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14 Exploring the Significance of Experimental and Computational Methods .. . 419
The initial step in MD simulations is selecting the file with the protein coordinates
(i.e., the structure with the highest resolution of atomic coordinates). Depending on
the experimental structural elucidation technique, not all atomic coordinates are
resolved, and there may be regions (gaps) without structural definition. However,
it is possible to use programs that perform structural comparisons of proteins and, by
similarity, complete the lacking regions, such as SWISS-MODEL [71]. Some MD
simulation software includes routines for preparing input files to perform this
verification and reconstruction of missing residues, such as the AMBERtools
[136]. Other aspects that require attention when conducting an MD simulation are
the definition of the statistical ensemble, the control of temperature and pressure, the
initial energy minimization conditions and system equilibration, the use of periodic
boundary conditions, the minimum image convention and cutoff radius, the treatment of long-range interactions, and the application of solvation methods for the
system [54]. Thus, once the parameters and conditions are obtained, the calculation
begins, proceeding through the minimization, heating, and production stages in a
classical MD simulation [116, 137].
More details on methodological aspects, validations, and analysis of MD simulations can be found in Chap. 8.
3.5 Sampling Strategies
According to Newton’s classical physics equations, several MD approaches are
successfully used to study the interaction and movement of atoms and molecules.
The numerical integration of Newton’s equations of motion for a system of
interacting particles provides successive system configurations, like a trajectory
film, showing the particles’ positions and velocities over a predetermined fixed
time. However, MD simulations require many numerical calculations due to the
small integration time steps of femtoseconds (10
simulate biologically relevant events (such as protein folding below microseconds,
-6
10
s) [138 ].
-15
s), which are necessary to
MD simulations have become an important and widespread method to explore
conformational space, being applied to studying the folding of small proteins
(around 80 amino acids) in their native structures [139]. Along with the precision
and robustness of diverse types of force fields (including solvent models), an aspect
that initially limits the e xtent of sampling in MD applications is the time scale a
conventional MD simulation can achieve. Typically, they are very short in describing real problems of protein systems with hundreds of thousands of atoms.
Several enhanced computational methods have been developed to address these
sampling issues using MD and expand the sampling range of classical MD simulations. Among these methods, one can include replica-exchange MD simulations
[140], steered MD [141], accelerated MD [142], metadynamics [143], and adaptive
420 A. H. Moraes et al.
steered MD [79]. Some methods use constrained potentials over force constants to
direct sampling over energy barriers and pre-defined reaction coordinates, as in
umbrella samplin g [144] and metadynamics [145]. However, these methods restrict
their use in predicting actual structures since the endpoint must be unknown in a
protein folding study. As an alternative, sampling methods that use energy as a
reaction coordinate have been developed, generalized ensemble methods such as
replica exchange MD (REMD), and methods that combine both methodologies
[146]. The REMD has becom e one of the most popular ab initio methodologies
for simulating protein folding, as it allows multi-node parallel computing architectures. In addition, the method can be used with stochastic Monte Carlo
simulations [147].
One can highlight some applications in this context, such as the ab initio structure
prediction of peptides and small proteins. Peptides have enormous potential for drug
development, bridging between small molecule-based drugs and proteins
[148]. Many naturally occurring peptides or those designed by computational
modeling have had their 3D structures predicted by MD-based methods. However,
peptide modeling via MD-base methods with sequence alignment is less reliable
than proteins [138].
3.6 Impact of Protein Structure and Dynamics on Drug
Design
The binding of proteins and ligands relies on physical and chemical interactions
between residues in the binding site of the protein and the ligand, ensuring specificity
and communication between active and allosteric sites. These interactions induce
structural changes in the binding pocket as the protein transitions from its ligandunbound (apo) to ligand-bound (holo) state, causing many differences between apo
and holo structures [149 ].
A study from 2005 examined 60 enzymes, revealing structural discrepancies
between their apo and holo forms [150]. However, most of the enzymes displayed
minimal deviations (≤1Å)inCα root mean square deviation (RMSD) between the
two states, their apo and holo states. Subsequently, in 2007 [151], 98 apo-holo pairs
were categorized into rigid, moderate, and flexible categories based on Cα RMSD
differences, highlighting preferences for polar–polar interactions in rigid proteins
and hydrophobic interactions in flexible ones. Exploring side chain orientations
across apo–holo pairs revealed increased flexibility at the binding site, often
overlooked in analyses focusing solely on residue backbones. Nevertheless, significant disparities can become apparent when analyzing side chain orientations across
apo–holo pairs [152]. One of the most visualized conformational changes upon
binding is the side chain rotameric stat e changes [153, 154].
The consensus from various studies suggests that while backbones undergo minor
conformational chang es, side chains explore a broader conformational space upon
ligand binding [149]. Consequently, holo-structures are generally preferred targets
for ligand docking due to differences in binding site structures. Holo-structure
14 Exploring the Significance of Experimental and Computational Methods .. . 421
superiority over apo counterparts was demonstrated in discriminating binders from
nonbinders [155], whereas significantly lower enrichment levels were observed in
apo structures compared to their holo counterparts [156]. Based on that information,
computational simulation for drug design methods that account for sidechain flexibility and multiple receptor conformations becomes crucial. Flexible SLIDE
docking can accommodate ligands within 2.5 Å of their crystal structure pose by
manipulating sidechain orientations in apo structures to address the challenges
associated with rigid docking [157]. While flexible docking offers a solution, proteins with significant backbone differences pose challenges. Ensemble docking,
employing MD simulations to obtain multiple receptor conformations, offers an
alternative approach [158, 159].
Furthermore, considering the available structural information of the system under
study is important for simulations. Molecular docking typically yields optimal
results when the molecule resembles the one crystallized [160 ]; RMSD <2.0 Å
corresponds to good docking simulations. However, achieving such similarity is not
always possible. In efforts to facilitate virtual screening using apo structures, protocols involving MD simulations to refine prote in structures were developed and
could be implemented before the virtual screening procedure. This strategy is
especially helpful when holo-structures are unavailable [149].
However, inherent flaws in current scoring functions, such as excessive simplifications, limited consideration of interaction energies, inaccuracies in predicting
binding affinities, may compromise the accurate identification of the putative binding mode of simulated compounds (docking poses), improper consideration of
oligomerization states, or the presence of multiple interfaces typically excluded
from docking calculations [161–163]. To mitigate these challenges, leveraging
external information whenever feasible and available can enhance the accuracy of
docking predictions. Pioneering methods like HADDOCK [164] and other protein–
protein docking approaches like pyDock [165], ZDOCK [166], and LightD ock [167]
have implemented protocols incor porating distance restraints to refine docking
calculations.
Although most enzymes do not undergo significant conformational changes when
interacting with a ligand, as previously discussed, understanding the system to be
modeled is always beneficial in selecting the appropriate structures. One example is
Abelson kinase (Abl), an enzyme whose deregulation causes chronic myeloid
leukemia and can exhibit different conformations depending on the ligand
[168]. Some inhibitors, such as dasatinib, inhibit an active form of the enzyme, in
which the activation segment is in an open conformation (Fig. 14.4). In contrast,
imatinib inhibits an inactive conformation of the protein, where the activation
segment is in a closed conformation. Due to steric effects, imatinib cannot bind to
the enzyme when the activation segment is in the extended conformation. Additionally, the activation segment’s conformation influences the P-loop conformation,
which must adopt a folded conformation when the activation segment closes to
avoid electrostatic repulsion [169, 170]. So, a detailed understanding of the conformations and specific interactions of ligands with enzym es is essential for selecting
422 A. H. Moraes et al.
Fig. 14.4 Superposition of Abl structures co-crystallized with imatinib in blue (PDB ID 2HYY)
and dasatinib in brown (PDB ID 2GQG). Residues with potential steric hindrance, depending on the
conformational state of the activation segment and P-loop, are shown [169, 170]. These steric
hindrances are highlighted as dotted spheres in yellow. The figure was generated using the software
PyMOL
and preparing the correct structures in computational simulations, directly influencing the accuracy and effectiveness of docking and molecular dynamics studies.
4 Remarks and Perspectives
In conclusion, integrating experimental and computational techniques is essential for
advancing drug design, particularly in understanding protein structure and dynamics.
The impact of conformational diversity between apo and holo states underscores the
need for comprehensive methods that capture the full range of structural variability.
Experimental methods provide critical insights into these structural changes, while
computational techniques, such as flexible docking and ensemble docking, address
the challenges of conformational flexibility. By combining these approaches,
researchers can enhance the accuracy of ligand binding predictions and better
understand the dynamic nature of protein–ligand interactions. This synergy between
experimental and computational strategies is crucial for developing effective therapeutics and optimizing drug discovery processes.
Moreover, we are at a transformative juncture in structural biology and protein
modeling with the advent of powerful tools like AlphaFold and RoseTTAFold.
14 Exploring the Significance of Experimental and Computational Methods .. . 423
These groundbreaking technologies have dramatically improved our ability to predict protein structures with unprecedented accuracy, which in turn, is set to influence
experimental techniques such as X-ray crystallography, NMR, and cryo-EM. As
these computational methods continue to evolve, they will complement and refine
traditional experimental approaches, thereby accelerating discoveries and deepening
our understanding of complex biomolecular systems. This convergence of computational and experimental strategies marks a new era in structural biology, promising
significant advancements in fundamental research and drug development.
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