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A P P L I C A T I O N N O T E Open Access
AMDock: a versatile graphical tool for
assisting molecular docking with Autodock
Vina and Autodock4
Mario S. Valdés-Tresanco
1*
, Mario E. Valdés-Tresanco
2,3
, Pedro A. Valiente
2,4
and Ernesto Moreno
1*
Abstract
AMDock (Assisted Molecular Docking) is a user-friendly graphical tool to assist in the docking of protein-ligand
complexes using Autodock Vina and AutoDock4, including the option of using the Autodock4Zn force field for
metalloproteins. AMDock integrates several external programs (Open Babel, PDB2PQR, AutoLigand, ADT scripts) to
accurately prepare the input structure files and to optimally define the search space, offering several alternatives
and different degrees of user supervision. For visualization of molecular structures, AMDock uses PyMOL, starting it
automatically with several predefined visualization schemes to aid in setting up the box defining the search space
and to visualize and analyze the docking results. One particularly useful feature implemented in AMDock is the off-
target docking procedure that allows to conduct ligand selectivity studies easily. In summary, AMDock’s functional
versatility makes it a very useful tool to conduct different docking studies, especially for beginners. The program is
available, either for Windows or Linux, at https://github.com/Valdes-Tresanco-MS.
Reviewers: This article was reviewed by Alexander Krah and Thomas Gaillard.
Keywords: AMDock, AutoDock4, AutoDock Vina, AutoDock4Zn, Docking, Graphical user interface
Background
Molecular docking has become a powerful tool for
lead discovery and optimization. A large number of
docking programs have been developed during the
last three decades, based on different search algo-
rithms and scoring functions. Aiming to make these
docking programs more user-friendly, especially to be-
ginners, different graphical user interfaces (GUIs)
have been developed to assist in the preparation of
molecular systems, the execution of the calculations
and/or the analysis of the results. Examples of avail-
able GUIs (developed mostly for AutoDock [1]and/or
Autodock Vina [2]) are AutoDock Tools (ADT), inte-
grated into the PMV graphical package [1], BDT [3],
DOVIS [4,5], VSDocker [6], AUDocker LE [7],
WinDock [8], DockoMatic [9], PyMOL AutoDock
plugin (PyMOL/AutoDock) [10], PyRx [11], MOLA
[12], DockingApp [13] and JADOPPT [14].
We present here a new multi-platform tool, AMDock
(Assisted Molecular Docking), whose main advantage
over its predecessors is the integration of several valu-
able external tools within a simple and intuitive graph-
ical interface that guides the users along well-established
docking protocols - using either Autodock4 or Auto-
Dock Vina - from system preparation to analysis of
results.
Functionalities and workflow
AMDock integrates functionalities from Autodock Vina
and Autodock4, ADT scripts, AutoLigand [15], Open
Babel [16], PDB2PQR [17] and PyMOL [18]. For pro-
teins containing a zinc ion in the active site, AMDock
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* Correspondence: mariosergiovaldes145@gmail.com;
emoreno@udem.edu.co
1
Faculty of Basic Sciences, University of Medellin, Medellin, Colombia
Full list of author information is available at the end of the article
Valdés-Tresanco et al. Biology Direct (2020) 15:12
https://doi.org/10.1186/s13062-020-00267-2
has the option of using the specially tailored Auto-
dock4Zn [19] parameters. AMDock is coded in Python
2.7 and is available for Windows and Linux. On Win-
dows, it is packaged together with all the integrated
tools, hence no additional software installation is re-
quired. On Linux, only Open Babel and PyMOL should
be installed (both tools are included in most popular
Linux repositories).
The AMDock main window has five tabs: 1) Home, 2)
Docking Options, 3) Results Analysis, 4) Configuration,
and 5) Info. A summary of AMDock’s functionalities
and workflow is presented below (Fig. 1) and discussed
afterwards in more detail.
In the “Home”tab, the user can select the docking
engine: Autodock Vina or Autodock4, with the add-
itional option of using the Autodock4Zn parameters.
Then the user is automatically directed to the
“Docking Options”tab, which contains four panels
that guide a sequential preparation of a docking
simulation.
Input files for AMDock
Minimally, the Cartesian coordinates of the ligand
and receptor molecules are needed, which can be pro-
vided in several common structure formats, e.g. PDB
or PDBQT for the protein, and PDB, PDBQT or
Mol2 for the ligand. If the protein coordinates come
together with a bound ligand, the coordinates of the
later are stored and can be used afterward to define
the search space.
The program works by following three main steps:
1- Preparing the docking input files:First,theuser
may set a pH value for the protonation of both
the ligand (optional, default value 7.4), using
Open Babel and the protein (default value: 7.4),
using PDB2PQR. Two different docking options
are available: a) “simple docking”,forpredicting
the binding mode of a single protein-ligand com-
plex, and b) “off-target docking”,forpredicting
the binding poses of a ligand with two different
receptors, i.e. the target and the off-target. Fi-
nally, the “Scoring”option included in this tab al-
lows to score an already existing protein-ligand
complex, using the Autodock Vina, Autodock4 or
Autodock4Zn functions. Once the docking or
scoring protocol has been selected, the input files
are prepared using ADT scripts.
2- Defining the search space: Four different approaches
can be used to define a box center and dimensions:
a) “Automatic”- the program uses AutoLigand to
predict possible binding sites and then a box with
optimal dimensions is centered on each AutoLigand
object,
1
at each predicted binding site. b) “Center
Fig. 1 AMDock workflow
Valdés-Tresanco et al. Biology Direct (2020) 15:12 Page 2 of 12
on Residue(s)”- AutoLigand is used to generate an
object with a volume in correspondence with the
ligand size, using as reference the geometric center
of the selected residues. Then, a box with optimal
dimensions is centered on the generated object. c)
“Center on Hetero”- a box is placed on the
geometric center of an existing ligand (if the
receptor was given in complex with a ligand), and
d) “Box”- the box center and dimensions are
defined by the user. The box generated with any of
these methods can be visualized in PyMOL and
easily modified at the user’s convenience using the
new AMDock plugin (adapted from [10]) embedded
in the PyMOL menu window.
3- Running the docking simulations and analyzing the
results: After running the molecular docking
calculations (started by clicking the “Run”button),
the user will be taken automatically to the “Results
Analysis”tab, where the Affinity, Estimated Ki
values and Ligand Efficiencies are listed for the
different binding poses.
The estimated Ki is a very useful value as it is more re-
lated to usually measured experimental parameters, as
compared to the affinity. Ligand efficiency (LE), on the
other hand, is an important informative parameter when
selecting a lead compound [20]. Here, LE is calculated
by using the following equation:
LE ¼
−ΔG
HA ;ð1Þ
where ΔG is the free energy of binding or the calculated
score value and HA is the number of heavy (non-hydro-
gen) atoms of the ligand. Compounds with LE > 0.3 are
highlighted as potential lead compounds [21].
The “Show in PyMOL”button starts PyMOL with a
customized visualization of the complex between the re-
ceptor and the selected pose (the lowest-energy ligand
pose is chosen by default). The resulting data through-
out the process is stored in a file (*.amdock), which can
be used to examine the results at any time later.
Different docking parameters can be set in the “Con-
figuration”tab, while the “Info”tab, gives access to
handy documentation, including a user manual and
references.
Visualization
AMDock relies on PyMOL for visualization at two dif-
ferent stages: 1) setting up the grid box location and
dimensions (the search space), and 2) analysis of the
docking results. PyMOL is a versatile and user-friendly
molecular analysis program which, besides, allows to
create high-quality images for publication. We have
coded in AMDock several predetermined PyMOL repre-
sentations for the two stages, selecting the visual design
and information that we considered optimal in each
case. These predefined representations can be modified
by the user within PyMOL.
Search space
The predetermined representations (in descending order
of complexity, according to the number of elements in
the visualization contents) are the following: 1) Box -a
simple representation where the protein under study ap-
pears as cartoon, together with the box with the specifi-
cations defined by the user (Fig. 2a); 2-Centered on
Hetero - includes the receptor protein (cartoon) and the
box with an optimal size centered on the selected previ-
ous ligand (sticks) (Fig. 2b); 3-Centered on Residue(s) -a
representation that allows the user to identify the resi-
dues that were selected to define the search space. The
protein is represented as cartoon, the selected residues
as sticks and the AutoLigand object as points. The calcu-
lated box is also showed, so that the user can easily
check and adjust (if necessary) its position and dimen-
sions (Fig. 2c). 4-Automatic –Here we intended to cre-
ate a simplified representation to show all the binding
sites predicted by AutoLigand. The protein is in cartoon,
each AutoLigand object is represented in sticks, sur-
rounded by a surface constructed on its neighboring res-
idues. Since docking simulations are to be performed for
each site predicted by AutoLigand, a box is generated
for each site, but showed only for a user-selected site
(Fig. 2d). As mentioned above, in any of these variants
the box center and size can be easily modified using the
AMDock plugin implemented in PyMOL.
Results analysis
The protein is represented in cartoon. Each ligand pose
is drawn in sticks and its polar contacts with the protein
are shown as dashed lines. A similar visualization is also
possible for both proteins if the “Off-Target Docking”
procedure was chosen (Fig. 3c). This allows a simultan-
eous comparison of ligand poses for both the target and
off-target proteins.
Case study: SAR405 binding selectivity - PI3Kγvs. Vps34
Phosphatidylinositol 3-kinase (PI3K) is an enzyme in-
volved in growth, proliferation, motility, survival, and
intracellular trafficking [22]. PI3K is also a promising
cancer target, with several of its inhibitors being
already in the clinical stage. A few of these inhibitors
are currently in phase III clinical trials and one of
1
AutoLigand Object is equivalent to the term “envelope”used by the
authors of AutoLigand and consists of a representation of a contiguous
region in space, filled with points that represent potential atomic
centers for ligand atoms [15]
Valdés-Tresanco et al. Biology Direct (2020) 15:12 Page 3 of 12
them, alpelisib, recently (May 2019) received the FDA
approval for use in the treatment of metastatic breast
cancer.
PI3K has several isoforms that are grouped in 3 dif-
ferent classes. Class I includes four different isoforms
(α,β,γand δ), while class III is composed of only
one protein, called Vps34 [22]. Because of their se-
quence and structural similarities, some inhibitors
may bind different isoforms, whereas several other in-
hibitors were designed to be isoform specific. Our re-
search group is currently focused on the identification
of PI3K inhibitors with the capability of inhibiting
PI3K orthologs found in different pathogenic microor-
ganisms, which express only the ancestral Vps34 iso-
form. For this purpose, AMDock represents a
valuable tool, particularly its “Off-Target Docking”op-
tion. Here we demonstrate its use with an exercise
that resembles our own research work.
Sar405 is a highly specific inhibitor of Vps34 (IC
50
=
1.2 nM), while its IC
50
for other isoforms is > 10
4
nM
[23]. A crystal structure of SAR405 in complex with hu-
man Vps34 is available in the Protein Data Bank [24]
(PDB code: 4oys). Here we use the human Vps34 as the
“Target”receptor, while the PI3K gamma isoform (PDB:
3apf) is used as the “Off-Target”receptor. Both struc-
tures contain a bound ligand in the active site, which is
convenient for generating the grid box. In the first step,
we select the docking program (Autodock Vina) and
thereafter a project folder is created in the computer
hard disk. After loading both protein structures, we take
advantage of their sequence similarity to use the avail-
able option of aligning and superimposing their struc-
tures using PyMOL, which makes possible defining a
common search space and simplifying the subsequent
analysis of the docking results. Next, the input files are
prepared automatically, which includes protonation of
titratable residues, merging of non-polar hydrogens
and ion/water removal. The center of the box is de-
fined based on the geometric center of the bound li-
gands (Fig. 3a), while the size of the box is defined
based on the radius of gyration of the ligand to be
docked [25], i.e. the SAR405 inhibitor in this case.
The initial ligand conformation (its torsion angles)
was randomized using ADT.
Fig. 2 Binding site visualization with PyMOL. aUser-defined box. This is an example used in tutorials with AutoDock4Zn and farnesyltransferase
(hFTase). bCentered on Hetero, (c) Centered on Residue(s) and (d) Automatic mode. Representations B, C and D correspond to Vps34
(PDB: 4uwh)
Valdés-Tresanco et al. Biology Direct (2020) 15:12 Page 4 of 12
Once the process is completed, the results show that
SAR405 is more selective for Vps34 (−9.2 kcal/mol) than
for Pi3Kγ(−7.3 kcal/mol) as expected (Fig. 3b). The pre-
dicted binding pose for SAR405 in Vps34 is close to the
crystal geometry (rmsd = 1.9 Å for all ligand atoms, rmsd =
0.5 Å for the ring core). Also, the predicted Ki value for
this complex is in the nanomolar range, which agrees with
the experimental value. On the other hand, a much higher
Ki value is predicted for the Pi3Kγ-SAR405 complex, and
the predicted binding pose differs significantly from the
crystallographic structure (rmsd= 4.7), as shown in Fig.
3c, which may explain the poor affinity value predicted by
AutoDock Vina. This study case has been incorporated as
a tutorial in the user manual, which is included in the
AMDock installation folder, and the wiki on Github
(https://github.com/Valdes-Tresanco-MS/AMDock-win/
wiki/4.3-Off-target-docking).
Discussion
AMDock provides a novel, easy-to-use and versatile
interface to work with two molecular docking engines,
Autodock4 and Autodock Vina, having different func-
tionalities and characteristics. AMDock should be very
useful to researchers with little experience in working
Fig. 3 Off-target docking of SAR405. aVisualization of the search space for docking, centered on known ligands. bAffinity comparison. c
Superposition of the best pose of SAR405 in complex with PI3Kγ(3apf) (protein in cyan cartoon and ligand in magenta sticks) on the reference
complex Vps34-SAR405 (4oys) (protein in gray cartoon and ligand in green sticks)
Valdés-Tresanco et al. Biology Direct (2020) 15:12 Page 5 of 12
with docking programs since no previous knowledge of
the particular functioning of these programs is needed.
Three different workflows (simple docking, off-target
docking and scoring) are included in the AMDock envir-
onment. We find the off-target docking procedure par-
ticularly helpful for conducting ligand selectivity studies
- a critical step in the drug design process.
Preparing the input files in a proper and consistent
way, as well as correctly defining the search space, are
critical issues when performing molecular docking stud-
ies. Several external programs/scripts are integrated into
AMDock to allow preparing the input files with minimal
effort while keeping control of the process. AMDock
uses OpenBabel and PDB2PQR for ligand and receptor
protonation, respectively, while the other GUIs men-
tioned in the Introduction use ADT for both receptor
and ligand protonation (with the exception of Dockin-
gApp, which uses also OpenBabel for ligand
protonation).
To define the search space, AMDock offers several op-
tions to set the position of the grid box in different sce-
narios, while the input ligand is used by default to
determine the box optimal dimensions, which decreases
the computational cost while optimizing the docking
process [25]. In this regard, only ADT and the PyMOL/
AutoDock plugin offer some limited options other than
a user-defined search space, but in any case the box size
must be defined by the user. In some of these GUIs, as
in DockingApp, the search space covers the entire recep-
tor, which leads to additional computational costs and
possibly compromises the accuracy of the simulations.
With other GUIs, the user must use an external applica-
tions such as ADT to define the box parameters.
The “Centered on Residue(s)”option is preferable
when the binding site residues are known. With this op-
tion, an object placed at the geometric center of the se-
lected residues is generated with AutoLigand on the
protein surface. This procedure optimizes both the loca-
tion and size of the search space. If the box was centered
instead on the geometric center of the selected residues,
a significant part of it will likely be embedded in the pro-
tein, demanding a larger size to cover the needed sam-
pling space (Fig. 4). The “Centered on Hetero”
alternative is useful for redocking studies on complexes
with crystallographic structures or when studying li-
gands with similar binding modes (Fig. 2b). The “Auto-
matic”option, on the other hand, is desirable when no
information regarding the binding site is available. In
this case, an independent docking run is performed for
every binding site predicted by AutoLigand (Fig. 2d).
Fig. 4 Comparison between a box (white) located at the geometric center of the selected residues (A:ILE:634, A:TYR:670, A:PHE:684, A:PHE:758,
A:ILE:760; in salmon) and an a box (magenta) centered on an object generated by AutoLigand from the geometric center of the selected
residues. In the later case, the box defines a more optimal ligand sampling space
Valdés-Tresanco et al. Biology Direct (2020) 15:12 Page 6 of 12
Table 1 Comparison of AMDock and AutoDock Tools features
Features AMDock AutoDock Tools
File formats Receptor: pdb, pdbqt pdb, mol2, pdbq, pdbqs,
pdbqt, pqr, cif
Ligand: pdb, pdbqt, mol2 pdb, pdbq, mol2
Protonation Receptor:
PDB2PQR Uses the last version
1
Uses PDB2PQR v1.2.1
pH value adjustment Yes No (default 7.0)
Experimental protonation state Only if the user enters a
protonated structure
2
Only histidines or when the user
enters a protonated structure
Ligand:
Open Babel Uses the last version
1
Basic implementation of Open
Babel v1.6
pH value adjustment Yes No (default value: 7.0)
Structure manipulation Flexible Side Chains Not implemented
2
Yes
Flexible Ligand Active torsions not implemented
2
Yes
Center Automatic
3
Possible binding sites are
determined with AutoLigand.
Docking is performed for each site.
The user must select a predicted
site and prepare the search space.
This should be repeated for each
site to be tested.
Center on Residues Centers the box on an AutoLigand
object, calculated for a group of
selected residues
Only on a selected atom
4
Center on Hetero Centers the search space in the
geometric center of a heteroatom set
found in the defined receptor pdb.
On selected heteroatoms or on
a ligand
5
Custom Box Box coordinates defined by the user. Box coordinates defined by the user.
Box Size Determined from the radius of
gyration of ligand, or set by the
user
6
Defined by the user.
Docking programs AutoDock4 Yes Yes
AutoDock Vina Yes Yes
Docking type Simple Yes Yes
Virtual Screening No
2
No
Off-target Docking Yes No
Covalent Docking No
2
Yes
Using Autodock4ZN Yes Command line
Hydrated docking No
2
Command line
Analysis of Results Simple docking Yes Yes
Virtual Screening No
2
Yes
Off-target docking Yes No
Covalent docking No
2
Yes
Autodock4 ZN docking Yes Yes
Hydrated docking No
2
Yes
Graphical Visualization Engine PyMOL Python Molecular Viewer
Capacity All the options included in PyMOL Protein-ligand interactions and cluster
manager for AutoDock4 results
Publication-quality images Easy high-resolution and custom
image generation
Easy low-resolution image generation.
Difficult high-resolution image generation
Maintenance Active development Inactive
Programming Python base Python 2.7.15
7
Python 2.6 (Inactive development)
Valdés-Tresanco et al. Biology Direct (2020) 15:12 Page 7 of 12
This way, the information from the AutoLigand ranking
method is combined with that of the docking engine,
without making an arbitrary selection of one of the pre-
dicted sites. This process is done automatically and the
results for each of the predicted binding sites can be vi-
sualized in PyMOL. Overall, the definition and
visualization of the box involves a minimal effort and
can always be modified, thus representing an advantage
not only for the novice user but also for experts.
Itisworthnotingthatwestandardizedtheboxsizetobe
in Angstroms to avoid commonly occurring errors, as re-
ported in different forums and mailing-lists. These errors
arise from the different ways in which the box dimensions
are defined in AutoDock (number of points + grid spacing)
and Autodock Vina (in angstroms), and may cause the
search space to be very small or too large, leading ultim-
ately to inconsistencies in the obtained docking results.
The integration of AMDock with PyMOL represents a
significant advantage. Indeed, PyMOL is a widely used
molecular viewer with great community support and ac-
tive development. Within PyMOL, docking results can
be analyzed with multiple tools, in particular with the
powerful Protein-ligand Interaction Profiler [26]. Other
applications such as ADT, PyRx or DockingApp have
their own graphical viewers. PyRx and DockingApp offer
simple solutions with limited analytical capabilities,
while ADT allows only for simple analysis of protein-
ligand interactions.
Furthermore, with AMDock it is possible to launch
docking simulations for metalloproteins using the Auto-
Dock’s Zn force field, wich is available in ADT only via
command line. Its off-target docking option, very useful
for drug repurposing studies, is available only in Docko-
matic and PyRx (in the later, only in the payment
version).
Most of the docking GUIs are focused on virtual
screening. Currently, AMDock does not have support
for virtual screening, however, we are currently working
on its implementation, to make it available in the next
program version.
Finally, and since ADT is probably the most widely
used docking GUI, we provide a more detailed compari-
son between AMDock and ADT (Table 1).
Conclusions
AMDock is a user-friendly GUI that works in a highly
intuitive and interactive manner, allowing to perform
molecular docking studies with Autodock4 and Auto-
Dock Vina with a minimal setup effort. These character-
istics make AMDock an attractive tool also for teaching
purposes. AMDock gathers features and procedures that
are not present in other similar programs. It includes re-
cent developments in AutoDock, such as the Auto-
dock4Zn parameterization. For our group, AMDock has
been very useful for estimating the selectivity profile of
different PI3K inhibitors over orthologous proteins in
several microorganisms. Further developments (hydrated
ligand, covalent docking and virtual screening) will be
included as docking options in future versions.
Reviewers’comments
Reviewer 1, Alexander Krah
Summary: Valdés-Tresanco et al. describe in their manu-
script entitled “AMDock: A versatile graphical tool for
assisting molecular docking with Autodock Vina and
Autodock4”the implementation of several molecular
modelling tools in a graphical user interface, which al-
lows to setup and perform docking simulations with
Autodock4 or Autodock Vina. I think the tool is inter-
esting and may allow individuals who just begin with
Table 1 Comparison of AMDock and AutoDock Tools features (Continued)
Features AMDock AutoDock Tools
Easy to use
8
Docking preparation 1 4
Analysis of Results 2 3
GUI simplicity 1 4
Process Log 1 4
Installation 2 2
Platform Linux and Windows Linux, Windows and Mac
1
Last version with Python 2.x support
2
Will be available in the next release
3
A common alternative is to do the so-called blind docking, in which the search space is defined to cover the entire receptor. This involves an increasing in the
sampling number so as not to compromise the accuracy of the docking, which leads to an increase in computational cost. Additionally, it can introduce false
positives by sampling sites with a different nature than the binding site. Results are usually questionable due to the non-convergence of the scoring functions
4
We describe the advantages of the method used in AMDock concerning this selection (Fig. 4)
5
It is possible to select an atom only if the heteroatoms of the receptor have not been removed. After that, these atoms must be removed and the receptor
should be redefined. Another possibility is entering a set of heteroatoms and directly select the option “center on ligand”. Both options have limitations and need
a deeper understanding of the ADT program
6
We describe the advantages of the method used in AMDock
7
Next version in python 3.x under development (https://github.com/Valdes-Tresanco-MS/AMDock-win-py3)
8
Our own evaluation using a 1–5 scale, where 1 is very easy and 5 is very difficult
Valdés-Tresanco et al. Biology Direct (2020) 15:12 Page 8 of 12
molecular docking to quickly go through the whole
process. However, I would like to ask for clarifications.
Response: We thank Dr. Krah for his positive com-
ment. Below we respond point-by-point his questions
and critical comments.
Mayor recommendations
1) Could the authors describe in more detail, how the
pKa for titratable groups of the ligand is calculated (e.g.
carboxylate or amine groups)?
Response: Ligand hydrogen atoms are added either
with Open Babel (default) or ADT. Open Babel uses a
set of fragments with known pKa to estimate the proton-
ation state of the ligand (Open Babel Documentation).
ADT adds hydrogens depending on the atom type, its
valence and minor chemical group considerations based
on a reimplementation of the PyBabel v1.6 module of
Open Babel (PyBabel documentation in ADT). The user
can decide whether to use AMDock's internal tools or
entering a protonated ligand. In the later case hydrogens
will not be added.
This point was also a concern of Reviewer 2. We have
added a statement on the optional protonation choice in
the subsection “Functionalities and workflow”. It reads:
“… (optional, default value 7.4), using Open Babel …”
2) Can the user set experimentally know protonation
states for protein residues? 3) Can protein residues be set
flexible, as this possibility is incorporated in ADT/Auto-
dock Vina.
Response: At the moment these functionalities are not
available. However, they will be incorporated in the next
AMDock version (development version in https://github.
com/Valdes-Tresanco-MS/AMDock-win-py3)
4) The authors report as an example of an off-target
binding prediction, resulting in a higher score for the tar-
get than the off-target. Could the authors test two more
examples, if targets with required structural and biophys-
ical information can be found:
a) different inhibitors binding in the same range to the
same protein bound to the same site?
Response: Three docking exercises included either in
the documentation or the manuscript involve three in-
hibitors having similar reported IC
50
values and the
same protein crystallographic structure (PDB code
4UWH, a Vps34-inhibitor complex)
- The re-docking exercise (https://github.com/Valdes-
Tresanco-MS/AMDock-win/wiki/4.2.2.1-re-Docking-ex-
periment) uses the crystallographic ligand from 4UWH:
compound No. 4 (IC
50
= 3nM) described by Pasquier et
al., 2014.
- In the “similar docking ligand”exercise (https://
github.com/Valdes-Tresanco-MS/AMDock-win/
wiki/4.2.2.2-Docking-a-similar-ligand), we use inhibitor
No. 31 (IC
50
= 2 nM, https://www.rcsb.org/ligand/7A5)
also described by Pasquier et al., 2014.
- Finally, inhibitor SAR405 (IC
50
= 1.2nM, https://
www.rcsb.org/ligand/1TT) is described as a case study in
the manuscript (https://github.com/Valdes-Tresanco-
MS/AMDock-win/wiki/4.3-Off-target-docking).
All inhibitors have similar IC
50
values and bind to
Vps34 in the same binding site. In all cases, AutoDock
Vina was able to reproduce the crystallographic com-
plex, with affinities of -9.2, -8.7 and -9.2 kcal/mol, re-
spectively, and estimated Ki values in the nanomolar
range.
It is important to mention that our case studies are
only representative examples of the methodologies im-
plemented in AMDock. As known from the literature,
the accuracy of the prediction depends on several struc-
tural factors, i.e., the protonation state, the quality of the
receptor structure, the particular side chain orientations
in the binding site (related to the induced fit effect),
among others. AMDock provides a platform for prepar-
ing docking files and optimize the search space. How-
ever, it does not influence the predictability of the
program used to perform the docking. As we remark in
the documentation, the interpretation of the results
must be based on empirical/experimental evidence,
which must be carefully studied by the user.
b) different inhibitors binding in the same range to the
same target bound to a different (potentially allosteric)
site?
Response: A new tutorial has been included in the
AMDock wiki (https://github.com/Valdes-Tresanco-MS/
AMDock-win/wiki/4.5.2-Docking-to-allosteric-binding-
sites) for such a system.
5) How does the program perform in comparison with
Autodock Vina and other tools, which incorporate Auto-
dock Vina?
Response: To date, most tools that incorporate
Autodock Vina are discontinued. We intend to pro-
vide a tool where routinary docking experiments
(simple docking, off-target docking, redocking, etc.)
can be performed with minimal effort. In terms of
speed, AMDock does not provide a better perform-
ance since it uses the standard Autodock Vina engine.
However, the integration of several externals tools to
prepare the docking files and define/optimize the
search space saves time while avoiding commonly-
made errors.
Reviewer 2, Thomas Gaillard
Summary: The manuscript of Valdés-Tresanco, Valdés-
Tresanco, Valiente, and Moreno presents AMDock, a
graphical tool aimed at facilitating molecular docking
with Autodock Vina and Autodock4. AMDock integrates
external programs (Open Babel, PDB2PQR, AutoLigand,
ADT scripts). Molecular visualization is performed with
PyMOL. The program is available for Windows and
Valdés-Tresanco et al. Biology Direct (2020) 15:12 Page 9 of 12
Linux and is distributed on Github (https://github.com/
Valdes-Tresanco-MS). Overall, the work presented is
valid and well written. The significance and originality
are however limited. There are indeed already many
existing GUIs facilitating docking calculations. In par-
ticular, the AutoDock Tools (ADT) interface to Auto-
dock Vina and Autodock4 is well conceived and offers
more functionalities than AMDock. The authors do not
provide a detailed comparison of AMDock and other
GUIs. To my opinion, the manuscript is not convincing
at demonstrating how AMDock is advantageous, at least
on some points, compared to existing tools. At last,
some methodological points need to be clarified in the
case study.
Response: We appreciate Dr. Gaillard’s comments and
agree with him in that other GUIs (ADT in particular)
offer several functionalites that are not available in
AMDock. Our goal, however, was not to outperform
ADT, but to provide a simple, easy-to-use tool with a
maximal optimization of the docking procedures. We
are currently working on expanding AMDock’s capabil-
ities, though we do believe that in its current state the
program can be useful in many aspects.
Mayor recommendations
1) A list of other docking GUIs is provided by the au-
thors in the Background section. A useful addition to the
manuscript, maybe in the Discussion section, would be a
comparative table of functionalities for these GUIs and
AMDock.
Response: In the Discussion section we now address in
more details the comparison between AMDock and
other tools mentioned in the Introduction section.
Please see the response to next point (2).
2) In particular, the authors should discuss what are
the advantages of their tool with respect to AutoDock
Tools (ADT), which is probably the most widely used
GUI for Autodock and Vina. ADT also offers many op-
tions for ligand and receptor preparation, docking input
file preparation, launching docking, results analysis, etc.
In addition, ADT includes its own visualization interface,
whereas AMDock depends on an external program
(PyMOL). - The authors claim that the main advantage
of their tool is "the integration of several valuable exter-
nal tools within a simple and intuitive graphical inter-
face that guides the users along well-established docking
protocols - using either Autodock4 or AutoDock Vina -
from system preparation to analysis of results" (p3). This
is rather vague and it would be helpful to clarify which
of these external tools offer a unique advantage over
existing GUIs.
Response: We have included in the Discussion section
a more comprehensive comparison between AMDock
and ADT –the new Table 1. We would like to
emphasize that our aim in developing AMDock is not to
replace ADT, but to create a helpful complement. From
our perspective, ADT can be a bit difficult to manipulate
for non-expert users. On the other hand, we continue
working to make AMDock a more versatile and robust
tool. Finally, it is worth mentioning that, to our know-
ledge, ADT development is not currently active.
3) In particular, a list of external tools integrated by
AMDock is provided (p3). Most of these tools are de-
scribed in the manuscript, at the exception of OpenBabel,
whose role is not discussed.
Response: Please see the response to point 1 by Re-
viewer 1.
4) I may have missed the point but it is not clear to me
what this procedure brings more than two standard
dockings with the target and off-target receptors.
Response: In principle, they are two separate standard
dockings. However, we do believe that carrying out
docking on both receptors at the same time is an advan-
tage. As pointed out by the Reviewer, the comparison
conditions should be similar in both cases. Here, we
allow the user to protonate both receptors to the same
pH, determine the optimal search space for both recep-
tors, run the docking simulations with the same selected
program and analyze the results in a simple comparative
format. Furthermore, the user can superimpose the re-
ceptors and obtain a visual comparison of both the
search space and the docking results. Carrying out all
these steps separately requires additional effort and may
lead to errors. This type of docking exercise is the basis
for inverse virtual screening used primarily in drug re-
purposing. We do intend to implement this feature in
future AMDock versions.
5) In the case study, the authors are docking an inhibi-
tor on target and off-target receptors and find that the
inhibitor indeed prefers the target. In such tests, it is im-
portant to ascertain the fairness of the comparison. It is
not clear how the initial conformation of the inhibitor is
chosen. If the docking is in any way biased in favor of the
known pose, the comparison with the off-target is not fair.
The authors need to make sure that the initial conform-
ation is randomized and that a sufficiently large search
box is used.
Response: We now describe in more details the
starting conditions, stating that the initial ligand con-
formation was randomized. The search space has the
same dimensions for both receptors, since the box
size depends on the radius of gyration of the ligand
(SAR405), which is a constant parameter in this exer-
cise. The center of the box is determined by the geo-
metric center of the co-crystallized ligands in both
complexes. As can be observed in Figure 3A,thetwo
boxes enclose their corresponding binding sites with
sufficient margins, so no bias is introduced for any of
the receptors.
Valdés-Tresanco et al. Biology Direct (2020) 15:12 Page 10 of 12
Minor recommendations
1) PyMOL is importantly used by AMDock. Could the
authors give information on the compatibility of AMDock
with the different versions of PyMOL?
Response: Previously, AMDock worked with PyMOL
1.8.5. Recently, we verified that the AMDock plugin is
compatible also with PyMOL v2.x. The current Win-
dows distribution works with PyMOL version 2.1. In
Linux, AMDock works with any installed PyMOL
version.
PyMOL is a robust and widely used visualization pro-
gram. Currently, its use as an external program in
AMDock implies limitations on its full exploitation. We
are currently working on its incorporation as a native
viewer for AMDock. This will be a significant advantage
since it will allow a direct exchange of information be-
tween AMDock and PyMOL (for example, to manipulate
the receptor and the ligand, for active visualization of
molecule preparation, creation, visualization and modifi-
cation of the search space, flexible side chain selection,
etc.).
2) Why version 2.7 of Python is used and not 3.*? Note
that 2.7 will not be maintained past 2020
Response: AMDock is programmed in Python 2.7 to
guarantee full compatibility with third-party programs
(e.g.: AutoDockTools, PDB2PQR, AutoLigand, etc.).
Some of these programs have been recently updated to
Python 3. ADTs are critical to prepare the input files for
docking with AutoDock or AutoDock Vina. We have re-
cently created a version of ADT in Python3 available
here (https://github.com/Valdes-Tresanco-MS/Auto-
DockTools_py3). We plan to migrate all the code to Py-
thon 3 in future versions (a developmental version in
Python3 is available here (https://github.com/Valdes-
Tresanco-MS/AMDock-win-py3).
3) In the case study, a step consists in "aligning and
superimposing their structures". It is not clear to me if it
is an AMDock or a PyMOL functionality
Response: It is done with PyMOL. A remark was
added in the Case study section.
4) The concept of an "AutoLigand object" is used in the
manuscript but not defined
Response: It is now defined in a footnote.
5) What "previous ligand" means on p7 l1?
Response: We define "previous ligand" as any fragment
of heteroatoms that appear co-crystallized with the pro-
tein in the PDB file. Ideally, the user should leave only
the atom sets of interest, i.e. inhibitors, fragments or
substrates found in the crystallographic structure. If a
protein-ligand complex is introduced as the receptor,
the coordinates of the ligand(s) are also stored. These
coordinates can be used in the search space determin-
ation option called “Centered on Hetero”, as explained
in the manuscript.
Abbreviations
GUI: Graphical user interface; ADT: AutoDockTools; PMV: Python molecular
viewer; AMDock: Assisted Molecular Docking; PI3K: Phosphatidylinositol 3-
kinase; LE: Ligand efficiency
Acknowledgements
Special thanks to the JetBrains company (https://www.jetbrains.com/) for
granting a free open source license to use their software.
Availability and requirements
Project name: AMDock: Assisted Molecular Docking with AutoDock4 and
Autodock Vina.
Project home page: https://github.com/Valdes-Tresanco-MS
Operating system(s): Windows and Linux.
Programming language: Python.
Other requirements: Python 2.7.
License: GPL version 3.
Any restrictions to use by non-academics: Restricted by the license and the
other softwares.
Authors’contributions
MSVT designed and did most of the programming work, with important
contributions from MEVT. They also produced a first draft of the manuscript.
PAV and EM contributed to the project design, supervised the entire work,
and undertook the final manuscript preparation. All authors read and
approved the final manuscript.
Funding
This work was supported by the University of Medellin and Minciencias
(grant 738-2016).
Availability of data and materials
The manual as well as tutorial files are included in the program installation
folder. Additionally, information such as common errors, frequently asked
questions, etc., can be found in the AMDock repository and the following
mailing-list: (https://groups.google.com/forum/#!forum/amdock).
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Faculty of Basic Sciences, University of Medellin, Medellin, Colombia.
2
Center
of Protein Studies, Faculty of Biology, University of Havana, 25 & J, 10400 La
Habana, Cuba.
3
Centre for Molecular Simulations and Department of
Biological Sciences, University of Calgary, Calgary, Alberta T2N 1N4, Canada.
4
Present address: Donnelly Centre for Cellular & Biomolecular Research
University of Toronto, 160 College St, Toronto, ON M5S 3E1, Canada.
Received: 13 May 2020 Accepted: 4 August 2020
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