Identification of key NOEs

In this tutorial we will provide you with guided examples for identification of key NOEs for a two-state eNOE calculation. Keep in mind that listed approach can be generalized to any other type of calculation.

In summary, our approach consists of following steps:

1. We extract all long range NOEs
2. For each long range NOE we run a two-state structure calculation missing this particular NOE
3. We evaluate structure calculations in terms of correlations (using PDBcor)

Software installation

This tutorial requires following software:

1. CYANA
2. Python3
3. PDBcor. In case if PDBcor is not yet installed, please go to this link and follow installation instructions. Later we will refer to the PDBcor installation path as PATH/To/PDBcor

Data preparation

Please follow the following steps:

1. Download the demo data.
2. Unpack the demo data

Execution

We recommend to use parallel computation for the execution as it will significantly reduce the total running time.

Please follow the following steps carefully (exact Linux commands are given below; you may copy them to a terminal):

First, edit the protein specific data in the folder data/. Make sure, that filenames are kept the same as in the demo. Advanced users can ignore it and fix PREP.cya for potential naming errors (keep in mind that RUN.cya will modify the distance restraints to filtered.upl and filtered.lol by removing a particular selected long distance NOE). Change the protein sequence in the root folder (same filename is not required).

Then, edit RUN.cya:

1. Edit upl and lol filenames

read upl data/combined.upl
read lol data/combined.lol

1. Edit the cyana engine

cyana:=~peterg/bin/cyana (if you are not sure what it is change it to cyana:=cyana)

Then, run the series of two-state structure calculations:

cyana RUN.cya

This creates a folder for each two-state structure calculation. In the calculation folder omitted NOE restraint is saved as a omitedrestraint.txt file and new restraint files are saved as filtered.upl and filtered.lol. Final structure is saved as bundle.pdb and final split structure (where each state is an independent model is saved as splitall.pdb).

Then, activate the PDBcor environment and run the correlation analysis:

source PATH/To/PDBcor/venv/bin/activate
cyana ANALYSE.cya

This creates a correlation/ subfolder in each structure calculation folder with a correlation value that can be read from the correlations_backbone.txt file.

Finally, collect results in a single file (results.txt):

cyana STAT_COLLECT.cya