Sequence aligner for accurate template-based protein structure prediction
Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful if good templates can be found. Although modern homology detection methods can find remote homologs with high sensitivity, the accuracy of template-based models generated from homology-detection-based alignments is often lower than that from ideal alignments.
We propose a new method that generates pairwise sequence alignments for more accurate template-based modeling. The proposed method trains a machine learning model using the structural alignment of known homologs. When calculating sequence alignments, instead of a fixed substitution matrix, this method dynamically predicts a substitution score from the trained model.
@article{10.1093/bioinformatics/btz483,
author = {Makigaki, Shuichiro and Ishida, Takashi},
title = "{Sequence alignment using machine learning for accurate template-based protein structure prediction}",
journal = {Bioinformatics},
year = {2019},
month = {06},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btz483},
url = {https://doi.org/10.1093/bioinformatics/btz483},
}
- Linux (recommended) and macOS X
- Python >=3.6
Open and read example.ipynb
.
Install requirements:
pip3 install -r requirements.txt
We also need FLANN [1] and Python binding.
git clone https://github.com/mariusmuja/flann
cd flann
### These two procedures are needed only for MacOSX
# brew install cmake hdf5
# git checkout 033b05b1 -b clang
mkdir build
cd build
### PYTHON_EXECUTABLE:FILEPATH and CMAKE_INSTALL_PREFIX should follow your environment.
### liblz4 is required.
cmake -DCMAKE_INSTALL_PREFIX=~/.local -DPYTHON_EXECUTABLE:FILEPATH=$(which python3) ..
make
make install
For non-root (--user
) installation, edit flann/src/python/CMakeLists.txt
:
# Around #10
COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/setup.py install
# Add --user
COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/setup.py install --user
Data used for the kNN index and the paper is available by dvc pull data.dvc
, which requires 140GB disk space.
- Marius Muja and David G. Lowe, "Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration", in International Conference on Computer Vision Theory and Applications (VISAPP'09), 2009