AtomPacker 0.3.0

A python package for packing nanoclusters into supramolecular cages

AtomPacker

A Python package for packing nanoclusters into supramolecular cages.

See also:

• Documentation
• GitHub repository
• Issues

Requirements

• ASE
• biopython
• MDAnalysis
• numpy
• plotly
• pyKVFinder

Installation

To install the latest release on PyPI, run:

pip install AtomPacker

Or, to install the development version, run:

pip install git+https://github.com/cnpem/AtomPacker.git

Usage

Packing nanoparticle atoms, based on ASE nanocluster, and filter atoms inside a target cavity.

>>> from AtomPacker import Cage
# 1: Load structure from file
>>> cage = Cage()
>>> cage.load("tests/data/ZOCXOH.pdb")
# Uncomment to preview the cage structure.
>>> # cage.preview()
# 2: Detect cavity
>>> cage.detect_cavity(step=0.25, probe_in=1.4, probe_out=10.0, removal_distance=1.0, volume_cutoff=5.0)
# Uncomment to preview the cavity structure for detection quality control.
>>> # cage.cavity.preview()
# Show volume
>>> print(f"Cavity volume: {cage.cavity.volume} A^3")
# Uncomment to save the cavity structure.
>>> # cage.cavity.save("tests/cavity.pdb")
# 3: Pack nanocluster into the cavity
>>> cage.pack(atom_type="Au", lattice_type="fcc", a=None, b=None, c=None)
# Uncomment to preview the cluster structure for quality control.
>>> # cage.cavity.preview()
# Uncomment to save the cluster structure.
>>> # cage.cluster.save("tests/cluster.pdb")
# Uncomment to preview the cage, cavity and cluster structure.
>>> # cage.preview(show_cavity=True, show_cluster=True)
# Show summary
>>> print(cage.cluster.summary)

Architecture

The package is organized as follows:

classDiagram
direction LR
Cage "1" o-- "1" Cavity : has
Cage "1" o-- "1..*" Cluster : fits in
Cavity "1" <|-- "1..*" Cluster : needs
namespace AtomPacker {
class Cage {
+ numpy.ndarray atomic
+ Cavity cavity
+ numpy.ndarray centroid
+ Cluster cluster
+ numpy.ndarray coordinates
+ MDAnalysis.Universe universe
+ detect_cavity(float step, float probe_in, float probe_out, float removal_distance, float volume_cutoff, str surface, int nthreads, bool verbose, Dict~str,Any~ **kwargs) Cavity
+ load(filename) MDAnalysis.Universe
+ pack(str lattice_type, str atom_type, float atom_radius, float a, float b, float c) ase.cluster.Cluster
+ preview(bool show_cavity, bool show_cluster, str renderer, Dict~str,Any~ **kwargs) void
# _build_cluster(str atom_type, str lattice_type, Tuple~float~ lattice_constants, numpy.ndarray center) ase.cluster.Cluster
# _filter_clashing_atoms(ase.cluster.Cluster cluster, float clashing_tolerance) ase.cluster.Cluster
# _filter_outside_cavity(ase.cluster.Cluster cluster) ase.cluster.Cluster
# _get_cluster_layers(str atom_type, float factor) numpy.ndarray            
}
class Cavity {
+ numpy.ndarray coordinates
+ numpy.ndarray grid
+ Universe universe
+ numpy.ndarray volume
# float step
# float probe_in
# float probe_out
# float removal_distance
# numpy.ndarray vertices
# float volume_cutoff
# str surface
+ preview(str renderer, float opacity, Dict~str,Any~ **kwargs) void
+ select_cavity(List~int~ indexes) void
+ save(str filename) void
# _get_universe() Universe
}
class Cluster {
+ str atom_type
+ numpy.ndarray coordinates
+ str lattice_type
+ Tuple~float~ lattice_constants
+ int number_of_atoms
+ int maximum_number_of_atoms
+ pandas.DataFrame summary
+ Universe universe
+ numpy.ndarray volume
# Cavity cavity
# ase.cluster.Cluster cluster
+ diameter(str method) float
+ preview(str renderer, float opacity, Dict~str,Any~ **kwargs) void
+ save(str filename) void
# _get_distances() numpy.ndarray
# _get_lattice_constants() Tuple~float~
# _get_radii() float
# _get_universe() Universe  
}
}

Citing

If you find AtomPacker useful for you, please cite the following references:

• Guerra, J. V. S., Ribeiro-Filho, H. V., Jara, G. E., Bortot, L. O., Pereira, J. G. C., & Lopes-de-Oliveira, P. S. (2021). pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science. BMC bioinformatics, 22(1), 607. https://doi.org/10.1186/s12859-021-04519-4.

• (manuscript in preparation) Guerra, J. V. S., Jara, G. E., Lopes-de-Oliveira, P. S. & Szalóki, G. (2024) Modellization of confined nanoparticles.

License

The software is licensed under the terms of the GNU General Public License version 3 (GPL3) and is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.