Conformational Changes in the Epidermal Growth Factor Receptor: Role of the Transmembrane Domain Investigated by Coarse-Grained MetaDynamics Free Energy Calculations

Ligand binding effects on the activation of the EGFR extracellular domain

The epidermal growth factor receptor (EGFR) is one of the most common target proteins in anti-cancer therapy. The binding of the EGF ligand to the EGFR extracellular domain (EGFR-ECD) promotes its inactive-to-active conformational transition (activation) but the relevant detailed mechanism remains elusive still. Here, the structural characterization and energetics of the EGFR-ECD conformational transition with and without the binding of the EGF are quantitatively explored using an innovative enhanced sampling MD simulation method. Intriguingly, the EGF offers hydrophobic interactions (e.g., EGF residues of Tyr44 and Leu47) and electrostatic interactions (e.g., the EGF residues of Glu5, Asp11, Asp17, and Arg41) to play a dominant role in dragging domain III to close the ligand binding domain gap. Subsequently, the correlation between domains III and II is enhanced through salt-bridges among Glu376, Arg403, and Arg405 from domain III and Glu293, Glu295, and Arg300 from domain II. Finally, the structural bending of domain II is regulated to facilitate the disengagement of domain II from domain IV. In this regard, the functional conformational transition of EGFR-ECD is a consequence of the cooperative motion of protein domains driven by the EGF ligand binding. The present study shows a detailed scenario of the EGF induced activation of EGFR-ECD and provides valuable information for drug discovery targeting the EGFR.

Structure and Dynamics of the EGF Receptor as Revealed by Experiments and Simulations and Its Relevance to Non-Small Cell Lung Cancer

The epidermal growth factor receptor (EGFR) is historically the prototypical receptor tyrosine kinase, being the first cloned and the first where the importance of ligand-induced dimer activation was ascertained. However, many years of structure determination has shown that EGFR is not completely understood. One challenge is that the many structure fragments stored at the PDB only provide a partial view because full-length proteins are flexible entities and dynamics play a key role in their functionality. Another challenge is the shortage of high-resolution data on functionally important higher-order complexes. Still, the interest in the structure/function relationships of EGFR remains unabated because of the crucial role played by oncogenic EGFR mutants in driving non-small cell lung cancer (NSCLC). Despite targeted therapies against EGFR setting a milestone in the treatment of this disease, ubiquitous drug resistance inevitably emerges after one year or so of treatment. The magnitude of the challenge has inspired novel strategies. Among these, the combination of multi-disciplinary experiments and molecular dynamic (MD) simulations have been pivotal in revealing the basic nature of EGFR monomers, dimers and multimers, and the structure-function relationships that underpin the mechanisms by which EGFR dysregulation contributes to the onset of NSCLC and resistance to treatment.

Multiconfigurational Coarse-Grained Molecular Dynamics

Standard low resolution coarse-grained modeling techniques have difficulty capturing multiple configurations of protein systems. Here, we present a method for creating accurate coarse-grained (CG) models with multiple configurations using a linear combination of functions or “states”. Individual CG models are created to capture the individual states, and the approximate coupling between the two states is determined from an all-atom potential of mean force. We show that the resulting multiconfiguration coarse-graining (MCCG) method accurately captures the transition state as well as the free energy between the two states. We have tested this method on the folding of dodecaalanine, as well as the amphipathic helix of endophilin.

Lipid-Protein Interplay in Dimerization of Juxtamembrane Domains of Epidermal Growth Factor Receptor

Transmembrane (TM) helix and juxtamembrane (JM) domains (TM-JM) bridge the extracellular and intracellular domains of single-pass membrane proteins, including epidermal growth factor receptor (EGFR). TM-JM dimerization plays a crucial role in regulation of EGFR kinase activity at the cytoplasmic side. Although the interaction of JM with membrane lipids is thought to be important to turn on EGF signaling, and phosphorylation of Thr654 on JM leads to desensitization, the underlying kinetic mechanisms remain unclear. In particular, how Thr654 phosphorylation regulates EGFR activity is largely unknown. Here, combining single-pair FRET imaging and nanodisc techniques, we showed that phosphatidylinositol 4,5-bis phosphate (PIP₂) facilitated JM dimerization effectively. We also found that Thr654 phosphorylation dissociated JM dimers in the membranes containing acidic lipids, suggesting that Thr654 phosphorylation electrostatically prevented the interaction with basic residues in JM and acidic lipids. Based on the single-molecule experiment, we clarified the kinetic pathways of the monomer (inactive state)-to-dimer (active state) transition of JM domains and alteration in the pathways depending on the membrane lipid species and Thr654 phosphorylation.

Understanding Membrane Domain-Partitioning Thermodynamics of Transmembrane Domains with Potential of Mean Force Calculations

The transmembrane domain (TMD) of membrane proteins plays an essential role in their dynamics and functions. Certain properties of TMDs, such as raft affinity and orientation, have been studied extensively both experimentally and computationally. However, the extent to which specific physicochemical properties of TMDs determine their membrane domain-partitioning thermodynamics is still far from clear. In this work, we propose an approach based on umbrella sampling molecular dynamics simulations of model membranes and idealized TMDs to quantify the effect of TMD physicochemical properties, namely, length, degree of hydrophobicity, and size of TMDs, on their membrane domain-partitioning thermodynamics. The results, which are fully consistent with previous experimental and simulation data, indicate that the concept of "hydrophobic mismatch" should go beyond differences in hydrophobic thickness to include mismatch in the degree of hydrophobicity between the TMD and the surrounding hydrocarbon lipid chains. Our method provides quantitative insights into the role of specific physicochemical features of TMDs in membrane localization and orientation, which will be broadly useful for predicting the raft affinity and membrane partitioning of any transmembrane protein.

Optimizing model representation for integrative structure determination of macromolecular assemblies

Integrative structure determination of macromolecular assemblies requires specifying the representation of the modeled structure, a scoring function for ranking alternative models based on diverse types of data, and a sampling method for generating these models. Structures are often represented at atomic resolution, although ad hoc simplified representations based on generic guidelines and/or trial and error are also used. In contrast, we introduce here the concept of optimizing representation. To illustrate this concept, the optimal representation is selected from a set of candidate representations based on an objective criterion that depends on varying amounts of information available for different parts of the structure. Specifically, an optimal representation is defined as the highest-resolution representation for which sampling is exhaustive at a precision commensurate with the precision of the representation. Thus, the method does not require an input structure and is applicable to any input information. We consider a space of representations in which a representation is a set of nonoverlapping, variable-length segments (i.e., coarse-grained beads) for each component protein sequence. We also implement a method for efficiently finding an optimal representation in our open-source Integrative Modeling Platform (IMP) software (https://integrativemodeling.org/). The approach is illustrated by application to three complexes of two subunits and a large assembly of 10 subunits. The optimized representation facilitates exhaustive sampling and thus can produce a more accurate model and a more accurate estimate of its uncertainty for larger structures than were possible previously.

Transmembrane domain dimerization induces cholesterol rafts in curved lipid bilayers

Are the dimerization of transmembrane (TM) domains and the reorganization of the lipid bilayer two independent events?

The architecture of EGFR's basal complexes reveals autoinhibition mechanisms in dimers and oligomers

Our current understanding of epidermal growth factor receptor (EGFR) autoinhibition is based on X-ray structural data of monomer and dimer receptor fragments and does not explain how mutations achieve ligand-independent phosphorylation. Using a repertoire of imaging technologies and simulations we reveal an extracellular head-to-head interaction through which ligand-free receptor polymer chains of various lengths assemble. The architecture of the head-to-head interaction prevents kinase-mediated dimerisation. The latter, afforded by mutation or intracellular treatments, splits the autoinhibited head-to-head polymers to form stalk-to-stalk flexible non-extended dimers structurally coupled across the plasma membrane to active asymmetric tyrosine kinase dimers, and extended dimers coupled to inactive symmetric kinase dimers. Contrary to the previously proposed main autoinhibitory function of the inactive symmetric kinase dimer, our data suggest that only dysregulated species bear populations of symmetric and asymmetric kinase dimers that coexist in equilibrium at the plasma membrane under the modulation of the C-terminal domain.

Atomistic mechanism of the constitutive activation of PDGFRA via its transmembrane domain

Single-point mutations in the transmembrane (TM) region of receptor tyrosine kinases (RTKs) can lead to abnormal ligand-independent activation. We use a combination of computational modeling, NMR spectroscopy and cell experiments to analyze in detail the mechanism of how TM domains contribute to the activation of wild-type (WT) PDGFRA and its oncogenic V536E mutant. Using a computational framework, we scan all positions in PDGFRA TM helix for identification of potential functional mutations for the WT and the mutant and reveal the relationship between the receptor activity and TM dimerization via different interfaces. This strategy also allows us design a novel activating mutation in the WT (I537D) and a compensatory mutation in the V536E background eliminating its constitutive activity (S541G). We show both computationally and experimentally that single-point mutations in the TM region reshape the TM dimer ensemble and delineate the structural and dynamic determinants of spontaneous activation of PDGFRA via its TM domain. Our atomistic picture of the coupling between TM dimerization and PDGFRA activation corroborates the data obtained for other RTKs and provides a foundation for developing novel modulators of the pathological activity of PDGFRA.

Interactions of the EphA2 Kinase Domain with PIPs in Membranes: Implications for Receptor Function

EphA2 is a member of the receptor tyrosine kinase family. Interactions of the cytoplasmic region of EphA2 with the cell membrane are functionally important and yet remain incompletely characterized. Molecular dynamics simulations combined with biochemical studies reveal the interactions of the transmembrane, juxtamembrane (JM), and kinase domains with the membrane. We describe how the kinase domain is oriented relative to the membrane and how the JM region can modulate this interaction. We highlight the role of phosphatidylinositol phosphates (PIPs) in mediating the interaction of the kinase domain with the membrane and, conversely, how positively charged patches at the kinase surface and in the JM region induce the formation of nanoclusters of PIP molecules in the membrane. Integration of these results with those from previous studies enable computational reconstitution of a near complete EphA2 receptor within a membrane, suggesting a role for receptor-lipid interactions in modulation of EphA2.

Computer Simulations Predict High Structural Heterogeneity of Functional State of NMDA Receptors

N-methyl-D-aspartate receptors (NMDARs)-i.e., transmembrane proteins expressed in neurons-play a central role in the molecular mechanisms of learning and memory formation. It is unclear how the known atomic structures of NMDARs determined by x-ray crystallography and electron cryomicroscopy (18 published Protein Data Bank entries) relate to the functional states of NMDARs inferred from electrophysiological recordings (multiple closed, open, preopen, etc. states). We address this problem by using molecular dynamics simulations at atomic resolution, a method successfully applied in the past to much smaller biomolecules. Our simulations predict that several conformations of NMDARs with experimentally determined geometries, including four "nonactive" electron cryomicroscopy structures, rapidly interconvert on submicrosecond timescales and therefore may correspond to the same functional state of the receptor (specifically, one of the closed states). This conclusion is not trivial because these conformational transitions involve changes in certain interatomic distances as large as tens of Å. The simulations also predict differences in the conformational dynamics of the apo and holo (i.e., agonist and coagonist bound) forms of the receptor on the microsecond timescale. To our knowledge, five new conformations of NMDARs, with geometries joining various features from different known experimental structures, are also predicted by the model. The main limitation of this work stems from its limited sampling (30 μs of aggregate length of molecular dynamics trajectories). Though this level significantly exceeds the sampling in previous simulations of parts of NMDARs, it is still much lower than the sampling recently achieved for smaller biomolecules (up to a few milliseconds), thus precluding, in particular, the observation of transitions between different functional states of NMDARs. Despite this limitation, such computational predictions may guide further experimental studies on the structure, dynamics, and function of NMDARs, for example by suggesting optimal locations of spectroscopic probes. Overall, atomic resolution simulations provide, to our knowledge, a novel perspective on the structure and dynamics of NMDARs, complementing information obtained by experimental methods.

Molecular simulations of self-assembling bio-inspired supramolecular systems and their connection to experiments

In bionanotechnology, the field of creating functional materials consisting of bio-inspired molecules, the function and shape of a nanostructure only appear through the assembly of many small molecules together. The large number of building blocks required to define a nanostructure combined with the many degrees of freedom in packing small molecules has long precluded molecular simulations, but recent advances in computational hardware as well as software have made classical simulations available to this strongly expanding field. Here, we review the state of the art in simulations of self-assembling bio-inspired supramolecular systems. We will first discuss progress in force fields, simulation protocols and enhanced sampling techniques using recent examples. Secondly, we will focus on efforts to enable the comparison of experimentally accessible observables and computational results. Experimental quantities that can be measured by microscopy, spectroscopy and scattering can be linked to simulation output either directly or indirectly, via quantum mechanical or semi-empirical techniques. Overall, we aim to provide an overview of the various computational approaches to understand not only the molecular architecture of nanostructures, but also the mechanism of their formation.

Dimer Interface of the Human Serotonin Transporter and Effect of the Membrane Composition

The oligomeric state of membrane proteins has recently emerged in many cases as having an effect on their function. However, the intrinsic dynamics of their spatial organization in cells and model systems makes it challenging to characterize. Here we use molecular dynamics (MD) simulations at multiple resolutions to determine the dimer conformation of the human serotonin transporter (hSERT). From self-assembly simulations we predict dimer candidates and subsequently quantify their relative strength. We use umbrella sampling (US) replica exchange MD simulations for which we present extensive analysis of their efficiency and improved sampling compared to regular US MD simulations. The data shows that the most stable hSERT dimer interface is symmetrical and involves transmembrane helix 12 (TM12), similar to the crystal structure of the bacterial homologue LeuT, but with a slightly different orientation. We also describe the supramolecular organization of hSERT from a 250 μs self-assembly simulation. Finally, the effects of the presence of phosphatidylinositol bisphosphate or cholesterol in the membrane model has been quantified for the TM12-TM12 predicted interface. Collectively, the presented data bring new insight to the area of protein and lipid interplay in biological membranes.

Bending Lipid Bilayers: A Closed-Form Collective Variable for Effective Free-Energy Landscapes in Quantitative Biology

Curvature-related processes are of major importance during protein-membrane interactions. The illusive simplicity of membrane reshaping masks a complex molecular process crucial for a wide range of biological functions like fusion, endo- and exocytosis, cell division, cytokinesis, and autophagy. To date, no functional expression of a reaction coordinate capable of biasing molecular dynamics simulations to produce membrane curvature has been reported. This represents a major drawback given that the adequate identification of proper collective variables to enhance sampling is fundamental for restrained dynamics techniques. In this work, we present a closed-form equation of a collective variable that induces bending in lipid bilayers in a controlled manner, allowing for straightforward calculation of free energy landscapes of important curvature-related events, using standard methods such as umbrella sampling and metadynamics. As a direct application of the collective variable, we calculate the bending free energies of a ternary lipid bilayer in the presence and the absence of a Bin/Amphiphysin/Rvs domain with an N-terminal amphipathic helix (N-BAR), a well-known peripheral membrane protein that induces curvature.

Fast Calculation of Protein-Protein Binding Free Energies Using Umbrella Sampling with a Coarse-Grained Model

Determination of protein–protein binding affinity values is key to understanding various underlying biological phenomena, such as how missense variations change protein–protein binding. Most existing non-rigorous (fast) and rigorous (slow) methods that rely on all-atom representation of the proteins force the user to choose between speed and accuracy. In an attempt to achieve balance between speed and accuracy, we have combined rigorous umbrella sampling molecular dynamics simulation with a coarse-grained protein model. We predicted the effect of missense variations on binding affinity by selecting three protein–protein systems and comparing results to empirical relative binding affinity values and to non-rigorous modeling approaches. We obtained significant improvement both in our ability to discern stabilizing from destabilizing missense variations and in the correlation between predicted and experimental values compared to non-rigorous approaches. Overall our results suggest that using a rigorous affinity calculation method with coarse-grained protein models could offer fast and reliable predictions of protein–protein binding free energies.

Protein crowding and lipid complexity influence the nanoscale dynamic organization of ion channels in cell membranes

Cell membranes are crowded and complex environments. To investigate the effect of protein-lipid interactions on dynamic organization in mammalian cell membranes, we have performed coarse-grained molecular dynamics simulations containing >100 copies of an inwardly rectifying potassium (Kir) channel which forms specific interactions with the regulatory lipid phosphatidylinositol 4,5-bisphosphate (PIP2). The tendency of protein molecules to cluster has the effect of organizing the membrane into dynamic compartments. At the same time, the diversity of lipids present has a marked effect on the clustering behavior of ion channels. Sub-diffusion of proteins and lipids is observed. Protein crowding alters the sub-diffusive behavior of proteins and lipids such as PIP2 which interact tightly with Kir channels. Protein crowding also affects bilayer properties, such as membrane undulations and bending rigidity, in a PIP2-dependent manner. This interplay between the diffusion and the dynamic organization of Kir channels may have important implications for channel function.

Excessive aggregation of membrane proteins in the Martini model

The coarse-grained Martini model is employed extensively to study membrane protein oligomerization. While this approach is exceptionally promising given its computational efficiency, it is alarming that a significant fraction of these studies demonstrate unrealistic protein clusters, whose formation is essentially an irreversible process. This suggests that the protein-protein interactions are exaggerated in the Martini model. If this held true, then it would limit the applicability of Martini to study multi-protein complexes, as the rapidly clustering proteins would not be able to properly sample the correct dimerization conformations. In this work we first demonstrate the excessive protein aggregation by comparing the dimerization free energies of helical transmembrane peptides obtained with the Martini model to those determined from FRET experiments. Second, we show that the predictions provided by the Martini model for the structures of transmembrane domain dimers are in poor agreement with the corresponding structures resolved using NMR. Next, we demonstrate that the first issue can be overcome by slightly scaling down the Martini protein-protein interactions in a manner, which does not interfere with the other Martini interaction parameters. By preventing excessive, irreversible, and non-selective aggregation of membrane proteins, this approach renders the consideration of lateral dynamics and protein-lipid interactions in crowded membranes by the Martini model more realistic. However, this adjusted model does not lead to an improvement in the predicted dimer structures. This implicates that the poor agreement between the Martini model and NMR structures cannot be cured by simply uniformly reducing the interactions between all protein beads. Instead, a careful amino-acid specific adjustment of the protein-protein interactions is likely required.

Gold nanoparticles with patterned surface monolayers for nanomedicine: current perspectives

Molecular self-assembly is a topic attracting intense scientific interest. Various strategies have been developed for construction of molecular aggregates with rationally designed properties, geometries, and dimensions that promise to provide solutions to both theoretical and practical problems in areas such as drug delivery, medical diagnostics, and biosensors, to name but a few. In this respect, gold nanoparticles covered with self-assembled monolayers presenting nanoscale surface patterns-typically patched, striped or Janus-like domains-represent an emerging field. These systems are particularly intriguing for use in bio-nanotechnology applications, as presence of such monolayers with three-dimensional (3D) morphology provides nanoparticles with surface-dependent properties that, in turn, affect their biological behavior. Comprehensive understanding of the physicochemical interactions occurring at the interface between these versatile nanomaterials and biological systems is therefore crucial to fully exploit their potential. This review aims to explore the current state of development of such patterned, self-assembled monolayer-protected gold nanoparticles, through step-by-step analysis of their conceptual design, synthetic procedures, predicted and determined surface characteristics, interactions with and performance in biological environments, and experimental and computational methods currently employed for their investigation.

Functions of intrinsic disorder in transmembrane proteins

Intrinsic disorder is common in integral membrane proteins, particularly in the intracellular domains. Despite this observation, these domains are not always recognized as being disordered. In this review, we will discuss the biological functions of intrinsically disordered regions of membrane proteins, and address why the flexibility afforded by disorder is mechanistically important. Intrinsically disordered regions are present in many common classes of membrane proteins including ion channels and transporters; G-protein coupled receptors (GPCRs), receptor tyrosine kinases and cytokine receptors. The functions of the disordered regions are many and varied. We will discuss selected examples including: (1) Organization of receptors, kinases, phosphatases and second messenger sources into signaling complexes. (2) Modulation of the membrane-embedded domain function by ball-and-chain like mechanisms. (3) Trafficking of membrane proteins. (4) Transient membrane associations. (5) Post-translational modifications most notably phosphorylation and (6) disorder-linked isoform dependent function. We finish the review by discussing the future challenges facing the membrane protein community regarding protein disorder.

CHARMM-GUI Martini Maker for modeling and simulation of complex bacterial membranes with lipopolysaccharides

A complex cell envelope, composed of a mixture of lipid types including lipopolysaccharides, protects bacteria from the external environment. Clearly, the proteins embedded within the various components of the cell envelope have an intricate relationship with their local environment. Therefore, to obtain meaningful results, molecular simulations need to mimic as far as possible this chemically heterogeneous system. However, setting up such systems for computational studies is far from trivial, and consequently the vast majority of simulations of outer membrane proteins still rely on oversimplified phospholipid membrane models. This work presents an update of CHARMM-GUI Martini Maker for coarse-grained modeling and simulation of complex bacterial membranes with lipopolysaccharides. The qualities of the outer membrane systems generated by Martini Maker are validated by simulating them in bilayer, vesicle, nanodisc, and micelle environments (with and without outer membrane proteins) using the Martini force field. We expect this new feature in Martini Maker to be a useful tool for modeling large, complicated bacterial outer membrane systems in a user-friendly manner. © 2017 Wiley Periodicals, Inc.

Into the Dynamics of a Supramolecular Polymer at Submolecular Resolution

To rationally design supramolecular polymers capable of self-healing or reconfiguring their structure in a dynamically controlled way, it is imperative to gain access into the intrinsic dynamics of the supramolecular polymer (dynamic exchange of monomers) while maintaining a high-resolution description of the monomer structure. But this is prohibitively difficult at experimental level. Here we show atomistic, coarse-grained modelling combined with advanced simulation approaches to characterize the molecular mechanisms and relative kinetics of monomer exchange in structural variants of a synthetic supramolecular polymer in different conditions. We can capture differences in supramolecular dynamics consistent with the experimental observations, revealing that monomer exchange in and out the fibres originates from the defects present in their supramolecular structure. At the same time, the submolecular resolution of this approach offers a molecular-level insight into the dynamics of these bioinspired materials, and a flexible tool to obtain structure-dynamics relationships for a variety of polymeric assemblies.Accessing the dynamics of soft self-assembled materials at high resolution is very difficult. Here the authors show atomistic and coarse-grained modelling combined with enhanced sampling to characterize the molecular mechanisms and kinetics of monomer exchange in synthetic supramolecular polymers.

Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

We describe and implement a computer-assisted approach for accelerating the exploration of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction of coarse-grained, macroscopic information from stochastic or atomistic simulations, such as molecular dynamics (MD). The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator toward unexplored phase-space regions by exploiting the smoothness of the gradually revealed intrinsic low-dimensional geometry of the FES.

Combining the MARTINI and Structure-Based Coarse-Grained Approaches for the Molecular Dynamics Studies of Conformational Transitions in Proteins

The application of coarse-grained (CG) models in biology is essential to access large length and time scales required for the description of many biological processes. The ELNEDIN protein model is based on the well-known MARTINI CG force-field and incorporates additionally harmonic bonds of a certain spring constant within a defined cutoff distance between pairs of residues, in order to preserve the native structure of the protein. In this case, the use of unbreakable harmonic bonds hinders the study of unfolding and folding processes. To overcome this barrier we have replaced the harmonic bonds with Lennard-Jones interactions based on the contact map of the native protein structure as is done in Go̅-like models. This model exhibits very good agreement with all-atom simulations and the ELNEDIN. Moreover, it can capture the structural motion linked to particular catalytic activity in the Man5B protein, in agreement with all-atom simulations. In addition, our model is based on the van der Waals radii, instead of a cutoff distance, which results in a smaller contact map. In conclusion, we anticipate that our model will provide further possibilities for studying biological systems based on the MARTINI CG force-field by using advanced-sampling methods, such as parallel tempering and metadynamics.

Ligand binding to telomeric G-quadruplex DNA investigated by funnel-metadynamics simulations

A thorough characterization of the binding interaction between a drug and its molecular target is fundamental to successfully lead drug design. We demonstrate that this characterization is also possible using the recently developed method of funnel-metadynamics (FM), here applied to investigate the binding of berberine to DNA G-quadruplex. We computed a quantitatively well-characterized free-energy landscape that allows identifying two low-energy ligand binding modes and the presence of higher energy prebinding states. We validated the accuracy of our calculations by steady-state fluorescence experiments. The good agreement between the theoretical and experimental binding free-energy value demonstrates that FM is a most reliable method to study ligand/DNA interaction.

G-quadruplexes (G4s) are higher-order DNA structures typically present at promoter regions of genes and telomeres. Here, the G4 formation decreases the replicative DNA at each cell cycle, finally leading to apoptosis. The ability to control this mitotic clock, particularly in cancer cells, is fascinating and passes through a rational understanding of the ligand/G4 interaction. We demonstrate that an accurate description of the ligand/G4 binding mechanism is possible using an innovative free-energy method called funnel-metadynamics (FM), which we have recently developed to investigate ligand/protein interaction. Using FM simulations, we have elucidated the binding mechanism of the anticancer alkaloid berberine to the human telomeric G4 (d[AG₃(T₂AG₃)₃]), computing also the binding free-energy landscape. Two ligand binding modes have been identified as the lowest energy states. Furthermore, we have found prebinding sites, which are preparatory to reach the final binding mode. In our simulations, the ions and the water molecules have been explicitly represented and the energetic contribution of the solvent during ligand binding evaluated. Our theoretical results provide an accurate estimate of the absolute ligand/DNA binding free energy (ΔGb0 = −10.3 ± 0.5 kcal/mol) that we validated through steady-state fluorescence binding assays. The good agreement between the theoretical and experimental value demonstrates that FM is a most powerful method to investigate ligand/DNA interaction and can be a useful tool for the rational design also of G4 ligands.

Convergence and Sampling in Determining Free Energy Landscapes for Membrane Protein Association

Potential of mean force (PMF) calculations are used to characterize the free energy landscape of protein–lipid and protein–protein association within membranes. Coarse-grained simulations allow binding free energies to be determined with reasonable statistical error. This accuracy relies on defining a good collective variable to describe the binding and unbinding transitions, and upon criteria for assessing the convergence of the simulation toward representative equilibrium sampling. As examples, we calculate protein–lipid binding PMFs for ANT/cardiolipin and Kir2.2/PIP₂, using umbrella sampling on a distance coordinate. These highlight the importance of replica exchange between windows for convergence. The use of two independent sets of simulations, initiated from bound and unbound states, provide strong evidence for simulation convergence. For a model protein–protein interaction within a membrane, center-of-mass distance is shown to be a poor collective variable for describing transmembrane helix–helix dimerization. Instead, we employ an alternative intermolecular distance matrix RMS (D_RMS) coordinate to obtain converged PMFs for the association of the glycophorin transmembrane domain. While the coarse-grained force field gives a reasonable K_d for dimerization, the majority of the bound population is revealed to be in a near-native conformation. Thus, the combination of a refined reaction coordinate with improved sampling reveals previously unnoticed complexities of the dimerization free energy landscape. We propose the use of replica-exchange umbrella sampling starting from different initial conditions as a robust approach for calculation of the binding energies in membrane simulations.