Electron density data, raw)
• Mol2 (Small molecules, e.g. ligands)
• SDF (Small molecules, e.g. ligands)
• PDBQT (Small molecules, e.g. ligands. Includes docking-specific fields.)
• Map (Electron density, e.g. from crystallography, Cryo EM. Processed using Fourier transforms)
• MTZ: If Gemmi is installed. (Electron density)
• AB1 (Sequence tracing)
• DAT (Amber force field data for small molecules)
• FRCMOD (Amber force field patch data for small molecules)
• Amber .lib files, e.g. with charge data for amino acids and proteins.
• GRO (Gromacs molecules)
• TOP (Gromacs topology) - WIP
• ORCA Input and output files (quantum chemistry; HF, DFT etc)
• XYZ (Minimal atom coordinate format)
• DCD (MD trajectories)
• XTC: If MDTraj is installed. (MD trajectories)

Planned:

• DNA (Exists in PlasCAD; needs to be decoupled)

Generic data types

This library includes a number of relatively generic data types which are returned by various load functions, and required to save data. These may be used in your application directly, or converted into a more specific format. Examples:

• AtomGeneric
• BondGeneric
• ResidueGeneric
• BondType
• LipidStandard

For Genbank, we recommend gb-io. We do not plan to support this format, due to this high quality library.

Each module represents a file format, and most have dedicated structs dedicated to operating on that format.

It operates using structs with public fields, which you can explore using the API docs, or your IDE. These structs generally include these three methods: new(), save() and load(). new() accepts &str for text files, and a R: Read + Seek for binary. save() and load() accept &Path. The Force Field formats use load_dat, save_frcmod instead, as they use the same structs for both formats.

Serial numbers

Serial numbers for atoms, residues, secondary structure, and chains are generally pulled directly from atom data files (mmCIF, Mol2 etc). These lists reference atoms, or residues, stored as Vec<u32>, with the u32 being the serial number. In your application, you may wish to adapt these generic types to custom ones that use index lookups instead of serial numbers. We use SNs here because they're more robust, and match the input files directly; add optimizations downstream, like converting to indices, and/or applying back-references. (e.g. the index of the residue an atom's in, in your derived Atom struct).

Orca interface

This library provides an interface to building Orca inputs, executing commands, and parsing outputs. It uses Rust data structures to contrain input choices into valid ones when possible, and allows you to integrate Orca into Rust programs and libraries. For example, ChemForma uses it to minimize energy on organic molecules, and augment traditional MD technique with quantum mechanics.

ORCA support in this library is limited to Rust only. If you wish to use Orca with Python, use OPI, the official FACCTS library.

Note: Orca support is currently limited to a subset of features. We plan to gradually expand this. If you're looking for specific functionality, please open an Issue or PR on Github.

Can generate and run ORCA commands, and parse the result. Example:

Loading building-block molecules from templates

We can load amino acids, nucleic acids, and lipids from Amber template files. These are standard molecule segments used to build common biological molecules. Example usage:

use dynamics::{LIPID_21_LIB, AMINO_19, RNA_LIB, OL24_LIB};

// Returns io::Result<HashMap<String, TemplateData>>
let lipids = load_templates(LIPID_21_LIB).unwrap();
let dna = load_templates(OL24_LIB).unwrap();
let amino_acids = load_templates(AMINIO19_LIB).unwrap();

Code examples

use std::path::PathBuf;
use bio_files::orca::{OrcaInput, Thermostat, Method, Task, BasisSet};

fn main() {
    let mut orca_inp = OrcaInput {
        task:
        method: Method::PBE0,
        basis_set: BasisSet::Def2Svp,
        atoms,
        ..Default::default()
    };

    orca_inp.keywords = vec![Keyword::D4Dispersion];

    println!("Orca input:\n{}\n", orca_inp.make_inp());
    // Save to disk if desired:
    orca_inp.save(Path::new("atoms.inp"))?;

    println!("Running Orca...");
    let orca_out = orca_inp.run().unwrap();

    println!("Orca OUT:\n{:?}\n", orca_out);

    // To run an ab-initio dynamics sim:
    let dyn = Dynamics {
        timestep: 0.5,
        init_vel: 310.,
        thermostat: Thermostat::Csvr,
        thermostat_temp: 310.,
        thermostat_timecon: 10.,
        traj_out_dir: PathBuf::from_str(
            "traj.xyz").unwrap(),
        steps: 200,
    };

    let orca_inp = OrcaInput {
        task: Task::Dynamics(dyn),
        method: Method::R2SCAN_c3,
        basis_set: BasisSet::Tzvp,
        atoms,
        ..Default::default()
    };

    orca_inp.run().unwrap();
}

Example use

Small molecule save and load, Python.

from biology_files import Sdf

sdf_data = Sdf.load("./molecules/DB03496.sdf")

sdf_data.atoms[0]
#AtomGeneric { serial_number: 1, posit: Vec3 { x: 2.3974, y: 1.1259, z: 2.5289 }, element: Chlorine,
type_in_res: None, force_field_type: None, occupancy: None, partial_charge: None, hetero: true }

sdf_data.atoms[0].posit
# [2.3974, 1.1259, 2.5289]

sdf_data.save("test.sdf")

mol2_data = sdf_data.to_mol2()
mol2_data.save("test.mol2")

# Load molecules from databases using identifiers:
mol = Sdf.load_drugbank("DB00198")
mol = Sdf.load_pubchem(12345)
mol = Sdf.load_pdbe("CPB")
mol = Mol2.load_amber_geostd("CPB")

peptide = MmCif.load_rcsb("8S6P")

# (See the Rust examples and API docs for more functionality; most
# is exposed in Python as well)

Small molecule save and load, Rust.

use bio_files::{Sdf, Mol2};

// ...
let sdf_data = Sdf::load(Path::new("./molecules/DB03496.sdf")) ?;

sdf_data.atoms[0]; // (as above)
sdf_data.atoms[0].posit;  // (as above, but lin_alg::Vec3))

sdf_data.save(Path::new("test.sdf")) ?;

let mol2_data: Mol2 = sdf_data.into();
mol2_data.save(Path::new("test.mol2")) ?;

let xyz_data = Xyz::load(Path::new("./atom_posits.xyz")) ?;


// Loading Force field parameters:
let p = Path::new("gaff2.dat")
let params = ForceFieldParams::load_dat(p) ?;


// Load electron density structure factors data, to be processed with a FFT:
let path = Path::new("8s6p_validation_2fo-fc_map_coef.cif");
let data = CifStructureFactors::new_from_path(path) ?;

// These functions aren't included; an example of turning loaded structure factor data
// into a density map.
let mut fft_planner = FftPlanner::new();
let dm = density_map_from_mmcif( & data, & mut fft_planner) ?;

// Or if you have a Map file:
let p = Path::new("8s6p.map");
let dm = DensityMap::load(path) ?;

// For MTZ files, or 2fo-fc:
let dm = DensityMap::from_sf_or_mtz(path, None) ?;

// Saving a density map:
dm.save(Path::new("8s6p.map")) ?;
// Uses the file extension `.mmcif` or `.mtz` to determine which format to save as.
dm.save_sf_or_mtz(Path::new("8s6p.mtz")) ?;

// Load molecules from databases using identifiers:
let mol = Sdf::load_drugbank("DB00198") ?;
let mol = Sdf::load_pubchem(12345) ?;
let mol = Sdf::load_pdbe("CPB") ?;
let mol = Mol2::load_amber_geostd("CPB") ?;

let peptide = MmCif::load_rcsb("8S6P") ?;

You can use similar syntax for mmCIF protein files.

Amber force fields

Reference the Amber 2025 Reference Manual, section 15 for details on how we parse its files, and how to use the results. In some cases, we change the format from the raw Amber data. For example, we store angles as radians (vice degrees), and σ vice R_min for Van der Waals parameters. Structs and fields are documented with reference manual references.

The Amber forcefield parameter format has fields which each contain a Vec of a certain type of data. (Bond stretching parameters, angle between 3 atoms, torsion/dihedral angles etc.) You may wish to parse these into a format that has faster lookups for your application.

Note that the above examples expect that your application has a struct representing the molecule that has From<Mol2>, and to_mol2(&self) (etc) methods. The details of these depend on the application. For example:

impl From<Sdf> for Molecule {
    fn from(m: Sdf) -> Self {
        // We've implemented `From<AtomGeneric>` and `From<ResidueGeneric>` for our application's `Atom` and
        // `Residue`
        let atoms = m.atoms.iter().map(|a| a.into()).collect();
        let residues = m.residues.iter().map(|r| r.into()).collect();

        Self::new(m.ident, atoms, m.chains.clone(), residues, None, None);
    }
}

A practical example of parsing a molecule from a mmCIF as parsed from bio_files into an application-specific format:

fn load() {
    let cif_data = mmcif::load("./1htm.cif");
    let mol: Molecule = cif_data.try_into().unwrap();
}

impl TryFrom<MmCif> for Molecule {
    type Error = io::Error;

    fn try_from(m: MmCif) -> Result<Self, Self::Error> {
        let mut atoms: Vec<_> = m.atoms.iter().map(|a| a.into()).collect();

        let mut residues = Vec::with_capacity(m.residues.len());
        for res in &m.residues {
            residues.push(Residue::from_generic(res, &atoms)?);
        }

        let mut chains = Vec::with_capacity(m.chains.len());
        for c in &m.chains {
            chains.push(Chain::from_generic(c, &atoms, &residues)?);
        }

        // Now that chains and residues are loaded, update atoms with their back-ref index.
        for atom in &mut atoms {
            for (i, res) in residues.iter().enumerate() {
                if res.atom_sns.contains(&atom.serial_number) {
                    atom.residue = Some(i);
                    break;
                }
            }

            for (i, chain) in chains.iter().enumerate() {
                if chain.atom_sns.contains(&atom.serial_number) {
                    atom.chain = Some(i);
                    break;
                }
            }
        }

        let mut result = Self::new(m.ident.clone(), atoms, chains, residues, None, None);

        result.experimental_method = m.experimental_method.clone();
        result.secondary_structure = m.secondary_structure.clone();

        result.bonds_hydrogen = Vec::new();
        result.adjacency_list = result.build_adjacency_list();

        Ok(result)
    }
}

A protein loading and prep example:

Python:

use biology_files::{Mol2, MmCif, ForceFieldParams, FfParamSet, prepare_peptide, load_prmtop};

mol = Mol2.load("CPB.mol2")
protein = MmCif.load("1c8k.cif")

param_set = FfParamSet.new_amber()
lig_specific = ForceFieldParams.load_frcmod("CPB.frcmod")

# Or, instead of loading atoms and mol-specific params separately:
# mol, lig_specific = load_prmtop("my_mol.prmtop")

# Add Hydrogens, force field type, and partial charge to atoms in the protein; these usually aren't
# included from RSCB PDB. You can also call `populate_hydrogens_dihedrals()`, and
# `populate_peptide_ff_and_q() separately. Add bonds.
protein.atoms, protein.bonds = prepare_peptide(
    protein.atoms,
    protein.bonds,
    protein.residues,
    protein.chains,
    param_set.peptide_ff_q_map,
    7.0,
)

Rust:

use bio_files::{MmCif, Mol2, ForceFieldParams, FfParamSet, prepare_peptide, load_prmtop};
use std::path::Path;

fn load() {
    let param_set = FfParamSet::new_amber().unwrap();

    let mut protein = MmCif::load(Path::new("1c8k.cif")).unwrap();
    let mol = Mol2::load(Path::new("CPB.mol2")).unwrap();
    let mol_specific = ForceFieldParams::load_frcmod(Path::new("CPB.frcmod")).unwrap();

    // Or, instead of loading atoms and mol-specific params separately:
    // let (mol, lig_specific) = load_prmtop("my_mol.prmtop");

    // Or, if you have a small molecule available in Amber Geostd, load it remotely:
    // let data = bio_apis::amber_geostd::load_mol_files("CPB");
    // let mol = Mol2::new(&data.mol2);
    // let mol_specific = ForceFieldParams::from_frcmod(&data.frcmod);

    // Add Hydrogens, force field type, and partial charge to atoms in the protein; these usually aren't
    // included from RSCB PDB. You can also call `populate_hydrogens_dihedrals()`, and
    // `populate_peptide_ff_and_q() separately. Add bonds.
    prepare_peptide(
        &mut protein.atoms,
        &mut protein.bonds,
        &mut protein.residues,
        &mut protein.chains,
        &param_set.peptide_ff_q_map.as_ref().unwrap(),
        7.0,
    )
        .unwrap();
}

MD trajectories example

use bio_files::dcd::DcdTrajectory;

fn main() {
    let path_dcd = Path::new("traj.dcd");
    let path_xtc = Path::new("traj.xtc");

    // Load trajectory files:
    let traj = DcdTrajectory::load(path_dcd).unwrap();
    // Or, if you have MDTraj installed, load XTC files:
    let traj = DcdTrajectory::load_xtc(path_xtc).unwrap();

    for frame in &traj.frames {
        println!("Time: {} unit cell: {:?}", frame.time, frame.unit_cell);
        println!("Positions:");
        for posit in &frame.atom_posits {
            // ...
        }
    }

    // To save:
    traj.save(path_dcd).unwrap();
    traj.save_xtc(path_xtc).unwrap();
}

Note: The Python version is currently missing support for some formats, and not all fields are exposed.

References

• Amber 2025 Reference Manual, section 15