<commit_before>---
layout: page
title: "Tutorial EDES / HADDOCK for ligand-protein docking"
tags: [GROMACS, HADDOCK, EDES, molecular dynamics, metadynamics, docking]
image:
feature: pages/banner_education-thin.jpg
---


This tutorial consists of the following sections:


* table of contents
{:toc}

<hr>
## Introduction

In this tutorial we will exploit enhanced sampling molecular dynamics, namely metadynamics [[1](#ref01)], to improve the predictive power of molecular docking of small ligands to a protein target in the framework of an ensemble docking strategy [[2](#ref02)]. Molecular docking is a widely used technique employed in Computer-Aided Drug Design (CADD) [[3](#ref03)] with the aim to drastically reduce the cost associated with the development of new drugs. A docking calculation can be grossly split into two main tasks:

* The search for putative binding poses of the ligand onto the selected protein target(s);

* The ranking of all generated poses, according to a given _scoring function_ which should reproduce at best the free energy of binding of the complex. In some cases, however, scoring functions do not aim to estimate the free energy of binding but they are just used to sort the poses in a way to distinguish the most stable ones from unstable complex conformations.


Docking is also the main computational method used in virtual screening (VS) campaigns aiming to the identification of putative drug candidates *in silico* [[3](#ref03),[4](#ref04),[5](#ref05)]. Clearly, a docking calculation should correctly rank native-like poses of virtually any compound as the top ones, thus assigning them the highest score (i.e. the lowest binding free energy). Both the above tasks i) and ii) can be implemented in different ways, however, they are both subject to failures due to the intrinsic limitations of docking algorithms. Examples of these limitations include the lack of protein flexibility (unability to sample bound-like conformations of the binding site) and problems in correctly accounting for entropic and de-solvation contributions  the scoring functions [[3](#ref03),[4](#ref04),[6](#ref06)].


The sampling problem, which is the focus of the present tutorial, is a crucial issue in presence of significant structural changes of the protein receptor due to the interaction with ligands (mechanism known as induced fit [[7](#ref07)]), which induces at least changes in the orientation of side chains and more generally in the shape of the binding site. This is particularly relevant in the search for new drug candidates, for which experimental information regarding their complexes with the target protein is not always available.


Several methods were developed in the past decades to deal with this issue, but a general and successful approach is still missing [[6](#ref06),[8](#ref08)]. Among the existing approaches, the ensemble-docking method was developed with the aim to account for the plasticity of the receptor by performing docking calculations on multiple conformations (of the receptor) obtained by experimental or computational means. The advantage of using multiple conformations is the increased probability to have in the pool of structures at least one featuring a bound-like conformation of the binding site, suitable to optimally dock the correct ligand(s). This approach was shown to improve the outcome of docking and VS calculations [[2](#ref02),[3](#ref03),[5](#ref05)].


In this tutorial we will show how to further improve the predictive power of molecular docking in cases where even a long unbiased MD simulation is unable to generate bound-like conformations of the target under study. This is usually the case of proteins undergoing large-scale conformational changes, often involving transitions between states separed by high free energy (compared to thermal energy) barriers. The target protein we will focus on during the tutorial, the T4 phage beta-glucosyltransferase, belongs to this category, as shown by the large distortion at the binding site (BS) upon binding of its substrate uridine diposphate (RMSD<sub>BS</sub>  = 2.8 A between unbound and bound structures). In this tutorial, different conformations of the protein generated by enhanced-sampling MD simulations will be used in ensemble docking runs. The results will be compared with those obtained using an ensemble of protein structures generated by an unbiased MD simulation of the apo system.


The following software packages will be used during the tutorial:

* [GROMACS2019](http://manual.gromacs.org/documentation/2019-beta1/index.html): a versatile package to perform molecular dynamics simulations and several related analyses;

* [PLUMED 2.5](https://www.plumed.org/doc-v2.5/user-doc/html/index.html): a package working with different MD simulation engines (such as GROMACS) used to implement different flavours of non-equilibrium MD simulations (among the others features), as well as for analysis purposes;

* [R](https://www.r-project.org/about.html): a data analysis software, used here to perform the cluster analysis on the MD trajectory;

* [HADDOCK2.4](https://wenmr.science.uu.nl/haddock2.4/): The HADDOCK web portal which allows to perform information-driven flexible docking among macromolecules and between a chemical compound and a macromolecule.


The content of this tutorial follows the workflow presented [in our recently published work]( [https://pubs.acs.org/doi/10.1021/acs.jcim.8b00730](https://pubs.acs.org/doi/10.1021/acs.jcim.8b00730)):

<NAME>, <NAME>, <NAME>, <NAME>, <NAME>, **Holo-like and Druggable Protein Conformations from Enhanced Sampling of Binding Pocket Volume and Shape**,  *J. Chem. Inf. Model*, 2019, 59, 4, 1515-1528, https://doi.org/10.1021/acs.jcim.8b00730

Throughout the tutorial, coloured text will be used to refer to questions or instructions.

<a class="prompt prompt-question">This is a question prompt: try answering it!</a>
<a class="prompt prompt-info">This is an instruction prompt: follow it!</a>
<a class="prompt prompt-pymol">This is a VMD prompt: write this in the Tk Console!</a>
<a class="prompt prompt-cmd">This is a Linux prompt: insert the commands in the terminal!</a>


<hr>
## System setup and MD simulations

First, let's create the directories needed for the tutorial. Open a terminal and type the following commands:

<a class="prompt prompt-cmd"><commit_msg>Added link to tutorial data and scripts<commit_after>---
layout: page
title: "Tutorial EDES / HADDOCK for ligand-protein docking"
tags: [GROMACS, HADDOCK, EDES, molecular dynamics, metadynamics, docking]
image:
feature: pages/banner_education-thin.jpg
---


This tutorial consists of the following sections:


* table of contents
{:toc}

<hr>
## Introduction

In this tutorial we will exploit enhanced sampling molecular dynamics, namely metadynamics [[1](#ref01)], to improve the predictive power of molecular docking of small ligands to a protein target in the framework of an ensemble docking strategy [[2](#ref02)]. Molecular docking is a widely used technique employed in Computer-Aided Drug Design (CADD) [[3](#ref03)] with the aim to drastically reduce the cost associated with the development of new drugs. A docking calculation can be grossly split into two main tasks:

* The search for putative binding poses of the ligand onto the selected protein target(s);

* The ranking of all generated poses, according to a given _scoring function_ which should reproduce at best the free energy of binding of the complex. In some cases, however, scoring functions do not aim to estimate the free energy of binding but they are just used to sort the poses in a way to distinguish the most stable ones from unstable complex conformations.


Docking is also the main computational method used in virtual screening (VS) campaigns aiming to the identification of putative drug candidates *in silico* [[3](#ref03),[4](#ref04),[5](#ref05)]. Clearly, a docking calculation should correctly rank native-like poses of virtually any compound as the top ones, thus assigning them the highest score (i.e. the lowest binding free energy). Both the above tasks i) and ii) can be implemented in different ways, however, they are both subject to failures due to the intrinsic limitations of docking algorithms. Examples of these limitations include the lack of protein flexibility (unability to sample bound-like conformations of the binding site) and problems in correctly accounting for entropic and de-solvation contributions  the scoring functions [[3](#ref03),[4](#ref04),[6](#ref06)].


The sampling problem, which is the focus of the present tutorial, is a crucial issue in presence of significant structural changes of the protein receptor due to the interaction with ligands (mechanism known as induced fit [[7](#ref07)]), which induces at least changes in the orientation of side chains and more generally in the shape of the binding site. This is particularly relevant in the search for new drug candidates, for which experimental information regarding their complexes with the target protein is not always available.


Several methods were developed in the past decades to deal with this issue, but a general and successful approach is still missing [[6](#ref06),[8](#ref08)]. Among the existing approaches, the ensemble-docking method was developed with the aim to account for the plasticity of the receptor by performing docking calculations on multiple conformations (of the receptor) obtained by experimental or computational means. The advantage of using multiple conformations is the increased probability to have in the pool of structures at least one featuring a bound-like conformation of the binding site, suitable to optimally dock the correct ligand(s). This approach was shown to improve the outcome of docking and VS calculations [[2](#ref02),[3](#ref03),[5](#ref05)].


In this tutorial we will show how to further improve the predictive power of molecular docking in cases where even a long unbiased MD simulation is unable to generate bound-like conformations of the target under study. This is usually the case of proteins undergoing large-scale conformational changes, often involving transitions between states separed by high free energy (compared to thermal energy) barriers. The target protein we will focus on during the tutorial, the T4 phage beta-glucosyltransferase, belongs to this category, as shown by the large distortion at the binding site (BS) upon binding of its substrate uridine diposphate (RMSD<sub>BS</sub>  = 2.8 A between unbound and bound structures). In this tutorial, different conformations of the protein generated by enhanced-sampling MD simulations will be used in ensemble docking runs. The results will be compared with those obtained using an ensemble of protein structures generated by an unbiased MD simulation of the apo system.


The content of this tutorial follows the workflow presented [in our recently published work]( [https://pubs.acs.org/doi/10.1021/acs.jcim.8b00730](https://pubs.acs.org/doi/10.1021/acs.jcim.8b00730)):

<NAME>, <NAME>, <NAME>, <NAME>, <NAME>, **Holo-like and Druggable Protein Conformations from Enhanced Sampling of Binding Pocket Volume and Shape**,  *J. Chem. Inf. Model*, 2019, 59, 4, 1515-1528, https://doi.org/10.1021/acs.jcim.8b00730

Throughout the tutorial, coloured text will be used to refer to questions or instructions.

<a class="prompt prompt-question">This is a question prompt: try answering it!</a>
<a class="prompt prompt-info">This is an instruction prompt: follow it!</a>
<a class="prompt prompt-pymol">This is a VMD prompt: write this in the Tk Console!</a>
<a class="prompt prompt-cmd">This is a Linux prompt: insert the commands in the terminal!</a>


<hr>
## Requirements

The following software packages will be used during the tutorial:

* [GROMACS2019](http://manual.gromacs.org/documentation/2019-beta1/index.html): a versatile package to perform molecular dynamics simulations and several related analyses;

* [PLUMED 2.5](https://www.plumed.org/doc-v2.5/user-doc/html/index.html): a package working with different MD simulation engines (such as GROMACS) used to implement different flavours of non-equilibrium MD simulations (among the others features), as well as for analysis purposes;

* [R](https://www.r-project.org/about.html): a data analysis software, used here to perform the cluster analysis on the MD trajectory;

* [HADDOCK2.4](https://wenmr.science.uu.nl/haddock2.4/): The HADDOCK web portal which allows to perform information-driven flexible docking among macromolecules and between a chemical compound and a macromolecule.


You can download from [here](https://surfdrive.surf.nl/files/index.php/s/uChxo21NAp2QxSl) an archive containing the `tutorial_files` directory used in this tutorial. 
This contains all the materials and scripts needed to run the tutorial.



<hr>
## System setup and MD simulations

First, let's create the directories needed for the tutorial. Open a terminal and type the following commands:

<a class="prompt prompt-cmd">
{'fraction_non_alphanumeric': 0.0683106141828764, 'fraction_numerical': 0.017996630418134478, 'mean_word_length': 5.128108869075551, 'pattern_counts': {'":': 0, '<': 40, '<?xml version=': 0, '>': 40, 'https://': 13, 'lorem ipsum': 0, 'www.': 4, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '3317074', 'n_tokens_mistral': 3455, 'n_tokens_neox': 3057, 'n_words': 1764}