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We propose a novel, information-theoretic, characterisation of cascades within the spatiotemporal dynamics of swarms, explicitly measuring the extent of collective communications. This is complemented by dynamic tracing of collective memory, as another element of distributed computation, which represents capacity for swarm coherence. The approach deals with both global and local information dynamics, ultimately discovering diverse ways in which an individual’s spatial position is related to its information processing role. It also allows us to contrast cascades that propagate conflicting information with waves of coordinated motion. Most importantly, our simulation experiments provide the first direct information-theoretic evidence (verified in a simulation setting) for the long-held conjecture that the information cascades occur in waves rippling through the swarm. Our experiments also exemplify how features of swarm dynamics, such as cascades’ wavefronts, can be filtered and predicted. We observed that maximal information transfer tends to follow the stage with maximal collective memory, and principles like this may be generalised in wider biological and social contexts.

Free energy calculations are fundamental to obtaining accurate theoretical estimates of many important biological phenomena including hydration energies, protein-ligand binding affinities and energetics of conformational changes. Unlike traditional free energy perturbation and thermodynamic integration methods, λ-dynamics treats the conventional "λ" as a dynamic variable in free energy simulations and simultaneously evaluates thermodynamic properties for multiple states in a single simulation. In the present paper, we provide an overview of the theory of λ-dynamics, including the use of biasing and restraining potentials to facilitate conformational sampling. We review how λ-dynamics has been used to rapidly and reliably compute relative hydration free energies and binding affinities for series of ligands, to accurately identify crystallographically observed binding modes starting from incorrect orientations, and to model the effects of mutations upon protein stability. Finally, we suggest how λ-dynamics may be extended to facilitate modeling efforts in structure-based drug design.

The interactions among associating (macro)molecules are dynamic, which adds to the complexity of molecular recognition. While ligand flexibility is well accounted for in computational drug design, the effective inclusion of receptor flexibility remains an important challenge. The relaxed complex scheme (RCS) is a promising computational methodology that combines the advantages of docking algorithms with dynamic structural information provided by molecular dynamics (MD) simulations, therefore explicitly accounting for the flexibility of both the receptor and the docked ligands. Here, we briefly review the RCS and discuss new extensions and improvements of this methodology in the context of ligand binding to two example targets: kinetoplastid RNA editing ligase 1 and the W191G cavity mutant of cytochrome c peroxidase. The RCS improvements include its extension to virtual screening, more rigorous characterization of local and global binding effects, and methods to improve its computational efficiency by reducing the receptor ensemble to a representative set of configurations. The choice of receptor ensemble, its influence on the predictive power of RCS, and the current limitations for an accurate treatment of the solvent contributions are also briefly discussed. Finally, we outline potential methodological improvements that we anticipate will assist future development.

Summary

Computer simulations in molecular biophysics describe in atomic detail structure, dynamics, and function of biological macromolecules. To assess the quality of these models and to pick up new mechanisms, comparisons with experimental measurements are made. Most comparisons examine thermodynamic and average structural properties. Here we discuss studies of dynamics and fluctuations in a protein. The diffusion of a small ligand between internal cavities in myoglobin, and its escape to solvent are considered. Qualitative and semi-quantitative agreements between experiment and simulation are obtained for the identities of the cavities that physically trap the ligand and for the connections between them. However, experimental and computational “doors” are at significant variance. Simulations suggest multiple gates while kinetic experiments point to one dominant exit.

To understand how the actin-polymerization-mediated movements in cells emerge from myriad individual protein–protein interactions, we developed a computational model of Listeria monocytogenes propulsion that explicitly simulates a large number of monomer-scale biochemical and mechanical interactions. The literature on actin networks and L. monocytogenes motility provides the foundation for a realistic mathematical/computer simulation, because most of the key rate constants governing actin network dynamics have been measured. We use a cluster of 80 Linux processors and our own suite of simulation and analysis software to characterize salient features of bacterial motion. Our “in silico reconstitution” produces qualitatively realistic bacterial motion with regard to speed and persistence of motion and actin tail morphology. The model also produces smaller scale emergent behavior; we demonstrate how the observed nano-saltatory motion of L. monocytogenes, in which runs punctuate pauses, can emerge from a cooperative binding and breaking of attachments between actin filaments and the bacterium. We describe our modeling methodology in detail, as it is likely to be useful for understanding any subcellular system in which the dynamics of many simple interactions lead to complex emergent behavior, e.g., lamellipodia and filopodia extension, cellular organization, and cytokinesis.

A detailed computer simulation explicitly simulates monomer- scale biochemical and mechanical interactions to characterize bacterial motion

The mechanisms of how ligands enter and leave the binding cavity of fatty acid binding proteins (FABPs) have been a puzzling question over decades. Liver fatty acid binding protein (LFABP) is a unique family member which accommodates two molecules of fatty acids in its cavity and exhibits the capability of interacting with a variety of ligands with different chemical structures and properties. Investigating the ligand dissociation processes of LFABP is thus a quite interesting topic, which however is rather difficult for both experimental approaches and ordinary simulation strategies. In the current study, random expulsion molecular dynamics simulation, which accelerates ligand motions for rapid dissociation, was used to explore the potential egress routes of ligands from LFABP. The results showed that the previously hypothesized “portal region” could be readily used for the dissociation of ligands at both the low affinity site and the high affinity site. Besides, one alternative portal was shown to be highly favorable for ligand egress from the high affinity site and be related to the unique structural feature of LFABP. This result lends strong support to the hypothesis from the previous NMR exchange studies, which in turn indicates an important role for this alternative portal. Another less favored potential portal located near the N-terminal end was also identified. Identification of the dissociation pathways will allow further mechanistic understanding of fatty acid uptake and release by computational and/or experimental techniques.

A full characterization of the thermodynamic forces underlying ligand-associated conformational changes in proteins is essential for understanding and manipulating diverse biological processes, including transport, signaling, and enzymatic activity. Recent experiments on the maltose binding protein (MBP) have provided valuable data about the different conformational states implicated in the ligand recognition process; however, a complete picture of the accessible pathways and the associated changes in free energy remains elusive. Here we describe results from advanced accelerated molecular dynamics (aMD) simulations, coupled with adaptively biased force (ABF) and thermodynamic integration (TI) free energy methods. The combination of approaches allows us to track the ligand recognition process on the microsecond time scale and provides a detailed characterization of the protein’s dynamic and the relative energy of stable states. We find that an induced-fit (IF) mechanism is most likely and that a mechanism involving both a conformational selection (CS) step and an IF step is also possible. The complete recognition process is best viewed as a “Pac Man” type action where the ligand is initially localized to one domain and naturally occurring hinge-bending vibrations in the protein are able to assist the recognition process by increasing the chances of a favorable encounter with side chains on the other domain, leading to a population shift. This interpretation is consistent with experiments and provides new insight into the complex recognition mechanism. The methods employed here are able to describe IF and CS effects and provide formally rigorous means of computing free energy changes. As such, they are superior to conventional MD and flexible docking alone and hold great promise for future development and applications to drug discovery.

Protein dynamics make important but poorly understood contributions to molecular recognition phenomena. To address this, we measure changes in fast protein dynamics that accompany the interaction of the arabinose-binding protein (ABP) with its ligand, d-galactose, using NMR relaxation and molecular dynamics simulation. These two approaches present an entirely consistent view of the dynamic changes that occur in the protein backbone upon ligand binding. Increases in the amplitude of motions are observed throughout the protein, with the exception of a few residues in the binding site, which show restriction of dynamics. These counter-intuitive results imply that a localised binding event causes a global increase in the extent of protein dynamics on the pico- to nanosecond timescale. This global dynamic change constitutes a substantial favourable entropic contribution to the free energy of ligand binding. These results suggest that the structure and dynamics of ABP may be adapted to exploit dynamic changes to reduce the entropic costs of binding.

Configurational entropy is thought to influence biomolecular processes, but there are still many open questions about this quantity, including its magnitude, its relationship to molecular structure, and the importance of correlation. The mutual information expansion (MIE) provides a novel and systematic approach to computing configurational entropy changes due to correlated motions from molecular simulations. Here, we present the first application of the MIE method to protein-ligand binding, using multiple molecular dynamics simulations (MMDSs) to study association of the UEV domain of the protein Tsg101 and an HIV-derived nonapeptide. The current investigation utilizes the second-order MIE approximation, which treats correlations between all pairs of degrees of freedom. The computed change in configurational entropy is large and is found to have a major contribution from changes in pairwise correlation. The results also reveal intricate structure-entropy relationships. Thus, the present analysis suggests that, in order for a model of binding to be accurate, it must include a careful accounting of configurational entropy changes.

Background

Despite computational challenges, elucidating conformations that a protein system assumes under physiologic conditions for the purpose of biological activity is a central problem in computational structural biology. While these conformations are associated with low energies in the energy surface that underlies the protein conformational space, few existing conformational search algorithms focus on explicitly sampling low-energy local minima in the protein energy surface.

Methods

This work proposes a novel probabilistic search framework, PLOW, that explicitly samples low-energy local minima in the protein energy surface. The framework combines algorithmic ingredients from evolutionary computation and computational structural biology to effectively explore the subspace of local minima. A greedy local search maps a conformation sampled in conformational space to a nearby local minimum. A perturbation move jumps out of a local minimum to obtain a new starting conformation for the greedy local search. The process repeats in an iterative fashion, resulting in a trajectory-based exploration of the subspace of local minima.

Results and conclusions

The analysis of PLOW's performance shows that, by navigating only the subspace of local minima, PLOW is able to sample conformations near a protein's native structure, either more effectively or as well as state-of-the-art methods that focus on reproducing the native structure for a protein system. Analysis of the actual subspace of local minima shows that PLOW samples this subspace more effectively that a naive sampling approach. Additional theoretical analysis reveals that the perturbation function employed by PLOW is key to its ability to sample a diverse set of low-energy conformations. This analysis also suggests directions for further research and novel applications for the proposed framework.

The efficient and accurate quantification of protein-ligand interactions using computational methods is still a challenging task. Two factors strongly contribute to the failure of docking methods to predict free energies of binding accurately: the insufficient incorporation of protein flexibility coupled to ligand binding and the neglected dynamics of the protein-ligand complex in current scoring schemes. We have developed a new methodology, named the ‘ligand-model’ concept, to sample protein conformations that are relevant for binding structurally diverse sets of ligands. In the ligand-model concept, molecular-dynamics (MD) simulations are performed with a virtual ligand, represented by a collection of functional groups that binds to the protein and dynamically changes its shape and properties during the simulation. The ligand model essentially represents a large ensemble of different chemical species binding to the same target protein. Representative protein structures were obtained from the MD simulation, and docking was performed into this ensemble of protein conformation. Similar binding poses were clustered, and the averaged score was utilized to re-rank the poses. We demonstrate that the ligand-model approach yields significant improvements in predicting native-like binding poses and quantifying binding affinities compared to static docking and ensemble docking simulations into protein structures generated from an apo MD simulation.

Ionotropic glutamate receptors (iGluRs) are enticing targets for pharmaceutical research; however, the search for selective ligands is a laborious experimental process. Here we introduce a purely computational procedure as an approach to evaluate ligand–iGluR pharmacology. The ligands are docked into the closed ligand-binding domain and during the molecular dynamics (MD) simulation the bi-lobed interface either opens (partial agonist/antagonist) or stays closed (agonist) according to the properties of the ligand. The procedure is tested with closely related set of analogs of the marine toxin dysiherbaine bound to GluK1 kainate receptor. The modeling is set against the abundant binding data and electrophysiological analyses to test reproducibility and predictive value of the procedure. The MD simulations produce detailed binding modes for analogs, which in turn are used to define structure–activity relationships. The simulations suggest correctly that majority of the analogs induce full domain closure (agonists) but also distinguish exceptions generated by partial agonists and antagonists. Moreover, we report ligand-induced opening of the GluK1 ligand-binding domain in free MD simulations. The strong correlation between in silico analysis and the experimental data imply that MD simulations can be utilized as a predictive tool for iGluR pharmacology and functional classification of ligands.

Background

Simulation methods can assist in describing and understanding complex networks of interacting proteins, providing fresh insights into the function and regulation of biological systems. Recent studies have investigated such processes by explicitly modelling the diffusion and interactions of individual molecules. In these approaches, two entities are considered to have interacted if they come within a set cutoff distance of each other.

Results

In this study, a new model of bimolecular interactions is presented that uses a simple, probability-based description of the reaction process. This description is well-suited to simulations on timescales relevant to biological systems (from seconds to hours), and provides an alternative to the previous description given by Smoluchowski. In the present approach (TFB) the diffusion process is explicitly taken into account in generating the probability that two freely diffusing chemical entities will interact within a given time interval. It is compared to the Smoluchowski method, as modified by Andrews and Bray (AB).

Conclusion

When implemented, the AB & TFB methods give equivalent results in a variety of situations relevant to biology. Overall, the Smoluchowski method as modified by Andrews and Bray emerges as the most simple, robust and efficient method for simulating biological diffusion-reaction processes currently available.

Prolyl oligopeptidase (POP) is considered as an important pharmaceutical target for the treatment of numerous diseases. Despite enormous studies on various aspects of POPs structure and function still some of the questions are intriguing like conformational dynamics of the protein and interplay between ligand entry/egress. Here, we have used molecular modeling and docking based approaches to unravel questions like differences in ligand binding affinities in three POP species (porcine, human and A. thaliana). Despite high sequence and structural similarity, they possess different affinities for the ligands. Interestingly, human POP was found to be more specific, selective and incapable of binding to a few planar ligands which showed extrapolation of porcine POP in human context is more complicated. Possible routes for substrate entry and product egress were also investigated by detailed analyses of molecular dynamics (MD) simulations for the three proteins. Trajectory analysis of bound and unbound forms of three species showed differences in conformational dynamics, especially variations in β-propeller pore size, which was found to be hidden by five lysine residues present on blades one and seven. During simulation, β-propeller pore size was increased by ∼2 Å in porcine ligand-bound form which might act as a passage for smaller product movement as free energy barrier was reduced, while there were no significant changes in human and A. thaliana POPs. We also suggest that these differences in pore size could lead to fundamental differences in mode of product egress among three species. This analysis also showed some functionally important residues which can be used further for in vitro mutagenesis and inhibitor design. This study can help us in better understanding of the etiology of POPs in several neurodegenerative diseases.

Background

Knowledge of the structure of proteins bound to known or potential ligands is crucial for biological understanding and drug design. Often the 3D structure of the protein is available in some conformation, but binding the ligand of interest may involve a large scale conformational change which is difficult to predict with existing methods.

Results

We describe how to generate ligand binding conformations of proteins that move by hinge bending, the largest class of motions. First, we predict the location of the hinge between domains. Second, we apply an Euler rotation to one of the domains about the hinge point. Third, we compute a short-time dynamical trajectory using Molecular Dynamics to equilibrate the protein and ligand and correct unnatural atomic positions. Fourth, we score the generated structures using a novel fitness function which favors closed or holo structures. By iterating the second through fourth steps we systematically minimize the fitness function, thus predicting the conformational change required for small ligand binding for five well studied proteins.

Conclusions

We demonstrate that the method in most cases successfully predicts the holo conformation given only an apo structure.

An analytical coarse-grained model (ACG) is introduced to represent individual macromolecules for simulation of dynamic processes in cells. In the ACG model, a macromolecular structure is treated as a fully coarse-grained entity with a uniform mass density without the explicit atomic details. The excluded volume and surface of the ACG macromolecular species are explicitly treated by a spherical harmonic representation in the present study (although ellipsoidal, solid, and radial augmented functions can be used), which can provide any desired accuracy and detail depending on the problem of interest. The present paper focuses on the description of the internal fluctuations of a single ACG macromolecule, modeled by the superposition of low frequency quasiharmonic modes from explicit molecular dynamics simulation. A procedure for estimating the amplitudes, time scales of the quasiharmonic motions and the corresponding phases is presented and used to synthesize the complex motion. The analytical description and numerical algorithm can provide an adequate representation of the internal protein fluctuations revealed from the corresponding atomistic simulations, although the internal motions of ACG macromolecules do not explore motions not exhibited in the dynamic simulations.

The nicotinic acetylcholine receptor (nAChR) is a member of the ligand-gated ion channel family and is implicated in many neurological events. Yet, the receptor is difficult to target without high-resolution structures. In contrast, the structure of the acetylcholine binding protein (AChBP) has been solved to high resolution, and it serves as a surrogate structure of the extra-cellular domain in nAChR. Here we conduct a virtual screening study of the AChBP using the relaxed-complex method, which involves a combination of molecular dynamics simulations (to achieve receptor structures) and ligand docking. The library screened through comes from the National Cancer Institute, and its ligands show great potential for binding AChBP in various manners. These ligands mimic the known binders of AChBP; a significant subset docks well against all species of the protein and some distinguish between the various structures. These novel ligands could serve as potential pharmaceuticals in the AChBP/nAChR systems.

Many polypeptides and small proteins can be readily engineered such that they only fold upon binding a specific target ligand. This approach couples target recognition with a considerable change in polymer structure and dynamics. Recent years have seen the development of a number of biosensors that couple these large changes to readily measurable optical (fluorescent) outputs. These sensors afford the detection of a wide variety of macromolecular targets including proteins, polypeptides, and nucleic acids. Here we describe the design of such biosensors, from the first iterations as protein engineering experiments, to the development of biosensors targeting a range of protein and nucleic acid targets.

We develop a new motion planning algorithm for a variant of a Dubins car with binary left/right steering and apply it to steerable needles, a new class of flexible bevel-tip medical needles that physicians can steer through soft tissue to reach clinical targets inaccessible to traditional stiff needles. Our method explicitly considers uncertainty in needle motion due to patient differences and the difficulty in predicting needle/tissue interaction. The planner computes optimal steering actions to maximize the probability that the needle will reach the desired target. Given a medical image with segmented obstacles and target, our method formulates the planning problem as a Markov Decision Process based on an efficient discretization of the state space, models motion uncertainty using probability distributions, and computes optimal steering actions using Dynamic Programming. This approach only requires parameters that can be directly extracted from images, allows fast computation of the optimal needle entry point, and enables intra-operative optimal steering of the needle using the pre-computed dynamic programming look-up table. We apply the method to generate motion plans for steerable needles to reach targets inaccessible to stiff needles, and we illustrate the importance of considering uncertainty during motion plan optimization.

The tumor suppressor protein p53 can lose its function upon single-point missense mutations in the core DNA-binding domain (“cancer mutants”). Activity can be restored by second-site suppressor mutations (“rescue mutants”). This paper relates the functional activity of p53 cancer and rescue mutants to their overall molecular dynamics (MD), without focusing on local structural details. A novel global measure of protein flexibility for the p53 core DNA-binding domain, the number of clusters at a certain RMSD cutoff, was computed by clustering over 0.7 µs of explicitly solvated all-atom MD simulations. For wild-type p53 and a sample of p53 cancer or rescue mutants, the number of clusters was a good predictor of in vivo p53 functional activity in cell-based assays. This number-of-clusters (NOC) metric was strongly correlated (r2 = 0.77) with reported values of experimentally measured ΔΔG protein thermodynamic stability. Interpreting the number of clusters as a measure of protein flexibility: (i) p53 cancer mutants were more flexible than wild-type protein, (ii) second-site rescue mutations decreased the flexibility of cancer mutants, and (iii) negative controls of non-rescue second-site mutants did not. This new method reflects the overall stability of the p53 core domain and can discriminate which second-site mutations restore activity to p53 cancer mutants.

Author Summary

p53 is a tumor suppressor protein that controls a central apoptotic pathway (programmed cell death). Thus, it is the most-mutated gene in human cancers. Due to the marginal stability of p53, a single mutation can abolish p53 function (“cancer mutants”), while a second mutation (or several) can restore it (“rescue mutants”). Restoring p53 function is a promising therapeutic goal that has been strongly supported by recent experimental results on mice. Understanding of the effects of p53 cancer and rescue mutations would be helpful for designing drugs that are able to achieve the same goal. The challenge is that cancer and rescue mutations are distributed widely in the protein, and experimental testing of all possible combinations of mutations is not feasible. This paper describes a simple computational metric that reflects the overall stability of the p53 core domain and can discriminate which second-site mutations restore activity to p53 cancer mutants.

We propose a theoretical formalism, molecular finite automata (MFA), to describe individual proteins as rule-based computing machines. The MFA formalism provides a framework for modeling individual protein behaviors and systems-level dynamics via construction of programmable and executable machines. Models specified within this formalism explicitly represent the context-sensitive dynamics of individual proteins driven by external inputs and represent protein-protein interactions as synchronized machine reconfigurations. Both deterministic and stochastic simulations can be applied to quantitatively compute the dynamics of MFA models. We apply the MFA formalism to model and simulate a simple example of a signal transduction system that involves a MAP kinase cascade and a scaffold protein.

Protein conformational change is an important consideration in ligand-docking screens, but it is difficult to predict. A simple way to account for protein flexibility is to soften the criterion for steric fit between ligand and receptor. A more comprehensive but more expensive method would be to sample multiple receptor conformations explicitly. Here, these two approaches are compared. A “soft” scoring function was created by attenuating the repulsive term in the Lennard-Jones potential, allowing for a closer approach between ligand and protein. The standard, “hard” Lennard-Jones potential was used for docking to multiple receptor conformations. The Available Chemicals Directory (ACD) was screened against two cavity sites in the T4 lysozyme. These sites undergo small but significant conformational changes on ligand binding, making them good systems for soft docking. The ACD was also screened against the drug target aldose reductase, which can undergo large conformational changes on ligand binding. We evaluated the ability of the scoring functions to identify known ligands from among the over 200 000 decoy molecules in the database. The soft potential was always better at identifying known ligands than the hard scoring function when only a single receptor conformation was used. Conversely, the soft function was worse at identifying known leads than the hard function when multiple receptor conformations were used. This was true even for the cavity sites and was especially true for aldose reductase. To test the multiple-conformation method predictively, we screened the ACD for molecules that preferentially docked to the expanded conformation of aldose reductase, known to bind larger ligands. Six novel molecules that ranked among the top 0.66% of hits from the multiple-conformation calculation, but ranked relatively poorly in the soft docking calculation, were tested experimentally for enzyme inhibition. Four of these six inhibited the enzyme, the best with an IC50 of 8 μM. Although ligands can get better scores in soft docking, the same is also true for decoys. The improved ranking of such decoys can come at the expense of true ligands.

We present an approach for constructing dynamic models for the simulation of gene regulatory networks from simple computational elements. Each element is called a “gene gate” and defines an input∕output relationship corresponding to the binding and production of transcription factors. The proposed reaction kinetics of the gene gates can be mapped onto stochastic processes and the standard ordinary differential equation (ODE) description. While the ODE approach requires fixing the system’s topology before its correct implementation, expressing them in stochastic π-calculus leads to a fully compositional scheme: network elements become autonomous and only the input∕output relationships fix their wiring. The modularity of our approach allows to pass easily from a basic first-level description to refined models which capture more details of the biological system. As an illustrative application we present the stochastic repressilator, an artificial cellular clock, which oscillates readily without any cooperative effects.

Single molecule visualization of protein–DNA complexes can reveal details of reaction mechanisms and macromolecular dynamics inaccessible to traditional biochemical assays. However, these techniques are often limited by the inherent difficulty of collecting statistically relevant information from experiments explicitly designed to look at single events. New approaches that increase throughput capacity of single molecule methods have the potential for making these techniques more readily applicable to a variety of biological questions involving different types of DNA transactions. Here we show that nanofabricated chromium barriers, which are located at strategic positions on a fused silica slide otherwise coated with a supported lipid bilayer, can be used to organize DNA molecules into molecular curtains. The DNA that makes up the curtains is visualized by total internal reflection fluorescence microscopy (TIRFM) allowing simultaneous imaging of hundreds or thousands of aligned molecules. These DNA curtains present a robust experimental platform portending massively parallel data acquisition of individual protein–DNA interactions in real time.

The initial coupling between ligand binding and channel gating in the human α7 nicotinic acetylcholine receptor (nAChR) has been investigated with targeted molecular dynamics (TMD) simulation. During the simulation, eight residues at the tip of the C-loop in two alternating subunits were forced to move toward a ligand-bound conformation as captured in the crystallographic structure of acetylcholine binding protein (AChBP) in complex with carbamoylcholine. Comparison of apo- and ligand-bound AChBP structures shows only minor rearrangements distal from the ligand-binding site. In contrast, comparison of apo and TMD simulation structures of the nAChR reveals significant changes toward the bottom of the ligand-binding domain. These structural rearrangements are subsequently translated to the pore domain, leading to a partly open channel within 4 ns of TMD simulation. Furthermore, we confirmed that two highly conserved residue pairs, one located near the ligand-binding pocket (Lys145 and Tyr188), and the other located toward the bottom of the ligand-binding domain (Arg206 and Glu45), are likely to play important roles in coupling agonist binding to channel gating. Overall, our simulations suggest that gating movements of the α7 receptor may involve relatively small structural changes within the ligand-binding domain, implying that the gating transition is energy-efficient and can be easily modulated by agonist binding/unbinding.

Synopsis

Nicotinic acetylcholine receptors are ligand-gated ion channels responsible for neurotransmitter-mediated signal transduction at synapses throughout the central and peripheral nervous systems. Binding of neurotransmitter molecules to subunit interfaces in the N-terminal extracellular domain induces structural rearrangements of the membrane-spanning domain permitting the influx of cations. A full understanding of how the conformational changes propagate from the ligand-binding site to the pore domain is of great interest to biologists, yet remains to be established. Using a special simulation technique known as targeted molecular dynamics, Cheng and colleagues probed the early stages of ligand-induced conformational rearrangements that may lead to channel opening. During the simulation, Cheng et al. observed a sequence of conformational changes that stem from the ligand-binding site to the transmembrane domain resulting in a wider channel. From these results, they suggest that gating movements may entail only small structural changes in the ligand-binding domain, implying that channel gating is energy-efficient and can readily be modulated by the binding/unbinding of agonist molecules.