cryoSBI - Simulation-based Inference for Cryo-EM

tests

Summary

cryoSBI is a Python module for simulation-based inference in cryo-electron microscopy. The module provides tools for simulating cryo-EM particles, training an amortized posterior model, and sampling from the posterior distribution. The code is based on the SBI libary Lampe, which is a using Pytorch.

Installing

To install the module you will have to dowload the repository and create a virtual environment with the required dependencies. You can create an environment for example with conda using the following command:

conda create -n cryoSBI python=3.10

After creating the virtual environment, you should install the required dependencies and the module.

Dependencies

Download this repository

git clone `https://github.com/DSilva27/cryo_em_SBI.git`

Navigate to the cloned repository and install the module

cd cryo_em_SBI

pip install .

Tutorial

An introduction tutorial can be found at tutorials/tutorial.ipynb. In this tutorial, we go through the whole process of making models for cryoSBI, training an amortized posterior, and analyzing the results. In the following section, I highlighted cryoSBI key features.

Generate model file to simulate cryo-EM particles

To generate a model file for simulating cryo-EM particles with the simulator provided in this module, you can use the command line tool models_to_tensor. You will need either a set of pdbs which are indexd or a trr trejectory file which contians all models. The tool will generate a model file that can be used to simulate cryo-EM particles.

models_to_tensor \
    --model_file path_to_models/pdb_{}.pdb \
    --output_file path_to_output_file/output.pt \
    --n_pdbs 100

The output file will be a Pytorch tensor with the shape (number of models, 3, number of pseudo atoms).

Simulating cryo-EM particles

To simulate cryo-EM particles, you can use the CryoEmSimulator class. The class takes in a simulation config file and simulates cryo-EM particles based on the parameters specified in the config file.

from cryo_sbi import CryoEmSimulator
simulator = CryoEmSimulator("path_to_simulation_config_file.json")
images, parameters = simulator.simulate(num_sim=10, return_parameters=True)

The simulation config file should be a json file with the following structure:

{
    "N_PIXELS": 128,
    "PIXEL_SIZE": 1.5,
    "SIGMA": [0.5, 5.0],
    "MODEL_FILE": "path_to_models/models.pt",
    "SHIFT": 25.0,
    "DEFOCUS": [0.5, 2.0],
    "SNR": [0.001, 0.5],
    "AMP": 0.1,
    "B_FACTOR": [1.0, 100.0]
}

The pixel size is defined in Angström (Å). The atom sigma defines the size of the Gaussians used to approximate the protein's electron density. Here, each Gaussian represents one amino acid, and while all Gaussians have the same sigma, the value is made to vary in the simulations. The shift is the offset of the protein from the image centre and is given in Angström (Å). The defocus of the microscope is given in units of micrometres (μm). The SNR (Signal-to-noise ratio) is unitless and defines the amount of noise in the simulated images. The Amplitude is a unitless parameter which ranges between 0 and 1. The B-factor is given in units of Angström squared (Å^2) and defines the decay rate of the CTF envelope function.

Training an amortized posterior model

Training of an amortized posterior can be done using the train_npe_model command line utility. The utility takes in an image config file, a train config file, and other training parameters. The utility trains a neural network to approximate the posterior distribution of the parameters given the images.

train_npe_model \
    --image_config_file path_to_simulation_config_file.json \
    --train_config_file path_to_train_config_file.json\
    --epochs 150 \
    --estimator_file posterior.estimator \
    --loss_file posterior.loss \
    --n_workers 4 \
    --simulation_batch_size 5120 \
    --train_device cuda

The training config file should be a json file with the following structure:

{
    "EMBEDDING": "RESNET18",
    "OUT_DIM": 256,
    "NUM_TRANSFORM": 5,
    "NUM_HIDDEN_FLOW": 10,
    "HIDDEN_DIM_FLOW": 256,
    "MODEL": "NSF",
    "LEARNING_RATE": 0.0003,
    "CLIP_GRADIENT": 5.0,
    "THETA_SHIFT": 25,
    "THETA_SCALE": 25,
    "BATCH_SIZE": 256
}

When training posterior for your own system, it's important to change THETA_SCALE and THETA_SHIFT. These two parameters normalize the conformational variable in cryoSBI. THETA_SHIFT and THETA_SCALE need to be adjusted according to the number of structures used in the prior. A good option is to set THETA_SHIFT and THETA_SCALE to the number of structures in the prior divided by two.

Loading the posterior after training

After training the estimator, loading it in Python can be done with the load_estimator in the estimator_utils module.

import cryo_sbi.utils.estimator_utils as est_utils
posterior = est_utils.load_estimator(
    config_file_path="path_to_config_file",
    estimator_path="path_to_estimator_file",
    device="cuda"
)

Inference

Sampling from the posterior distribution can be done using the sample_posterior function in the estimator_utils module. The function takes in an estimator, images, and other parameters and returns samples from the posterior distribution.

import cryo_sbi.utils.estimator_utils as est_utils
samples = est_utils.sample_posterior(
    estimator=posterior,
    images=images,
    num_samples=20000,
    batch_size=100,
    device="cuda",
)

The Pytorch tensor containing the samples will have the shape (number of samples, number of images). In order to visualize the posterior for each image you can use matplotlib. We can quickly generate a histogram with 50 bins with the following piece of code.

import matplotlib.pyplot as plt
idx_image = 0 # posterior for image with index 0
plt.hist(samples[:, idx_image].flatten(), np.linspace(0, simulator.max_index, 50))

In this case the x-axis is just the index of the structures in increasing order.

Latent space

Computing the latent features for simulated or experimental particles can be done using the compute_latent_repr function in the estimator_utils module. The function needs a trained posterior estimator and images and computes the latent representation for each image.

import cryo_sbi.utils.estimator_utils as est_utils
latent_vecs = est_utils.compute_latent_repr(
    compute_latent_repr(
    estimator=posterior,
    images=images,
    batch_size=100,
    device="cuda",
)

After we computed the latent representation for the images, one possible way to visualize the latent space is to use UMAP . UMAP generates a two-dimensional representation of the latent space, which should allow us to analyze its important features.

import umap
reducer = umap.UMAP(metric="euclidian", n_components=2, n_neighbors=50)
embedding = reducer.fit_transform(latent_vecs.numpy())

We can quickly visualize the 2d latent space with matplotlib.

import matplotlib.pyplot as plt
plt.scatter(
    embedding[:, 0],
    embedding[:, 1],
)

flatironinstitute/cryo_em_SBI