\brief Assignment of sequence to backbone fragments traced in a cryo-EM map

EMSequenceFinder is a method for assigning amino acid residue sequence to backbone fragments traced in an input cryo-electron microscopy (cryo-EM) map. EMSequenceFinder relies on a Bayesian scoring function for ranking 20 standard amino acid residue types at a given backbone position, based on the fit to a density map, map resolution, and secondary structure propensity. The fit to a density is quantified by a convolutional neural network that was trained on 5.56 million amino acid residue densities extracted from cryo-EM maps at 3–10 Å resolution and corresponding atomic structure models deposited in the Electron Microscopy Data Bank (EMDB). For more information, see Mondal et al, 2025.

Dependencies:

In addition to IMP's own dependencies, EMSequenceFinder also requires

• The STRIDE command line tool for secondary structure prediction
• The mrcfile, scipy, scikit-learn, statsmodels, pandas, and tensorflow Python packages

One way to get these dependencies is via conda-forge. In order for TensorFlow prediction to work correctly on GPUs with libdevice, you may have to run export XLA_FLAGS=--xla_gpu_cuda_data_dir=$CONDA_PREFIX

emseqfinder: command line tool to run emseqfinder protocol {#emseqfinder}

The protocol is typically run using the emseqfinder command line tool in the following fashion:

1. Add input files. Create directories pdb_files, cryoem_maps and fasta_files containing input files in .pdb, .map and .fasta format respectively. Name all three files for a given run with the same stem (e.g. EMD-8637.pdb, EMD-8637.map, EMD-8637.fasta).
2. Run the pipeline with emseqfinder batch.

Upon successful execution, the following output files will be generated:

• *_ML_side_ML_prob.dat contains fragment-wise sequence scores.
• batch_matching_results.txt contains overall sequence matching accuracy per structure.

Info

Author(s): Dibyendu Mondal, Vipul Kumar

Maintainer: benmwebb

License: LGPL This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.

Publications:

• D. Mondal, V. Kumar, T. Satler, R. Ramachandran, D. Saltzberg, I. Chemmama, K.B. Pilla, I. Echeverria, B.M. Webb, M. Gupta, K.A. Verba, A. Sali. Recognizing amino acid sidechains in a medium resolution cryo-electron density map. Prot Sci 34, e70217, 2025.
• See [main IMP papers list](@ref publications).