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Chapter 9
Conformational Sampling of Proteins:
Methods for Simulate Protein Plasticity
and Ensemble Docking
Ana Ligia Scott, Simone Queiroz Pantaleão, and Eric Allison Philot
Abstract The understanding of protein function by emphasizing the critical role of
structural dynamics, or protein plasticity, in biological processes is a field of
knowledge in constant evolution. It highlights that proteins are not static entities
but exhibit intrinsic, nonrandom movements crucial for their interactions with other
molecules. Advances in cryoelectron microscopy (cryo-EM) and crystallography
have provided snapshots of proteins in various conformations, revealing the
dynamic nature of macromolecules. Different mechanisms of molecular recognition,
such as conformational selection and induced fit, could be simulated by using hybrid
methods that integrate molecular dynamics and normal mode analysis to study
protein conformational spaces. These approaches have significant implications for
drug discovery, as they allow for more accurate modeling of protein–ligand interactions by considering the inherent flexibility of protein structures. Methods such as
MDeNM, which integrates normal modes into molecular dynamics simulations, is
highlighted for its ability to promote significant conformational changes, facilitating
the flexible fitting of atomic structures into cryo-EM maps and revealing complex
protein motions. Collective molecular dynamics (coMD) and ClustENM are also
discussed, emphasizing their roles in conformational sampling and protein–ligand
interactions. Ensemble docking strategies, including the recent essential dynamic
ensemble docking (EDED) protocol, are reviewed for their potential in drug design,
particularly in improvi ng the accuracy of molecular docking by considering the
flexibility of both ligands and target proteins. These methodologies, combined with
advancements in force fields and postdocking techniques, contribute to a more
comprehensive understanding and prediction of biomolecular interactions.
A. L. Scott · S. Q. Pantaleão (✉)
Center for Mathematics, Computing and Cognition, Federal University of ABC, Santo André,
São Paulo, Brazil
e-mail: simone.qrzp@gmail.com
E. A. Philot
Educational and Research Institute, Molecular Oncology Research Center, Barretos Cancer
Hospital, Barretos, São Paulo, Brazil
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
V. G. Maltarollo (ed.), Computer-Aided and Machine Learning-Driven Drug
Design, Computer-Aided Drug Discovery and Design 3,
https://doi.org/10.1007/978-3-031-76718-0_9
243
244 A. L. Scott et al.
Keywords Protein plasticity · Conformational sampling · Normal modes ·
Ensemble docking
1 Introduction
For a long time, the idea that the protein structure de fines its function has been well
accepted. In recent decades, this concept has been adjusted to include a new element:
protein structural dynamics or plasticity. This chapter seeks to discuss the importance of protein local and global movements and thei r relevance for interaction with
other macromolecules or small molecules.
Proteins are not static entities; they have nonrandom intrinsic movements that are
extremely important for their function, often because of the interaction with different
binding partners. Recent developments in cryoelectron microscopy (cryo-EM) and
crystallography techniques have revealed multiple snapshots of increasingly large
and flexible systems [51]. Cryo-EM has increasingly been used in structural biology
to characterize not only a single structure but an ensemble of conformations, often
relevant to biological function, particularly for supramolecular assembly [8, 48 ].
Thanks to radically new technological advances in both microscope hardware and
image processing software, termed the resolution revolution, cryo-EM has dramatically improved in the last years and it can now capture information about biomolecular structure in multiple states to near-atomic resolution [44]. Macromolecules
adopt many conformations in solution depending on their structure and shape, which
determine their dynamics and function. These conformations exhibit unique
structure-encoded dynamic properties that have a profound influence on their biological function [5, 58]. Spectacular progress has been achieved in cryogenic
electron microscopy (cryo-EM) through technological advances in both microscope
hardware and image processing software (the so-called resolution revolution). CryoEM can now capture information about biomolecular structures in multiple states to
near-atomic resolution [44]. Not only has the number of structures solved by CryoEM increased steadily in recent years, but it has also allowed the characterization of
ensembles of conformations.
Macromolecules can adopt multiple conformations in solution depending on their
structure and shape, which are directly linked to their function. These conformations
exhibit unique structure-encoded dynamic properties that have a profound influence
on their biological function [5, 58]. Several studies have indicated that these
dynamic properties are primarily determined by the topology of native contacts [36].
In the last decade, a series of studies established the role of structural dynamics,
also called intrinsic dynamics, facilitating, if not driving, the interactions, and
functioning of biomolecular systems in the cell [5 , 51, 58]. Therefore, it is crucial
to explore different conformations to model dynamical processes and determine
stable states [71]. Such studies require both experimental and computational efforts.
In this context, theoretical methods are becoming essential for understanding how
different conformers interconvert to mediate biological function due to the accumulated structural information. A large number of studies based on coarse-grained
9 Conformational Sampling of Proteins: Methods for Simulate... 245
Fig. 9.1 Molecular
recognition mechanisms
models (such as the Elastic Network Model, ENM) or full atomic normal mode
analysis (NMA) have been applied to investigate macromolecular structures and the
molecular mecha nisms of their conformational changes [36]. Integrated methods
that combine NMA (using ENM or complete atomic models), molecular dynamics,
and experimental data from biophysical techniques, such as X-ray crystallography,
are useful for studying the structural dynamics of macromolecules.
Related to molecular recognition mechanisms, it is necessary to better understand
whether the interaction of proteins with binding partners occurs through possibilities: (i) conformational selection; (ii) induced adjustment, or even (iii) a combination
of both. Figure 9.1 illustrates these three situations. In the first, the target visits
several conformations energetically acce ssible by intrinsic motions and the ligand
chooses the best (Fig. 9.1a). In the induced adjustment, the ligand binding to the
target and provokes a conformational change to a lower energy level (Fig. 9.1b). The
third consists of the combinations of both: there is a conformational change before
the ligand binding to DNA after, as illustrated in Fig. 9.1c.
There are several examples illustrating each of these three cases and it is important to consider their role in the molecular interaction to improve the drug discovery
methods/software. In several cases, it is very important to consider this target’s
plasticity when we perform in silico studies, such as molecular docking (please, see
Chap. 7). In recent years, the importance of considering fitting effects in molecular
docking calculations was widely recognized in the molecular modeling community.
Although small-scale protein side chain movements are now accounted for in many
high-end docking strategies, the explicit modeling of large-scale protein movements
is still a challenging task. Ensemble-based methods have been introduced taking into
account several protein conformations in Drug Design and/or Discovery. Some
software couples a technique to sample the target with its docking algorithm.
We can use two distinct strategies to perform an ensemble docking (simulating
the conformational selection): (a) use software that integrates sampling and docking
algorithms and (b) use a software/method to sample the conformations for the Target
246 A. L. Scott et al.
and/or the ligand as a first step and after use software to simulate the docking of
several conformations. This second strategy allows the use of methods more efficiently to generate the conformers for the target and/or ligand. In this case, we can
use several different methods: Molecular Dynamics (please, see Chap. 8),
Metadynamics, and Hybrid methods that combine Elastic Network Models or
Normal Mode all atoms with methods such as Molecular Dynamics, Monte Carlo,
and Metadynamics. We should remember that several times it is important to
simulate the adjustment induced after the complexes are formed. There are several
examples of protein–ligand where this kind of molecular motion is important to
make the complexes more stabl e or facilitate the catalyses of substrates.
MDeNM coupled with ensemble docking helped in elucidating the mechanisms
guiding the recognition of substrates and inhibitors by the sulfotransferase [21]. In
the next section, we discuss some of these hybrid methods and applications to
simulate ensemble docking protein–ligand and protein–protein.
2 Hybrid Methods to Sample the Protein Conformational
Space
The protein plasticity, corresponding to the collective and noncollective motions,
has become important in understanding the relationship between sequence, structure
and function; and consequently, various aspects of biological processes such as [36]:
(i) The interaction of proteins with other macromolecules or with small molecules.
(ii) The molecular mechanisms involved in metabolic cycles.
(iii) The effect of mutations on the structure and function of proteins and others.
These protein movements can be classified according to different aspects. Yang
and collaborators classify protein movements [68] into three classes: (1) local
movements of protein fragments; (2) domain movements; and (3) movements
involving more than one unit.
In another way, Bahar’s group proposed the following classification [41]:
(i) Evolution of motions in the global (the three motions with lower frequency).
(ii) Low-frequency motions (from mode 4 until 20).
(iii) Low-to-intermedi ate frequency (from mode 21 to 60).
(iv) High-frequency (modes >60).
This classification is very interesting and useful for some kinds of analysis [41].
But in this text, we will use a generic classification using the nomenclature: collective and noncollective movements.
Collective Movements involve a considerable part of the molecule moving in a
coordinated manner, leading to a global conformational change. For example, the
ordered collective movements of one domain or more than one domain [6, 62]. In the
case of involving more than one domain, we can imagine that this movement is
9 Conformational Sampling of Proteins: Methods for Simulate... 247
composed of several collective (coordinated) movements, independent or not [37].
The collective one can be considered the first 20 motions.
Noncollective movements involve a small portion of the protein generally the
result of a stimulus. As an example, we can mention an allosteric conformational
change due to a small ligand binding. Another example that can be cited involves
secondary structure transitions that result in local folding or unfolding (change in
secondary structure) of the molecule, as can occur in the case of the Prion protein
[22, 42].
Despite the great advances in scalable codes, graphics processing units (GPUs),
and parallelization of simulation algorithms [51, 53, 56] allow us to simulate
increasingly larger systems and therefore longer biological times such as entire
bacterial cytoplasm in the submicrosecond range [69]. Still, for most proteins,
these time scales cover a small part of the structural picture, and longer simulations
are only accessible with special-purpose supercomputers like Anton (https://www.
psc.edu/resources/anton-3/)[20, 63]. In addition to these technical aspects, there is a
fundamental “sampling problem.”
Several studies are showing that the way configuration spaces are sampled can be
more critical than the simulation length. Normal Modes calculation using all-atom
models or coarse-grained methods such as Elastic Network Models can predict with
impressive accuracy, just from the general shape of a protein, not only the experimentally observed conformational changes but also entire sequences of intermediates structures [52]. The hybrid approaches allow for efficient conformational space
sampling, considering both slow and fast movements [36]. In recent years, several
hybrid methods have been developed to take into account local and global movements in conformational sampling such as MDeNM, CoMD, Dynomics, VMOD,
and others [12, 36].
These methods have been used for many purposes [15, 23], such as: (i) efficiently
sampling the conformational space of macromolecules; (ii) refining biomolecular
complexes and assemblies; (iii) integrating experimental data for improving the
determination of conformational ensembles; and (iv) conducting biomedical applications such as the study of allosteric movements, docking, and the impact of
mutations.
Efficient conformational sampling is essential to identify stable structures as well
as transition states of macromolecules and to calculate free energy differences
[7]. Kaynak et al. [35] provided a comparative analysis of such hybrid methods
developed for conformational space efficient sampling and the possible transitions
between functional states [35]. They compared four methods: ClustENM [39], its
recent extension ClustENMD [35], collective MD (CoMD) [27], and MD with
excited NMs (MDeNM) [12]. The first three hybrid methods combine Elastic
Network Models with minimizations and/or molecular dynamics to sample the
conformational space of macromolecules or macromolecular complexes. The last
method, MDeNM, combines normal mode calculation based on a force field (either
with all atoms or just their alpha carbons) with MD to sample the conformers. In this
chapter, the authors show that these methods can be more efficient than classical
molecular dynamics, sampling some open, closed and intermediate states of
248 A. L. Scott et al.
Fig. 9.2 Key combinations of methods for protein structural studies of proteins. Hybrid methods
may combine elastic network model (ENM)-based or force-field (FF)-based or anisotropic network
models NMA with various kinds of simulations such as Molecular Dynamics (MD) simulations,
Monte Carlo and can improve the heterogeneity conformational of proteins
macromolecules. Another hybrid method that considers all-atom normal mode
calculation is VMOD. The main methods present in hybrid approaches that use
normal modes are summarized in Fig. 9.2.
2.1 MDeNM
The method MD with excited Normal Modes (MDeNM) was developed to promote
large conformational changes during molecular dynamics simulations by incorporating normal modes. This technique is very useful for obtaining conformational
changes that are rarely achieved by standard MD simulations unless excessively long
simulations are carried out [12]. The MDeNM approach consists of a multireplica
protocol designed to enhance conformational exploration in a subspace defined by a
set of low-frequency normal modes, considering the dockings with the localized
motions occurring within the Cartesian space [14]. A proof-of-concept study has
shown that MDeNM can be a very efficient tool for performing flexible fitting of
atomic structures into cryo-EM maps [13]. Each replica starts from an equilibrated
9 Conformational Sampling of Proteins: Methods for Simulate... 249
initial structure whose normal modes are calculated, and the system is driven
dynamically along a direction obtained by a linear combination of selected lowestfrequency modes. An efficient conformational sampling is essential for identifying
stable, transient, and transition states of macromolecules and calculating free energy
differences.
A recent MDeNM new version updates the direction used to move the system
during simulations, taking into account the stru ctural and energetic constraints
imposed by the system itself and the environment, which allows the system to
explore new paths, especially considering complex movements [14]. Without the
need to recalculate the NMs like other methods (such as ClustENM, ACM,
ACM-PCA, NMA-ITS), the advantage of this approach is to describe alternative
paths in the conformational landscape using only a few initial modes as input.
The adaptive MDeNM (aMDeNM—[59]) has results compatible with several
available methods when simulating simple large-scale movements such as a hinge or
twisting movements while showing better performances considering complex movements involving reorganizations between protein domains [35]. Quiroz et al. combined the use of MDeNM with the Markov Model—PyEMMA [57] to study the
effects of phosphorylation on the meta-state transition of the Human Dopamine
Transporter (hDAT) and obtained relevant results [57].
Duda et al. used MDeNM to elucidate molecular mechanisms that guide the
recognition of diverse substrates and inhibitors by SULT1A1. The hybrid method
allowed them to explore the conformational space in a broad way for SULT1A1 in
complex and to clarify a littl e more the mechanisms of binding of the protein to the
substrate and the inhibitor [21 ].
2.2 Collective Molecular Dynamics (coMD)
The coMD method uses an Anisotropic Network Model (ANM) to guide the
molecular dynamics for the system. ANM modes are selected with the help of a
Monte Carlo (MC)/Metropolis scheme that allows the system to occasionally
diverge from the shortest path and bypass energy barriers. This allows movements
not to be limited to proceeding along a subset of low-frequency modes, which is
quite interesting and may include a stochastic factor in the generation of
conformations.
The use of Molecular Dynamics with a complete model of atoms at the step of the
ANM allows us to generate coordinates of perfectly compacted side chain atoms and
avoid nonphysical distortions in the geometry of the protein: As the ANM is defined
exclusively by the contact topology between residues, changes in geometry affect
the adaptively generated ANM modes in the next cycle of the simulations. Therefore, the basic approach is to deform the structure collectively along the modes
predicted by the anisotropic network model, upon selecting them via a Monte Carlo/
Metropolis algorithm from among the complete pool of all accessible modes. The
250 A. L. Scott et al.
CoMD method was proposed by [27] and implemented in the package Prody
developed by Bahar’s group [71, 72].
2.3 ClustENM and ClustENMD
ClustENM [39] is a fully automated conformational sampling method composed of
multiple generations/cycles consisting of each of the following steps: (1) generating
conformers by deformation along global NMs calculating using the lattice model
anisotropic (ANM) [4], (2) clustering of generated conformers and (3) relaxation of
cluster representatives. ANM modes are updated for each parent conformer in each
generation. A series of deformations along the ± directions of some global modes
(3–5) are performed, occurring a specific root mean square deviation (RMSD) for
deformation (Step 1). Representative conformers selected to calculate the next
generation of conformers are relaxed by energy minimization (EM) in implicit
solvent (Step 3). Kurkcuoglu and Bovin discussed (pre and postdocking sampling
of conformational changes using ClustENM and HADDOCK for protein–protein
and protein–DNA). They applied ClustENM combined with HADDOCK software
to perform pre and postdocking sampling to incorporate the effect of plasticity in
protein–protein and protein–DNA complexes [28]. The authors concluded that
conformational selection (presampling and docking) has the greatest impact on the
quality of the final models especially in the case of protein–protein complexes. They
also observed that for protein–DNA complexes, in contrast, the induced tuning stage
of the pipeline (postsampling) significantly improved the quality of the final product
models.
3 Ensemble Docking
Ensemble docking is a strategy that carries multiple independent molecular docking
calculations (please, see Chap. 7), each of them using different target conformations,
either from side-chains or larger backbone variation. This ensemble mimics the
macromolecule movements, despite using individual rigid conformations. It is
possible to discuss Ensemble Docking in two distinct contexts but similar in some
aspects: treating problems involving complex of protein–ligand; and protein–protein
interaction. These problems are important for the problem of Drug Design and/or
Discovery.
Currently, protein–protein dockings also have an important guideline for success,
which consists of understanding the modulation of protein–protein interactions, i.e.,
understanding which are primordial and which are advisory, even acting in different
regions, with allosteric mechanisms. Facts like this can be observed through the
stabilization of flexible loops by extensive sampling of the protein’s conformational
space.
9 Conformational Sampling of Proteins: Methods for Simulate... 251
Molecular dynamics have aspects that need to be constantly observed for biological systems, from adequate force fields to realistic sampling. In the study by [3], the
author also mentions that an improvement in the force fields applied with the
inclusion of electronic polarization together with quantum mechanics simulations
also helps to provide good informat ion about the biological system.
A computer program for coupling flexible ligands to flexible proteins using a
genetic algorithm, FITTED 1.0, was developed by [11]. This tool includes the
ProCESS and SMART modules which correspond to the protein/ligand configuration steps. Its validation was obtained by molecular docking of biological systems
involved with protein inhibitors such as HIV-1 protease, among others.
There are two very distinct strategic approaches for Drug Design, the first consists
of structure-based drug design (SBDD) and the other of ligand-based drug design
(LBDD). Both can be applied to analyze the properties and characteristics of
bioactive ligands. In the following session we discuss the main differences between
their uses and applicati ons in the docking of molecules.
Ensemble docking represents a molecular docking strategy in which the first step
is to generate an ensemble of structural conformations of a biological target, usually
obtained by molecular dynamics simulations, in which candidate molecules are
subsequently docked. More than the number of coupli ngs/target structures used,
what determines the success of the approach is the selection of which structures are
the most representative of the system of interest. When information such as the
flexibility and thermal fluctuation of the atoms is taken into account, it is possible to
explore conformational substates of these complexes that may correspond to important specific moments in the shape of the binding pockets. The need for more realistic
simulations has led to the development of studies using a combination of techniques,
which form the basis of ensemble docking. As reported by [3], the computational
design of ligands based on the structure has been strategically improving since 1970.
With computational advances, it has become possible to carry out more complete
simulations, considering a greater number of structures with diverse states in terms
of flexibility and the chemical space of the ligands. The author points out that some
historical events have marked the field, such as in 1999 when work began to consider
the flexibility of ligand pharmacophores. The main key points of the ensemble
docking technique are illustrated in Fig. 9.3.
McKay et al. [43] proposed a new proposal for ensemble coupling, known as
essential dynamic ensemble docking (EDED), the authors proposed this protocol for
agonists of the PAC1 receptor, a class B GPCR, where three million compounds
from the ZINC database were screened, with experimental validation of 23 compounds for PAC1 targeting. Its differential is that this technique can reduce the
number of false negatives in screening and improve precision in the search for
bioactive molecules. In this case, an important point is to obtain and select chemically significant receptor models for ligand docking. The technique consists of
parameterizing the ligand with an OPLS3 force field. The trajectory frames with
Principal Components (PCAs) values closest to the centers by RMSD measurement
were adopted as receptors for the ensemble docking.
252 A. L. Scott et al.
Fig. 9.3 Key points of the ensemble docking technique
The difference with other techniques is that this approach allowed us to arrive at a
minimum of relevant receptors in the region of the binding pocket, as opposed to
other techniques which generate many conformations of protein structures but not all
of them relevant to the region in which the ligand will act, demonstrating that it is
also possible to arrive at good docking results using a homology model.
Authors who generally follow the LBDD line also involve molecular activities,
but related to other techniques, such as QSAR models with subsequent steps of
molecular dynamics and study of postdocking normal modes. Two dist inct lines can
be interesting depending on the focus of each research. In the LBDD approach, the
focus on knowledge generation seeks to elucidate the relationship between the
structure and physicochemical attributes of molecules with their biological activity.
The structure– activity relationship (SAR) considers pharmacophore models of the
ligands that identify the main characteristics responsible for the activity, the quantitative structure–activity relationships (QSAR), providing quantitative predictions
of activity, similarity, and combinations of chemical descriptors. In the work of Shim
et al. [64], it is mentioned that 2D-QSAR is based on the calculation of physicochemical, electronic, topological and shape descriptors and is not closely related to
the molecular docking technique. These are models that require preprocessing, the
values are normalized and correlated, or redundant descriptors can be eliminated
using the partial least squares (PLS) technique or principal component analysis
(PCA), which require cross-validation, randomization or a set of external tests.
The author, [64], also mentions other types of QSARs, now related to molecular
docking, as they use 3D descriptors in bioactive conformations, such as in comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA), which depend on the chemical and spatial characteristics of the
molecules, such as molecular volume, surface area, solvation ΔG, dipole moments,
HOMO and LUMO, based on the molecular interaction field in a 3D grid around the
molecules with polar probes. The molecular docking step is used to align the ligands
in their bioactive conformation. Statistical methods such as PLS or PCA are applied
and related to biological activities. There is a type of model, known as 4D-QSAR,
which takes flexibility into account and uses multiple conformations. 5D-QSAR, on
the other hand, builds a pseudoreceptor based on information from the ligand to