Skip to content

A deep learning method that identifies improved protein conformational states in trajectory data from refinement simulations

Notifications You must be signed in to change notification settings

OneAngstrom/DeepTrajectory

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

17 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

DeepTrajectory

DeepTrajectory is a deep recurrent neural network based on gated recurrent units to identify improved conformational states from refinement trajectory data in order to assist accurate protein structure prediction.

The pre-print is available on bioRxiv.

This repository contains the source code of the model together with helper functions to measure the performance during training and validation.

Data-set availability DOI

The data-set for training and testing can be downloaded from https://zenodo.org/record/1183354. This webpage contains links to the raw PDB files of all trajectories used in this work, the feature table in CSV format and the cross-validation assignment as a CSV file.

Usage

The following python dependencies are required to run the code: tensorflow (version 1.0.0), scikit-learn, numpy, pandas.

The ipython notebook in src/training_example.ipynb shows an example how to train the model. Please make sure that you have downloaded and extracted the CSV data to the sub folder data/.

About

A deep learning method that identifies improved protein conformational states in trajectory data from refinement simulations

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published