Atomic Detail Research Articles

Unlocking the unfolded structure of ubiquitin: Combining time-resolved x-ray solution scattering and molecular dynamics to generate unfolded ensembles.

The unfolding dynamics of ubiquitin were studied using a combination of x-ray solution scattering (XSS) and molecular dynamics (MD) simulations. The kinetic analysis of the XSS ubiquitin signals showed that the protein unfolds through a two-state process, independent of the presence of destabilizing salts. In order to characterize the ensemble of unfolded states in atomic detail, the experimental XSS results were used as a constraint in the MD simulations through the incorporation of x-ray scattering derived potential to drive the folded ubiquitin structure toward sampling unfolded states consistent with the XSS signals. We detail how biased MD simulations provide insight into unfolded states that are otherwise difficult to resolve and underscore how experimental XSS data can be combined with MD to efficiently sample structures away from the native state. Our results indicate that ubiquitin samples unfolded in states with a high degree of loss in secondary structure yet without a collapse to a molten globule or fully solvated extended chain. Finally, we propose how using biased-MD can significantly decrease the computational time and resources required to sample experimentally relevant nonequilibrium states.

Partitioning of an Enzyme-Polymer Surfactant Nanocomplex into Lipid-Rich Cellular Compartments Drives In Situ Hydrolysis of Organophosphates.

Most organophosphates (OPs) are hydrophobic, and after exposure, can sequester into lipophilic regions within the body, such as adipose tissue, resulting in long term chronic effects. Consequently, there is an urgent need for therapeutic agents that can decontaminate OPs in these hydrophobic regions. Accordingly, an enzyme-polymer surfactant nanocomplex is designed and tested comprising chemically supercharged phosphotriesterase (Agrobacterium radiobacter; arPTE) electrostatically conjugated to amphiphilic polymer surfactant chains ([cat.arPTE][S-]). Experimentally-derived structural data are combined with molecular dynamics (MD) simulations to provide atomic level detail on conformational ensembles of the nanocomplex using dielectric constants relevant to aqueous and lipidic microenvironments. These show the formation of a compact admicelle pseudophase surfactant corona under aqueous conditions, which reconfigures to yield an extended conformation at a low dielectric constant, providing insight into the mechanism underpinning cell membrane binding. Significantly, it demonstrated that [cat.arPTE][S-] spontaneously binds to human mesenchymal stem cell membranes (hMSCs), resulting in on-cell OP hydrolysis. Moreover, the nanoconstruct can endocytose and partition into the intracellular fatty vacuoles of adipocytes and hydrolyze sequestered OP.

Molecular dynamics simulations of nucleosomes are coming of age

AbstractUnderstanding the function of eukaryotic genomes, including the human genome, is undoubtedly one of the major scientific challenges of the 21st century. The cornerstone of eukaryotic genome organization is nucleosomes—elementary building blocks of chromatin about 10 nm in size that wrap DNA around an octamer of histone proteins. Nucleosomes are integral players in all genomic processes, including transcription, DNA replication and repair. They mediate genome regulation at the epigenetic level, bridging the discrete nature of the genetic information encoded in DNA with the analog physical nature of the intermolecular interactions required to access that information. Due to their relatively large size and dynamic nature, nucleosomes are difficult objects for experimental characterization. Molecular dynamics (MD) simulations have emerged over the years as a useful tool to complement experimental studies. Particularly in recent years, advances in computing power, refinement of MD force fields and codes have opened up new frontiers in terms of simulation timescales and quality for nucleosomes and related systems. It has become possible to elucidate in atomistic detail their functional dynamics modes such as DNA unwrapping and sliding, to characterize the effects of epigenetic modifications, DNA and protein sequence variation on nucleosome structure and stability, to describe the mechanisms governing nucleosome interactions with chromatin‐associated proteins and the formation of supranucleosome structures. In this review, we systematically analyzed all‐atom MD simulation studies of nucleosomes and related structures published since 2018 and discussed their relevance in the context of older studies, experimental data, and related coarse‐grained and multiscale studies.This article is categorized under: Software > Molecular Modeling Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Biochemistry and Biophysics

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.

Proteins are inherently dynamic, and their conformational ensembles are functionally important in biology. Large-scale motions may govern protein structure-function relationship, and numerous transient but stable conformations of intrinsically disordered proteins (IDPs) can play a crucial role in biological function. Investigating conformational ensembles to understand regulations and disease-related aggregations of IDPs is challenging both experimentally and computationally. In this paper we first introduced an unsupervised deep learning-based model, termed Internal Coordinate Net (ICoN), which learns the physical principles of conformational changes from molecular dynamics (MD) simulation data. Second, we selected interpolating data points in the learned latent space that rapidly identify novel synthetic conformations with sophisticated and large-scale sidechains and backbone arrangements. Third, with the highly dynamic amyloid-β 1-42 (Aβ42) monomer, our deep learning model provided a comprehensive sampling of Aβ42's conformational landscape. Analysis of these synthetic conformations revealed conformational clusters that can be used to rationalize experimental findings. Additionally, the method can identify novel conformations with important interactions in atomistic details that are not included in the training data. New synthetic conformations showed distinct sidechain rearrangements that are probed by our EPR and amino acid substitution studies. This approach is highly transferable and can be used for any available data for training. The work also demonstrated the ability for deep learning to utilize learned natural atomistic motions in protein conformation sampling.

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.

Proteins are inherently dynamic, and their conformational ensembles are functionally important in biology. Large-scale motions may govern protein structure-function relationship, and numerous transient but stable conformations of intrinsically disordered proteins (IDPs) can play a crucial role in biological function. Investigating conformational ensembles to understand regulations and disease-related aggregations of IDPs is challenging both experimentally and computationally. In this paper first an unsupervised deep learning-based model, termed Internal Coordinate Net (ICoN), is developed that learns the physical principles of conformational changes from molecular dynamics (MD) simulation data. Second, interpolating data points in the learned latent space are selected that rapidly identify novel synthetic conformations with sophisticated and large-scale sidechains and backbone arrangements. Third, with the highly dynamic amyloid-β1-42 (Aβ42) monomer, our deep learning model provided a comprehensive sampling of Aβ42's conformational landscape. Analysis of these synthetic conformations revealed conformational clusters that can be used to rationalize experimental findings. Additionally, the method can identify novel conformations with important interactions in atomistic details that are not included in the training data. New synthetic conformations showed distinct sidechain rearrangements that are probed by our EPR and amino acid substitution studies. The proposed approach is highly transferable and can be used for any available data for training. The work also demonstrated the ability for deep learning to utilize learned natural atomistic motions in protein conformation sampling.

Structural basis for recruitment of peptidoglycan endopeptidase MepS by lipoprotein NlpI

Peptidoglycan (PG) sacculi surround the cytoplasmic membrane, maintaining cell integrity by withstanding internal turgor pressure. During cell growth, PG endopeptidases cleave the crosslinks of the fully closed sacculi, allowing for the incorporation of new glycan strands and expansion of the peptidoglycan mesh. Outer-membrane-anchored NlpI associates with hydrolases and synthases near PG synthesis complexes, facilitating spatially close PG hydrolysis. Here, we present the structure of adaptor NlpI in complex with the endopeptidase MepS, revealing atomic details of how NlpI recruits multiple MepS molecules and subsequently influences PG expansion. NlpI binding elicits a disorder-to-order transition in the intrinsically disordered N-terminal of MepS, concomitantly promoting the dimerization of monomeric MepS. This results in the alignment of two asymmetric MepS dimers respectively located on the two opposite sides of the dimerization interface of NlpI, thus enhancing MepS activity in PG hydrolysis. Notably, the protein level of MepS is primarily modulated by the tail-specific protease Prc, which is known to interact with NlpI. The structure of the Prc-NlpI-MepS complex demonstrates that NlpI brings together MepS and Prc, leading to the efficient MepS degradation by Prc. Collectively, our results provide structural insights into the NlpI-enabled avidity effect of cellular endopeptidases and NlpI-directed MepS degradation by Prc.

Substrate Turnover Dynamics Guide Ketol-Acid Reductoisomerase Redesign for Increased Specific Activity.

The task of adapting enzymes for specific applications is often hampered by our incomplete ability to tune and tailor catalytic functions, particularly when seeking increased activity. Here, we develop and demonstrate a rational approach to address this challenge, applied to ketol-acid reductoisomerase (KARI), which has uses in industrial-scale isobutanol production. While traditional structure-based computational enzyme redesign strategies typically focus on the enzyme-bound ground state (GS) and transition state (TS), we postulated that additionally treating the underlying dynamics of complete turnover events that connect and pass through both states could further elucidate the structural properties affecting catalysis and help identify mutations that lead to increased catalytic activity. To examine the dynamics of substrate conversion with atomistic detail, we adapted and applied computational methods based on path sampling techniques to gather thousands of QM/MM simulations of attempted substrate turnover events by KARI: both productive (reactive) and unproductive (nonreactive) attempts. From these data, machine learning models were constructed and used to identify specific conformational features (interatomic distances, angles, and torsions) associated with successful, productive catalysis. Multistate protein redesign techniques were then used to select mutations that stabilized reactive-like structures over nonreactive-like ones while also meeting additional criteria consistent with enhanced specific activity. This procedure resulted in eight high-confidence enzyme mutants with a significant improvement in calculated specific activity relative to wild type (WT), with the fastest variant's increase in calculated k cat being (2 ± 1) × 104-fold. Collectively, these results suggest that introducing mutations designed to increase the population of reaction-promoting conformations of the enzyme-substrate complex before it reaches the barrier can provide an effective approach to engineering improved enzyme catalysts.

Effect of Solvent Composition on Non-DLVO Forces and Oriented Attachment of Zinc Oxide Nanoparticles.

Oriented attachment (OA) occurs when nanoparticles in solution align their crystallographic axes prior to colliding and subsequently fuse into single crystals. Traditional colloidal theories such as DLVO provide a framework for evaluating OA but fail to capture key particle interactions due to the atomistic details of both the crystal structure and the interfacial solution structure. Using zinc oxide as a model system, we investigated the effect of the solvent on short-ranged and long-ranged particle interactions and the resulting OA mechanism. In situ TEM imaging showed that ZnO nanocrystals in toluene undergo long-range attraction comparable to 1kT at separations of 10 nm and 3kT near particle contact. These observations were rationalized by considering non-DLVO interactions, namely, dipole-dipole forces and torques between the polar ZnO nanocrystals. Langevin dynamics simulations showed stronger interactions in toluene compared to methanol solvents, consistent with the experimental results. Concurrently, we performed atomic force microscopy measurements using ZnO-coated probes for the short-ranged interaction. Our data are relevant to another type of non-DLVO interaction, namely, the repulsive solvation force. Specifically, the solvation force was stronger in water compared to ethanol and methanol, due to the stronger hydrogen bonding and denser packing of water molecules at the interface. Our results highlight the importance of non-DLVO forces in a general framework for understanding and predicting particle aggregation and attachment.

A Practical Guide to All-Atom and Coarse-Grained Molecular Dynamics Simulations Using Amber and Gromacs: A Case Study of Disulfide-Bond Impact on the Intrinsically Disordered Amyloid Beta.

Intrinsically disordered proteins (IDPs) pose challenges to conventional experimental techniques due to their large-scale conformational fluctuations and transient structural elements. This work presents computational methods for studying IDPs at various resolutions using the Amber and Gromacs packages with both all-atom (Amber ff19SB with the OPC water model) and coarse-grained (Martini 3 and SIRAH) approaches. The effectiveness of these methodologies is demonstrated by examining the monomeric form of amyloid-β (Aβ42), an IDP, with and without disulfide bonds at different resolutions. Our results clearly show that the addition of a disulfide bond decreases the β-content of Aβ42; however, it increases the tendency of the monomeric Aβ42 to form fibril-like conformations, explaining the various aggregation rates observed in experiments. Moreover, analysis of the monomeric Aβ42 compactness, secondary structure content, and comparison between calculated and experimental chemical shifts demonstrates that all three methods provide a reasonable choice to study IDPs; however, coarse-grained approaches may lack some atomistic details, such as secondary structure recognition, due to the simplifications used. In general, this study not only explains the role of disulfide bonds in Aβ42 but also provides a step-by-step protocol for setting up, conducting, and analyzing molecular dynamics (MD) simulations, which is adaptable for studying other biomacromolecules, including folded and disordered proteins and peptides.

Mechanistic insights into G-protein coupling with an agonist-bound G-protein-coupled receptor.

G-protein-coupled receptors (GPCRs) activate heterotrimeric G proteins by promoting guanine nucleotide exchange. Here, we investigate the coupling of G proteins with GPCRs and describe the events that ultimately lead to the ejection of GDP from its binding pocket in the Gα subunit, the rate-limiting step during G-protein activation. Using molecular dynamics simulations, we investigate the temporal progression of structural rearrangements of GDP-bound Gs protein (Gs·GDP; hereafter GsGDP) upon coupling to the β2-adrenergic receptor (β2AR) in atomic detail. The binding of GsGDP to the β2AR is followed by long-range allosteric effects that significantly reduce the energy needed for GDP release: the opening of α1-αF helices, the displacement of the αG helix and the opening of the α-helical domain. Signal propagation to the Gs occurs through an extended receptor interface, including a lysine-rich motif at the intracellular end of a kinked transmembrane helix 6, which was confirmed by site-directed mutagenesis and functional assays. From this β2AR-GsGDP intermediate, Gs undergoes an in-plane rotation along the receptor axis to approach the β2AR-Gsempty state. The simulations shed light on how the structural elements at the receptor-G-protein interface may interact to transmit the signal over 30 Å to the nucleotide-binding site. Our analysis extends the current limited view of nucleotide-free snapshots to include additional states and structural features responsible for signaling and G-protein coupling specificity.

Curvature Footprints of Transmembrane Proteins in Simulations with the Martini Force Field.

Membranes play essential roles in biological systems and are tremendously diverse in the topologies and chemical and elastic properties that define their functions. In many cases, a given membrane may display considerable heterogeneity, with localized clusters of lipids and proteins exhibiting distinct characteristics compared to adjoining regions. These lipid-protein assemblies can span nanometers to micrometers and are associated with cellular processes such as transport and signaling. While lipid-protein assemblages are dynamic, they can be stabilized by coupling between local membrane composition and shape. Due to the inherent difficulty in resolving atomistic details of membrane proteins in their native lipid environments, these complexes are notoriously challenging to study experimentally; however, molecular dynamics (MD) simulations might be a viable alternative. Here, we aim to assess the utility of coarse-grained (CG) MD simulations with the Martini force field for studying membrane curvature induced by transmembrane (TM) proteins that are reported to generate local curvature. The direction and magnitude of curvature induced by five different TM proteins, as well as certain lipid-protein and protein-protein interactions, were found to be in good agreement with available reference data.

Crystal structure of a tetrameric RNA G-quadruplex formed by hexanucleotide repeat expansions of C9orf72 in ALS/FTD.

The abnormal GGGGCC hexanucleotide repeat expansions (HREs) in C9orf72 cause the fatal neurodegenerative diseases including amyotrophic lateral sclerosis and frontotemporal dementia. The transcribed RNA HREs, short for r(G4C2)n, can form toxic RNA foci which sequestrate RNA binding proteins and impair RNA processing, ultimately leading to neurodegeneration. Here, we determined the crystal structure of r(G4C2)2, which folds into a parallel tetrameric G-quadruplex composed of two four-layer dimeric G-quadruplex via 5'-to-5' stacking in coordination with a K+ ion. Notably, the two C bases locate at 3'- end stack on the outer G-tetrad with the assistance of two additional K+ ions. The high-resolution structure reported here lays a foundation in understanding the mechanism of neurological toxicity of RNA HREs. Furthermore, the atomic details provide a structural basis for the development of potential therapeutic agents against the fatal neurodegenerative diseases ALS/FTD.

Metalloid transporters in plants: bridging the gap in molecular structure and physiological exaptation

Abstract The rhizosphere contains both essential nutrients and potentially harmful substances for plant growth. Plants, as sessile organisms, must efficiently absorb the necessary nutrients while actively avoiding the uptake of toxic compounds. Metalloids, elements that exhibit properties of both metals and non-metals, can have different effects on plant growth, from being essential and beneficial to being toxic. This toxicity arises due to either the dosage of exposure or the specific elemental type. To utilize or detoxify these elements, plants have developed various transporters regulating their uptake and distribution in plants. Genomic sequence analysis suggests that such transporter families exist throughout the plant kingdom, from chlorophytes to higher plants. These transporters form defined families with related transport preferences. The isoforms within these families have evolved with specialized functions regulated by defined selectivity. Hence, understanding the chemistry of transporters to atomic detail is important to achieve the desired genetic modifications for crop improvement. We outline various adaptations in plant transport systems to deal with metalloids, including their uptake, distribution, detoxification, and homeostasis in plant tissues. Structural parallels are drawn to other nutrient transporter systems to support emerging themes of functional diversity of active sites of transporters, elucidating plant adaptations to utilize and extrude metalloid concentrations. Considering the observed physiological importance of metalloids, this review highlights the shared and disparate features in metalloid transport systems and their corresponding nutrient transporters.

Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences.

Phosphorylations are the most common and extensively studied post-translational modification (PTM) of proteins in eukaryotes. They constitute a major regulatory mechanism, modulating protein function, protein-protein interactions, as well as subcellular localization. Phosphorylation sites are preferably located in intrinsically disordered regions and have been shown to trigger structural rearrangements and order-to-disorder transitions. They can therefore have a significant effect on protein backbone dynamics or conformation, but only sparse experimental data are available. To obtain a more general description of how and when phosphorylations have a significant effect on protein behavior, molecular dynamics (MD) currently provides the only suitable framework to study these effects at a large scale in atomistic detail. This study develops a systematic MD simulation framework to explore the influence of phosphorylations on the local backbone dynamics and conformational propensities of proteins. Through a series of glycine-backbone peptides, we studied the effects of amino acid residues including the three most common phosphorylations (Ser, Thr, and Tyr), on local backbone dynamics and conformational propensities. We further extended our study to investigate the interactions of all such residues between position i to positions i + 1, i + 2, i + 3, and i + 4 in such peptides. The final data set comprises structural ensembles for 3393 sequences with more than 1 μs of sampling for each ensemble. To validate the relevance of the results, the structural and conformational properties extracted from the MD simulations are compared to NMR data from the Biological Magnetic Resonance Data Bank. The systematic nature of this study enables the projection of the gained knowledge onto any phosphorylation site in the proteome and provides a general framework for the study of further PTMs. The full data set is publicly available, as a training and reference set.

Molecular storytelling: a conceptual framework for teaching and learning with molecular case studies

Molecular case studies (MCSs) provide educational opportunities to explore biomolecular structure and function using data from public bioinformatics resources. The conceptual basis for the design of MCSs has yet to be fully discussed in the literature, so we present molecular storytelling as a conceptual framework for teaching with case studies. Whether the case study aims to understand the biology of a specific disease and design its treatments or track the evolution of a biosynthetic pathway, vast amounts of structural and functional data, freely available in public bioinformatics resources, can facilitate rich explorations in atomic detail. To help biology and chemistry educators use these resources for instruction, a community of scholars collaborated to create the Molecular CaseNet. This community uses storytelling to explore biomolecular structure and function while teaching biology and chemistry. In this article, we define the structure of an MCS and present an example. Then, we articulate the evolution of a conceptual framework for developing and using MCSs. Finally, we related our framework to the development of technological, pedagogical, and content knowledge (TPCK) for educators in the Molecular CaseNet. The report conceptualizes an interdisciplinary framework for teaching about the molecular world and informs lesson design and education research.

Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development.

The opioid crisis in the United States is a significant public health issue, with a nearly threefold increase in opioid-related fatalities between 1999 and 2014. In response to this crisis, society has made numerous efforts to mitigate its impact. Recent advancements in understanding the structural intricacies of the κ opioid receptor (KOR) have improved our knowledge of how opioids interact with their receptors, triggering downstream signaling pathways that lead to pain relief. This review concentrates on the KOR, offering crucial structural insights into the binding mechanisms of both agonists and antagonists to the receptor. Through comparative analysis of the atomic details of the binding site, distinct interactions specific to agonists and antagonists have been identified. These insights not only enhance our understanding of ligand binding mechanisms but also shed light on potential pathways for developing new opioid analgesics with an improved risk-benefit profile.

Na+/K+-ATPase: More than an Electrogenic Pump.

The sodium pump, or Na+/K+-ATPase (NKA), is an essential enzyme found in the plasma membrane of all animal cells. Its primary role is to transport sodium (Na+) and potassium (K+) ions across the cell membrane, using energy from ATP hydrolysis. This transport creates and maintains an electrochemical gradient, which is crucial for various cellular processes, including cell volume regulation, electrical excitability, and secondary active transport. Although the role of NKA as a pump was discovered and demonstrated several decades ago, it remains the subject of intense research. Current studies aim to delve deeper into several aspects of this molecular entity, such as describing its structure and mode of operation in atomic detail, understanding its molecular and functional diversity, and examining the consequences of its malfunction due to structural alterations. Additionally, researchers are investigating the effects of various substances that amplify or decrease its pumping activity. Beyond its role as a pump, growing evidence indicates that in various cell types, NKA also functions as a receptor for cardiac glycosides like ouabain. This receptor activity triggers the activation of various signaling pathways, producing significant morphological and physiological effects. In this report, we present the results of a comprehensive review of the most outstanding studies of the past five years. We highlight the progress made regarding this new concept of NKA and the various cardiac glycosides that influence it. Furthermore, we emphasize NKA's role in epithelial physiology, particularly its function as a receptor for cardiac glycosides that trigger intracellular signals regulating cell-cell contacts, proliferation, differentiation, and adhesion. We also analyze the role of NKA β-subunits as cell adhesion molecules in glia and epithelial cells.

Thrombin - A Molecular Dynamics Perspective.

Thrombin is a crucial enzyme involved in blood coagulation, essential for maintaining circulatory system integrity and preventing excessive bleeding. However, thrombin is also implicated in pathological conditions such as thrombosis and cancer. Despite the application of various experimental techniques, including X-ray crystallography, NMR spectroscopy, and HDXMS, none of these methods can precisely detect thrombin's dynamics and conformational ensembles at high spatial and temporal resolution. Fortunately, molecular dynamics (MD) simulation, a computational technique that allows the investigation of molecular functions and dynamics in atomic detail, can be used to explore thrombin behavior. This review summarizes recent MD simulation studies on thrombin and its interactions with other biomolecules. Specifically, the 17 studies discussed here provide insights into thrombin's switch between 'slow' and 'fast' forms, active and inactive forms, the role of Na+ binding, the effects of light chain mutation, and thrombin's interactions with other biomolecules. The findings of these studies have significant implications for developing new therapies for thrombosis and cancer. By understanding thrombin's complex behavior, researchers can design more effective drugs and treatments that target thrombin.

High-resolution in situ structures of mammalian respiratory supercomplexes

Mitochondria play a pivotal part in ATP energy production through oxidative phosphorylation, which occurs within the inner membrane through a series of respiratory complexes1–4. Despite extensive in vitro structural studies, determining the atomic details of their molecular mechanisms in physiological states remains a major challenge, primarily because of loss of the native environment during purification. Here we directly image porcine mitochondria using an in situ cryo-electron microscopy approach. This enables us to determine the structures of various high-order assemblies of respiratory supercomplexes in their native states. We identify four main supercomplex organizations: I1III2IV1, I1III2IV2, I2III2IV2 and I2III4IV2, which potentially expand into higher-order arrays on the inner membranes. These diverse supercomplexes are largely formed by ‘protein–lipids–protein’ interactions, which in turn have a substantial impact on the local geometry of the surrounding membranes. Our in situ structures also capture numerous reactive intermediates within these respiratory supercomplexes, shedding light on the dynamic processes of the ubiquinone/ubiquinol exchange mechanism in complex I and the Q-cycle in complex III. Structural comparison of supercomplexes from mitochondria treated under different conditions indicates a possible correlation between conformational states of complexes I and III, probably in response to environmental changes. By preserving the native membrane environment, our approach enables structural studies of mitochondrial respiratory supercomplexes in reaction at high resolution across multiple scales, from atomic-level details to the broader subcellular context.

High Pressure Promotes Binding of the Allosteric Inhibitor Zn2+-Cyclen in Crystals of Activated H-Ras.

In this work, we experimentally investigate the potency of high pressure to drive a protein toward an excited state where an inhibitor targeted for this state can bind. Ras proteins are small GTPases cycling between active GTP-bound and inactive GDP-bound states. Various states of GTP-bound Ras in active conformation coexist in solution, amongst them, state 2 which binds to effectors, and state 1, weakly populated at ambient conditions, which has a low affinity for effectors. Zn2+-cyclen is an allosteric inhibitor of Ras protein, designed to bind specifically to the state 1. In H-Ras(wt).Mg2+.GppNHp crystals soaked with Zn2+-cyclen, no binding could be observed, as expected in the state 2 conformation which is the dominant state at ambient pressure. Interestingly, Zn2+-cyclen binding is observed at 500 MPa pressure, close to the nucleotide, in Ras protein that is driven by pressure to a state 1 conformer. The unknown binding mode of Zn2+-cyclen to H-Ras can thus be fully characterized in atomic details. As a more general conjunction from our study, high pressure x-ray crystallography turns out to be a powerful method to induce transitions allowing drug binding in proteins that are in low-populated conformations at ambient conditions, enabling the design of specific inhibitors.