MultiMM: An OpenMM-based software for whole genome 3D structure reconstruction

MultiMM is an OpenMM model designed for modeling the 3D structure of the whole human genome. Its distinguishing feature is that it is multiscale, meaning it aims to model different levels of chromatin organization, from smaller scales (nucleosomes) to the level of chromosomal territories. The algorithm is both fast and accurate. A key feature enabling its speed is GPU parallelization via OpenMM, along with smart assumptions that assist the optimizer in finding the global minimum. One such fundamental assumption is the use of a Hilbert curve as the initial structure. This helps MultiMM to converge faster because the initial structure is already highly compacted.

After running the MultiMM model, users obtain a genome-wide structure. Users can color different chromosomes or compartments, or they can visualize individual chromosomes separately. MultiMM is simple to use, as it is based on modifying a single configuration file. Here we explain its usage.

Key Features

• OpenMM based.
• User-friendly software available via PyPI.
• Efficient simulation of chromatin interactions using 3D conformations.
• Can simulate the scales of nucleosomes, TADs, compartments, chromosomal territories and interactions with lamina. Scalable simulations across different force fields and resolution levels.
• Compatible with modern GPU-accelerated simulation libraries. CPU acceleration can also be done.

About Operating Systems

MultiMM has been tested primarily on Linux-based operating systems, with successful tests in Ubuntu, Debian, and Red Hat-based distributions. It is also possible to run it on macOS, though without CUDA support, which is helpful for accelerating computations. We do not recommend running MultiMM on Windows systems.

Installation

MultiMM can be easily installed with pip:

pip install MultiMM

PyPI software: https://pypi.org/project/MultiMM/.

Input Data

MultiMM relies on three types of datasets:

• Loop interactions in bedpe format (mandatory).
• Compartmentalization data in bed format (optional).
• ATAC-Seq p-value data in .BigWig format (optional).

For loop interactions, users need to provide a file with interactions between anchor 1 and anchor 2, and their strength. The file must be in .bedpe format, should contain 7 columns, and should not include a header. An example:

chr10	100225000	100230000	chr10	100420000	100425000	95
chr10	100225000	100230000	chr10	101005000	101010000	56
chr10	101190000	101195000	chr10	101370000	101375000	152
chr10	101190000	101200000	chr10	101470000	101480000	181
chr10	101600000	101605000	chr10	101805000	101810000	152

The file may contain interactions for all chromosomes, which MultiMM can automatically model. For single-cell data, users can provide a file with the second and third columns identical (the same for the fifth and sixth columns) and strength set to 1.

For (sub)compartment interactions, the file should be in the format produced by the CALDER software: https://github.com/CSOgroup/CALDER2. Users do not need to run CALDER specifically, but the file format must match. The file should contain at least the first four columns with chromosome, regions, and the subcompartment label. Example:

chr1	700001	900000	A.1.2.2.2.2.2.2	0.875	.	700001	900000	#FF4848
chr1	900001	1400000	A.1.1.1.1.2.1.1.1.1.1	1	.	900001	1400000	#FF0000
chr1	1400001	1850000	A.1.1.1.1.2.1.2.2.2.1	1	.	1400001	1850000	#FF0000
chr1	1850001	2100000	B.1.1.2.2.1.2.1	0.5	.	1850001	2100000	#DADAFF

For ATAC-Seq data, users should provide a file with p-values in BigWig format. The pyBigWig library is required, which does not work on Windows systems.

Note: At present, MultiMM only works for human genome data. The code may run for other organisms with some debugging and modifications.

Usage

All the model's parameters are specified in a config.ini file. This file should have the following format:

[Main]

; Platform selection
PLATFORM = OpenCL

; Input data
FORCEFIELD_PATH = forcefields/ff.xml
LOOPS_PATH = /home/skorsak/Data/Rao/GSE63525_GM12878_primary+replicate_HiCCUPS_looplist_hg19.bedpe
COMPARTMENT_PATH = /home/skorsak/Data/Rao/subcompartments_primary_replicate/sub_compartments/all_sub_compartments.bed
ATACSEQ_PATH = /home/skorsak/Data/encode/ATAC-Seq/ENCSR637XSC_GM12878/ENCFF667MDI_pval.bigWig
OUT_PATH = application_note

; Simulation Parameters
N_BEADS = 50000
SHUFFLE_CHROMS = True
NUC_DO_INTERPOLATION = True

; Enable forcefield for genome-wide simulation
SC_USE_SPHERICAL_CONTAINER = True
CHB_USE_CHROMOSOMAL_BLOCKS = True
SCB_USE_SUBCOMPARTMENT_BLOCKS = True
IBL_USE_B_LAMINA_INTERACTION = True
CF_USE_CENTRAL_FORCE = True

; Simulation Parameters
SIM_RUN_MD = True
SIM_SAMPLING_STEP = 50
SIM_N_STEPS = 1000
TRJ_FRAMES = 100

After specifying the parameters and forces, users can run the following command in the terminal:

MultiMM -c config.ini

The software will output a folder with the resulting structure and plots showing compartment distribution.

Example data can be found on Google Drive: https://drive.google.com/drive/folders/1nFAPE4pCaHpeL5nw6nq0VvfUFoc24aXm?usp=sharing. Note that this data is publicly available from Rao et al. The subcompartment predictions were made using CALDER, and the ATAC-Seq data is from ENCODE.

In the examples folder, we provide example configuration files for different modeling scenarios.

Vizualization

For vizualization purposes, if you would like to import the whole genome structure you may use the command,

import simulation.plots as splt

splt.viz_chroms(sim_path)

For sim_path you should add the output folder directory path (add comps=Fase in case that you do not need compartment coloring). Otherwise, in case that the user would like to model a particular region, without using compartment or chromosome coloring (pretty much any cif structure), they can type,

import simulation.plots as splt
import simulation.utils as suts

V = suts.get_coordinates_cif(cif_path)
splt.viz_structure(V)

We would like once again to thank people who developed pyvista library and allow us to have fast and good vizualizations of large chromatin structure.

Simulation Arguments

MultiMM has numerous configurable parameters. Below is a description of each argument and its default values. The defaults have been tested for genome-wide simulation but can be modified in the configuration file if needed. Units are typically assumed based on OpenMM conventions, though explicit unit specification is not required.

This version should now be more precise and polished. Let me know if you'd like to adjust anything further!

Argument Name	Type	Value	Units	Description
PLATFORM	str	None	None	name of the platform. Available choices: CPU, OpenCL, CUDA.
DEVICE	str	None	None	device index for CUDA or OpenCL (count from 0)
INITIAL_STRUCTURE_PATH	str	None	None	Path to CIF file.
BUILD_INITIAL_STRUCTURE	bool	True	None	To build a new initial structure.
INITIAL_STRUCTURE_TYPE	str	hilbert	None	you can choose between: hilbert, circle, rw, confined_rw, self_avoiding_rw, helix, spiral, sphere, knot.
FORCEFIELD_PATH	str	None	None	Path to XML file with forcefield.
N_BEADS	int	50000	None	Number of Simulation Beads.
COMPARTMENT_PATH	str	None	None	It should be a .bed file with subcompartments from Calder.
LOOPS_PATH	str	None	None	A .bedpe file path with loops. It is required.
ATACSEQ_PATH	str	None	None	A .bw or .BigWig file path with atacseq data for nucleosome interpolation. It is not required.
OUT_PATH	str	results	None	Output folder name.
LOC_START	int	None	None	Starting region coordinate (in case that you do not want to model the whole genome).
LOC_END	int	None	None	Ending region coordinate (in case that you do not want to model the whole genome).
CHROM	str	None	None	Chromosome that corresponds the the modelling region of interest (in case that you do not want to model the whole genome).
SHUFFLE_CHROMS	bool	False	None	Shuffle the chromosomes.
SHUFFLING_SEED	int	0	None	Shuffling random seed.
SAVE_PLOTS	bool	True	None	Save plots.
POL_USE_HARMONIC_BOND	bool	True	None	Use harmonic bond interaction for consecutive beads ($i,i\pm 1$).
POL_HARMONIC_BOND_R0	float	0.1	nm	Harmonic bond distance equilibrium constant.
POL_HARMONIC_BOND_K	float	300000.0	kJ/mol/nm^2	harmonic bond force constant
POL_USE_HARMONIC_ANGLE	bool	True	None	Use harmonic angle interaction for consecutuve beads ($i,i\pm 1, i\pm 2$).
POL_HARMONIC_ANGLE_R0	float	pi	None (radians)	Equilibrium angle of harmonic angle force.
POL_HARMONIC_ANGLE_CONSTANT_K	float	100.0	kJ/mol/radian^2	Harmonic angle force constant.
LE_USE_HARMONIC_BOND	bool	True	None	Use harmonic bond interaction for long range loops.
LE_FIXED_DISTANCES	bool	False	None	For fixed distances between loops. False if you want to correlate with the heatmap strength.
LE_HARMONIC_BOND_R0	float	0.1	None	Harmonic bond distance equilibrium constant for long-range loops.
LE_HARMONIC_BOND_K	float	30000.0	kJ/mol/nm^2	Harmonic bond force constant for long-range loops.
EV_USE_EXCLUDED_VOLUME	bool	True	None	Use excluded volume $V_{ev}=\epsilon\left(\frac{\sigma}{r+r_{\text{small}}}\right)^{\alpha}$.
EV_EPSILON	float	100.0	kJ/mol	Epsilon parameter - strength of excluded volume.
EV_R_SMALL	float	0.05	nm	Add something small in denominator to make it not exploding all the time.
EV_POWER	float	3.0	None	Power $\alpha$ in the exponent of EV potential.
SC_USE_SPHERICAL_CONTAINER	bool	False	None	Use Spherical container.
SC_RADIUS1	float	None	nm	Inner spherical container radius.
SC_RADIUS2	float	None	nm	Outer spherical container radius.
SC_SCALE	float	1000.0	kJ/mol/nm^2	Spherical container scaling factor
CHB_USE_CHROMOSOMAL_BLOCKS	bool	False	None	Use Chromosomal Blocks.
CHB_KC	float	0.3	nm^(-4)	Block copolymer width parameter.
CHB_DE	float	1e-5	kJ/mol	Energy factor for block copolymer chromosomal model.
COB_USE_COMPARTMENT_BLOCKS	bool	False	kJ/mol	Use Compartment Blocks.
COB_DISTANCE	float	None	nm	Block copolymer equilibrium distance for chromosomal blocks.
COB_EA	float	1.0	kJ/mol	Energy strength for A compartment.
COB_EB	float	2.0	kJ/mol	Energy strength for B compartment.
SCB_USE_SUBCOMPARTMENT_BLOCKS	bool	False	None	Use Subcompartment Blocks.
SCB_DISTANCE	float	None	nm	Block copolymer equilibrium distance for chromosomal blocks.
SCB_EA1	float	1.0	kJ/mol	Energy strength for A1 compartment.
SCB_EA2	float	1.33	kJ/mol	Energy strength for A2 compartment.
SCB_EB1	float	1.66	kJ/mol	Energy strength for B1 compartment.
SCB_EB2	float	2.0	kJ/mol	Energy strength for B2 compartment.
IBL_USE_B_LAMINA_INTERACTION	bool	False	None	Interactions of B compartment with lamina.
IBL_SCALE	float	400.0	kJ/mol	Scaling factor for B comoartment interaction with lamina.
CF_USE_CENTRAL_FORCE	bool	False	None	Attraction of smaller chromosomes to the nucleolus (in the center).
CF_STRENGTH	float	10.0	kJ/mol	Strength of Attraction of Smaller Chromosomes
NUC_DO_INTERPOLATION	bool	False	None	Attraction of smaller chromosomes.
NUC_RADIUS	float	0.1	None	The radius of the single nucleosome helix.
POINTS_PER_NUC	int	20	None	The number of points that consist a nucleosome helix.
PHI_NORM	float	pi/5	None	Zig zag angle.
SIM_RUN_MD	bool	False	None	Do you want to run MD simulation?
SIM_N_STEPS	int	None	None	Number of steps in MD simulation
SIM_ERROR_TOLERANCE	float	0.01	None	Error tolerance for variable MD simulation
SIM_AMD_ALPHA	float	100.0	kJ/mol	Alpha of AMD simulation.
SIM_AMD_E	float	1000.0	kJ/mol	E (energy) of AMD simulation.
SIM_SAMPLING_STEP	int	100	None	It determines in t how many steps we save a structure.
SIM_INTEGRATOR_TYPE	str	langevin	None	Possible choices: variable_nagevin, langevin, variable_verlet, verlet, amd, brownian.
SIM_INTEGRATOR_STEP	Quantity	1	fsec	The step of integrator.
SIM_FRICTION_COEFF	float	0.5	1/psec	Friction coefficient (Used only with langevin and brownian integrators).
SIM_SET_INITIAL_VELOCITIES	bool	False	None	Sets initial velocities based on Boltzmann distribution.
SIM_TEMPERATURE	Quantity	310	kelvin	Simulation temperature
TRJ_FRAMES	int	2000	None	Number of trajectory frames to save.

Copyrights

The software is freely distributed under the GNU license and everybody can use it how they want, improve it, or apply it to their research interests. In case that the software would be used for research, we would like that you will cite our paper:

• Korsak, Sevastianos, Krzysztof Banecki, and Dariusz Plewczynski. "Multiscale Molecular Modelling of Chromatin with MultiMM: From Nucleosomes to the Whole Genome." bioRxiv (2024): 2024-07.

Please, communicate with the authors in case that you would like to contribute in this model, and you would like to improve it.