Docking pose refinement tutorial
======================================================
The aim of this tutorial is to refine a Glide docking pose of flu virus hemagglutinin inhibitor JNJ4796.

1. Protein file
+++++++++++++++++++++++

We will be using a preprocessed docking pose of JNJ4796 ligand extracted from PDB 6CF7 in hemagglutinin structure (PDB code 5W6T).
You can download the structure :download:`here <../files/docking.pdb>` .

The superposition of Glide docking output (green) with the native pose (pink) resulted in the ligand RMSD of 7.68Å. Let's see, if we can improve that with PELE's induced fit simulation!

.. image:: ../img/induced_fit_tutorial_1.png
  :width: 400
  :align: center


2. PELE input file
++++++++++++++++++++

Create ``input.yaml`` file in your working directory, it should contain the following flags:

    - **system** - path to the protein-ligand PDB file
    - **chain** - ligand chain ID, here ``Z``
    - **resname** - ligand residue name, in our case ``LIG``
    - **induced_fit_long** - to run an exhaustive induced fit simulation
    - **atom_dist** - atoms used to track the progress of the simulation, we will use one from the ligand and one from the receptor, following ``chain ID:residue number:atom name`` format
    - **cpus** - number of CPUs you want to use for the simulation (we suggest a minimum of 50 for a normal simulation, but you could lower it for training purposes only)
    - **seed** - random seed to ensure reproducibility.

..  code-block:: yaml

    system: 'docking.pdb'
    chain: 'Z'
    resname: 'LIG'
    induced_fit_long: true
    seed: 12345
    cpus: 50
    atom_dist:
    - "A:29:N" # nitrogen of Ser29 in the binding site
    - "Z:1:C13"
    #pele_licenses: /gpfs/projects/bsc72/PELE++/mniv/V1.6.1/license/ #(Example MN4 - Need it if complain about licenses)

**We strongly recommend running a test first to ensure all your input files are valid.** Simply include ``test: true`` in your input.yaml and launch the simulation, it will only use 5 CPUs. If it finishes correctly, you can remove the test flag and start a full production run.
Otherwise, inspect the logs and correct any mistakes indicated in the error codes.


3. Launching the simulation
+++++++++++++++++++++++++++++

Once you have ``docking.pdb`` and ``input.yaml`` in your working directory, you can launch the simulation using one of the following methods:

    - **directly** on command line using ``python -m pele_platform.main input.yaml``

    - submit a slurm file to the **queue system** (ask your IT manager, if you are not sure how to do it). In our case, the slurm file is called ``slurm.sl`` and we can launch it on the command line using ``sbatch slurm.sl``

Example slurm file:

.. code-block:: console

    #!/bin/bash
    #SBATCH -J PELE
    #SBATCH --output=mpi_%j.out
    #SBATCH --error=mpi_%j.err
    #SBATCH --ntasks=50
    #SBATCH --mem-per-cpu=1000

    python -m pele_platform.main input.yaml

You can download ready slurm files for :download:`MareNostrum<../files/slurm_mn.sl>` and the :download:`NBD cluster <../files/slurm_nbd.sl>`.
If you are running the simulation on the NBD cluster, you have to include ``usesrun: true`` in your input.yaml!

4. Analysis of the results
++++++++++++++++++++++++++++

Inside your ``LIG_Pele`` directory, you will find several folders containing simulation configuration and data, raw output as well as ``results`` folder, which
we will be mostly concerned with in this tutorial.

If you want to understand more about the content of the ``LIG_Pele`` directory, you can refer to the PELE++ documentation:

    - `Data folder <https://nostrumbiodiscovery.github.io/pele_docs/molecularParameters.html>`_
    - `pele.conf <https://nostrumbiodiscovery.github.io/pele_docs/GeneralStructure/GeneralStructure.html>`_
    - `adaptive.conf <https://adaptivepele.github.io/AdaptivePELE/Examples.html#control-file-outline>`_

a. Plots
-------------

In the ``LIG_Pele/results/Plots`` folder you will find several plots, you can inspect them to get the general idea of the progression of the simulation.
For example, you can see a clear relationship between the ligand's solvent accessible surface area and binding energy on ``currentEnergy_Binding_Energy_sasaLig_plot.png``.

.. image:: ../img/induced_tutorial_sasa.png
  :width: 400
  :align: center

b. Selected binding modes
-------------------------

The software extracts the top 100 lowest binding energy structures in ``LIG_Pele/results/BestStructs/``. Additionally, it clusters all poses based on
ligand heavy atom coordinates, the best energy representative of each cluster can be found in ``LIG_Pele/results/clusters/``. The figure below shows
a representative of cluster 5 (green) superposed with the native pose (pink, PDB code 6CF7), the resulting RMSD is 4.99Å.

.. image:: ../img/induced_tutorial_cluster5.png
  :width: 400
  :align: center

For more information regarding the outputs of the tutorial see `Output files <outputs.html>`_