Molecules 2015, 20, 14265-14292; doi:10.

3390/molecules200814265
OPEN ACCESS

molecules
ISSN 1420-3049
www.mdpi.com/journal/molecules
Review

Evolution of Macromolecular Docking Techniques: The Case

Study of Nickel and Iron Metabolism in Pathogenic Bacteria
Francesco Musiani * and Stefano Ciurli *

Laboratory of Bioinorganic Chemistry, Department of Pharmacy and Biotechnology,

University of Bologna, Viale G. Fanin 40, Bologna I-40127, Italy

* Authors to whom correspondence should be addressed;

E-Mails: francesco.musiani@unibo.it (F.M.); stefano.ciurli@unibo.it (S.C.);
Tel.: +39-051-2096236 (F.M.); +39-051-2096204 (S.C.); Fax: +39-051-2096203 (F.M. & S.C.).

Academic Editor: Rino Ragno

Received: 28 April 2015 / Accepted: 28 July 2015 / Published: 5 August 2015

Abstract: The interaction between macromolecules is a fundamental aspect of most biological

processes. The computational techniques used to study protein-protein and protein-nucleic
acid interactions have evolved in the last few years because of the development of new
algorithms that allow the a priori incorporation, in the docking process, of experimentally
derived information, together with the possibility of accounting for the flexibility of the
interacting molecules. Here we review the results and the evolution of the techniques used
to study the interaction between metallo-proteins and DNA operators, all involved in the nickel
and iron metabolism of pathogenic bacteria, focusing in particular on Helicobacter pylori
(Hp). In the first part of the article we discuss the methods used to calculate the structure of
complexes of proteins involved in the activation of the nickel-dependent enzyme urease.
In the second part of the article, we concentrate on two applications of protein-DNA docking
conducted on the transcription factors HpFur (ferric uptake regulator) and HpNikR (nickel
regulator). In both cases we discuss the technical expedients used to take into account the
conformational variability of the multi-domain proteins involved in the calculations.

Keywords: macromolecular docking; urease; urease metallocenter biosynthesis; NikR; Fur;

Helicobacter pylori
Molecules 2015, 20 14266

1. Introduction

Living organisms rely on protein-protein and on protein-nucleic acid interactions to perform their
functions [1–4]. Considering only protein-protein binary interactions, this number can go from ca. 10,000
in Escherichia coli [5], to ca. 18,000 in higher plants of the Arabidopsis genus [6], to 150,000–500,000 in
human cells [7,8]. Despite their evident importance, the interactions between macromolecules are not fully
understood at the structural level. Indeed, only a fraction of the putatively analyzable unique protein-protein
interfaces are so far available from high-resolution X-ray crystallography and nuclear magnetic resonance
(NMR) spectroscopy [9]. Structural information can also be obtained from low-resolution experimental
techniques, such as cryo-electron microscopy or small-angle X-ray scattering which, however, do not provide
enough molecular details of the interacting surfaces [10]. Experimental techniques can thus be complemented
by computational docking methods aimed to model the quaternary structure of complexes formed by two
or more interacting macromolecules. Protein-protein complexes are the most commonly-attempted targets
of such modelling [10–15], followed by protein-nucleic acid complexes [16–18]. In the classical (or ab initio)
docking methods, the calculation itself only produces plausible candidate structures. These candidates
must then be ranked a posteriori using scoring functions or validation/filtering procedures that use
experimental data to identify structures that are most likely to occur in nature. In recent years, integrative
and information-driven algorithms changed the classical ab initio docking procedure by encoding a priori
information derived from experimentally-identified or predicted protein interfaces to drive the docking
process [10].
Here we review and discuss the evolution of the techniques used by us and by other groups to study
the interactions between macromolecules in two macro-test cases: (i) the prediction of the complexes
formed by the accessory proteins involved in the activation of the nickel-dependent enzyme urease; and
(ii) the prediction of protein-DNA complexes involving two bacterial transcriptional factor, namely the
ferric uptake regulator (Fur) and the nickel regulator NikR, involved in cellular iron and nickel metabolism.

2. Macromolecular Docking Overview

Macromolecular docking is usually defined as the structural prediction of a molecular complex

starting from the separated structures of its members. A typical docking algorithm involves three steps
(see Scheme 1). The first step consists of the exploration of the large number of possible orientations
and conformations that the members of a complex can assume in tridimensional space. This is usually done
through a “search algorithm”. This exploration step is then followed by a scoring step of the resulting model
complexes using an appropriate criterion. Finally, the procedure is often concluded by the refinement of
a restricted number of the obtained structures of the complex.

2.1. Search Algorithms

In typical docking algorithms, one molecule is fixed (the so-called “static molecule”) and the other
molecule is translated and rotated around (the so-called “moving molecule”). Depending on the search
strategies, current ab initio docking programs can be divided into three general categories: (i) exhaustive
global search; (ii) local shape feature matching; and (iii) randomized search algorithms [14].
Molecules 2015, 20 14267

Scheme 1. Macromolecular docking flow-chart.

The global search algorithms perform a scan over the three rotational and the three translational
degrees of freedom. Given the huge number of possible orientations to be sampled, it is computationally
prohibitive to evaluate all the binding complexes using a standard search method. To reduce the
computational cost, two types of approaches have been developed for this type of exhaustive global search:
fast Fourier transform (FFT) correlation and direct search algorithms [14]. The FFT-based algorithm
accelerates the search process in the translational space by moving to the imaginary space through
several FFT calculations. In the direct search algorithm approach, the molecular shape of two interacting
molecules is first mapped onto a tridimensional grid and the shape matching is then directly performed
in the Cartesian grid space to find the geometric fit between the two molecules.
In local shape feature-matching algorithms, the interacting molecules are represented by their shapes,
such as the molecular surface. The search algorithms find those matches that give a good local shape
complementarity between the two molecules. Local shape feature-matching algorithms also tend to
generate more binding orientations towards those sites with good shape complementarity. Therefore, a
clustering step is often necessary after the docking calculation to remove the redundancy in the final
solutions [14].
In randomized search algorithms, the moving molecule is randomly placed around the binding site
for a local search or around the whole static molecule for a global search based on a certain number of
rules. The placement procedure can be improved by using additional information, such as the molecular
surfaces, to generate more realistic initial binding orientations. Then, from their starting positions, each
of the initially generated orientations is optimized and/or refined through a multistage sampling and/or
multiscale modeling approach that use stochastic algorithms, such as genetic algorithms and/or Monte
Carlo methods [14].
Molecules 2015, 20 14268

Docking search algorithms can use information extracted from experimental or bioinformatics sources
to guide the formation of the complex. In this way, the sampling can be limited to the regions defined
by the data and the number of unrealistic solutions can be dramatically reduced. On the other hand, the
inclusion of experimental/prediction data does not guarantee that all complexes are correct [10].

2.2. Scoring Functions

There are three general classes of scoring functions: (i) force field; (ii) empirical; and (iii)
knowledge-based, even if some algorithms use a combination of two or all of them [19].
In the case of force field scoring functions, the interaction affinities are estimated by considering
physical atomic interactions, such as van der Waals and electrostatic interactions and bond stretching/
bending/torsional forces [19]. Force field functions and parameters are usually derived from both
experimental data and quantum mechanical calculations. Because the binding of the molecules normally
takes place in the presence of water, the desolvation energies of the interacting molecules are sometimes
taken into account using implicit solvation methods [19].
Empirical scoring functions estimate the binding affinity of a complex on the basis of a set of weighted
energy terms [19]. The energy coefficients are determined by fitting the binding affinity data of a training
set of protein-ligand complexes with known three-dimensional structures. Compared to the force field
scoring functions, the empirical scoring functions are much faster in binding score calculations due to
their simple energy terms.
The knowledge-based (also known as statistical potentials) scoring functions are based on statistical
observations of intermolecular close contacts in structural databases that are used to derive “potentials
of mean force”. This method is based on the assumption that close intermolecular interactions between
certain types of atoms or functional groups that occur more frequently than one would expect by a
random distribution are likely to be energetically favorable and therefore contribute favorably to binding
affinity [20].

2.3. A Peculiar Case: Protein-DNA Docking

DNA is considered a difficult target for macromolecular docking because of its inherent flexibility
summed with the scarcity of the information to define the DNA-binding interfaces. DNA can exhibit
large conformational changes upon binding to a protein, which can greatly modify the shape of the
interaction surface. As a direct consequence, the total conformational space that needs to be searched in
order to find favorable conformations becomes too computationally expensive [21]. In the present review
we discuss protein-DNA calculations performed using the program Haddock [22,23] and a two-stage
docking approach specifically developed for the calculation of protein-DNA complexes [21]. These
calculations yielded good performances [24] and are described in the examples discussed here. Haddock
(High Ambiguity Driven biomolecular DOCKing) implements an approach that uses biochemical and/or
biophysical interaction data to drive the docking process. The calculation is guided by defining selected
residues as “active” in the protein-protein or protein-DNA interaction. The docking algorithm rewards
the complexes that have these active residues on the interaction interface. Haddock simulations are
composed of three rounds: (i) a rigid body energy minimization that produces a user-defined number of
putative docking complexes (usually 1000); (ii) a semi-flexible simulated annealing carried out on the
Molecules 2015, 20 14269

best solutions calculated in the first round and found on the basis of the intermolecular energy (habitually
200); and (iii) an explicit water refinement carried out on the same structures of the previous step.
Haddock has been implemented by using the program CNS [25] for structure calculations and python
scripts derived from ARIA [26] for automation. The solutions are then clustered on the basis of the
pairwise backbone RMSD and further analyzed for structural and functional congruence. The cut-off for
the clustering of protein-protein docking solution is typically set to 7.5 Å. The protein-DNA docking
protocol developed for Haddock starts with the generation of a model for the unbound DNA operator
using the DNA analysis and rebuilding software 3DNA implemented in the 3D-DART server [27].
In the first docking round, additional restraints are introduced for the DNA fragment to maintain base
planarity and Watson-Crick bonds. Subsequently, the DNA conformation in the docked resulting
structures are analyzed using the program DART [27] in order to determine trends in DNA bending and
twisting, a type of information that is used to generate an ensemble of custom DNA models representing
the accessible conformations. A second, and final, docking round is then carried out following the same
approach described for the first round, but this time including the ensemble of pre-bent DNA models
generated above and representing various degrees of conformational flexibility of the nucleic acid. In
this round, the conformational freedom of the DNA molecule is restricted at the semi-flexible refinement
stage (see below) in order to prevent helical deformation.
Here below we describe and discuss specific examples of macromolecular docking applied to proteins
involved in the cellular metabolism of nickel and iron.

3. Urease Activation

Urease is a nickel dependent enzyme that catalyzes urea hydrolysis in the last step of organic nitrogen
mineralization to give ammonia and carbamate, which spontaneously decomposes to give a second
molecule of ammonia and bicarbonate. The reaction products cause an overall pH increase that has
negative effects both on human and animal health, as well as on the ecosphere [28,29]. The structures of
urease from several bacteria and from higher plants have revealed the molecular architecture of the
enzyme (Figure 1A) [28,29]. In the fully-conserved urease active site, two Ni(II) ions are bridged by the
oxygen atoms of a carbamylated lysine residue and bound to two histidines. One Ni(II) ion is additionally
bound to an aspartate carboxylate oxygen. The coordination geometry of the Ni(II) ions is completed by
a water molecule bound to each metal ion and by a nickel-bridging hydroxide ion (Figure 1A) [28,29].
In vivo, holo-urease is post-translationally synthesized starting from apo-urease and following an
activation process that involves CO2 uptake for lysine carbamylation, hydrolysis of GTP, and Ni(II)
delivery into its active site. These events are typically carried out by four specific accessory proteins
named UreD, UreF, UreG, and UreE [28]. The “classical” hypothesis for this process [28,30] involves
the sequential binding of UreD, UreF, and UreG [or of a preformed aggregate of UreD, UreF, and UreG
(UreDFG)] to obtain a pre-activation complex that carbamylates the active site lysine side chain and
further binds Ni(II) ions delivered by UreE [28,30] through a route driven by GTP hydrolysis (Figure 1B,
blue arrows path). Little is known on the functional properties of UreD [31], apparently the first protein
that binds apo-urease. UreF is proposed to bind the urease-UreD complex through a direct interaction
with UreD [32] and favors the formation of the urease-UreDFG complex [33]. UreG is responsible for
coupling GTP hydrolysis to the process of urease activation and it is proposed to catalyze, in the presence
Molecules 2015, 20 14270

of CO2, the formation of carboxyphosphate, an excellent carbamylation agent for the metal-binding
lysine in the urease active site [33]. UreG is the first case of an intrinsically disordered enzyme [34], which
can retain enzymatic activity owing to the rigidity of the active-site environment [35]. UreF has been
also proposed to regulate the function and the folding of UreG, acting as a GTPase-activating protein
(GAP) [36].

Figure 1. (A) Ribbon diagram of urease from S. pasteurii, K. aerogenes, H. pylori, and jack
bean. Ribbons are colored to evidence the chains composing the protomers. Ni(II) ions are
reported as red spheres. The proteins are seen through the ternary axis (top panels). The
bottom panels are rotated by 90° around the horizontal axis vs. the top panels. The inset
reports the details of Ni(II) coordination geometry of in native urease active site; (B) Schematic
representation of the proposed mechanisms for urease activation.
Molecules 2015, 20 14271

Finally, UreE is proposed to be the metallo-chaperone in charge of delivering Ni(II) ions [37],
and actively transferring Ni(II) to urease apo-protein within the apourease-UreDFG complex in a
GTP-dependent activation process [38]. Indeed, in vivo studies using yeast two-hybrid analysis [39,40]
and coimmunoprecipitation assays [40], as well as calorimetry and NMR spectroscopy [41] indicated a
direct interaction between UreE and UreG from Helicobacter pylori (Hp).
Even though the structure of urease bound to any of the accessory proteins is not yet available, the
crystal structure of the (UreF)2 homodimer [42] and the structure of the (UreDF)2 [43] and (UreDFG)2 [44]
complexes from H. pylori have been recently reported (Figure 2A). The crystal structure of the Hp(UreDFG)2
complex contains two copies of each of UreF, UreD, and UreG, related by two-fold symmetry, forming
a dimer of heterotrimers (Figure 2A). HpUreF adopts an all-helical fold that consists of a bundle of nine
α-helices arranged in an antiparallel fashion. The UreF dimerization occurs through α-helices 2, 3, 8 and
9 [42]. HpUreD fold consists of 17 β-strands and two α-helices located near the C-terminus. The structure
is dominated by two mixed strand β-sheets [43]. The topology of HpUreG is characteristic of the SIMIBI
class GTPases [45] and the protein was co-crystalized with one GDP molecule per monomer. The
sequences of UreG feature a Cys-Pro-His (CPH) conserved motif that is fundamental for the metal binding
properties of the protein [41,46]. In the Hp(UreDFG)2 structure (as well in previously calculated model
structures [47,48]) the CPH motif is found in a cleft at the UreG dimerization interface (Figure 2A) [44].
Interestingly, despite the fact that SpUreG and HpUreG model structures previously published by our
group were based on distant homologues [34,47–49], the experimental structure of Hp(UreG)2 found in
the Hp(UreDFG)2 complex and the model structure of dimeric HpUreG are very similar (Figure 2B),
with a root mean square deviation (RMSD) of only 1.8 Å for the Cα atoms. Structural information on
UreE proteins from various bacteria has been derived from numerous crystallographic studies: UreE
from Sporosarcina pasteurii (formerly known as Bacillus pasteurii, SpUreE) [50,51], Klebsiella aerogenes
(KaUreE) [52], and H. pylori (HpUreE) [53,54] display a similar fold made by a symmetric homo-dimer,
with each monomer composed of two domains connected by flexible linkers (Figure 2C). The fully
conserved metal binding site is located on the surface of the C-terminal domain, at the dimerization
interface. The N-terminal domain has been found in slightly different orientations with respect to the
C-terminal domain, suggesting that some inter-domain conformational freedom is available for this
protein, a feature possibly related to induced-fit processes that occur during the formation of protein-protein
complexes involving the other urease chaperones [55]. On the other hand, this means that the UreE
N-terminal domain conformations observed in the solid state could not be the same needed for the correct
formation of the complex with UreG [41]. The entirety of the structural information from crystallography,
together with UV-VIS spectroscopy, light scattering experiments, and GTPase activity assays performed
on HpUreG, recently suggested a new mechanism for the biosynthesis of the urease active site (Figure 1B,
orange arrows path) [56]. In the new pathway, the Ni(II)-bound UreE dimer binds two apo-UreG
monomers, facilitating GTP uptake by UreG in the presence of Mg(II) ions. The UreG binding to UreE
can, in principle, occur either in a single or in a multistep process. In the (UreEG)2 complex, the Ni(II)
ion is then translocated from UreE to UreG. Subsequently, the pre-formed (UreDF)2 complex competes
with UreE for the Ni(II)-(UreG)2 dimer to form the supercomplex apo-urease/Ni(II)-(UreDFG)2. Finally,
the GTP hydrolysis performed by UreG is catalyzed by KHCO3/NH4HCO3 to complete the nickel
insertion into the apo-urease.
Molecules 2015, 20 14272

Figure 2. (A) Ribbon diagram of the (UreDFG)2 crystal structure from H. pylori (PDB id
4HI0) [44]. HpUreD, HpUreF, and HpUreG chains are colored in light green, yellow and
dark red, respectively, with the darker and lighter portions highlighting the different
monomers. The views in the top and bottom panel are rotated by 90° along the horizontal
axis. The inset reports the position of the residues forming the HpUreG Cys-Pro-His
conserved motif; (B) Comparison between the HpUreG dimeric crystal structure, as found
in the Hp(UreDFG)2 complex, and the homology model structure calculated in ref. [47]; (C)
Ribbon scheme of the structure of SpUreE homodimer (PDB id: 1EAR); the two monomers
are colored in light blue, with the darker and lighter portions representing the two monomers.
The views in the top and bottom panel are rotated by 90° along the horizontal axis. In the
inset is reported the detail of the SpUreE metal binding site.

3.1. Calculation of the Structure of the UreEG Complex

The first attempt to model the UreE-UreG complex from H. pylori (Hp(UreEG)) was published in
2009 [41]. The calculation was prompted by a number of experimental observations: (i) SpUreG and
Mycobacterium tuberculosis UreG (MtUreG) are dimers in native conditions, while KaUreG and
HpUreG are monomeric proteins [47]; (ii) Zn(II)-driven protein dimerization occurs in vitro for
HpUreG, with one Zn(II) ion binding at the protein dimerization interface using the conserved cysteine
and histidine residues from the CPH motif from each monomer [47]; (iii) Zn(II) and Ni(II) share the
same binding site at the interface of the protein dimer in the SpUreE crystal structure [50], with Zn(II)
Molecules 2015, 20 14273

affinity ten times lower than Ni(II)-affinity for KaUreE [57], while the thermodynamics of Zn(II) binding
to HpUreE is very similar to that of Ni(II) [41]; (iv) the HpUreEG protein complex contains two monomers
of HpUreG dimerized onto one HpUreE dimer [41], with an interaction stabilized by the presence of
Zn(II) and not by Ni(II) [41], suggesting a role for Zn(II) in promoting the UreE interaction network.
Thus, the hypothesis at the basis of the docking calculation was that the two conserved metal binding
sites found on dimeric UreE and UreG are able to come in close contact during the formation of the
Hp(UreEG)2 complex. The only UreE-available structures at that time were those from S. pasteurii and
K. aerogenes, while there was no structure for UreG from any organism. Thus, the structures of HpUreE
and HpUreG were modelled starting from homologue experimental structure [41]. In the case of HpUreE
the templates were the crystal structures of SpUreE and KaUreE (PDB id: 1EAR [50] and 1GMW [52],
respectively) following a previously established procedure [55], while for HpUreG the template structure
was HypB from Methanocaldococcus jannaschii (MjHypB, PDB id: 2HF8 [58]), an homologue GTPase
involved in the biosynthesis of [Ni,Fe]-hydrogenase [47]. The RosettaDock software [59] was used to
calculate an initial complex between the model structure of dimeric HpUreG, and the central C-terminal
domains of dimeric HpUreE. This step allowed the calculations of a complex not biased by the conformation
of the HpUreE N-terminal domains. The protocol used by RosettaDock included (i) prepacking of the
partners to remove clashes in the free monomers; (ii) global search of rigid-body orientations; and (iii)
clustering of the low-energy models and selection of the largest cluster as prediction. The global search
step starts from a large number of random initial orientations and brings the partners into glancing
contact and removes clashes. It then proceeds by optimizing rigid-body orientation at a low-resolution
level (where each amino acid side-chain is represented by a centroid pseudo-atom that is positioned according
to an average position determined from a set of known structures from the PDB), and subsequently builds
initial side-chain conformations using a Monte Carlo search through a backbone-dependent rotamer
library. Finally, the protocol uses ca. 50 cycles of Monte Carlo minimization to optimize the side-chain
and rigid-body degrees of freedom using a free energy function dominated by short-range Lennard-Jones
and hydrogen-bonding interactions, and an implicit solvation model [59]. A search of 1000 complexes
was carried out by randomly translating and rotating the initial positions of the interacting proteins. The
complex with the best RosettaDock score was selected among all generated models for the subsequent
refining cycle, carried out by applying 1000 times a perturbation to the starting structure. The Cα trace
of this complex and the crystal structures of MjHypB, SpUreE, and KaUreE were used as templates to
build 200 structural models of the Hp(UreEG)2 complex using the Modeller software [60]. The best
model was selected on the basis of the lowest value of the Modeller objective function. This modelling step
allowed the addition of the HpUreE N-terminal domains to the structure of the calculated model complex
between the C-terminal domain of HpUreE and HpUreG. The final Hp(UreEG)2 model (Figure 3A)
showed the two proteins facing each other along their extended axes, and only limited modifications of
the proteins backbone, restricted both in extent and in topology distribution, were necessary in order to
optimize the docking procedure. The central pocket formed on the HpUreG surface around the conserved
CPH motif matches the shape and volume of the protruding metal binding region on the surface of HpUreE.
The shallow crevice formed between the central C-terminal domain and the peripheral N-terminal domain
of HpUreE is filled with the bulge found on the surface of HpUreG around the rim of the protein
dimerization interface, and an overall continuous contact surface, with optimal electrostatic complementarity,
was obtained (Figure 3B) [41].
Molecules 2015, 20 14274

Figure 3. (A,C) Ribbon schemes and solvent-excluded surfaces of the model complexes of
the Hp(UreEG)2 (A) and the Bp(UreEG)2 (C) structures. Ribbons are colored as in Figure 2.
Residues found at the interface of the complex and known to be involved in metal binding
(HpUreE H102, SpUreE H100 and UreG CPH motif residues), as well as the positions of the
GTPγS and GDP molecules (bound to HpUreG and to SpUreG, respectively), are shown as
ball and stick models and the atoms are colored accordingly to the atom type; (B,D) Solvent
excluded surfaces of two components of the Hp(UreEG)2 (B) and the Bp(UreEG)2 (D)
complexes orientated in order to expose the interaction surfaces. The surface is colored
according to the surface electrostatic potential (blue, positive; red, negative); (E) Superposition
of the structures and of the inertia ellipsoid of Hp(UreEG)2 (in orange) on the Bp(UreEG)2
(in blue) model complex. Left and right panels are rotated by 90° along the horizontal axis,
keeping the UreG model as a reference; (F) Ribbon diagram and solvent excluded surface
of the best HpUreDFGE docked complex. Ribbons are colored as in Figure 2. Left and right
panels are rotated by 90° along the horizontal axis.
Molecules 2015, 20 14275

In 2014, new data provided by NMR allowed a deeper characterization of the SpUreE residues in
contact with SpUreG [61]. These new findings, in general agreement with the proposed region of
HpUreE interacting with HpUreG in the docking calculations performed earlier on the same proteins
from H. pylori [41], prompted the calculation of the SpUreEG complex driven by the NMR experimental
information. The calculation used the latest published structure of SpUreE (PDB id: 4L3K [51]) as well
as a model of SpUreG calculated based on its high degree of homology (sequence identity = 58%) with
HpUreG, whose crystal structure in complex with HpUreF and HpUreD had been released in 2013 [44].
In order to take into account the flexibility of the SpUreE domains, the five low-frequency normal
conformational modes of the dimeric SpUreE crystal structure were calculated using the el Némo
web-server [62] and a recently published protocol (see ref. [63] and below). The eleven protein
conformations determined using this analysis, which comprised the starting structure as well as the ten
structures derived by applying the perturbations consistent with each calculated normal mode to the starting
structure, were utilized to build a library of structures to be used in the subsequent docking stages. The
SpUreE dimeric structure was docked onto the SpUreG dimeric model complex using the data-driven
docking program Haddock 2.1 [22,23] (see above for a general description of the program). For SpUreE,
the active residues were those identified by NMR chemical shift and signal intensity perturbations. For
SpUreG, the residues in the conserved CPH motif [34] and the most conserved residues on the same side
of the protein determined using the server ConSurf [64–66] were used to guide the docking. The general
features of the calculated Sp(UreEG)2 complex (Figure 3C) [61] are similar to those observed in the
previously reported complex for Hp(UreEG)2 [41]. The SpUreE residues in direct contact with SpUreG
are in good agreement with the experimental data, witnessing the correct outcome of the docking
simulation [61]. The SpUreE surface in the metal binding region is mainly hydrophobic, with some spots
in correspondence of positively charged residues. The electrostatic potential mapped on the protein
surface of SpUreG shows that the interaction region is mainly negatively charged (Figure 3D). Therefore,
the presence of a divalent cation bound to the SpUreE metal binding site could efficiently change the
electrostatic properties of the protein surface, allowing a more favorable interaction with the negatively
charged surface of SpUreG [61], consistently with the stabilization of the UreEG complex in H. pylori
upon metal binding [41]. The main difference between the model complexes from S. pasteurii and
H. pylori is an anticlockwise rotation of the SpUreE dimer around the vertical axis of about 35 degrees
(Figure 3E) [61].

3.2. Calculation of the Structure of the UreDFGE Complex

The calculation of the model of the HpUreGE complex released in 2009 (see above) [41], together with
the publication of the HpUreDF crystal structure in 2011 (PDB id: 3SF5 [43]), provided the potential to
investigate the structural propertied of the putative UreDFGE supercomplex using macromolecular
docking, because all the pieces of the puzzle were finally available. A bioinformatics analysis conducted
by combining the results obtained from ConSurf [64–66] and ProBis [67–69], together with experimental
data from the mutagenesis studies conducted on UreG from H. pylori [43] and K. aerogenes [70],
allowed us to identify a restricted set of residues not already involved in other protein-protein interactions
on the surfaces of HpUreF and HpUreG. ConSurf calculates surface residue conservation, while ProBiS
predicts surface regions containing structural similarity with known protein binding sites based on
Molecules 2015, 20 14276

geometric and physicochemical parameters. Therefore, the HpUreGE complex was docked onto the
HpUreDF complex using the docking program Haddock and a protocol identical to that utilized for the
calculation of the SpUreGE complex (see above) [61]. The structural models resulting from this docking
process were clustered on the basis of their RMSD, allowing the identification of the two most populated
clusters (composed of 58 and 51 models, respectively) at a similar Haddock score. The two clusters are
indeed representative of the same HpUreDF-HpUreGE model complex because they differ only by the
rotation of the symmetric Hp(UreG)2 homodimer by approximately 180 degrees along a vertical axis
perpendicular to the surface of HpUreF. In the resulting modeled structure of the HpUreDFGE complex
(Figure 3F), UreG is in direct contact with UreF through weak van der Waals interactions and a number
of polar interactions. The shallow crevice formed at the interface between the two UreF monomers is in
close contact with the convex surface of the UreG dimer (Figure 3F). Moreover, structural details in
the UreF-UreG interaction surface supported the proposition of a role for UreF as a GTPase activating
protein (GAP) [36]. Subsequent to the publication of the paper describing the model of the HpUreDFGE
complex [71], the crystal structure of the HpUreDFG complex was released (PDB id: 4HI0, [44]). This
structure showed that the general secondary and quaternary structure of HpUreG agrees well with the
model structure previously calculated [47], and that the binding site of HpUreG onto the HpUreDF
complex surface is the same as the one identified earlier [71]. The crystal structure of the complex does
not include HpUreE. Unexpectedly, the main difference between the calculated model of the HpUreDFGE
complex and the crystal structure of the HpUreDFG complex is the orientation of the HpUreG protein
dimer with respect to the HpUreF interacting protein: HpUreG interacts with HpUreF using the surface
patch that was predicted to interact with HpUreE on the basis of experimental data [41]. In order to
explain this difference, it has been proposed that HpUreG, being an intrinsically partially-disordered
protein, undergoes the known moonlighting behavior [72], changing its partner as needed along a
metabolic pathway [56,71].

3.3. Calculation of the Structure of the Urease-UreDFG Complex

In 2012, Ligabue-Braun et al., attempted to build a model of the interaction between K. aerogenes
urease (KAU) and three of the required accessory proteins KaUreD, KaUreF, and KaUreG, in their
monomeric oligomerization state [73]. KAU (as well as S. pasteurii urease) is a trimer of trimers formed
by the three structural urease proteins: UreA, UreB, and UreC [28,29]. The adopted strategy included (i)
the homology modelling of the structure of KaUreG based on the structure of MjHypB (the structure of
HpUreG was not available at the time of the publication) and the homology modelling of the monomeric
KaUreD and KaUreF based on the structure of Hp(UreDF)2 structure (PDB id: 3SF5, [43]); and (ii) a
stepwise docking procedure in which the monomers of KaUreD, KaUreF, and finally KaUreG were
docked on the crystal structure of KAU [73]. All the docking steps, with the exception of the last stage
(binding of KaUreG), were performed without imposing any restraints. The authors used three different
rigid-body docking programs: PatchDock [74], Hex [75], and PIPER [76] via ClusPro 2.0 [77]. PatchDock
divides the surface of the two interacting biomolecules into patches according to the surface shape. The
obtained patches correspond to patterns that visually distinguish between puzzle pieces. Once the patches
are identified, they can be superimposed using shape-matching algorithms that go through three major
stages: (i) molecular shape representation; (ii) surface patch matching; and (iii) filtering and scoring.
Molecules 2015, 20 14277

Both Hex and PIPER use a FFT method [78] to perform a rigid-body search of the proteins’ orientations.
Hex accelerates the search by using an expansion of the molecular surface and electric field in spherical
harmonics. Fourier correlations between the expansion coefficients are used to simplify the problem of
calculating the complementarity between the surfaces in different orientations to that of look up in a table
of pre-calculated overlap integrals [79]. PIPER approximates the interaction matrix by its eigenvectors
corresponding to the few dominant eigenvalues, resulting in an energy expression written as the sum of
a few correlation functions, and by using repeated FFT calculations [76]. In all cases, the results were
subsequently clustered with MMTSB Tool Set [80] by hierarchical clustering based on mutual RMSD and
evaluated in terms of relative energy with FoldX [81]. The obtained complexes (Figure 4) were checked
against experimental small-angle X-ray scattering (SAXS) profiles obtained for the K. aerogenes
KAU-UreDFG supercomplex [73] as well as against results derived from cross-linking, mutagenesis,
and pull-down experiments. Theoretical SAXS profiles were calculated using the FoXS server [82] and
compared to the experimental data for the KAU-KaUreD and KAU-KaUreDF complexes [83].

Figure 4. Ribbon scheme of the KAU–UreDFG model complex as calculated in ref. [73].
Ureases subunits and accessory protein are colored as in Figures 1 and 2, respectively. The
position of the KAU active sites is indicated by red circles. The views in the top and bottom
panel are rotated by 90° along the horizontal axis.
Molecules 2015, 20 14278

The model of the supercomplex is in agreement with pull-down [46] and cross-linking [32] assays.
On the other hand, the hinge region of KaUreB (residues 1–19), which is essential for proper urease
activation [84], does not bind directly to any accessory protein. The authors suggested that this region
could be required for a proper “gating” of the active site for activation [73]. Furthermore, the accessory
proteins bind far away from the urease active site, and a clear pathway for nickel traffic towards the
active site was not identified [73]. Finally, the spatial assembly of the urease-UreDFG supercomplex
observed in the case of K. aerogenes could not be a general case. Indeed, ureases from H. pylori or jack
bean assume different quaternary structures (the former is a tetramer of trimers of dimers, while the latter
is a dimer of trimers of monomers, see Figure 1A) and in such arrangements there is no room for the
accessory proteins in the same position of the complex proposed by Ligabue-Braun et al. [73]. The
authors suggest that H. pylori represents an exception among bacterial ureases, with such difference
reflected in a different structural organization of its activation complex, while for plant ureases they
hypothesize that the trimer of monomers is activated before the formation of the final protein structure.

4. HpFur-DNA and HpNikR-DNA Complexes

Fur and NikR are two important metal-responsive regulators involved in iron and nickel homeostasis
and function by controlling gene expression in H. pylori. Fur and NikR can bind independently at distinct
operators, and also compete for overlapping operators in some co-regulated gene promoters, suggesting
a link between iron and nickel regulation [85].

4.1. HpFur-DNA Complexes

Iron holds a central position in the host-pathogen interplay in mammals and plants [86]. For instance,
iron deficiency is the most common nutritional stress in aquatic environments [87]. In order to manage
iron limitation, cells have developed a large number of responses that enable maintenance of iron
homeostasis through precise changes in gene expression. The latter allow the cells to adapt their physiology
to the environment and to prevent nutrient overloads that would be highly poisonous or lead to oxidative
stress [88]. The control of iron metabolism and its coupling with regulation of defenses against oxidative
stress is carried out by Fur in most prokaryotic organisms. In H. pylori, Fur is able to act as a transcriptional
commutator switch that exploits the alternative readout of DNA grooves to mediate opposite output
regulation for the same input signal [63]. In particular, when Fe(II) ions are abundant, gene transcription
is repressed by HpFur in an iron-dependent manner, conforming to the classic Fe(II)-Fur (holo-Fur)
repression paradigm [89], in which the iron ion acts as corepressor. Accordingly, the iron-repressible
Fur targets include genes involved in Fe(II) ions uptake, such as the prototypical frpB1 gene, which
needs to be expressed only under iron-starving conditions [90]. On the other hand, when iron is scarce,
apo-HpFur represses transcription of a different set of genes, including the iron-inducible pfr gene, which
codes for a ferritin involved in iron storage, thus demanding derepression only under iron-replete
conditions [91]. In this case, the Fe(II) cofactor ion acts as an inducer, rather than a corepressor.
The structure of Fur is a homo-dimer built through a head-to-head interaction of the C-termini of the
protein. Each monomer is formed by a DNA-binding domain (DBD) at the N-terminal and a metal binding
domain (MBD, also called dimerization domain) at the C-terminal (Figure 5A). Each Fur monomer binds
metal ions in three sites: a regulatory site that can bind one Fe(II) (site S1), a structural site binding one
Molecules 2015, 20 14279

Zn(II) (S2), and a not fully conserved site with uncertain function (S3) (Figure 5A) [92]. Size exclusion
chromatography indicated that HpFur is a dimer in solution and that it is able to tetramerize, and further
multimerize, in the presence of divalent metal ions such as Fe(II) and Mn(II), even in the absence of
DNA [63].
To gain insight in the molecular mechanism responsible for the distinctive recognition of apo- and
holo-operators, docking simulations were run using the program Haddock and the crystal structure of
holo-HpFur (C78S and C150S mutant, PDB code: 2XIG [93]). In this structure there are three Zn(II) ions
bound to each of the metal binding site. Curiously, in the regulatory site the Zn(II) ion is found in two
different coordination geometries: in one chain of the dimer the metal ion is tetra-coordinated (S2t in
Figure 5A), while in the second chain the Zn(II) ion adopts a distorted octahedral geometry (S2o). This
discrepancy in the coordination geometry can be due to the presence of Zn(II) ions instead of the
physiological Fe(II) ions in the crystallization milieu. Thus, the first step was to use homology modelling
to reconstruct a model of the wild type holo-HpFur featuring both the regulatory metal binding sites in
a tetrahedral coordination geometry [63]. The calculation was performed using the Modeller software [60]
and the coordination geometry was induced by introducing appropriate symmetry, bond and angle
constraints. The model structure of apo-Fur was obtained by depletion of Zn(II) ions from the model
structure of holo-Fur. In order to take into account the experimentally-observed mobility of the DBDs
with respect to the MBD [92,93], two libraries of possible Fur conformations were created by analyzing
the low-frequency normal modes of the apo- and holo- protein models by using the elNémo web server [62].
In particular, eleven protein conformations (the starting structure, in addition to the structures derived
by applying to the starting structure, the perturbation of every calculated normal mode in the two possible
directions) determined using this analysis were utilized to build a library of structures for each HpFur
metalation state to be used in subsequent docking calculations together with the apo- and holo-operator
(OPIpfr and OPIfrpB hereafter, respectively) [63]. A starting model for the unbound DNA operators were
generated using the DNA analysis and rebuilding software 3DNA implemented in the 3D-DART server [27].
The docking calculations were carried out by considering all the possible combinations between apo-
and holo-HpFur and the two operators (i.e., apo-HpFur/OPIpfr, holo-HpFur/OPIpfr, apo-HpFur/OPIfrpB,
holo-HpFur/OPIfrpB). In Fur, α-helix 4 is known to be part of the DNA recognition process [94] and in
particular the conserved Y65 (Y55 in E. coli numeration) [95] appears to be involved in the binding.
Thus, the calculations on the protein side were guided defining as “active” the residue Y65, while the
surface residues around it were considered “passive” residues [63]. In the apo- and holo-operators, the
base pairs identified in footprinting assay were defined as “active” (bases −19, −18, −7, −6 for OPIpfr
and from −25 to −21 and from −19 to −15 for OPIfrpB, see Figure 5B,C) [63]. In all cases, the docking
protocol was optimized by running calculations in the presence and absence of additional restraints on
the symmetry of the complex and on the DNA bases [63].
Molecules 2015, 20 14280

Figure 5. (A) Ribbon scheme and details of the metal binding sites of HpFur crystal
structure. Ribbons for DBD and MBD in left panel are colored in cyan and in orange,
respectively, while the unstructured region linking the two domains is in yellow. Zn(II) ions
are in purple. In the right panels, metal binding residues are reported as sticks colored
according to atom type; (B,C) Model structures of the HpFur-DNA: best docking models
resulting for HpFur-OPIpfr (B) and Fur-OPIfrpB (C) complexes. The protein is reported as
ribbon diagram colored from blue in the proximity of the N-terminal to red at the C-terminus.
Zn(II) ions are in purple. The DNA is in blue, with the exception of the active residues that
are in red.

In the case of OPIpfr complexes, the best result was obtained for the apo-Fur/OPIpfr model (Figure 5B)
with the application of restraints to take into account the symmetry of the complex and the DNA bases
known to be involved in the interaction. The calculated protein-DNA complex features the axis connecting
the two DNA-binding domains nearly parallel to the DNA major axes. The Fur DNA-binding domains
insert the loop between α-helices 1 and 2, as well as the first five residues of α-helix 4, in the major
groove of the apo-operator in correspondence with the two regions identified by footprinting assay. The
apo-operator assumes a convex conformation with respect to the Fur position, broadening the major
grooves exposed to the protein and thus promoting protein binding. On the other hand, in simulations
carried out on the OPIfrpB operator, we observed a different orientation of the protein with respect to
Molecules 2015, 20 14281

the considered DNA fragment. In all the docking calculations involving OPIfrpB, HpFur clamps the
holo-operator positioning the axis connecting the DNA-binding domains perpendicularly to the DNA
major axis. These results, together with the experimental observation of the formation of a complex
involving OPIfrpB and tetrameric holo-HpFur, prompted us to perform a simulation in which we included
two HpFur dimers together with the holo-operator. In these simulations we included additional restraints
derived from the electrostatic properties of the residues found on the surface of the DNA-binding
domain. In particular, our hypothesis was based on the observation that K24 and N58 are placed in good
position for inter-protein interaction in the case of the formation of a tetramer build by connecting two
Fur dimers across the DNA-binding domain. In the resulting best complex (Figure 5C), the two Fur
dimers envelop the DNA using both the DNA-binding domain and the dimerization domain, completely
covering the large region of the operator identified by footprinting assay. The Fur tetramer does not bind
DNA perpendicularly to the operator major axes, but is tilted by about 30 degrees, allowing both
Fur dimers to putatively interact with DNA regions that are feebly detected in the footprinting assay
(Figure 5C) [63].

4.2. HpNikR-DNA Complex

NikR is a transcription factor that regulates the expression of genes coding for proteins involved in
nickel metabolism [28,96]. It is a highly homologous protein, found in ca. 30 species of bacteria and
archea. The Ni(II)-bound NikR from E. coli (EcNikR) binds to DNA and represses the transcription of
the nikABCDE operon, which codes for a specific Ni(II) membrane uptake ABC transporter [97,98]. On
the other hand, NikR from H. pylori (HpNikR) is a pleiotropic regulator of several genes, acting as a
nickel-dependent repressor of HpNikR itself and of the Ni(II) permease NixA, as well as a nickel-dependent
activator of urease operon [99,100]. Numerous crystal structures of NikR [101–103] have established
that this protein is a homo-tetramer, made of a dimer of dimers, constituted by two domains (Figure 6A).
One domain is the central metal binding domain (MBD), made of the C-terminal portion of the protein
responsible for tetramerization. This domain hosts four regulatory metal binding sites symmetrically
located at the tetramerization interface, where Ni(II) ions bind three fully conserved histidines and one
cysteine residues in a square planar coordination geometry (HHHC site hereafter, Figure 6A). Some
crystal structures of full-length HpNikR [101,103], showing partial occupancy of the square planar HHHC
sites as well as additional Ni(II) binding sites, have been interpreted as indicating means for HpNikR to
recognize different DNA sequences depending on the level of Ni(II) concentration and cytoplasmic pH,
but no evidence has been obtained so far to support this hypothesis The MBD is flanked by two peripheral
DNA binding domains (DBD), separated by flexible linkers. Each DBD is made of the dimerization of
the N-terminal portion of the protein and features a ribbon-helix-helix motif for DNA binding, typical
of prokaryotic transcription factors [104], in which two anti-parallel N-terminal β-strands from opposite
protomers make a two-stranded anti-parallel β-sheet that contacts the major groove (Figure 6A). Three
distinct conformations of NikR have been observed in the solid-state, characterized by the position
of the DBDs with respect to the central MBD, depending on the conformation of the flexible link
between the domains: cis, open, and trans (Figure 6B). The crystal structure of the EcNikR-DNA complex
has shown that the cis conformation of the Ni(II)-bound protein is able to bind DNA with the MBD
that keeps DBDs at the right distance to contact one-half-site of a two-fold symmetric DNA operator
Molecules 2015, 20 14282

(Figure 6A) [105]. The Ni(II) binding to the MBD of HpNikR results in an increase of the protein
affinity to DNA, as determined by calorimetric titrations [106]. The way through which Ni(II) binding
propagates the information for DNA binding at the MBD along the protein chain to the DBDs, has been
investigated both experimentally [100,107,108], and computationally, using atomistic molecular dynamics
simulations [100,109,110]. The conclusions suggest the occurrence of an ensemble of interconverting
structures in solution, spanning the open, trans and cis conformers, both for the apo and the holo-protein.
This implies that Ni(II) binding does not induce a conformational rearrangement of the protein towards
a specific cis conformation able to bind DNA, but rather unlocks the movement of the two peripheral
N-terminal DNA-binding domains with respect to the central C-terminal metal binding domain [100,110].

Figure 6. (A) Ribbon scheme of the crystal structure of holo-EcNikR in complex with DNA.
DBD are in orange and blue, while MBD are in light orange and light orange. Nickel ions
are reported as dark red spheres and DNA is in green. The inset reports the details of the
Ni(II) binding site. Nickel binding residues are reported as sticks colored according to atom
type; (B) Schematic representation of the conformations of NikR found in the available
crystal structures; (C) Ribbon diagram and solvent-excluded surface of the model structure
of the HpNikR-DNA complex. HpNikR ribbons are colored in dark and light grey in the
DBD and MBD, respectively. Active and passive residues used to guide the docking are in
red and orange, respectively. Ni(II) ions are reported as dark brown spheres. The DNA
ribbons are in dark red for the HpNikR operator and in light and dark blue for the terminal
regions. The inset shows the details of HpNikR residues in the DBD in direct contact with
the DNA major groove.
Molecules 2015, 20 14283

Some molecular details of the HpNikR interaction with the urease operator OPureA, based primarily
on NMR spectroscopy in solution, have recently been reported [111]. Considering that the size of the
full-length protein prevents the characterization of the HpNikR-OPureA interaction using only NMR, the
two protein domains were investigated separately. An NMR-based analysis of the perturbations, induced
on the DBD by the presence of OPureA, provided information useful to guide the subsequent docking
calculation aimed to build a computational model of the HpNikR-OPureA complex [111]. The homology
model of the holo-HpNikR in the cis conformation was calculated in a previous study [100]. The modeling
was based on the structure of NikR from E. coli in the cis conformation (PDB ID: 2HZV, resolution
3.00 Å) [105]. The alignment between the sequences of NikR from all the available crystal structures
(H. pylori, E. coli, and P. horikoshii) was produced using ClustalW [112] and manually adjusted in order
to match the primary and secondary structure of the proteins. The obtained alignment was used to calculate
50 structural models of the cis conformation of tetrameric HpNikR using the program Modeller [60].
Four square-planar Ni(II) ions were included in the modeling, bound in the well-known metal binding
sites involving H88 from one monomer and H99, H101 and C107 from the adjacent monomer [100,111].
A starting model for the unbound OPureA operator was generated using the DNA analysis and rebuilding
software 3DNA implemented in the 3D-DART server [27]. OPureA comprises nucleotides from −56 to
−87 with respect to the urease operon transcriptional start site in H. pylori strain G27 [111]. In order to
avoid biasing effects due to the highly charged DNA termini, twenty nucleotides were added to each
side of the operator by using the H. pylori strain G27 genome. The model was generated in the canonical
B-DNA conformation. The holo-HpNikR model structure in the cis conformation was docked onto
OPureA using the data-driven docking program HADDOCK 2.1 and the same protocol used previously
in the case of Fur (see ref. [63] and above). The calculation was guided by selecting the protein residues
found by NMR to be involved in the interaction with DNA (R12, F13, S14, V15, S16, S36, R37, L40),
as well as the operator’s nucleotides (from −56 to −87 with respect to the urease operon transcriptional
start site in H. pylori strain G27).
The NMR-based docking model of the interaction complex between the full-length HpNikR and
OPureA (Figure 6C), obtained using the NMR-based constraints described above, has strong analogies
with the crystal structure of the analogous complex experimentally determined in E. coli [105]. The
major axes of HpNikR and OPureA are almost parallel, with the latter uninterruptedly making contacts
with HpNikR, in agreement with DNAase I footprinting assays [106]. The DNA major axis is slightly
bent, even though not as much as in the E. coli structure, while the major groove is more open in the
region of interaction with the DBD domain, causing in turn shrinkage of the minor groove in the same
region (Figure 6C). HpNikR appears to interact with DNA mainly by inserting residues 12–14 of the DBD
into the major groove in the proximity of nucleotides positioned from −24 to −26 and from −80 to −82
with respect to the urease operon transcriptional start site in H. pylori strain G27 (Figure 6C) [111].

5. Perspectives

The determination of the structure of the complexes between proteins, as well as between proteins
and gene transcription operators, is an important step not only in the advancement of the basic knowledge
of a metabolic process, but also in the identification of new targets for the development of new drugs.
Molecules 2015, 20 14284

Moreover, several diseases are caused by alterations of transcription and/or protein-DNA interactions,
making these mechanisms highly attractive targets for therapy [113].
Current methods and libraries for drug discovery work well with a limited number of targets, such as
enzymes, ion channels or receptors, which feature a well-defined and structurally-stable concave binding
sites. The G protein-coupled receptors (GPCRs), protein kinases and proteases super-families are good
examples of this kind of targets with “druggable” characteristics [114–118]. Targeting protein-protein
interfaces (PPIs) of multi-protein complexes that mediate cell regulation (long regarded by many as
“undruggable”) has become a subject of intense activity in recent years [119]. Within protein-protein
complexes of known structure, PPIs are generally large (ca. 1500–3000 Å2), flat, and relatively
featureless [120], making the design of small molecule antagonists a challenging task. Furthermore, in the
case of protein-DNA interactions, many factors have led to consider transcription as“undruggable” [121].
Indeed, several DNA binding proteins lack ligand-binding domains or intrinsic enzymatic activities [121].
Moreover, some of the protein domains involved in DNA recognition or protein recognition are
intrinsically disordered in the absence of interacting partners [122,123]. Finally, the complexity of the
interactions involving multi-point contacts over large surfaces, the lack of defined pockets suitable for a
rational design of small molecules, and the presence of metal ions are critical issues that need to be
addressed [121].
The development of protocols to mimic these difficult interactions is a key answer to these pressing
needs, with the potential to offer the molecular details of new druggable targets. First it has been recognized
that energetic hotspots contribute much of interface interaction free energy in many PPIs [124], but also
it has been noted that many interactions involve continuous epitopes constituting defined grooves or
series of small specific pockets [125]. Such observations lead to the development of stapled α-helical
peptides and other proteo-mimetic approaches to drugging interfaces [126]. In a different approach, it has
been noted that fragments might gain low-affinity “footholds” on PPIs, and that these might be elaborated
to apt modulators of multi-protein assemblies where knowledge of the structures of the complexes is
available [114]. In the latter examples, the experimental determination or the computational prediction
of the structural details of the protein-protein or the protein-DNA interface is mandatory to the study of
the interaction interfaces. In this aim, the macromolecular docking protocols discussed in this review
have evolved in the last twenty years thanks to the development of new algorithms and the availability of
more computational power, and have become an important complement to the experimental techniques.

Acknowledgements

FM was supported by the University of Bologna through the FARB Program (Finanziamenti
dell’Alma Mater Studiorum alla Ricerca di Base) “Modulation of protein-DNA interactions with small
molecules: novel opportunities for drug design”.

Conflicts of Interest

The authors declare no conflict of interest.

Molecules 2015, 20 14285

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