Tinker-HP fighting against the COVID-19

You can find here our data representing 15.14-microsecond AMOEBA simulation of the apo enzyme started from the apo enzyme structure determined by X-ray crystallography (PDB entry 6LU7) with frames saved every 0.1-nanosecond.

The AMOEBA_15mics_sampling trajectory is divided in sampling iterations X that represent the (X-1)^th adaptive sampling iteration. The iteration1 is the first of the fourteen 10ns trajectories that we used to start our adaptive sampling scheme.

For each iteration, we give the protein atoms only files (protein.dcd) and the all atoms files trajectory file (protein_water.arc).

We also provide the 6LU7 pdb file, the protease topology file and the protease+water topology file, both taken from Riken, doi: 10.17632/vpps4vhryg.2 and a de-biasing score file of the whole 15.14-microsecond trajectory.

We provide all cluster structures and reduced cluster structures.

We used the following settings:

1. DESRES: 1 cluster
   • X=1 structures: 1000
2. Riken: 3 clusters
   • X=1 structures: 299
   • X=2 structures: 1000
   • X=3 structures: 1000
3. Tinker-HP: 5 clusters
   • X=1 structures: 1000
   • X=2 structures: 1000
   • X=3 structures: 599
   • X=4 structures: 1000
   • X=5 structures: 899

For each Tinker-HP clusters, we also provide a de-biasing score file for full clusters and reduced clusters

We provide de-biased_observable.py a python script to compute de-biased observable average. It takes as arguments:

1. name of the de-biasing score file, i.e, ’de-biasing_score’ (column file)
2. name of the computed observable file, i.e, ’observable’ (column file).

Additionally, we provide de-biased_histogram.py6 to compute de-biased histogram and kernel density estimation of a given observable. It takes the same arguments and gives as output a picture (PNG format).

Finally, We added two python script examples: de-biased_observable_ex.py and de-biased_histogram_ex.py

They should be run with the following command :

Libraries needed: numpy, pandas, statsmodels, matplotlib

For further information about the de-biasing please refer to the paper method section.

Citation

The use of any AMOEBA trajectory data in any reports or publications of results obtained with the trajectory data should be acknowledged by including a citation to:

We would like to acknowledge the exceptional work of D. E. Shaw Research and RIKEN Center for Biosystems Dynamics Research, from which we used datas. Please cite:

The DESRES and Riken trajectory datasets are released under a Creative Commons Attribution 4.0 International Public License, a copy of which is contained in the file CC4_License.txt provided in http://www.deshawresearch.com/resources_sarscov2.html/

This trajectory may be viewed using the VMD version 1.8.7 or later (or any other tool capable of reading files in ARC and DCD format). The VMD software is available from the Theoretical and Computational Biophysics Group at the University of Illinois at Urbana-Champaign. The reference is: