Proteins as dynamic entitiesThe three-dimensional folded structure of the amino acid chain constituting the protein entity is often depicted more or less as a static macromolecule, considering that proteins fold into a ‘correct’ and ‘final’ conformation cap-tured by, for example, x-ray crystallography or NMR spectroscopy. These static representa-tions are in many cases deceptive, and it has long been known the highly dynamic nature of proteins. Early studies of hydrogen–deuterium exchange [1] and x-ray diffraction experiments on myoglobin and hemoglobin [2,3] strengthened the view that proteins are dynamical and flex-ible entities. The latter study in fact showed that conformational changes were needed in order for O
2 to escape from the binding pocket. It soon
became acknowledged that proteins and biomol-ecules such as nucleic acids possess important dynamical properties [4,5]. The inherent dynam-ics of any protein is chemically coded in the pro-tein structure and, hence, in the sequence, and is in most cases intimately linked to its function [6]. The structure–dynamics relationship allows the protein to adapt and alternate its conformation as a response to environmental stimuli and to interact with different binding partners at dif-ferent times or locations. This property enables the modulation of the vast number of biological processes in living organisms.
Proteins and nucleic acids possess a rich hier-archy of different motions and conformational
events that span a large timescale (from fem-toseconds up to several hours), and involve collective motions as well as local flexibility of the biomolecule (FiguRe 1). Bond vibrations occur on the matter of femtoseconds (10-15 s), sidechain rotations and loop motions on the pico- (10-12 s), nano- (10-9 s), and microsecond (10-6 s) timescales. Breathing modes and larger conformational changes such as domain move-ments happen during several tens to hundreds of nanoseconds, allosteric regulation on the microsecond timescale, and protein folding from microseconds to several minutes and hours. Of these events, the most striking conformational changes are often triggered by environmental changes (e.g., ligand binding, change in pH, temperature and protein–protein interactions). Nevertheless, many of the listed events are intrinsic to the proteins; they move as a result of thermal energy (equilibrium dynamics).
Ligand-induced conformational changes are, in particular, essential to molecular motors that convert chemical energy to mechanical work. Motor proteins are often allosterically regulated. In these systems, allosterism enables alternating switching of conformational states and func-tionality of distinct units of the protein, and is often elicited by ligand binding. Allosteric interactions between identical ligand-binding sites (homotropic interactions) or different sites (heterotropic interactions) in the protein mol-ecule can result either in positive or negative
Dynamics, flexibility and ligand-induced conformational changes in biological macromolecules: a computational approach
Biomolecules possess important dynamical properties that enable them to adapt and alternate their conformation as a response to environmental stimuli. Recent advancements in computational resources and methodology allow a higher capability to mimic in vitro conditions and open up the possibility of studying large systems over longer timescales. Here, we describe commonly used computational approaches for studying the dynamic properties of proteins. We review a selected set of simulation studies on ligand-induced changes in the chaperonin GroEL–GroES, a molecular folding machine, maltose-binding protein, a prototypical member of the periplasmic binding proteins, and the bacterial ribosomal A-site, focusing on aminoglycoside antibiotic recognition. We also discuss a recent quantitative reconstruction of the binding process of benzamidine and trypsin. These studies contribute to the understanding and further development of the medicinal regulation of large biomolecular systems.
Lars Skjærven*1,2,3, Nathalie Reuter2,4 & Aurora Martinez1
1Department of Biomedicine, University of Bergen, Jonas Lies vei 91, N-5009 Bergen, Norway 2Computational Biology Unit University Research, Bergen, Norway 3Present address: The European Molecular Biology Laboratory, Meyerhofstr. 1, 69117 Heidelberg, Germany 4Department of Molecular Biology, University of Bergen, Thormøhlensgt. 55, N-5008 Bergen, Norway*Author for correspondence: Tel.: +49 6221 387 8964 Fax: +49 6221 387 8519 E-mail: [email protected]
Future Med. Chem. (2011) 3(16)2080 future science group
cooperativity and the conformational changes can span several tens of angstroms. These fas-cinating allosteric events lie at the heart of the regulatory mechanisms of many biological pro-cesses, and keen attention has been paid in order to understand the detailed mechanisms at play.
Experimental procedures, such as x-ray crys-tallography, provide invaluable high-resolution structural information of proteins in a crys-talline environment. Generally, cryo-electron microscopy (EM) deals with proteins that are not crystallized or packed, contrary to x-ray crys-tallography, but this approach is, in most cases, limited to low resolution and detailed dynamic properties are customarily not accessible. The 3D-cartesian coordinates obtained by these approaches are typically an observable average of the thermal fluctuations at stable end-states of the functional cycle. Thus, the transitions between these states are not observed. NMR is, in many cases, a better source of obtaining information about dynamics and flexibility but, is generally, limited to relatively small proteins and nucleic acids.
Biomolecular simulations, for example, molecular dynamics (MD) simulations, have the capacity to mimic in vitro conditions and provide a detailed picture of the configurational dynamics in proteins and nucleic acids. Principal component ana lysis (PCA) of MD simulations and normal-mode ana lysis (NMA) provide effective means to describe and predict pro-tein slow motions. Thus, these techniques have the potential to complement the experimental
structural procedures. The aim of this review is to highlight some of the capabilities of biomo-lecular simulations, in particular related to the investigation of ligand-induced conformational changes in proteins. We focus on how MD simu-lations, NMA and PCA can be utilized in order to gain valuable insights regarding the dynamics and flexibility of a large, allosterically regulated molecular machine, the chaperonin GroEL. Describing allosteric mechanisms in which ligand-induced structural perturbation at one site of the protein affects the structure, dynamics and/or biochemical properties at other sites, is of particular interest. We start by reviewing the classical models for molecular recognition and allostery, and we turn our attention to describing some of the most popular computational tools. We then discuss and summarize results from a selected set of simulation studies of the GroEL chaperone, which is also an important research focus at our laboratory. In addition, we review a recent study on the maltose-binding protein to emphasize the importance of enhanced sam-pling approaches in MD simulations. Further, we describe a simulation study of the complete reconstruction of the binding process between an inhibitor–enzyme (benzamidine–trypsin) complex obtained from distributed comput-ing. Finally, we underline important advances that have been made on simulations of nucleic acids and we therefore dedicate the last section of this review to RNA simulations, with the particular focus of antibiotics binding to the bacterial ribosome.
Figure 1. Timescales of conformational events in proteins.
Key Terms
Allosteric interactions: Indirect (distant) interactions between distinct binding sites of the protein.
Molecular dynamics simulations: Computational modeling method to simulate the time-dependent development of a molecule.
Normal-mode ana lysis: Computational modeling method to analyze the vibrational harmonic oscillating motions of a molecule.
Molecular machines: Large proteins that convert chemical energy to mechanical work.
Chaperonin: Class of ring-like chaperone proteins that aid in the correct folding of other proteins.
Models for molecular recognition & allosteryWhen Fischer released the lock-and-key concept for molecular recognition in 1894, the preva-lent view of proteins was as rigid bodies [7]. This model anticipated that the protein had a defined space in which the binding partners would fit perfectly into. Early notion that proteins were not static but could adapt their shape (plasticity) to fit the binding partner led to the induced-fit concept by Koshland in 1958 [8]. In this model, it was assumed that both the receptor and the ligand mutually adapted to each other, thus pro-viding the complementarity that is observed in protein–ligand and protein–protein complexes.
As the proteins possess inherent dynamics, they constantly move on the multidimensional energy landscape and, thus, sample conforma-tions which are thermally accessible. On this notion, an alternative to the induced-fit model was postulated [9–12], reflecting the concept that the unbound state exists as an ensemble of conformations: all conformations pre-exist in dynamic equilibrium (pre-existing equilibrium model). In this model, the weakly populated, high-energy conformations constitute the bind-ing competent conformers and are, thus, respon-sible for binding to partners (by conformational selection) (FiguRe 2).
In the case of a low-energetic barrier between the conformers corresponding to the ligand-free and ligand-bound states, the outcome of ligand binding is a shift in the equilibrium towards these conformers (referred to as the popula-tion-shift model) [9–12]. Both experimental and computational studies of ligand binding have been interpreted in the direction of pre-existing equilibrium [13–17]. Particularly interesting is the fact that basal activities are sustained even in the absence of activating ligands; for example, for G-protein-coupled receptors [18]. However, according to recent MD simulations, this model is not compatible with the usually very fast pro-tein–protein association [19]. In response to this, Grunberg et al. suggested a unifying mechanism between the conformer selection and the induced-fit model, later termed the flexible protein rec-ognition model by Teilum et al. [5]. This model acknowledges three different steps in the binding process: encounter, recognition, and final step. The first step starts with the diffusional encoun-ter complex in which the two separate molecules have the same conformational ensembles as free in solution. Subsequently, the most binding competent of the free conformational ensembles
are recognized (conformational selection), and short-range forces become more important in the aligned complex, forming a recognition complex. At this stage, the two molecules can no longer be considered independent. Finally, the bind-ing between the two induces mutual adapta-tion (induced fit) to form the complementarities observed in the final (bound) complex.
The relevance of both induced-fit and popula-tion-shift mechanisms has been demonstrated by theoretical and experimental studies [16,20]. The two mechanisms are not necessarily mutually exclusive (as also depicted in FiguRe 2), although Okazaki and Takada postulate that the binding of small ligands to proteins proceeds mainly via the population-shift model, while binding of larger ligands follows the induced-fit mechanism [16].
The conformational response to various environmental changes can be of very different nature resulting in only local changes (i.e., move-ments of a few amino acids) or much larger movements (i.e., domains). Moreover, binding to one site of the protein might induce confor-mational responses in another distant region of the protein. This coupling between two sepa-rate sites is known as allosterism and usually has crucial implications for the regulation of cellular processes where the protein is implicated [21].
Originally, allosteric systems were described as oligomeric proteins made up of identical subunits in a symmetric assembly. This allosteric model, generally referred to as the Monod, Wyman and Changeux (MWC) model [22], proposed that the ligand-induced conformational changes mainly affected the quaternary structure and consisted
Figure 2. Protein–ligand recognition models. (A) All conformations pre-exist in solution and the ligand binds selectively to the binding-competent conformer (conformational selection). (B) Protein–ligand binding induces mutual adaptation of the two units. Adapted with permission from [51].
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Future Med. Chem. (2011) 3(16)2082 future science group
of concerted rotations of the subunits in the oligomeric complex, where symmetry is main-tained. An alternative proposition, referred to as the Koshland, Nemethy and Filmer (KNF) model, was based on a sequential model in which the subunits changed conformation one at a time upon ligand binding [23].
Accumulating new insight has revealed the existence of allostery, not only in oligomeric systems but also in monomeric and single domain proteins [24]. It has also been suggested that all proteins possess allosteric features [25]. Moreover, the MWC model does not handle negative cooperativity, thus, the alternative KNF model appears more suitable to explain allosteric changes in certain systems [26], although MWC has proven its usability in a number of cases (e.g., review by Cui and Karplus [27]). Results have also been interpreted in the direction of a com-bination of these models (MWC and KNF) [28]. Hence, how allosteric regulation is achieved in particular systems, often cannot be explained solely by these traditional models. The mecha-nisms involved might be different and span a wide range of functional motions.
Allostery has also been explained in the direc-tion of the population-shift mechanism [12]. Characteristic for this interpretation is that the allosteric conformational transition occurs even in the absence of the activating ligand [25]. Thus, ligand binding does not induce a set of sequential steps along a well-defined path from one site to another in the protein. Rather, allo-stery, ‘derives from a redistribution of the con-formational ensemble’ [25]. This model is often denoted as the ‘new view’. However, this is a debated topic, since it is claimed to be the basis for the original MWC model [27,29].
In the case of signal propagation in allosteric proteins, the existence of multiple propagation pathways has been suggested [30]. Central in this hypothesis is the idea that ligand binding induces local stress, which destabilizes the struc-ture, leading to perturbations that propagate to distant sites possibly via multiple pathways. A similar concept, referred to as proteinquakes, has been formulated to explain a mechanistic model for protein relaxation [31]. Providing a plausible view on signal propagation in proteins, this anal-ogy has been used to explain, for example, ligand escape in myoglobin [32].
Molecular modelingIn the ongoing venture of understanding bio-logical processes, the computational toolbox
has proven its usability to predict and describe, in particular, dynamic properties of biologi-cal macromolecules. During the last decades, a myriad of computational tools has emerged for this purpose. Among the most popular techniques are NMA [33,34] and MD simula-tions [35], although methods such as Brownian dynamics [36] and Monte Carlo simula-tions [37,38] are also widely used. All-atom unbi-ased MD simulations have a superior potential to reveal the protein flexibility, dynamics and detailed mechanisms involved in, for example, allosteric regulation and conformational tran-sitions [27,39–42]. On the other hand, NMA is particularly useful for describing flexibil-ity and providing information on slow pro-tein motions [43–46], which is inaccessible by conventional MD simulations.
Computational tools rely on representing the molecule (or protein) at various levels of detail to mimic and investigate its proper-ties and behavior. The appropriate model and the level of detail is typically a compromize between computational expense and struc-tural complexity. One of the most used mod-els includes representing each atom in the pro-tein as a single particle (ball) with an assigned radius, mass, and charge (referred to as the all-atom model) (FiguRe 3A) . Incorporating several atoms into one pseudo-atom is also a common strategy in order to reduce the com-plexity of the system, for example, by treating the hydrogen and carbon atoms as single inter-action centers (referred to as the united-atom model) (FiguRe 3B). Coarse-grained models go even further in reducing the complexity of the system; for example, by only considering the Ca-atoms of the protein chain (FiguRe 3C). In these models, bonded interactions are repre-sented as springs having a particular equilib-rium distance. Utilizing such a model involves the approximation of representing the protein with classical mechanics in contrast to quan-tum mechanics. Hence, this means that the electrons are not explicitly represented (as they are in quantum mechanics).
The potential energy of such a model can readily be estimated with a force field, which is the association of a suitable potential energy function and the collection of parameters for each particle (atom) in the system. Thus, the potential energy (V) is a function of the atomic coordinates (r) of the system. The most commonly used force fields dedicated for biomolecules (e.g., Amber [47], Charmm [48],
Key Term
Force field: Set of parameters and associated energy function to calculate the potential energy of a molecule.
φ
r
θ
l
Figure 4. Illustration of the terms included in a potential energy function.
Gromos [49]) comply with two important principles: additivity and transferability. The force field allows for calculation of the energy of the molecule as a sum of potentials, and to transfer the parameters between similar chemical groups.
A typical, all-atom energy function typi-cally consists of the sum of bonded (E
bonded) and
nonbonded (Enonbonded
) terms:
V(r) E Ebonded non bonded= + -
equAtion 1The bonded (E
bonded) terms describe the bond
stretching, angle bending and torsional rota-tions (dihedral angles), while the nonbonded (E
nonbonded) terms account for the electrostatic
and van der Waals’ repulsion and attraction (FiguRe 4). The bonded terms have the following form:
E 21 k l l 2
1 k
21 k 1 cos n
l 0bonds
0bonds
dihedrals
= - + -
+ + -
i i
z d
i
z z z
^ ^
^
h h
h6 @
/ /
/equAtion 2
The energy associated with bond stretching and angle bending is calculated as the sum over all bonds and angles in the molecule and is a function of the force constant ‘k’ and deviations from the equilibrium values (l and q denote the instantaneous bond length and bond angle, respectively, while l
0 and q
0 denote the equi-
librium values). The dihedral term represents the energy of changing the torsional angle (f) defined by four bound atoms. Here, nf and df represents the (atom type-specific) periodicity and phase-shift parameter, respectively.
The nonbonded interactions are commonly treated by the terms:
E 21
rA
rB
21
rq q
non bondedij12ij
ij6ij
ij ij
i j
ij= - +- e o/ /
equAtion 3The first term represents the van der Waals’
interactions between two atoms and is com-monly modeled by a 12–6 Lennard-Jones poten-tial. It consists of a repulsive (r
ij-12) short-range
term modeling the high energy of overlapping electron orbitals, and a longer range attraction (by dispersion) force (r
ij-6). Here, A
ij and B
ij are
coefficients determined from experiments. The second nonbonded term models the electro-static interactions between pairs of atoms by a Coulombic potential as a function of the two partial charges (q
i and q
j) and the distance (r
ij)
between them.
A B C
Figure 3. Common modeling strategies at three different levels of detail. (A) All-atom, (B) united-atom and (C) Ca representations of the protein are shown.
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� MD simulationsIn this section, we will provide a brief and non-technical introduction to MD simulations (see [50] for a more technical introduction, and [42,51] for applications of the method). Central to the MD methodology is the combination of the force field and Newton’s laws of motion. As described earlier, the force field reports on the total potential energy of the molecule as a func-tion of the coordinates (or the conformation) of the molecule. More importantly in this context is that the force field also provides the force (and the direction of the force) acting upon atom A due to the interactions of all the other atoms in the system. Utilizing Newton’s second law of motion allows (through numerical integra-tion) the calculation of the position and veloc-ity of atom A after a small step in time (t + Dt). Commonly, this propagation time step (Dt) is 1–2 fs (10-15 s) long. A time evolution propagator is, thus, applied for millions of steps to calculate the set of coordinates of the atoms in the system along a time axis. In the case of a step size of 1 fs, a 1 ns long MD simulation thus consists of 106 propagation steps, indicating the computational expense. The small time step (1–2 fs) is needed to obtain smooth and accurate trajectories and avoid unstable motion of the macromolecule. As a consequence, conventional MD simulations are generally restricted to precise but limited conformational sampling. Thus, only relatively small conformational events are accessible, and crossing of large barriers on the potential energy surface is generally not reachable.
The power of MD simulations lies in the potential to complement experimental data, first of all by providing a dynamic model at an atomic resolution of the biomolecule at a given temperature. It allows measuring time-depen-dent properties and following individual particle motion along time, studying dynamics and con-formational events at different conditions, and answering detailed questions about the molecule. Such a study is, however, highly sensitive to the quality and resolution of the starting structure, which is commonly obtained experimentally. The lack of a high-resolution starting structure can potentially lead to artifacts and instabilities in the simulation.
Commonly used programs for classical MD simulations of biomolecules are Amber [52,201], Charmm [53,202], Gromos [54,203], Gromacs [55,204], NAMD [56,205], and Desmond [206]. The devel-opers of these programs (and others) are also the main contributors to the associated force fields.
� Approaches for enhanced sampling in MD simulationsDue to the level of computational complex-ity in classical MD simulations, the approach remains limited to studying dynamics and conformational events in biomolecules on the nanosecond to microsecond time scale. Thus, there is a significant gap between accessible time scales and the time scales for many physiological processes. To close this gap and overcome sam-pling issues in MD simulations, several variants have emerged.
One of these variants is targeted MD (TMD) [57]. In this approach, the starting simu-lation is guided towards a final target structure. The driving force is calculated based on the root-mean-square deviation between the instantaneous and target structure, generally driving the confor-mation in a straight line to the target. Application of this approach includes simulating the transi-tion pathways between two conformers [58], but also ‘guided’ protein folding [59]. The application of TMD has found widespread use, notably for allosteric proteins, such as the nicotinic acetyl-choline receptor [60] and GroEL [61], but also for molecular machines, such as the F
1-ATPase [62].
While TMD imposes a force to drive a conforma-tional transition on the entire molecule, steered MD [63,64] can be used to drive/pull a set of atoms (e.g., ligand dissociation and forced unfolding) of the system along a defined reaction coordinate (e.g., distance or angle between atoms).
Another concept for enhanced conforma-tional sampling in MD simulations without adding an explicit external driving force includes smoothing the energy landscape with a biasing potential. Generally this is done by decreasing the depth of the energy well, or, alternatively, lowering the energy barrier, allowing for a faster transition to neighboring local minima. Pioneering this concept, Grubmüller and col-leagues developed the MD variant termed conformational flooding [65,66]. An equivalent approach is the accelerated MD (aMD) pro-posed by Hamelberg et al. [67]. In general, these techniques efficiently avoid the simulation to be trapped in a local energetic minimum on the energy landscape. A related approach, termed metadynamics [68], allows the definition of sev-eral reaction coordinates to drive a particular process. In a recent study on diffusion of anti-biotics through a membrane protein channel, Hajjar and co-workers defined the antibiotic ori-entation and specific interactions with the chan-nel in an attempt to overcome the simplicity and
Key Term
Conformational sampling: The collection of protein conformations collected through simulations.
the approximation arising from the application of a single reaction coordinate [69].
Of particular interest for extensive sampling of phase space are replica exchange simulations, also known as parallel tempering [70]. For biomol-ecules, replica exchange MD (REMD) simula-tions, introduced by Sugita and Okamoto [71], is the most widely used replica exchange approach. Replica exchange was, however, originally for-mulated to extend sampling in Monte Carlo simulations [72]. The concept of REMD relies on simulating N parallel replicas (or copies of the system). Each individual simulation is performed at different temperatures, ranging from low to high (normally, between 270 and 600 K). During the simulations, configurations are swapped (or exchanged) between the N par-allel simulations. Generally, high-temperature MD simulations traverse a large conformational space, and low temperature simulations yield a more precise sampling around a local energetic minimum. Exchanging representative configu-rations from the high- to the low-temperature simulations, allows low-temperature sampling of representative high-temperature phase space. REMD simulations are mainly used for protein-folding experiments.
In addition, several other important and help-ful techniques have been developed to overcome the sampling issues in MD simulations. To mention some: temperature aMD [73], transi-tion path sampling [74], milestoning [75], nudged elastic band [76], locally enhanced sampling [77] and leap dynamics [78].
Although clever algorithms for enhanced sampling and the continuous increase in com-putational power will allow for longer and more extensive MD simulations, such as the recent microsecond simulation of the nicotinic recep-tor [79], attempts to accelerate the simulations by means of hardware persist. Notably, the devel-opment of special purpose computing facilities, such as the Anton MD super-computer [80], rep-resents a major step forward in accessing more physiological time-scales in MD simulations [81]. Distributed computing, in which ordinary desk-top computers are connected together into a large grid, represents another trend to access massive computational resources. The Folding@home project by Pande [82] is one successful example. Another one is GPUGRID, which specializes in taking advantage of the advanced graphics pro-cessing units in many new computers [83]. Both of these projects rely on volunteers worldwide to donate computing time to the project.
� Extracting long time-scale dynamics from short time-scale simulationsPCA, also known as essential dynamics when applied to MD simulations, is an established procedure to reduce the dimensionality of the complex trajectories obtained from the simula-tions [84,85]. Generally PCA consists of a linear transformation of the data into a set of orthogo-nal uncorrelated variables called principal com-ponents (PCs). This is done so that the PCs are ordered with respect to the variance of the pro-jection of the original data onto the PCs. When applied to protein ensembles (e.g., MD trajectory or, alternatively, from a large set of experimen-tally obtained structures), the PCs describe a set of independent motions of the molecule. Since the PCs are ranked, the first PCs, thus, describe the largest variance (i.e., fluctuations) along the ensemble of conformations (for a more compre-hensive introduction to PCA, see [86]). Thus, PCA can readily be utilized to assess the major conformational differences in an ensemble of structures [87].
Interestingly, the motional directionalities (PCs) obtained from PCA of MD trajectories have shown to be in good agreement with pro-tein slow motions [86]. Notably, the PCs gener-ally correlate with known conformational transi-tions (e.g., between open and closed structures). On this notion, short MD simulations can also be used as a means to study protein motion on much longer time scales [88,89]. However, PCA is sensitive to the input trajectory; for exam-ple, which window of the simulation to use. In particular, the ordering of the modes has been shown to be difficult to reproduce using independent simulations [86].
� Normal-mode ana lysisWhile MD simulations provide a time depen-dent development of individual fluctuations, NMA is the analytical approach to study the vibrational harmonic oscillating motions of a mechanical system. The motions considered are in the immediate vicinity of its equilibrium, and they do not cross energy barriers. NMA classi-fies all possible motions a protein can undergo with respect to their energetic cost. The vibra-tional low-frequency, or low-energetic modes, typically correspond to motions of collective character while the higher frequency modes cor-respond to local deformations (see [90] for a more comprehensive introduction to NMA).
Classical all-atom NMA (aaNMA) relies on the same empirical force field used for MD
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simulations (equAtions 1–3) [33], although most applications to proteins involve a simplifica-tion of this due to the high computational cost (e.g., by only considering torsional angles as the dynamic variables) [91]. The normal modes are obtained by calculating and diagonalizing the matrix of the second derivatives of the potential energy (referred to as the Hessian matrix), V, at a local minimum of the molecule. The nor-mal mode vectors are obtained through solving the eigenvalue problem (ATVA = l). The nor-mal modes, and thus the pattern of motions, are specified by the eigenvectors (A
k) and their
associated eigenvalues (lk). The eigenvalues
are related to the energetic cost associated with displacing the system along the eigenvectors.
The memory requirement for diagonalization of the 3N × 3N large Hessian matrix (where N is the number of atoms) is the main computational limitation of NMA. To overcome this obsta-cle several methods have been developed [90], based on a reduction of the degrees of freedom (e.g., by holding bond lengths and angles fixed) or by considering only rotations and transla-tions of the residues. In combination with clever algorithms for diagonalization aaNMA has been successfully applied to proteins of sev-eral thousands residues [90]. Another interest-ing approach is the elastic network model where the atomic force field is replaced by a simpli-fied Hookean potential [92,93]. Employing this in conjunction with a coarse-grained model, where only the Ca atoms are considered, sig-nificantly reduces the size of the Hessian matrix and, thus, the computational cost of the NMA. Coarse-grained NMA (cgNMA) is thus read-ily applicable to proteins of several thousand amino acids.
While PCA (on MD trajectories) and NMA yield comparable results [86], the advantage of NMA lies in the simplicity of the calculation. In particular, elastic network model–NMA is far less computationally demanding than per-forming a MD simulation, and also more robust and less prone to errors. Its limitations lie in the lack of capability to cross energy barriers on the potential energy surface. Moreover, the motions studied by NMA are harmonic, while proteins are known to be highly anharmonic.
The chaperonin GroELGroEL is an ATP-dependent molecular chaper-one belonging to the chaperonin family. It pro-vides a cage-like protective environment that facilitates protein folding [94,95]. The Type I
chaperonin GroE system from Escherichia coli is essential for cell viability by mediating fold-ing of approximately 5–10% of all proteins in the bacterium [96]. The large product of the GroE operon is denoted GroEL and is a mul-timeric protein organized in two heptameric barrel-like rings stacked together back-to-back (FiguRe 5A & B) [97]. The heptameric ring is com-posed of seven identical subunits (548 residues each), which are divided into three distinctive domains (FiguRe 5C & F): equatorial (residues 1–133, 409–548), intermediate (134–190, 377–408) and apical (191–376) domains, separated by two hinges that facilitate large conformational transitions in the complex [97].
The two rings work off-phase with respect to each other so that only one of them is fold-ing-active at a given time [98]. This is regulated allosterically with positive cooperativity within a ring (intra-ring), and negative cooperativity between the rings (inter-ring) [99]. GroEL works together with the co-chaperonin GroES (the small product of the GroE operon) that func-tions as a ‘lid’, and encapsulates the substrate protein in the folding cage (FiguRe 5D & e) [98].
Structural investigations, notably by x-ray crystallography and cryo-EM, have revealed large-scale conformational transitions driven by ATP binding and hydrolysis [98,100,101]. Specifically, ATP binding to one of the rings primes it as folding active and is termed the cis ring. The opposite ring, which is folding inactive at this time in the cycle, is termed the trans ring. Association with ATP induces the R-state (all seven subunits adopt the r conformation; here, a capital letter refers to the state of one ring, while lowercase [t and r] refers to the conformation of the individual subunits). Conversely, the unlig-anded (closed) heptamer is termed the T-state. The cooperative ATP binding within one ring leads not only to intra-ring concerted domain movements occurring with positive cooperativ-ity (in the cis ring), but also inter-ring negative cooperativity, thus, preventing ATP-binding to the trans ring (FiguRe 6) [99]. Hence, the event of ATP binding to the cis ring is transduced in both directions in the complex. Subsequent GroES binding induces the transition to the elongated and fully open heptamer (termed the R -́state) enabling unfolded substrate proteins to enter the GroEL cavity (FiguRe 6) [98]. ATP hydrolysis in the cis ring induces the R´́ con-formation with weaker association to GroES (FiguRe 6C & D) [100,102]. This event reverses the conformational shifts in the trans ring, priming
it for ATP- and substrate binding. The succeed-ing ATP binding at this stage triggers the ejec-tion of the cis ligands on the opposite side of the complex, more than 100 Å away (FiguRe 7D & e).
� Dynamics, flexibility & allostery of GroELDespite this extensive functional and structural insight available for the GroEL system, only limited information exists on the relationship between the protein structure and its dynamics. Moreover, the underlying mechanisms involved in ATP binding and the subsequent conforma-tional transitions are not yet fully understood at residue and atomic level. Describing allosteric
mechanisms in which structural perturbation at one site of the protein affects the structure, dynamics and/or biochemical properties at other sites is of particular interest.
Numerous computational studies have focused on drawing a more detailed picture of the conformational transitions and mechanisms involved in the positive cooperativity within a ring and the negative cooperativity between the two rings [61,103–112]. Although many of these studies provide essential structural and dynami-cal information of the allosteric behavior of the GroEL machinery, none have yet provided a full-scale monitoring of the allosteric conformational
A B C
D E F
Figure 5. The GroEL assembly. A GroEL heptameric ring with its seven subunits shown in (A). The unliganded GroEL complex is shown in (B) with two subunits (one from each ring) highlighted in colors. The GroEL subunit folds into three distinct domains; apical (green), intermediate (red), and equatorial (blue). A subunit in the unliganded and ADP-bound conformation is shown in (C) and (F), respectively. GroES is shown in (D), while the GroEL–GroES–ADP7 complex is shown in (E). The figure was prepared with PDB codes 1XCK and 1AON.
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transitions. Unbiased MD simulation is the computational procedure that perhaps has con-tributed the most to the understanding of the dynamic properties of proteins in general [41,42]. For GroEL however, only relatively short simu-lations of the GroEL subunit [61,103,110] have been carried out prior to the recent reports on extensive unbiased MD simulations of both the GroEL subunit and the complete GroEL assembly [L Skjærven et al., Submitted Manuscript], performed in our laboratories [86,113]. In this review, we focus on these later studies, which contribute in advancing the understanding of the GroEL folding machine as they provide a model for the structure–dynamics relationship and detailed interactions potentially important for the transitions along the functional cycle. Due to the extensive amount of information obtained from long MD simulations the selected studies also represent an important contribu-tion to the methodological aspects of probing the detailed mechanisms in large protein sys-tems. Thus, the procedures utilized should be highly applicable for systems similar in size and complexity to GroEL.
� Equilibrium dynamics in GroELRecent MD simulations and NMA on the GroEL subunit and complete oligomeric struc-ture [86,113] reveal the GroEL machinery not only
GroES
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Figure 6. Illustration of the GroEL–GroES reaction cycle. Unliganded GroEL with high affinity to non-native substrate proteins is shown in (A). ATP-binding facilitates association with GroES, which results in the encapsulation of the non-native protein (B). ATP hydrolysis occurs after 8–10 s (C) and enables ligand binding to the opposite ring (D). This event terminates one cycle of GroEL-mediated protein folding (E).Adapted with permission from [94].
Figure 7. Equilibrium dynamics of the chaperonin GroEL. The figure represents the ensemble of conformations in the ATP-free (apo) t-state. The figure was prepared with VMD [207].
as a very flexible protein designed to adapt its shape/conformation, but also as a very dynamic entity. That is, even in the unliganded closed T-state, GroEL moves on the energy landscape and visits a relatively large conformational space, revealing the protein as an ensemble of confor-mations (FiguRe 7) [114], in contrast to the static picture usually provided by x-ray crystallogra-phy. In fact, the subunit simulations indicate that the R-state is intrinsically accessible for the subunit (FiguRe 8A), even in the absence of bound nucleotide (pre-existing equilibrium), similar to what has been previously suggested experimentally [115,116] and also described for several other proteins [4,13,17]. Actually, the most densely populated area is not directly around the closed T-state, but rather in between the t- and R-state. These simulations, thus, propose a dynamic equilibrium for the subunit between the defined conformational states (FiguRe 8).
� Nucleotide-dependent conformational sampling By performing extensive simulations of the isolated GroEL subunit starting from both the open and closed structures (with and without bound nucleotide) we were able to probe the effects of nucleotide binding to the subunit alone [113]. These simulations revealed the abil-ity of the subunit to retain its dynamics and flexibility even in an isolated environment, sepa-rated from the neighboring subunits. Thus, the
ATP-bound simulations sample the transition from the T-state all the way to a structure that is close to the fully open R´́ -state (FiguRe 8B). A nearly complete opposite transition is observed by removing ADP from the fully open R´́ -state conformation.
A pronounced feature of the simulations is a shift in the conformational sampling as a response to ATP-binding in the subunit [113]. We were not able to detect a defined propagation pathway in which allosteric signals are transmit-ted from the ATP binding site(s) within the sub-unit. Although a cascade-like propagation pat-tern has been suggested [110], our simulations did not show the same property. Thus, the proposed model that allostery ‘derives from a redistribu-tion of the conformational ensemble’ [25], seems appropriate in the case of GroEL. The observed effects of ATP binding could perhaps also accommodate to the proteinquakes model [31]; the event of ligand binding releases strains in the structure allowing for larger conformational sampling. In fact, what we observe can be char-acterized as breathing modes intrinsic to the protein, and which are similar in both the ATP-free and ATP-bound simulations. Nevertheless, the ATP-bound state seems to sample a larger conformational space.
� Detailed effects upon ATP-bindingThe simulations described earlier were also able to reveal detailed changes in the atom–atom
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Figure 8. Conformational sampling of the GroEL subunit. (A) ATP-free and (B) ATP-bound simulations are shown (300 ns long). The plots show projections of conformers onto the two first principal components (PC1 and PC2; obtained from all available experimentally structures). Gray dots represent conformations of the subunit collected from x-ray data. Orange, red, and green dots depict the closed (t), semi-open (r), open (r’’) x-ray structures. Density-shaded blue dots depict conformers from the molecular dynamics simulations. Reproduced with permission from [113].
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interaction pattern upon ATP-binding [113]. Measuring all inter-atomic distances along two simulations (e.g., ATP-free and ATP-bound) to map the (statistically significant) differences in the interaction patterns can identify a network of residues potentially important for the allosteric transitions (FiguRe 9). In the equatorial domain we identify, in particular, one site that seems to have a controlling effect of the allostery in GroEL: the aQ–loop–b16 (i.e., residues Asp-473–Asn-475) interacts tightly with helix aE only in the absence of ATP, possibly due to tighter binding between Lys-34 and Glu-483 (FiguRe 10). This region is in vicinity to residues earlier highlighted to be important for the inter-ring allosteric communication (e.g., Glu-461 and Glu-452) [117].
� Long-range interactionsThe cooperative binding of seven ATP molecules and GroES to the cis ring is the main functional determinant for the T to R´ transition to occur (intra-ring positive cooperativity). Such bind-ing prevents ATP-binding to the opposite trans ring (inter-ring negative cooperativity), more than 60 Å distance to the cis binding sites. MD simulations of the entire GroEL oligomer with and without seven ATP molecules bound to the cis ring probe the initial phases of the allosteric communication, both at the inter- and intra-ring level [L Skjærven et al., Submitted Manuscript]. These simulations reveal a reorganization of the inter-ring contacts upon ATP-binding, yield-ing an out of register alignment between two helices D (one from cis, and one trans ring), similar to what it was suggested in a previous cryo-EM study [100]. In particular, the Glu-434–Arg-430 contact is identified to be impor-tant for this conformational change. In fact, the study shows by performing simulations of the GroEL
E434K mutant that this interaction is
essential for the proper inter-ring allosteric com-munication [L Skjærven et al., Submitted Manuscript]. Thus, these preliminary results reveal the capa-bility of MD simulations to capture long-range allosterically triggered structural transitions.
� Coarse-grained modelsMaking use of approximations such as coarse graining (e.g., where only the Cas are consid-ered) for large proteins will often be a necessity for large protein assemblies. Thus, such simplified models have emerged as the preferred approach to perform NMA on proteins containing sev-eral thousands of residues [92,93,118,119]. Recently,
we performed an extensive comparison between PCA, cgNMA and aaNMA, using the GroEL subunit [86]. This study revealed remarkably sim-ilar results between the three approaches. In fact, a general one-to-one correspondence between the resulting eigenvectors was found. The confor-mational subspaces were also very similar, with a root-mean-square inner product (see [120] for a precise mathematical definition) value of 0.84 between the aaNMA and cgNMA (provid-ing a marker between 0 to 1 for the similarity between ten lowest frequency modes). Moreover, both aaNMA and cgNMA correspond well with the known conformational transition in GroEL; only five modes were needed to capture over 80% of the conformational difference. Our results, thus, show that both the all-atom and coarse-grained models globally describe similar type of displacements of the subunit domains, and that the displacements are in good agreement with both x-ray data and previous studies.
Earlier studies on the same topics have con-centrated on nucleic acid systems [121], or on a quantitative basis on a range of much smaller proteins [122]. In contrast to our findings with GroEL [86], the study by Kondrashov et al. shows that aaNMA has an advantage over cgNMA in predicting the directionality, but both tech-niques yield results in good agreement with experiments [122]. The results from our work add to the accumulating validations of cgNMA for studying protein slow motions.
Enhancing conformational sampling; ligand recognition in maltose-binding proteinAlthough the rapid advancement in compu-tational power currently allows classical MD simulations at a microsecond timescale [123], many conformational events are still out of reach. Conformational transitions are often particularly difficult to sample as classical MD simulations often get trapped in local ener-getic minima. To overcome such energy barri-ers, and to increase conformational sampling, Hamelberg et al. developed aMD [67]. In this approach the energy wells in the potential energy landscape are made shallower by a biasing poten-tial (to the true potential). aMD has, success-fully, been applied to several protein systems to explore conformational transitions out of reach by classical MD simulations [124–126].
A recent study focusing on the bacterial maltose-binding protein (MBP) provides inter-esting insight to the ligand–protein recognition
Figure 9. Map of unique inter-atomic contacts revealed by molecular dynamics simulations. The map shows contacts, which are unique either to the ATP-bound (blue dots) or the ATP-free (green triangles) simulations. Two residues are assumed to interact if the minimal distance between them is <4 Å. Only contacts existing in at least half of the structures are considered. Red circles depict contacts that are found in both the x-ray and molecular dynamics ana lysis. Secondary structure elements are indicated schematically with helices in black and strands in gray. Reproduced with permission from [113].
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Figure 10. Detailed changes upon nucleotide binding revealed by MD-simulations. (A) The interactions between Leu134 and Asn475, which are found to be unique to the ATP-free simulation due to a conformational change involving Asn475. (B) The unique interaction between Lys34 and Glu483, which are found significantly stronger in the ATP-bound simulations than the ATP-free. The light blue structure represents the nucleotide-bound conformation, while the gray structure is the nucleotide-free structure. Nucleotide (here, ADP) is shown in stick and surface representation.Reproduced with permission from [113].
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process of this particular system (FiguRe 11) [127]. Previous x-ray crystallographic studies of MBP have revealed the presence of two substrate-dependent conformations (closed holo, and open apo). An induced-fit mechanism is traditionally put forward to describe the conformational changes occurring upon substrate binding [128]. However, more recent NMR data suggested a pre-existing equilibrium between the apo and holo forms of the protein [129].
The aMD simulations of MBP showed that the protein samples a significant conformational space, even in the absence of bound ligand [127]. Thus, the study gives further support for a pre-existing equilibrium model. However, the simulations were not able to sample the entire open-to-closed transition (FiguRe 12). Thus, the authors propose a two-step transition mecha-nism (as also described earlier; see ‘Models for molecular recognition and allostery’ [5,19]).
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Figure 11. Maltose-binding protein conformational transition upon substrate binding. The figure was prepared with PDB codes 1OMP (open) and 3MBP (closed).
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Figure 12. Conformational sampling of maltose-binding protein. (A) Classical molecular dynamics (MD) and (B) accelerated MD simulations are shown. The plots show projections of conformers onto the two first principal components (PC1 and PC2; obtained from available experimentally structures). Red and black dots depict closed and open x-ray structures. Density-shaded blue dots depict conformers from the MD simulations. Reproduced with permission from [127].
This involves substrate binding to the weakly populated conformers (termed the ‘semi-closed’ form), yielding an encounter complex. Subsequently, the semi-closed to closed transi-tion involves mutual adaptation between the substrate and protein (i.e., induced-fit).
Simulating the binding process of an enzyme–inhibitor complexIn this section we will review a simulation study performed on a distributed graphics processing unit-based infrastructure, known as GPUGRID [83], which provides extensive computational resources to perform MD simu-lations. The infrastructure consists of multiple NVIDIA graphics cards joined together in a massive grid. Anyone with a specific NVIDIA graphics card can donate computing time to the grid in order to allow scientists to perform biomolecular simulations on it.
Buch et al. utilized this extensive amount of computational resources to simulate the complete binding process of the inhibitor benzamidine to b-trypsin [130]. Trypsin, a serine protease, cleaves peptide bonds next to positively charged amino acids (Arg and Lys). Similar to other serine prote-ases, trypsin possesses a catalytic triad consisting of His-57, Asp-102, and Ser-195. Benzamidine is a reversible specific inhibitor of trypsin with a structure that mimics the side chains of arginine and lysine. Buch et al. were able to conduct a total of 495 MD simulations, each 100 ns long, starting from the separated complex, with the ligand placed 35 Å away from the binding site. In total, 187 of their simulations were able to reproduce the binding of benzamidine, with an root-mean-square deviation fit below 2 Å. In some of the simulations the authors report that benzamidine associates and reaches the pose after just 10–15 ns, others after 90 ns. As expected on these time scales, the majority of the simulations do not reproduce the binding pose at all.
From the simulations, which reproduce the binding pose, the authors are able to report on the presence of three metastable states of the complex (S1, S2 and S3), in addition to the bulk S0 and final bound state S4 (FiguRe 13). State S0 represents the bulk, and S1 is the initial contact between the protein and ligand and consists of weak-hydrophobic interactions. In S2, the ligand is more stably bound to the receptor stabilized by stacking interactions with hydrophobic residues. In state S3 the authors report on several hydrogen bonds between the receptor and ligand, and thus a stronger binding than in S1 and S2. State S4 is the
final bound state, where the ligand has a strong interaction with Asp-189. Transitions between S0 and S1 are reported, as well as from sites S0/S1 to S3/S4. However, in the transition between S2 and S3 it is assumed that the ligand diffuse back and rebind to S3, instead of binding directly from S2 to S4 (the final bound state). The binding to S4 appears to come preferentially from S3, and the authors are able to explain how the benzamidine ‘roll’ over the surface from S3 in order to reach S4.
The approach for structure-based drug design is mainly oriented around identifying the lowest energy binding mode of a ligand to a receptor. In this approach the investigation of the recep-tor–ligand recognition is normally neglected. However, being able to monitor the binding pro-cess at an atomic resolution can potentially be crucial for the understanding of how to control and change the recognition and binding process. Free diffusion MD simulations can, thus, aid in this process and provide additional information on how a potential drug will interact with a drug candidate.
S4
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Figure 13. Metastable states of the trypsin benzamidine complex. The figure shows trypsin in surface representation and benzamidine in stick representation, colored light green and orange, respectively. The predicted metastable states (S1–S4) of the trypsin benzamidine complex are labeled. S0 depicts the bulk. A simplified representation of the transition modes is depicted with red arrows. The figure was prepared with PDB code 3PTB. Data from [130].
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Ribosomal antibiotic recognition & interactionFinally, we will discuss two recent examples of computational studies on bacterial rRNA [131,132]. These studies are of particular interest since they provide direct insights into mechanisms of RNA–aminoglycoside antibiotic recognition and inter-action. Mutations in the bacterial ribosome can, potentially, lead to drug-resistant bacterial vari-ants, which represent a significant challenge, as ribosomes are important drug targets. Detailed studies of the ligand binding and effects of nucle-otide mutations in RNA opens novel possibilities for rational structure-based drug design.
Aminoglycoside antibiotics target the bacte-rial ribosomal A-site of the bacterial 30S ribo-somal subunit (see FiguRe 14 for the complex with paromomycin). Association affects directly the conformation of two rRNA nucleotides (A1492 and A1493), which get locked in a flipped-out position, causing misreading errors and protein defects, as observed by x-ray crystallography [133] and NMR [134]. A recent MD study by Vaiana and Sanbonmatsu focused on the mechanisms of rec-ognition of the aminoglycoside antibiotic genta-micin by the bacterial ribosomal A-site [132]. They
performed extensive MD simulations of both the ‘free’ A-site, and the A-site with bound gentami-cin, observing that the conformation of the two bases energetically prefers the flipped-in state in the absence of gentamicin (FiguRe 15). However, A1493 is found to be significantly more mobile than A1492. While flipping of A1492 involves crossing of an energy barrier of 3.5 kcal/mol, the barrier for A1493 is predicted to be 1.3 kcal/mol. Both flipping events are observed in the simu-lations of the ‘free’ A-site (even in the absence of gentamicin). On this notion the authors pro-pose that gentamicin does not induce the con-formational change (flipped-in to flipped-out), but rather binds selectively to the low-populated flipped-out states (termed stochastic gating, but is equivalent to conformational selection).
Specific mutations in the A-site introduce bac-terial resistance to, among others, paromomycin, preventing the efficient use of these antibiotics. To gain insights on the basis and mechanisms of this type of aminoglycoside resistance a recent study modeled three single mutants (G1491A, G1491U, U1495C) and one double mutant (U1406C/U1495A) [131]. By performing MD simulations on the mutant structures, bare and in complex with paromomycin, the authors were able to report on direct effects related to paromomycin resistance; in particular the internal dynamics and shape of the binding site can change significantly due to single-point mutations. For the G1491A and G1491U mutants a shift in the base pairing was observed leading to higher population of the flipped-in state of the A1492 and A1493 nucleo-tides than in the native structure. Moreover, when simulating the complex of paromomycin with a double mutant, the initial conformation of the drug was severely disrupted and many hydrogen bonds were broken, showing that upon this double substitution, the A-site/paromomycin association is not stable (FiguRe 16). This finding points to the importance of observing internal dynamics of biomolecules when trying to design new drugs. The intrinsic movements of the target can either limit or enhance the effectiveness of drugs target-ing their active sites. Alternatively, the presence of a small molecule can induce big changes in the conformation and dynamics of the target. These effects are of high importance and, some-times, they cannot properly be accounted for by the experimental procedures, which often only provide static structures. Especially in the new field of designing drugs targeting RNA molecules, the dynamic description is necessary due to high flexibility of these structures.
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Figure 14. Ribosomal A-site. Here, paromomycin (bound) is shown with stick and surface representation. The ribosomal A-site is shown in cartoon representation and the two flipped-out nucleotides (A1492 and A1493) are labeled and shown as sticks. The figure was prepared with PDB code 1J7T.
Key Terms
Aminoglycosides: Class of antibiotic (dideoxystreptamine derivatives) that acts on a wide range of Gram-negative bacteria.
Ribosomal A-site: rRNA fragment that is responsible for discriminating between the cognate and noncognate tRNA.
Future perspective: from modeling to drug targetsMolecular machines and, in particular, chap-erone systems, represent important drug target candidates. In fact, Hsp90, another chaperone
system, has been successfully targeted and entered clinical trials over a decade ago in the battle against cancer [109]. Several drug can-didates, with the specific aim of inhibiting Hsp90 by occupying the ATP-binding site,
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Figure 15. Energetically accessible flipped in- and flipped-out states of A1492 and A1493 of the ribosomal A-site. Free-energy landscape of base-flipping coordinates from simulations (A) without and (B) with gentamicin. Representative conformers depict the different states.Reproduced with permission from [132].
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Figure 16. Modeling of mutant variants of the ribosomal A-site. (A) Wild-type A-site with bound paromomycin. Throughout the simulation paromomycin remains stably bound. (B) Snapshot of the double mutant variant of the A-site where paromomycin rotates out of the binding pocket. Reproduced with permission from [131].
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have emerged in recent years, and also ten suc-cessful drugs entered the clinic [110]. Although chaperonins are not yet established drug tar-gets, their important role in folding processes points to a, so far underdeveloped, potential for drug design.
In structure-based drug design, the impor-tance of the flexibility of drug targets has long been acknowledged although it is often difficult to take it into account, mostly owing to concep-tual and technical difficulties. Many approaches have been proposed, using either local (e.g., side chain) flexibility or global flexibility using various data, computational or experimental, to generate an ensemble of structures representative of the overall target conformational space. These techniques have unequivocally demonstrated the importance of including receptor flexibility in docking and in silico screening procedures [135]. The relaxed complex scheme is one pioneering example; it makes use of MD simulations of the drug target to generate an ensemble of confor-mations onto which the potential ligands will be docked, thus, explicitly accounting for the flexibility of the receptor [136,137].
Finally, as drug targets, allosteric proteins present both challenges and opportunities for development of specific drugs. The application of standard structure-determination methods such as x-ray crystallography to the understand-ing of their multiple conformational stages and conformational transitions required for
biological function is often hampered by the large flexibility within this important protein class. The continuous progress in MD simula-tion approaches, frequently in combination with evolutionary-based sequence analyses, opens new avenues for the discovery and development of drugs targeting allosteric regulatory sites, distal to the actual active site. We, thus, envi-sion that computational strategies will increase their impact in the development of specific medications targeting inhibitory or activating allosteric sites, only present in intermediate conformational states.
AcknowledgementsThe authors would like to thank Jarl Underhaug (Department for Biomedicine, University of Bergen, Norway) for contributing Figures 1 and 6, and Julia Romanowska (Department of Biophysics, University of Warsaw, Poland) for discussions about RNA–antibiotic recognition, as well as providing Figure 16.
Financial & competing interests disclosureThe authors would like to thank the Nanobasic program at University of Bergen, The Norwegian Research Council and Bergens Forskningsstiftelse for funding. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials dis-cussed in the manuscript apart from those disclosed.
No writing assistance was utilized in the production of this manuscript.
Executive summary
� Biomolecules, such as proteins and nucleic acids, possess important dynamical properties, which enable them to adapt and alternate its conformation as a response to specific metabolites and environmental stimuli.
� Molecular machines, which convert chemical energy to mechanical work, are often allosterically regulated; binding at one site of the protein affects the binding affinity at a different (distant) site of the protein.
� Ligand recognition and binding can induce a conformational change (‘induced fit’); ligands can also bind selectively to a conformation that pre-exists (‘conformational selection’).
� The chaperonin GroEL is an ATP-dependent molecular machine that undergoes large conformational changes.
� The allosteric regulatory mechanisms in GroEL are not well understood at a detailed level; computational studies can enhance the understanding of the underlying mechanisms of ligand-induced conformational changes.
� Modeling indicates that the equilibrium dynamics in GroEL is significant, and that the semi-open form (r’) is visited even in the absence of activating ligand.
� Free diffusion molecular dynamics simulations have recently shown the remarkable capability to reconstruct the binding process of an enzyme–inhibitor complex.
� Specific antibiotics target ribosomal RNA causing misreading errors and protein defects, but mutations of the RNA represent a significant challenge in healthcare.
� Simulations of the A-site of ribosomal RNA in complex with antibiotics reveals the mechanism of RNA–antibiotic recognition and aid the design of novel therapeutics.
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