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SINATRA-Pro-0.0.1
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Python3 package for SINATRA Pro.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | SINATRA-Pro-0.0.1 |
| نام | SINATRA-Pro |
| نسخه کتابخانه | 0.0.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Wai Shing Tang |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/lcrawlab/SINATRA-Pro |
| آدرس اینترنتی | https://pypi.org/project/SINATRA-Pro/ |
| مجوز | MIT License |
# SINATRA Pro
Protein Structure and Conformation Analysis using Topological Summary Statistics.
## Introduction
The sub-image selection problem is to identify physical regions that most explain the variation between two classes of three dimensional shapes. [SINATRA](https://github.com/lcrawlab/SINATRA) is a statistical pipeline for carrying out sub-image analyses using topological summary statistics (Wang et al. 2021). SINATRA Pro is an adaptation of the SINATRA framework for structure-based applications in protein dynamics. The general algorithm follows four key steps:
1. 3D shapes of protein structures (represented as triangular meshes) are summarized by a collection of vectors (or curves) detailing their topology (e.g., Euler characteristics, persistence diagrams, etc).
2. A statistical model is used to classify the shapes based on their topological summaries. Here, we make use of a Gaussian process classification model with a probit link function.
3. After fitting the model, an association measure is computed for each topological feature (e.g., centrality measures, posterior inclusion probabilities, p-values, etc).
4. Association measures are mapped back onto the original protein structures via a reconstruction algorithm, thus, highlighting atomic or residue-level positions that best explain the variation between two ensembles.
Through detailed simulations, we assess the power of our algorithm as a function of its free parameters. As an application of our pipeline, we conduct feature selection for identifying minute conformational changes in five independent protein systems of varying complexities.
## Package Details
Code for implementing the SINATRA Pro pipeline was written in Python 3 (version 3.6.9). As part of this procedure:
1. Reading of trajectory files, alignment of protein structures, and neighbor search algorithms are done using the [MDAnalysis](https://www.mdanalysis.org) package (Gowers et. al. 2016, Michaud-Agrawal et. al. 2011).
2. Most athematical calculations are performed using [NumPy](https://numpy.org) and [SciPy](https://www.scipy.org).
3. Inference for the Gaussian process classification (GPC) model was done using elliptical slice sampling (Murray, Prescott, and MacKay 2010).
4. Association measures are computed for the Euler characteristic curves using the relative centrality criterion (RATE), which is a variable selection measure for nonlinear and nonparametric statistical methods (see Crawford et al. 2019 and Ish-Horowicz et al. 2019).
## Dependencies
The SINATRA Pro package depends on the following Python 3 packages:
numpy >= 1.18.0
scipy >= 1.5.0
mdanalysis >= 0.20.0
fast-histogram >= 0.9
joblib >= 0.16.0
## Python Package Download
To install the package:
pip3 install SINATRA-Pro
To load the package:
import sinatra_pro
To run the application:
python3 -m sinatra_pro
usage: __main__.py [-h] [-pa PROTA] [-pb PROTB] [-sa STRUCT_FILE_A]
[-ta TRAJ_FILE_A] [-sb STRUCT_FILE_B] [-tb TRAJ_FILE_B]
[-dir DIRECTORY] [-pl] [-nc N_CORE] [-n N_SAMPLE]
[-of OFFSET] [-s SELECTION] [-r RADIUS] [-hs] [-et EC_TYPE]
[-c N_CONE] [-d N_DIRECTION_PER_CONE] [-t CAP_RADIUS]
[-l N_FILTRATION] [-bw BANDWIDTH] [-sm SAMPLING_METHOD]
[-nm N_MCMC] [-ll] [-v] [-no]
optional arguments:
-h, --help show this help message and exit
-pa PROTA, --protA PROTA
name of protein A for file naming
-pb PROTB, --protB PROTB
name of protein B for file naming
-sa STRUCT_FILE_A, --struct_file_A STRUCT_FILE_A
structure file for protein A (.gro)
-ta TRAJ_FILE_A, --traj_file_A TRAJ_FILE_A
trajectory file for protein A (.xtc)
-sb STRUCT_FILE_B, --struct_file_B STRUCT_FILE_B
structure file for protein B (.gro)
-tb TRAJ_FILE_B, --traj_file_B TRAJ_FILE_B
trajectory file for protein B (.xtc)
-dir DIRECTORY, --directory DIRECTORY
directory for output files
-fp, --from_pdb start from sets of PDB structures instead of
trajectories
-pa PDBPATH_A, --pdbpath_A PDBPATH_A
directory containing PDB structures for protein A
-pb PDBPATH_B, --pdbpath_B PDBPATH_B
directory containing PDB structures for protein B
-pr PDB_REFERENCE, --pdb_reference PDB_REFERENCE
PDB structure for visualization from protein A
-pl, --parallel
use multiple CPU cores for calculations
-nc N_CORE, --n_core N_CORE
number of core for parallel computing, default: use
all cores
-n N_SAMPLE, --n_sample N_SAMPLE
number of sample drawn from trajectory, default: 10
-of OFFSET, --offset OFFSET
starting frame for sample drawn from trajectory,
default: 0
-s SELECTION, --selection SELECTION
selection for protein, default: all protein
-r RADIUS, --radius RADIUS
radius for simplicial construction, default: 2.0
-hs, --hemisphere distribute directions over hemisphere instead of whole
sphere
-et EC_TYPE, --ec_type EC_TYPE
type of Euler characteristic measure (DECT/ECT/SECT),
default: DECT
-c N_CONE, --n_cone N_CONE
number of cone, default: 1
-d N_DIRECTION_PER_CONE, --n_direction_per_cone N_DIRECTION_PER_CONE
number of direction per cone, default: 1
-t CAP_RADIUS, --cap_radius CAP_RADIUS
cap radius, default: 0.8
-l N_FILTRATION, --n_filtration N_FILTRATION
number of filtration step, default: 20
-bw BANDWIDTH, --bandwidth BANDWIDTH
bandwidth for elliptical slice sampling, default: 0.01
-sm SAMPLING_METHOD, --sampling_method SAMPLING_METHOD
sampling method, default: ESS
-nm N_MCMC, --n_mcmc N_MCMC
number of sample from ESS
-ll, --logistic_likelihood
use logistic likelihood instead of probit likelihood
-lr, --low_rank use low rank matrix approximations to compute the RATE
values
-v, --verbose verbose
-no, --name_offset name folder with offset
## Examples of Running the Package
Starting from MD trajectories
python3 -m sinatra_pro --protA WT --protB R164S \
--directory "WT_R164S_65_213_no164sc_2.0" \
--n_sample 10 \
--struct_file_A "WT.gro" \
--traj_file_A "WT.xtc" \
--struct_file_B "R164S.gro" \
--traj_file_B "R164S.xtc" \
--selection "protein and resid 65:213 and not (resid 164 and not backbone)" \
--radius 2.0 \
--n_cone 4 \
--n_direction_per_cone 4 \
--cap_radius 0.80 \
--ec_type "DECT" \
--n_filtration 60 \
--n_mcmc 100000 \
--parallel \
--n_core 4 --verbose
Starting from aligned PDB structures
python3 -m sinatra_pro --protA WT --protB R164S \
--directory "WT_R164S_65_230_2.0" \
--n_sample 10 \
--from_pdb \
--pdbpath_A "WT_R164S_65_230_2.0/pdb/WT_offset_0/" \
--pdbpath_B "WT_R164S_65_230_2.0/pdb/R164S_offset_0/" \
--pdb_reference "WT_R164S_65_230_2.0/pdb/WT_offset_0/WT_frame0.pdb" \
--radius 2.0 \
--n_cone 1 \
--n_direction_per_cone 1 \
--cap_radius 0.80 \
--ec_type "DECT" \
--n_filtration 20 \
--n_mcmc 10000 \
--parallel \
--n_core 4 --verbose
Other code specific to analyses conducted in the paper can be found in the repo [SINATRA_Pro_Paper_Results](https://github.com/lcrawlab/SINATRA_Pro_Paper_Results).
## Questions and Feedback
For questions or concerns, please contact [Wai Shing Tang](mailto:wai_shing_tang@brown.edu) or [Lorin Crawford](mailto:lcrawford@microsoft.com). We appreciate any feedback you may have with our repository and instructions for running the software.
## Relevant Citations
Wai Shing Tang*, Gabriel Monteiro da Silva*, Henry Kirveslahti, Erin Skeens, Bibo Feng, Timothy Sudijono, Kevin K. Yang, Sayan Mukherjee, Brenda Rubenstein, and Lorin Crawford. Topological data analytic approach for discovering biophysical signatures in protein dynamics. _bioRxiv_.
نیازمندی
| مقدار | نام |
|---|---|
| >=1.18.0 | numpy |
| >=1.5.0 | scipy |
| >=0.20.0 | MDAnalysis |
| >=0.9 | fast-histogram |
| >=0.16.0 | joblib |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl SINATRA-Pro-0.0.1:
pip install SINATRA-Pro-0.0.1.whl
نصب پکیج tar.gz SINATRA-Pro-0.0.1:
pip install SINATRA-Pro-0.0.1.tar.gz