Conformational Transition Pathway Research Articles

Studying the Collective Functional Response of a Receptor in Alchemical Ligand Binding Free Energy Simulations with Accelerated Solvation Layer Dynamics.

Ligand binding free energy simulations (LB-FES) that involve sampling of protein functional conformations have been longstanding challenges in research on molecular recognition. Particularly, modeling of the conformational transition pathway and design of the heuristic biasing mechanism are severe bottlenecks for the existing enhanced configurational sampling (ECS) methods. Inspired by the key role of hydration in regulating conformational dynamics of macromolecules, this report proposes a novel ECS approach that facilitates binding-associated structural dynamics by accelerated hydration transitions in combination with the λ-exchange of free energy perturbation (FEP). Two challenging protein-ligand binding processes involving large configurational transitions of the receptor are studied, with hydration transitions at binding sites accelerated by Hamiltonian-simulated annealing of the hydration layer. Without the need for pathway analysis or ad hoc barrier flattening potential, LB-FES were performed with FEP/λ-exchange molecular dynamics simulation at a minor overhead for annealing of the hydration layer. The LB-FES studies showed that the accelerated rehydration significantly enhances the collective conformational transitions of the receptor, and convergence of binding affinity calculations is obtained at a sweet-spot simulation time scale. Alchemical LB-FES with the proposed ECS strategy is free from the effort of trial and error for the setup and realizes efficient on-the-fly sampling for the collective functional response of the receptor and bound water and therefore presents a practical approach to high-throughput screening in drug discovery.

Probing Conformational Transition of TRPV5 Induced by Mechanical Force Using Coarse-Grained Molecular Dynamics.

Transient receptor potential vanilloid 5 (TRPV5) is a calcium-selective TRP channel that plays a crucial role in calcium homeostasis regulation. However, there are still many issues that need to be addressed, such as the specific conformational transition of TRPV5 and the specific functions of each structure in cation gating. Here, we build a model of the calcium ion transport protein from Xenopus oocytes in the presence of the lipid membrane and water molecules. Due to the activation process of ion channels are global and collective, coarse-grained molecular dynamics (CG-MD) simulations of the potential of mean force along the conformational transition pathway are performed. The CG-MD simulations show that the S6 helix plays a vital role in the TRPV5 conformational transition. Most importantly, these simulated trajectories indicate that the activation of ion channels happens before the extension and rotation of S6 helices, revealing that TRPV5 has a unique gating mechanism different from TRPV6. The present work demonstrates how the mechanical force acting on the S6 helix opens the TRPV5 channel gates. These results deepen our understanding of the TRPV5 gating mechanism.

Efficient discovery of multiple minimum action pathways using Gaussian process

We present a new efficient transition pathway search method based on the least action principle and the Gaussian process regression method. Most pathway search methods developed so far rely on string representations, which approximate a transition pathway by a series of slowly varying system replicas. Such string methods are computationally expensive in general because they require many replicas to obtain smooth pathways. Here, we present an approach employing the Gaussian process regression method, which infers the shape of a potential energy surface with a few observed data and Gaussian-shaped kernel functions. We demonstrate a drastic elevation of computing efficiency of the method about five orders of magnitude than existing methods. Further, to demonstrate its real-world capabilities, we apply our method to find multiple conformational transition pathways of alanine dipeptide using a quantum mechanical potential. Owing to the improved efficiency of our method, Gaussian process action optimization (GPAO), we obtain the multiple transition pathways of alanine dipeptide and calculate their transition probabilities successfully with density-functional theory (DFT) accuracy. In addition, GPAO successfully finds the isomerization pathways of small molecules and the rearrangement of atoms on a metallic surface.

Machine learning guided sampling of protein transition pathways.

Capturing a protein conformational transition pathway is an important yet difficult task in biophysics. For example, X-ray-crystallography/cryo-EM techniques have captured open/closed states or inward/outward-facing conformations of many ion-channels/transporters; yet, it remains a challenge to experimentally capture transitioning pathways between these ground states due to their rather short-spanned nature. Multiple advanced techniques based on molecular dynamics (MD) simulations have provided great insights to fill such gaps by predicting hypothetical transition states and pathways. Such MD-based techniques, including both long equilibrium simulations and enhanced sampling ones, have provided insights to better understand how putative transition state conformations and pathways would look like. However, MD-based techniques generally take a long time; therefore, it would be of great utility to sample putative transition states along putative transition pathways more efficiently. We present a machine learning model, developed using variational autoencoder (VAE), with the goal of sampling the transition pathway between different protein's conformational states. Using VAE, which takes in protein raw trajectory coordinates generated by equilibrium MD simulations as input, the model is able to reconstruct a structurally close output. Such a VAE can therefore reduce the high-dimensional protein description in the full space into a meaningful low-dimensional representation. In this low-dimensional space, we can interpolate between the two states of interest. The interpolated pathway represents a putative transition-pathway in the higher-dimensional space, learned by VAE. After reconstruction of the sampled transition pathway into the full protein space, secondary structures, RMSDs, and other quality-control properties are used and compared with the functional mechanism of the protein. The reconstructed putative transition pathway can be used in further MD simulations. This machine learning guided MD approach will accelerate studying the full transition pathways of protein conformations by seeding good starting points generated by VAE for iterative MD simulations.

Computation of conformational transitions and activation of Src tyrosine kinase.

A protein molecule can adapt different structural forms depending on solution conditions or ligation state. The stable forms can be defined experimentally, but the transition pathways and metastable states between them cannot. A computational method to explore equilibrium conformational transition pathways is the adaptive bias path optimization (ABPO) algorithm. ABPO, implemented in the CHARMM program, differs from other path methods by using enhanced sampling along a path between two known states without restraining the system to the reduced coordinates of the evolving path. ABPO converges to a physically reasonable path even in the case of a highly rugged energy landscape and is amenable to assessing the power of the reduced variables chosen to explore the transition. ABPO was applied to three cases where a conformational transition is localized to one part of the polypeptide chain. With proper selection of the reduced coordinate set, sampling without restraints converged to a physically reasonable path. We also applied ABPO to a more complex transition of Src tyrosine kinase catalytic domain that undergoes a more complex transition between enzymatically down‑regulated and activated conformations. The results using coarse-grained and all‑atom models identified a switched electrostatic network (SEN) that could confer an entropic advantage to reduce energetic barriers by limiting the search space of the activation loop. The richness in atomic detail provided by the ABPO path reveals how the SEN couples the dynamics of the key αC‑helix and the activation loop, which provided a new rationale for the highly conserved HRD sequence of kinases. Further, the results related to energetics of the rotation of the αC‑helix provide a rationale for the interactions observed in a number of kinase regulatory complexes, some for which the basis for regulation from inspection of the structure along is puzzling.

Coevolution and smFRET Enhances Conformation Sampling and FRET Experimental Design in Tandem PDZ1-2 Proteins.

The structural flexibility of proteins is crucial for their functions. Many experimental and computational approaches can probe protein dynamics across a range of time and length-scales. Integrative approaches synthesize the complementary outputs of these techniques and provide a comprehensive view of the dynamic conformational space of proteins, including the functionally relevant limiting conformational states and transition pathways between them. Here, we introduce an integrative paradigm to model the conformational states of multidomain proteins. As a model system, we use the first two tandem PDZ domains of postsynaptic density protein 95. First, we utilize available sequence information collected from genomic databases to identify potential amino acid interactions in the PDZ1-2 tandem that underlie modeling of the functionally relevant conformations maintained through evolution. This was accomplished through combination of coarse-grained structural modeling with outputs from direct coupling analysis measuring amino acid coevolution, a hybrid approach called SBM+DCA. We recapitulated five distinct, experimentally derived PDZ1-2 tandem conformations. In addition, SBM+DCA unveiled an unidentified, twisted conformation of the PDZ1-2 tandem. Finally, we implemented an integrative framework for the design of single-molecule Förster resonance energy transfer (smFRET) experiments incorporating the outputs of SBM+DCA with simulated FRET observables. This resulting FRET network is designed to mutually resolve the predicted limiting state conformations through global analysis. Using simulated FRET observables, we demonstrate that structural modeling with the newly designed FRET network is expected to outperform a previously used empirical FRET network at resolving all states simultaneously. Integrative approaches to experimental design have the potential to provide a new level of detail in characterizing the evolutionarily conserved conformational landscapes of proteins, and thus new insights into functional relevance of protein dynamics in biological function.

The Conformational Transition Pathways and Hidden Intermediates in DFG-Flip Process of c-Met Kinase Revealed by Metadynamics Simulations.

Protein kinases intrinsically translate their conformations between active and inactive states, which is key to their enzymatic activities. The conformational flipping of the three-residue conservative motif, Asp-Phe-Gly (DFG), is crucial for many kinases' biological functions. Obtaining a detailed demonstration of the DFG flipping process and its corresponding dynamical and thermodynamical features could broaden our understanding of kinases' conformation-activity relationship. In this study, we employed metadynamics simulation, a widely used enhanced sampling technique, to analyze the conformational transition pathways of the DFG flipping for the c-Met kinase. The corresponding free energy landscape suggested two distinct transition pathways between the "DFG-in" and "DFG-out" states of the DFG-flip from c-Met. On the basis of the orientation direction of the F1223 residue, we correspondingly named the two pathways the "DFG-up" path, featuring forming a commonly discovered "DFG-up" transition state, and the "DFG-down" path, a unique transition pathway with F1223 rotating along the opposite direction away from the hydrophobic cavity. The free energies along the two pathways were then calculated using the Path Collective Variable (PCV) metadynamics simulation. The simulation results showed that, though having similar free energy barriers, the free energy cuve for the DFG-down path suggested a two-step conformational transition mechanism, while that for the DFG-up path showed the one-step transition feature. The c-Met DFG flipping mechanism and the new intermediate state discovered in this work could provide a deeper understanding of the conformation-activity relationship for c-Met and, possibly, reveal a new conformational state as the drug target for c-Met and other similar kinases.

Computation of the Protein Conformational Transition Pathway on Ligand Binding by Linear Response-Driven Molecular Dynamics.

While extremely important for relating the protein structure to its biological function, determination of the protein conformational transition pathway upon ligand binding is made difficult due to the transient nature of intermediates, a large and rugged conformational space, and coupling between protein dynamics and ligand-protein interactions. Existing methods that rely on prior knowledge of the bound (holo) state structure are restrictive. A second concern relates to the correspondence of intermediates obtained to the metastable states on the apo → holo transition pathway. Here, we have taken the protein apo structure and ligand-binding site as only inputs and combined an elastic network model (ENM) representation of the protein Hamiltonian with linear response theory (LRT) for protein-ligand interactions to identify the set of slow normal modes of protein vibrations that have a high overlap with the direction of the protein conformational change. The structural displacement along the chosen direction was performed using excited normal modes molecular dynamics (MDeNM) simulations rather than by the direct use of LRT. Herein, the MDeNM excitation velocity was optimized on-the-fly on the basis of its coupling to protein dynamics and ligand-protein interactions. Thus, a determined set of structures was validated against crystallographic and simulation data on four protein-ligand systems, namely, adenylate kinase-di(adenosine-5')pentaphosphate, ribose binding protein-β-d-ribopyranose, DNA β-glucosyltransferase-uridine-5'-diphosphate, and G-protein α subunit-guanosine-5'-triphosphate, which present important differences in protein conformational heterogeneity, ligand binding mechanism, viz. induced-fit or conformational selection, extent, and nonlinearity in protein conformational changes upon ligand binding, and presence of allosteric effects. The obtained set of intermediates was used as an input to path metadynamics simulations to obtain the free energy profile for the apo → holo transition.

Protein conformational transitions explored by a morphing approach based on normal mode analysis in internal coordinates.

Large-scale conformational changes are essential for proteins to function properly. Given that these transition events rarely occur, however, it is challenging to comprehend their underlying mechanisms through experimental and theoretical approaches. In this study, we propose a new computational methodology called internal coordinate normal mode-guided elastic network interpolation (ICONGENI) to predict conformational transition pathways in proteins. Its basic approach is to sample intermediate conformations by interpolating the interatomic distance between two end-point conformations with the degrees of freedom constrained by the low-frequency dynamics afforded by normal mode analysis in internal coordinates. For validation of ICONGENI, it is applied to proteins that undergo open-closed transitions, and the simulation results (i.e., simulated transition pathways) are compared with those of another technique, to demonstrate that ICONGENI can explore highly reliable pathways in terms of thermal and chemical stability. Furthermore, we generate an ensemble of transition pathways through ICONGENI and investigate the possibility of using this method to reveal the transition mechanisms even when there are unknown metastable states on rough energy landscapes.

Capillary Electrophoresis-based Hydrogen/Deuterium Exchange for Conformational Characterization of Proteins with Top-down Mass Spectrometry.

Resolving conformational heterogeneity of multiple protein states that coexist in solution remains one of the main obstacles in the characterization of protein therapeutics and the determination of the conformational transition pathways critical for biological functions, ranging from molecular recognition to enzymatic catalysis. Hydrogen/deuterium exchange (HDX) reaction coupled with top-down mass spectrometric (MS) analysis provides a means to characterize protein higher-order structures and dynamics in a conformer-specific manner. The conformational resolving power of this technique is highly dependent on the efficiencies of separating protein states at the intact protein level and minimizing the residual non-deuterated protic content during the HDX reactions. Here we describe a capillary electrophoresis (CE)-based variant of the HDX MS approach that aims to improve the conformational resolution. In this approach, proteins undergo HDX reactions while migrating through a deuterated background electrolyte solution (BGE) during the capillary electrophoretic separation. Different protein states or proteoforms that coexist in solution can be efficiently separated based on their differing charge-to-size ratios. The difference in electrophoretic mobility between proteins and protic solvent molecules minimizes the residual non-deuterated solvent, resulting in a nearly complete deuterating environment during the HDX process. The flow-through microvial CE-MS interface allows efficient electrospray ionization of the eluted protein species following a rapid mixing with the quenching and denaturing modifier solution at the outlet of the sprayer. The online top-down MS analysis measures the global deuteration level of the eluted intact protein species, and subsequently, the deuteration of their gas-phase fragments. This paper demonstrates this approach in differential HDX for systems, including the natural protein variants coexisting in milk.

Insulin-induced conformational changes in the full-length insulin receptor: structural insights gained from molecular modeling analyses.

Insulin receptor plays an important role in the regulation of energy metabolism. Dysfunction of insulin receptor (IR) can lead to many disease states, such as diabetes mellitus. Deciphering the complex dynamic structures of human IR and its mechanism of activation would greatly aid in understanding IR-mediated signaling pathways and also in designing new drugs (including nonpeptidal insulin analogs) to treat diabetes mellitus. Experimental evidence about IR structures has been gradually obtained by biologists over the past three decades. Based on available experimental structures of IR in different states, here we employ molecular modeling approach to construct the full-length IR structures in different states and model its structural and conformational changes during insulin-induced IR activation. Several key possible intermediate states are constructed based on structural alignment, rotation, and computational modeling. Based on the structures of the full-length IR in different states, it appears that there are two possible conformational transition pathways: one is symmetric and the other one is asymmetric. Structural changes and motions of different domains of the full-length IR along the pathways are analyzed. The role of insulin binding to IR in facilitating the conformational transition of the receptor is analyzed. Information and insights derived from our present structural modeling analyses may aid in understanding the complex dynamic, structural, and conformational changes during the process of IR activation.

Conformational Transition Pathway of GkPOT

GkPOT, a Proton-Coupled coupled Oligopeptide Transporter transporter (POTs) from Geobacillus Kaustophilus, is among the symporter members of the Major Facilitator Superfamily (MFS) of transporters. Functional and structural analysis of various POTs suggest that the uptake of small peptides and peptide- like molecules to the cell across the cell membrane, via the alternating access mechanism, utilizes an inward directed proton electrochemical gradient the source of energy for their active symport function. The transition between the inward-facing (IF) and outward-facing (OF) conformations is important in understanding the structural relationship between large-scale protein conformational changes and substrate translocation that has often times remained elusive due to the short time scales of molecular dynamics simulations (MD) limitations of computational and experimental effort techniques. Using an extensive set of enhanced sampling techniques, we have characterized the conformational transition pathway of GkPOT. We have aimed at describing the large-scale conformational changes between the IF and OF states while in the presence and absence of substrate (dialanine peptide). Nonequlibrium pulling simulations were initiated from the known inward facing IF conformation and steered the system toward the unknown outward facing OF conformation using orientation based collective variables defined on different transmembrane helices. The transition pathways are far from equilibrium and are smoothed with string method with swarms of trajectories (SMwST), thus generating microseconds of biased and unbiased simlulations. Free energy profiles of the conformational transition pathways were obtained from a novel maximum-likelihood estimation method. Our results indicate that the presence of ligand significantly changes the conformational landscape with respect to the apo system which contains a greater degree of conformational variability.

Generating Protein Ensembles by Applying Forces to an Improved Elastic Network Model

The lack of experimental structures showing sufficient conformational diversity introduces limitations for the study of protein mechanisms. Computed ensembles of protein conformations can aid in investigations of functional mechanisms. Application of our recent development of a protein packing based hinge prediction method (PACKMAN) and an improved elastic network model to study protein global motions (hd-ANM) together enable improved protein ensemble generations. Here, we apply random forces on the surface to generate the ensemble. Multiple independent Monte Carlo Markov Chain force applications and energy based sampling is implemented to obtain a protein ensemble. We observe a 70% PCA overlap between an ensemble generated by using our method with that of the corresponding protein crystal structure ensemble available in the PDB database. Moreover, results provide mechanistic insights into the conformational transition pathway of proteins. We aim to utilize native and mutant protein ensembles generated using our method to estimate the change in global motion of protein and the allosteric pathway changes induced by a point mutation.

Conformational Changes of Protein Analyzed Based on Structural Perturbation Method

Proteins perform their biological functions through their conformational changes due to ligand-binding. Though atomistic simulations have allowed for understanding the conformational dynamics of proteins, they are computationally restrictive in revealing the conformational transition pathway of proteins. In this work, we consider an elastic network model (ENM) with introducing the structural perturbation method, which mimics the breakage or formation of native contacts during the conformational changes, for gaining insight into the conformational transition pathway of proteins. It is shown that ENM with structural perturbation method enables the characterization of the conformational transition of adenylate kinase as a model protein. In addition, the low-frequency normal modes of adenylate kinase are found to play a role in its conformational transition. Our study sheds light on ENM with structural perturbation method for studying the conformational transitions of large protein complexes.

Structural Heterogeneities of the Ribosome: New Frontiers and Opportunities for Cryo-EM.

The extent of ribosomal heterogeneity has caught increasing interest over the past few years, as recent studies have highlighted the presence of structural variations of the ribosome. More precisely, the heterogeneity of the ribosome covers multiple scales, including the dynamical aspects of ribosomal motion at the single particle level, specialization at the cellular and subcellular scale, or evolutionary differences across species. Upon solving the ribosome atomic structure at medium to high resolution, cryogenic electron microscopy (cryo-EM) has enabled investigating all these forms of heterogeneity. In this review, we present some recent advances in quantifying ribosome heterogeneity, with a focus on the conformational and evolutionary variations of the ribosome and their functional implications. These efforts highlight the need for new computational methods and comparative tools, to comprehensively model the continuous conformational transition pathways of the ribosome, as well as its evolution. While developing these methods presents some important challenges, it also provides an opportunity to extend our interpretation and usage of cryo-EM data, which would more generally benefit the study of molecular dynamics and evolution of proteins and other complexes.

Enhanced Conformational Sampling Method Based on Anomaly Detection Parallel Cascade Selection Molecular Dynamics: ad-PaCS-MD.

In the present study, we propose a rare-event sampling method called anomaly detection parallel cascade selection molecular dynamics (ad-PaCS-MD). The original PaCS-MD was designed to generate conformational transition pathways from a given reactant to a product when the latter is known a priori. As an extension of the original method, ad-PaCS-MD has been designed to efficiently search transition pathways from a given reactant without referring to a given product. In ad-PaCS-MD, rarely occurring but essential states (configurations) of proteins for the transitions are identified based on the degrees of an anomaly. In more detail, ad-PaCS-MD adopts an algorithm called an anomaly detection generative adversarial network (anoGAN) as a measure for detecting rarely occurring states to be resampled. Here, the essential configurations with higher degrees of the anomaly are selected with anoGAN and intensively resampled by restarting short-time MD simulations from the selected configurations. By repeating the detections and resampling of configurations with the higher degrees of the anomaly, ad-PaCS-MD automatically and efficiently promotes the rare events and gives a wide range of the free energy landscape by combining with the Markov state model construction. As demonstrations, open-closed transitions of two globular proteins (T4 lysozyme and maltose-binding protein) were promoted with ad-PaCS-MD by referring only to the given starting configurations. In each demonstration, ad-PaCS-MD promoted the large-amplitude open-closed transitions with nanosecond-order simulation times. In conclusion, our demonstrations showed a higher conformational sampling efficiency for ad-PaCS-MD than conventional MD (CMD) because CMD required computational costs of more than microsecond-order simulation times to promote the rare events.

Enhancing Biomolecular Sampling with Reinforcement Learning: A Tree Search Molecular Dynamics Simulation Method.

This paper proposes a novel molecular simulation method, called tree search molecular dynamics (TS-MD), to accelerate the sampling of conformational transition pathways, which require considerable computation. In TS-MD, a tree search algorithm, called upper confidence bounds for trees, which is a type of reinforcement learning algorithm, is applied to sample the transition pathway. By learning from the results of the previous simulations, TS-MD efficiently searches conformational space and avoids being trapped in local stable structures. TS-MD exhibits better performance than parallel cascade selection molecular dynamics, which is one of the state-of-the-art methods, for the folding of miniproteins, Chignolin and Trp-cage, in explicit water.

Conformational transitions of bio-molecular systems studied using adaptive bond bending elastic network model

Conformational transitions in proteins are essential for biological functions. Such transitions involve short-lived conformational states that are difficult to detect experimentally. Motivated by the utility and efficiency of elastic network models for describing the collective dynamics of proteins, in this study we present a new adaptive bond bending elastic network model for studying the conformational transition pathway of adenylate kinase and leucine transporter. Assuming that all the native contacts within these proteins are identical, we assigned a bond binding energy to the native contacts and computed the free energy of each image along the transition pathway. After plotting the free energy profiles, we found a few metastable intermediate states, separated by the free energy barriers. The observed intermediate states and the transition pathways for all proteins were in good agreement with those of the other methods and molecular dynamics simulations.

Coarse-grained free-energy simulations of conformational state transitions in an adenosine 5′-triphosphate-binding cassette exporter

ATP-binding cassette exporters transport many substrates out of cellular membranes via alternating between inward-facing and outward-facing conformations. Despite extensive research efforts over the past decades, understanding of the molecular mechanism remains elusive. As these large-scale conformational movements are global and collective, we have previously performed extensive coarse-grained molecular dynamics simulations of the potential of mean force along the conformational transition pathway [J. Phys. Chem. B 119, 1295 (2015)]. However, the occluded conformational state, in which both the internal and external gate are closed, was not determined in the calculated free energy profile. In this work, we extend the above methods to the calculation of the free energy profile along the reaction coordinate, d1–d2, which are the COM distances between the two sides of the internal (d1) and the external gate (d2). The potential of mean force is thus obtained to identify the transition pathway, along which several outward-facing, inward-facing, and occluded state structures are predicted in good agreement with structural experiments. Our coarse-grained molecular dynamics free-energy simulations demonstrate that the internal gate is closed before the external gate is open during the inward-facing to outward-facing transition and vice versa during the inward-facing to outward-facing transition. Our results capture the unidirectional feature of substrate translocation via the exporter, which is functionally important in biology. This finding is different from the previous result, in which both the internal and external gates are open reported in an X-ray experiment [Proc. Natl. Acad. Sci. USA 104, 19005 (2007)]. Our study sheds light on the molecular mechanism of the state transitions in an ATP-binding cassette exporter.

Dynamics and Thermodynamics of Ibuprofen Conformational Isomerism at the Crystal/Solution Interface.

Conformational flexibility of molecules involved in crystal growth and dissolution is rarely investigated in detail and usually considered to be negligible in the formulation of mesoscopic models of crystal growth. In this work, we set out to investigate the conformational isomerism of ibuprofen as it approaches and is incorporated in the morphologically dominant {100} crystal face, in a range of different solvents: water, 1-butanol, toluene, cyclohexanone, cyclohexane, acetonitrile, and trichloromethane. To this end, we combine extensive molecular dynamics and well-tempered metadynamics simulations to estimate the equilibrium distribution of conformers, compute conformer-conformer transition rates, and extract the characteristic relaxation time of the conformer population in solution, adsorbed at the solid/liquid interface, incorporated in the crystal in contact with the mother solution, and in the crystal bulk. We find that, while the conformational equilibrium distribution is weakly dependent on the solvent, relaxation times are instead significantly affected by it. Furthermore, differences in the relaxation dynamics are enhanced on the crystal surface, where conformational transitions become slower and specific conformational transition pathways are hindered. This leads us to observe that the dominant mechanisms of conformational transition can also change significantly moving from the bulk solution to the crystal interface, even for a small molecule with limited conformational flexibility such as ibuprofen. Our findings suggest that understanding conformational flexibility is key to provide an accurate description of the solid/liquid interface during crystal dissolution and growth, and therefore, its relevance should be systematically assessed in the formulation of mesoscopic growth models.