Beyond ManifoldEM

This repository contains code for generating results shown in our paper Beyond ManifoldEM: Geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections (Seitz, Frank* and Schwander*). This work was developed in the Frank research group at Columbia University in collaboration with Peter Schwander at the University of Wisconsin-Milwaukee (UWM).

The algorithms presented here in their current form are developed for analyzing synthetic data. Custom synthetic datasets can be generated using code in our previous repository with additional information provided in the corresponding paper: Simulation of Cryo-EM Ensembles from Atomic Models of Molecules Exhibiting Continuous Conformations (Seitz, Acosta-Reyes, Schwander, Frank*). We have also deposited our complete synthetic dataset in the IEEE DataPort, which is a mirror of the 2019 repository with the addition of several refinements for processing the provided data.

Users interested in applying these methods to experimental datasets should consult the ManifoldEM Matlab suite or ManifoldEM Python suite, with the latter featuring a comprehensive GUI, user manual and robust automation strategies. As well, our ManifoldEM ESPER method can be used to further refine ManifoldEM outputs given that certain prerequisites are met, with detailed information provided in our ESPER paper: Recovery of Conformational Continuum From Single-Particle Cryo-EM Images: Optimization of ManifoldEM Informed by Ground Truth (Seitz, Acosta-Reyes, Maji, Schwander, Frank*).

Environment:

First, install Anaconda, and with Anaconda sourced, create a new Anaconda environment:

conda create -n beyondMEM python=3

Next, activate this environment via conda activate beyondMEM, and install the following packages:

• pip install numpy
• pip install matplotlib
• pip install scipy
• pip install -U scikit-learn scipy matplotlib
• pip install mrcfile
• pip install latex #if texlive installed (see below)

LaTex can be additionally installed (e.g. via TeX Live); if not, syntax for figure generation in these scripts will need to be individually altered. Once these packages are installed within the Anaconda environment, the environment must be initiated each time before running these scripts via the command conda activate beyondMEM. When you are done using the environment, always exit via conda deactivate.

Additional Software:

In addition to the Anaconda environment detailed above, the PyMOL and Chimera packages may also prove useful if users wish to recreate distance matrices for ACS and EDM files, respectively, as well as any earlier files in our pipeline from scratch. For the latter, see our synthetic generation repository.

Usage:

Detailed instructions and comments for all procedures are provided in the code. We have also supplied several pre-generated datasets for reproducing outputs of our analysis and related figures.

Attribution:

cite one or more of the ChimeraX references, mainly:

If this research or code is useful in your work, please cite one or more of the ManifoldEM references, mainly:

• (Beyond ManifoldEM: manuscript information TBD)
• E. Seitz, "Beyond ManifoldEM Data and Code Repository," Zenodo, 2023, doi: 10.5281/zenodo.7657567
• E. Seitz, F. Acosta-Reyes, S. Maji, P. Schwander and J. Frank, "Recovery of Conformational Continuum From Single-Particle Cryo-EM Images: Optimization of ManifoldEM Informed by Ground Truth," in IEEE Transactions on Computational Imaging, vol. 8, pp. 462-478, 2022, doi: 10.1109/TCI.2022.3174801.

License:

Copyright (C) 2018-2023 Evan Seitz

The software, code sample and their documentation made available on this website could include technical or other mistakes, inaccuracies or typographical errors. We may make changes to the software or documentation made available on its web site at any time without prior notice. We assume no responsibility for errors or omissions in the software or documentation available from its web site. For further details, please see the LICENSE file.