Protein conformational flexibility modulates kinetics and thermodynamics of drug binding

Identifying Conformational Isomers of Organic Molecules in Solution via Unsupervised Clustering

We present a systematic approach for the identification of statistically relevant conformational macrostates of organic molecules from molecular dynamics trajectories. The approach applies to molecules characterized by an arbitrary number of torsional degrees of freedom and enables the transferability of the macrostates definition across different environments. We formulate a dissimilarity measure between molecular configurations that incorporates information on the characteristic energetic cost associated with transitions along all relevant torsional degrees of freedom. Such metric is employed to perform unsupervised clustering of molecular configurations based on the Fast Search and Find of Density Peaks algorithm. We apply this method to investigate the equilibrium conformational ensemble of Sildenafil, a conformationally complex pharmaceutical compound, in different environments including the crystal bulk, the gas phase, and three different solvents (acetonitrile, 1-butanol, and toluene). We demonstrate that while Sildenafil can adopt more than 100 metastable conformational configurations, only 12 are significantly populated across all of the environments investigated. Despite the complexity of the conformational space, we find that the most abundant conformers in solution are the closest to the conformers found in the most common Sildenafil crystal phase.

A rebinding-assay for measuring extreme kinetics using label-free biosensors

In vitro kinetic measurements allow mechanistic characterization of binding interactions and are particularly valuable throughout drug discovery, from confirmation of on-target binding in early discovery to fine-tuning of drug-binding properties in pre-clinical development. Early chemical matter often exhibits transient kinetics, which remain challenging to measure in a routine drug discovery setting. For example, characterization of irreversible inhibitors has classically relied on the alkylation rate constant, yet this metric fails to resolve its fundamental constituent rate constants, which drive reversible binding kinetics and affinity complex inactivation. In other cases, extremely rapid association processes, which can approach the diffusion limit, also remain challenging to measure. To address these limitations, a practical kinetic rebinding assay is introduced that may be applied for kinetic screening and characterization of compounds. The new capabilities afforded by this probe-based assay emerge from mixed-phase partitioning in a flow-injection configuration and have been implemented using label-free biosensing. A finite element analysis-based biosensor model, simulating inhibition of rebinding within a crowded hydrogel milieu, provided surrogate test data that enabled development and validation of an algebraic model for estimation of kinetic interaction constants. An experimental proof-of-principle demonstrating estimation of the association rate constant, decoupled from the dissociation process, provided further validation.

In Silico Design and Selection of New Tetrahydroisoquinoline-Based CD44 Antagonist Candidates

CD44 promotes metastasis, chemoresistance, and stemness in different types of cancer and is a target for the development of new anti-cancer therapies. All CD44 isoforms share a common N-terminal domain that binds to hyaluronic acid (HA). Herein, we used a computational approach to design new potential CD44 antagonists and evaluate their target-binding ability. By analyzing 30 crystal structures of the HA-binding domain (CD44HAbd), we characterized a subdomain that binds to 1,2,3,4-tetrahydroisoquinoline (THQ)-containing compounds and is adjacent to residues essential for HA interaction. By computational combinatorial chemistry (CCC), we designed 168,190 molecules and compared their conformers to a pharmacophore containing the key features of the crystallographic THQ binding mode. Approximately 0.01% of the compounds matched the pharmacophore and were analyzed by computational docking and molecular dynamics (MD). We identified two compounds, Can125 and Can159, that bound to human CD44HAbd (hCD44HAbd) in explicit-solvent MD simulations and therefore may elicit CD44 blockage. These compounds can be easily synthesized by multicomponent reactions for activity testing and their binding mode, reported here, could be helpful in the design of more potent CD44 antagonists.

Identifying the natural polyphenol catechin as a multi-targeted agent against SARS-CoV-2 for the plausible therapy of COVID-19: an integrated computational approach

The global pandemic crisis, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has claimed the lives of millions of people across the world. Development and testing of anti-SARS-CoV-2 drugs or vaccines have not turned to be realistic within the timeframe needed to combat this pandemic. Here, we report a comprehensive computational approach to identify the multi-targeted drug molecules against the SARS-CoV-2 proteins, whichare crucially involved in the viral-host interaction, replication of the virus inside the host, disease progression and transmission of coronavirus infection. Virtual screening of 75 FDA-approved potential antiviral drugs against the target proteins, spike (S) glycoprotein, human angiotensin-converting enzyme 2 (hACE2), 3-chymotrypsin-like cysteine protease (3CLpro), cathepsin L (CTSL), nucleocapsid protein, RNA-dependent RNA polymerase (RdRp) and non-structural protein 6 (NSP6), resulted in the selection of seven drugs which preferentially bind to the target proteins. Further, the molecular interactions determined by molecular dynamics simulation revealed that among the 75 drug molecules, catechin can effectively bind to 3CLpro, CTSL, RBD of S protein, NSP6 and nucleocapsid protein. It is more conveniently involved in key molecular interactions, showing binding free energy (ΔGbind) in the range of -5.09 kcal/mol (CTSL) to -26.09 kcal/mol (NSP6). At the binding pocket, catechin is majorly stabilized by the hydrophobic interactions, displays ΔEvdW values: -7.59 to -37.39 kcal/mol. Thus, the structural insights of better binding affinity and favorable molecular interaction of catechin toward multiple target proteins signify that catechin can be potentially explored as a multi-targeted agent against COVID-19.

Thermodynamic Dissection of Potency and Selectivity of Cytosolic Hsp90 Inhibitors

The cytosolic Hsp90-selective inhibitor TAS-116 has an acceptable safety profile and promising antitumor activity in clinical trials. We examined the binding characteristics of TAS-116 and its analogs to determine the impact of the ligand binding mode on selectivity for cytosolic Hsp90. Analyses of the co-crystal structure of Hsp90 and inhibitor TAS-116 suggest that TAS-116 interacts with the ATP-binding pocket, the ATP lid region, and the hydrophobic pocket. A competitive isothermal titration calorimetry analysis confirmed that a small fragment of TAS-116 (THS-510) docks into the lid region and hydrophobic pockets without binding to the ATP-binding pocket. THS-510 exhibited enthalpy-driven binding to Hsp90α and selectively inhibited cytosolic Hsp90 activity. The heat capacity change of THS-510 binding was positive, likely due to the induced conformational rearrangement of Hsp90. Thus, we concluded that interactions with the hydrophobic pocket of Hsp90 determine potency and selectivity of TAS-116 and derivatives for the cytosolic Hsp90 isoform.

Bioinformatic analysis of subfamily-specific regions in 3D-structures of homologs to study functional diversity and conformational plasticity in protein superfamilies

Local 3D-structural differences in homologous proteins contribute to functional diversity observed in a superfamily, but so far received little attention as bioinformatic analysis was usually carried out at the level of amino acid sequences. We have developed Zebra3D - the first-of-its-kind bioinformatic software for systematic analysis of 3D-alignments of protein families using machine learning. The new tool identifies subfamily-specific regions (SSRs) - patterns of local 3D-structure (i.e. single residues, loops, or secondary structure fragments) that are spatially equivalent within families/subfamilies, but are different among them, and thus can be associated with functional diversity and function-related conformational plasticity. Bioinformatic analysis of protein superfamilies by Zebra3D can be used to study 3D-determinants of catalytic activity and specific accommodation of ligands, help to prepare focused libraries for directed evolution or assist development of chimeric enzymes with novel properties by exchange of equivalent regions between homologs, and to characterize plasticity in binding sites. A companion Mustguseal web-server is available to automatically construct a 3D-alignment of functionally diverse proteins, thus reducing the minimal input required to operate Zebra3D to a single PDB code. The Zebra3D + Mustguseal combined approach provides the opportunity to systematically explore the value of SSRs in superfamilies and to use this information for protein design and drug discovery. The software is available open-access at https://biokinet.belozersky.msu.ru/Zebra3D.

Flexibility and mobility of SARS-CoV-2-related protein structures

The worldwide CoVid-19 pandemic has led to an unprecedented push across the whole of the scientific community to develop a potent antiviral drug and vaccine as soon as possible. Existing academic, governmental and industrial institutions and companies have engaged in large-scale screening of existing drugs, in vitro, in vivo and in silico. Here, we are using in silico modelling of possible SARS-CoV-2 drug targets, as deposited on the Protein Databank (PDB), and ascertain their dynamics, flexibility and rigidity. For example, for the SARS-CoV-2 spike protein-using its complete homo-trimer configuration with 2905 residues-our method identifies a large-scale opening and closing of the S1 subunit through movement of the S[Formula: see text] domain. We compute the full structural information of this process, allowing for docking studies with possible drug structures. In a dedicated database, we present similarly detailed results for the further, nearly 300, thus far resolved SARS-CoV-2-related protein structures in the PDB.

Enhancing water sampling of buried binding sites using nonequilibrium candidate Monte Carlo

Water molecules can be found interacting with the surface and within cavities in proteins. However, water exchange between bulk and buried hydration sites can be slow compared to simulation timescales, thus leading to the inefficient sampling of the locations of water. This can pose problems for free energy calculations for computer-aided drug design. Here, we apply a hybrid method that combines nonequilibrium candidate Monte Carlo (NCMC) simulations and molecular dynamics (MD) to enhance sampling of water in specific areas of a system, such as the binding site of a protein. Our approach uses NCMC to gradually remove interactions between a selected water molecule and its environment, then translates the water to a new region, before turning the interactions back on. This approach of gradual removal of interactions, followed by a move and then reintroduction of interactions, allows the environment to relax in response to the proposed water translation, improving acceptance of moves and thereby accelerating water exchange and sampling. We validate this approach on several test systems including the ligand-bound MUP-1 and HSP90 proteins with buried crystallographic waters removed. We show that our BLUES (NCMC/MD) method enhances water sampling relative to normal MD when applied to these systems. Thus, this approach provides a strategy to improve water sampling in molecular simulations which may be useful in practical applications in drug discovery and biomolecular design.

Current and Future Challenges in Modern Drug Discovery

Drug discovery is an expensive, time-consuming, and risky business. To avoid late-stage failure, learnings from past projects and the development of new approaches are crucial. New modalities and emerging new target spaces allow the exploration of unprecedented indications or to address so far undrugable targets. Late-stage attrition is usually attributed to the lack of efficacy or to compound-related safety issues. Efficacy has been shown to be related to a strong genetic link to human disease, a better understanding of the target biology, and the availability of biomarkers to bridge from animals to humans. Compound safety can be improved by ligand optimization, which is becoming increasingly demanding for difficult targets. Therefore, new strategies include the design of allosteric ligands, covalent binders, and other modalities. Design methods currently heavily rely on artificial intelligence and advanced computational methods such as free energy calculations and quantum chemistry. Especially for quantum chemical methods, a more detailed overview is given in this chapter.

An Appraisal of Computer Simulation Approaches in Elucidating Biomolecular Recognition Pathways

Computer simulation approaches in biomolecular recognition processes have come a long way. In this Perspective, we highlight a series of recent success stories in which computer simulations have played a remarkable role in elucidating the atomic resolution mechanism of kinetic processes of protein-ligand binding in a quantitative fashion. In particular, we show that a robust combination of unbiased simulation, harnessed by a high-fidelity computing environment, and Markov state modeling approaches has been instrumental in revealing novel protein-ligand recognition pathways in multiple systems. We also elucidate the role of recent developments in enhanced sampling approaches in providing the much-needed impetus in accelerating simulation of the ligand recognition process. We identify multiple key issues, including force fields and the sampling bottleneck, which are currently preventing the field from achieving quantitative reconstruction of experimental measurements. Finally, we suggest a possible way forward via adoption of multiscale approaches and coarse-grained simulations as next steps toward efficient elucidation of ligand binding kinetics.

Effect of pretreatment by supercritical fluids on antioxidant activity of protein hydrolyzate from quinoa (Chenopodium quinoa Willd.)

The effect of two pretreatments on the antioxidant activity was evaluated in quinoa protein hydrolysate, using supercritical CO2 extraction and ethanol as cosolvent, this type of pretreatment was compared to a conventional petroleum ether extraction method without recovery of bioactive compounds. The extractions were carried out at a temperature of 55°C and a pressure of 23 MPa using ethanol (7-8 g quinoa/100 ml); the CO2 mass flow was 35 g/min, the extraction time was 240 min and the particle size was 500 µm, enzyme COROLASE® 7089 was applied for enzymatic hydrolysis, finally ABTS test assessed antioxidant activity. A significant effect was found on the degree of hydrolysis (23.93%) and antioxidant activity (1,181.64 μmol TE/g protein) compared to a conventional method (24.33%) and (1,448.84 μmol TE/g protein). In conclusion, our results suggest that the use of supercritical CO2 and the addition of ethanol as cosolvent are the interesting green technology, to recovery oil and separate phenolic compounds prior to enzymatic hydrolysis to avoid interference with biological activities from quinoa protein hydrolysates, and shows highest antioxidant activity to be incorporate in food products.

Redox-Driven In Situ Helix Reversal of Graphene-Based Hydrogels

Controlling the handedness of dynamic helical nanostructures of supramolecular assemblies by external stimuli is of great fundamental significance with appealing morphology-dependent applications. Significantly, access to in situ chirality transformation of dynamic multistimuli-responsive systems can provide channels for real-time monitoring of the transfer processes in biological systems. However, efforts to achieve helix inversion in an all-gel-state and to comprehend the phenomena at a molecular scale are scarce. Herein, we introduce an example of supramolecular hydrogel in which graphene oxide (GO) incorporation leads to opposite helicity of the l-phenylalanine derivative (LPFEG) upon UV irradiation. The gelator modulates different degrees of packing that are responsible for the initial construction of right-handed nanofibers in GO surfaces and for the change in helix to preferred left-handedness in RGO surfaces caused by GO reduction. Specifically, LPFEG shows a mixture of right- and left-handed nanofibers with an appropriate exposure to UV light. A thermal-reversible transformation of chirality is also discovered in the supramolecular assemblies, allowing a dynamic and invertible flip of helicity upon heating and cooling. The morphology transformation makes the hybrid an ideal candidate for application in a precisely controlled drug delivery process. It can unexpectedly serve as a photosensitizer and a carrier for enantioselective absorption of specific chiral drugs enantiomer (l-dopa and S-naproxen sodium) and also exhibit on-demand drug release due to the helix reversal induced by light irradiation. Our results illustrate how the surface reactivity can direct the helical organization of adsorbed fibers, which in turn provide control over enantioselective absorption of chiral drug enantiomers, further giving rise to on-demand drug release due to handedness inversion upon UV irradiation.

A comprehensive approach to X-ray crystallography for drug discovery at a synchrotron facility - The example of Diamond Light Source

A detailed understanding of the interactions between drugs and their targets is crucial to develop the best possible therapeutic agents. Structure-based drug design relies on the availability of high-resolution structures obtained primarily through X-ray crystallography. Collecting and analysing quickly a large quantity of structural data is crucial to accelerate drug discovery pipelines. Researchers from academia and industry can access the highly automated macromolecular crystallography (MX) beamlines of Diamond Light Source, the UK national synchrotron, to rapidly collect diffraction data from large numbers of crystals. With seven beamlines dedicated to MX, Diamond offers bespoke solutions for a wide variety of user requirements. Working in synergy with state-of-the-art laboratories and other life science instruments to provide an integrated offering, the MX beamlines provide innovative and multidisciplinary approaches to the determination of structures of new pharmacological targets as well as the efficient study of protein-ligand complexes.

Mechanisms of ligand binding

Many processes in chemistry and biology involve interactions of a ligand with its molecular target. Interest in the mechanism governing such interactions has dominated theoretical and experimental analysis for over a century. The interpretation of molecular recognition has evolved from a simple rigid body association of the ligand with its target to appreciation of the key role played by conformational transitions. Two conceptually distinct descriptions have had a profound impact on our understanding of mechanisms of ligand binding. The first description, referred to as induced fit, assumes that conformational changes follow the initial binding step to optimize the complex between the ligand and its target. The second description, referred to as conformational selection, assumes that the free target exists in multiple conformations in equilibrium and that the ligand selects the optimal one for binding. Both descriptions can be merged into more complex reaction schemes that better describe the functional repertoire of macromolecular systems. This review deals with basic mechanisms of ligand binding, with special emphasis on induced fit, conformational selection, and their mathematical foundations to provide rigorous context for the analysis and interpretation of experimental data. We show that conformational selection is a surprisingly versatile mechanism that includes induced fit as a mathematical special case and even captures kinetic properties of more complex reaction schemes. These features make conformational selection a dominant mechanism of molecular recognition in biology, consistent with the rich conformational landscape accessible to biological macromolecules being unraveled by structural biology.

Structural Fluctuations of Aromatic Residues in an Apo-Form Reveal Cryptic Binding Sites: Implications for Fragment-Based Drug Design

Cryptic sites are binding pockets that are transiently formed in an apo form or that are induced by ligand binding. The investigation of cryptic sites is crucial for drug discovery, since these sites are ubiquitous in disease-related human proteins, and targeting them expands the number of drug targets greatly. However, although many computational studies have attempted to identify cryptic sites, the detection remains challenging. Here, we aimed to characterize and detect cryptic sites in terms of structural fluctuations in an apo form, investigating proteins each of which possesses a cryptic site. From their X-ray structures, we saw that aromatic residues tended to be found in cryptic sites. To examine structural fluctuations of the apo forms, we performed molecular dynamics (MD) simulations, producing probability distributions of the solvent-accessible surface area per aromatic residue. To detect aromatic residues in cryptic sites, we have proposed a "cryptic-site index" based on the distribution, demonstrating the performance via several measures, such as recall and specificity. Besides, we found that high-ranking aromatic residues were likely to probe concaves in a cryptic site. This implies that such fluctuations provide a profile of scaffolds of compounds with the potential to bind to a particular cryptic site.

Destabilisation of the structure of transthyretin is driven by Ca2

Tetrameric transthyretin (TTR) transports thyroid hormones and retinol in plasma and cerebrospinal fluid and performs protective functions under stress conditions. Ageing and mutations result in TTR destabilisation and the formation of the amyloid deposits that dysregulate Ca²⁺ homeostasis. Our aim was to determine whether Ca²⁺ affects the structural stability of TTR. We show, using multiple techniques, that Ca²⁺ does not induce prevalent TTR dissociation and/or oligomerisation. However, in the presence of Ca²⁺, TTR exhibits altered conformational flexibility and different interactions with the solvent molecules. These structural changes lead to the formation of the sub-populations of non-native TTR conformers and to the destabilisation of the structure of TTR. Moreover, the sub-population of TTR molecules undergoes fragmentation that is augmented by Ca²⁺. We postulate that Ca²⁺ constitutes the structural and functional switch between the native and non-native forms of TTR, and therefore tip the balance towards age-dependent pathological calcification.

Disulfide Reduction Allosterically Destabilizes the β-Ladder Subdomain Assembly within the NS1 Dimer of ZIKV

The Zika virus (ZIKV) was responsible for a recent debilitating epidemic that till date has no cure. A potential way to reduce ZIKV virulence is to limit the action of the nonstructural proteins involved in its viral replication. One such protein, NS1, encoded as a monomer by the viral genome, plays a major role via symmetric oligomerization. We examine the homodimeric structure of the dominant β-ladder segment of NS1 with extensive all atom molecular dynamics. We find it stably bounded by two spatially separated interaction clusters (C1 and C2) with significant differences in the nature of their interactions. Four pairs of distal, intramonomeric disulfide bonds are found to be coupled to the stability, local structure, and wettability of the interfacial region. Symmetric reduction of the intramonomeric disulfides triggers marked dynamical heterogeneity, interfacial wettability, and asymmetric salt-bridging propensity. Harnessing the model-free Lipari-Szabo based formalism for estimation of conformational entropy (S_conf), we find clear signatures of heterogeneity in the monomeric conformational entropies. The observed asymmetry, very small in the unperturbed state, expands significantly in the reduced states. This allosteric effect is most noticeable in the electrostatically bound C2 cluster that underlies the greatest stability in the unperturbed state. Allosteric induction of conformational and thermodynamic asymmetry is expected to affect the pathways leading to symmetric higher-ordered oligomerization, and thereby affect crucial replication pathways.

Elucidating the potential neurotoxicity of chiral phenthoate: Molecular insight from experimental and computational studies

Chiral organophosphorus pollutants are existed ubiquitously in the ecological environment, but the enantioselective toxicities of these nerve agents to humans and their molecular bases have not been fully elucidated. Using experimental and computational approaches, this story was to explore the neurotoxic response process of the target acetylcholinesterase (AChE) to chiral phenthoate and further decipher the microscopic mechanism of such toxicological effect at the enantiomeric level. The results showed that the toxic reaction of AChE with chiral phenthoate exhibited significant enantioselectivity, and (R)-phenthoate (K＝1.486 × 10⁵ M^-1) has a bioaffinity for the nerve enzyme nearly three times that of (S)-phenthoate (K＝4.503 × 10⁴ M^-1). Dynamic research outcomes interpreted the wet experiments, and the inherent conformational flexibility of the target enzyme has a great influence on the enantioselective neurotoxicological action processes, especially reflected in the conformational changes of the three key loop regions (i.e. residues His-447, Gly-448, and Tyr-449; residues Gly-122, Phe-123, and Tyr-124; and residues Thr-75, Leu-76, and Tyr-77) around the reaction patch. This was supported by the quantitative results of conformational studies derived from circular dichroism spectroscopy (α-helix: 34.7%→30.2%/31.6%; β-sheet: 23.6%→19.5%/20.7%; turn: 19.2%→22.4%/21.9%; and random coil: 22.5%→27.9%/25.8%). Meanwhile, via analyzing the modes of toxic action and free energies, we can find that (R)-phenthoate has a strong inhibitory effect on the enzymatic activity of AChE, as compared with (S)-phenthoate, and electrostatic energy (-23.79/－17.77 kJ mol^-1) played a critical role in toxicological reactions. These points were the underlying causes of chiral phenthoate displaying different degrees of enantioselective neurotoxicity.

Recent progress in molecular simulation methods for drug binding kinetics

Due to the contribution of drug-target binding kinetics to drug efficacy, there is a high level of interest in developing methods to predict drug-target binding kinetic parameters. During the review period, a wide range of enhanced sampling molecular dynamics simulation-based methods has been developed for computing drug-target binding kinetics and studying binding and unbinding mechanisms. Here, we assess the performance of these methods considering two benchmark systems in detail: mutant T4 lysozyme-ligand complexes and a large set of N-HSP90-inhibitor complexes. The results indicate that some of the simulation methods can already be usefully applied in drug discovery or lead optimization programs but that further studies on more high-quality experimental benchmark datasets are necessary to improve and validate computational methods.

Effect of Protein Flexibility from Coarse-Grained Elastic Network Parameterizations on the Calculation of Free Energy Profiles of Ligand Binding

The characterization of the affinity and binding mechanism of specific molecules to a protein active site is scientifically and industrially relevant for many applications. In principle, this information can be obtained using molecular dynamics (MD) simulations by calculating the free energy profile of the process. However, this is a computationally demanding calculation. Currently, coarse-grained (CG) force fields are very well implemented for MD simulations of biomolecular systems. These computationally efficient force fields are a major advantage to the study of large model systems and/or those requiring long simulation times. The Martini model is currently one of the most popular CG force fields for these systems. For the specific case of protein simulations, to correctly maintain the macromolecular three-dimensional structure, the Martini model needs to include an elastic network (EN). In this work, the effect of protein flexibility, as induced by three EN models compatible with the Martini force field, was tested on the calculation of free energy profiles for protein-ligand binding. The EN models used were ElNeDyn, GoMartini, and GEN. The binding of triolein (TOG) and triacetin (TAG) to a lipase protein (thermomyces lanuginosa lipase-TLL) was used as a case study. The results show that inclusion of greater flexibility in the CG parameterization of proteins is of high importance in the calculation of the free energy profiles of protein-ligand systems. However, care must be taken in order to avoid unjustified large protein deformations. In addition, due to molecular flexibility there may be no absolute need for the center of the ligand to reach the center of the protein-binding site. The calculation of the energy profile to a distance of about 0.5 nm from the active site center can be sufficient to differentiate the affinity of different ligands to a protein.

Mapping the underlying mechanisms of fibrinogen benzothiazole drug interactions using computational and experimental approaches

Three-dimensional conformational crystallographic binding-modes are of paramount importance to understand the docking mechanism of protein-ligand interactions and to identify potential "leading drugs" conformers towards rational drugs-design. Herein, we present an integrated computational-experimental study tackling the problem of multiple binding modes among the ligand 3-(2-Benzothiazolylthio)-propane sulfonic acid (BTS) and the fibrinogen receptor (E-region). Based on molecular docking simulations, we found that the free energy of binding values for nine of different BTS-docking complexes (i.e., BTS-pose_1-9) were very close. We have also identified a docking-mechanism of BTS-interaction mainly based on non-covalent hydrophobic interactions with H-bond contacts stabilizing the fibrinogen-BTS docking complexes. Interestingly, the different BTS-poses_1-9 were found to be able to block the fibrinogen binding site (E-region) by inducing local perturbations in effector and allosteric residues, reducing the degree of collectivity in its flexibility normal modes. As such, we theoretically suggest that the BTS-binding modes can significantly affect the physiological condition of the unoccupied fibrinogen protein structure by bringing global and local perturbations in the frequency domain spectra. The proposed theoretical mechanisms, the interactions involved and the conformational changes suggested, were further corroborated by different experimental techniques such as isothermal titration calorimetry (ITC), zeta potential, UV-vis, fluorescence and small angle X-ray scattering (SAXS). The combined results shall open new avenues towards the application of complex supra-molecular information in rational drugs-design.

Probing molecular mechanism of inhibitor bindings to bromodomain-containing protein 4 based on molecular dynamics simulations and principal component analysis

It is well known that bromodomain-containing protein 4 (BRD4) has been thought as a promising target utilized for treating various human diseases, such as inflammatory disorders, malignant tumours, acute myelogenous leukaemia (AML), bone diseases, etc. For this study, molecular dynamics (MD) simulations, binding free energy calculations, and principal component analysis (PCA) were integrated together to uncover binding modes of inhibitors 8P9, 8PU, and 8PX to BRD4(1). The results obtained from binding free energy calculations show that van der Waals interactions act as the main regulator in bindings of inhibitors to BRD4(1). The information stemming from PCA reveals that inhibitor associations extremely affect conformational changes, internal dynamics, and movement patterns of BRD4(1). Residue-based free energy decomposition method was wielded to unveil contributions of independent residues to inhibitor bindings and the data signify that hydrogen bonding interactions and hydrophobic interactions are decisive factors affecting bindings of inhibitors to BRD4(1). Meanwhile, eight residues Trp81, Pro82, Val87, Leu92, Leu94, Cys136, Asn140, and Ile146 are recognized as the common hot interaction spots of three inhibitors with BRD4(1). The results from this work are expected to provide a meaningfully theoretical guidance for design and development of effective inhibitors inhibiting of the activity of BRD4.

Exhaustive search of the configurational space of heat-shock protein 90 with its inhibitor by multicanonical molecular dynamics based dynamic docking

Multicanonical molecular dynamics based dynamic docking was used to exhaustively search the configurational space of an inhibitor binding to the N-terminal domain of heat-shock protein 90 (Hsp90). The obtained structures at 300 K cover a wide structural ensemble, with the top two clusters ranked by their free energy coinciding with the native binding site. The representative structure of the most stable cluster reproduced the experimental binding configuration, but an interesting conformational change in Hsp90 could be observed. The combined effects of solvation and ligand binding shift the equilibrium from a preferred loop-in conformation in the unbound state to an α-helical one in the bound state for the flexible lid region of Hsp90. Thus, our dynamic docking method is effective at predicting the native binding site while exhaustively sampling a wide configurational space, modulating the protein structure upon binding.

Multisecond ligand dissociation dynamics from atomistic simulations

Coarse-graining of fully atomistic molecular dynamics simulations is a long-standing goal in order to allow the description of processes occurring on biologically relevant timescales. For example, the prediction of pathways, rates and rate-limiting steps in protein-ligand unbinding is crucial for modern drug discovery. To achieve the enhanced sampling, we perform dissipation-corrected targeted molecular dynamics simulations, which yield free energy and friction profiles of molecular processes under consideration. Subsequently, we use these fields to perform temperature-boosted Langevin simulations which account for the desired kinetics occurring on multisecond timescales and beyond. Adopting the dissociation of solvated sodium chloride, trypsin-benzamidine and Hsp90-inhibitor protein-ligand complexes as test problems, we reproduce rates from molecular dynamics simulation and experiments within a factor of 2–20, and dissociation constants within a factor of 1–4. Analysis of friction profiles reveals that binding and unbinding dynamics are mediated by changes of the surrounding hydration shells in all investigated systems.

Protein-ligand unbinding processes are out of reach for atomistic simulations due to time-scale involved. Here the authors demonstrate an approach relying on dissipation-corrected targeted molecular dynamics that enables to provide binding and unbinding rates with a speed-up of several orders of magnitude.

Emergence of Ferrichelatase Activity in a Siderophore-Binding Protein Supports an Iron Shuttle in Bacteria

Siderophores are small-molecule high-affinity multidentate chelators selective for ferric iron that are produced by pathogenic microbes to aid in nutrient sequestration and enhance virulence. In Gram-positive bacteria, the currently accepted paradigm in siderophore-mediated iron acquisition is that effluxed metal-free siderophores extract ferric iron from biological sources and the resulting ferric siderophore complex undergoes diffusion-controlled association with a surface-displayed siderophore-binding protein (SBP) followed by ABC permease-mediated translocation across the cell envelope powered by ATP hydrolysis. Here we show that a more efficient paradigm is possible in Gram-positive bacteria where extracellular metal-free siderophores associate directly with apo-SBPs on the cell surface and serve as non-covalent cofactors that enable the holo-SBPs to non-reductively extract ferric iron directly from host metalloproteins with so-called "ferrichelatase" activity. The resulting SBP-bound ferric siderophore complex is ready for import through an associated membrane permease and enzymatic turnover is achieved through cofactor replacement from the readily available pool of extracellular siderophores. This new "iron shuttle" model closes a major knowledge gap in microbial iron acquisition and defines new roles of the siderophore and SBP as cofactor and enzyme, respectively, in addition to the classically accepted roles as a transport substrate and receptor pair. We propose the formal name "siderophore-dependent ferrichelatases" for this new class of catalytic SBPs.

In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method

Accumulated evidence suggests that binding kinetic properties—especially dissociation rate constant or drug-target residence time—are crucial factors affecting drug potency. However, quantitative prediction of kinetic properties has always been a challenging task in drug discovery. In this study, the VolSurf method was successfully applied to quantitatively predict the k_off values of the small ligands of heat shock protein 90α (HSP90α), adenosine receptor (AR) and p38 mitogen-activated protein kinase (p38 MAPK). The results showed that few VolSurf descriptors can efficiently capture the key ligand surface properties related to dissociation rate; the resulting models demonstrated to be extremely simple, robust and predictive in comparison with available prediction methods. Therefore, it can be concluded that the VolSurf-based prediction method can be widely applied in the ligand-receptor binding kinetics and de novo drug design researches.

A Chemical Biology Approach to the Chaperome in Cancer-HSP90 and Beyond

Cancer is often associated with alterations in the chaperome, a collection of chaperones, cochaperones, and other cofactors. Changes in the expression levels of components of the chaperome, in the interaction strength among chaperome components, alterations in chaperome constituency, and in the cellular location of chaperome members, are all hallmarks of cancer. Here we aim to provide an overview on how chemical biology has played a role in deciphering such complexity in the biology of the chaperome in cancer and in other diseases. The focus here is narrow and on pathologic changes in the chaperome executed by enhancing the interaction strength between components of distinct chaperome pathways, specifically between those of HSP90 and HSP70 pathways. We will review chemical tools and chemical probe-based assays, with a focus on HSP90. We will discuss how kinetic binding, not classical equilibrium binding, is most appropriate in the development of drugs and probes for the chaperome in disease. We will then present our view on how chaperome inhibitors may become potential drugs and diagnostics in cancer.

Identification of novel DPP-IV inhibitory peptides from Atlantic salmon (Salmo salar) skin

The aim of this study was to identify dipeptidyl peptidase IV (DPP-IV) inhibitory peptides from salmon skin collagen hydrolysate, and to evaluate the possible inhibition mechanism of DPP-IV and peptide. Salmon skin collagen was hydrolyzed by pepsin, trypsin, papain, or Alcalase 2.4 L, separately. Trypsin hydrolysate (10 mg/mL) showed the highest inhibitory activity of 66.12 ± 0.68%. The hydrolysate was separated into three fractions by ultrafiltration, and the inhibitory IC₅₀ of M1 (molecular weight <3 kDa) was 1.54 ± 0.06 mg/mL. M1 was separated by gel chromatography and RP-HPLC; A10 was the highest inhibitory fraction in the 12 fractions, i.e., IC₅₀ was 0.79 ± 0.13 mg/mL. A novel peptide LDKVFR with the IC₅₀ value of 0.1 ± 0.03 mg/mL (128.71 μM) was identified from A10. Molecular docking revealed that six hydrogen bonds and eight hydrophobic interactions between LDKVFR and DPP-IV were contributed to DPP-IV inhibition.

Assessment of the Cruzain Cysteine Protease Reversible and Irreversible Covalent Inhibition Mechanism

Reversible and irreversible covalent ligands are advanced cysteine protease inhibitors in the drug development pipeline. K777 is an irreversible inhibitor of cruzain, a necessary enzyme for the survival of the Trypanosoma cruzi (T. cruzi) parasite, the causative agent of Chagas disease. Despite their importance, irreversible covalent inhibitors are still often avoided due to the risk of adverse effects. Herein, we replaced the K777 vinyl sulfone group with a nitrile moiety to obtain a reversible covalent inhibitor (Neq0682) of cysteine protease. Then, we used advanced experimental and computational techniques to explore details of the inhibition mechanism of cruzain by reversible and irreversible inhibitors. The isothermal titration calorimetry (ITC) analysis shows that inhibition of cruzain by an irreversible inhibitor is thermodynamically more favorable than by a reversible one. The hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) and Molecular Dynamics (MD) simulations were used to explore the mechanism of the reaction inhibition of cruzain by K777 and Neq0682. The calculated free energy profiles show that the Cys25 nucleophilic attack and His162 proton transfer occur in a single step for a reversible inhibitor and two steps for an irreversible covalent inhibitor. The hybrid QM/MM calculated free energies for the inhibition reaction correspond to -26.7 and -5.9 kcal mol^-1 for K777 and Neq0682 at the MP2/MM level, respectively. These results indicate that the ΔG of the reaction is very negative for the process involving K777, consequently, the covalent adduct cannot revert to a noncovalent protein-ligand complex, and its binding tends to be irreversible. Overall, the present study provides insights into a covalent inhibition mechanism of cysteine proteases.

The Arabidopsis (ASHH2) CW domain binds monomethylated K4 of the histone H3 tail through conformational selection

Chromatin post-translational modifications are thought to be important for epigenetic effects on gene expression. Methylation of histone N-terminal tail lysine residues constitutes one of many such modifications, executed by families of histone lysine methyltransferase (HKMTase). One such protein is ASHH2 from the flowering plant Arabidopsis thaliana, equipped with the interaction domain, CW, and the HKMTase domain, SET. The CW domain of ASHH2 is a selective binder of monomethylation at lysine 4 on histone H3 (H3K4me1) and likely helps the enzyme dock correctly onto chromatin sites. The study of CW and related interaction domains has so far been emphasizing lock-key models, missing important aspects of histone-tail CW interactions. We here present an analysis of the ASHH2 CW-H3K4me1 complex using NMR and molecular dynamics, as well as mutation and affinity studies of flexible coils. β-augmentation and rearrangement of coils coincide with changes in the flexibility of the complex, in particular the η1, η3 and C-terminal coils, but also in the β1 and β2 strands and the C-terminal part of the ligand. Furthermore, we show that mutating residues with outlier dynamic behaviour affect the complex binding affinity despite these not being in direct contact with the ligand. Overall, the binding process is consistent with conformational selection. We propose that this binding mechanism presents an advantage when searching for the correct post-translational modification state among the highly modified and flexible histone tails, and also that the binding shifts the catalytic SET domain towards the nucleosome. DATABASES: Structural data are available in the PDB database under the accession code 6QXZ. Resonance assignments for CW42 in its apo- and holo-forms are available in the BMRB database under the accession code 27251.

Structural dynamics and allostery of Rab proteins: strategies for drug discovery and design

Rab proteins represent the largest family of the Rab superfamily guanosine triphosphatase (GTPase). Aberrant human Rab proteins are associated with multiple diseases, including cancers and neurological disorders. Rab subfamily members display subtle conformational variations that render specificity in their physiological functions and can be targeted for subfamily-specific drug design. However, drug discovery efforts have not focused much on targeting Rab allosteric non-nucleotide binding sites which are subjected to less evolutionary pressures to be conserved, hence are likely to offer subfamily specificity and may be less prone to undesirable off-target interactions and side effects. To discover druggable allosteric binding sites, Rab structural dynamics need to be first incorporated using multiple experimentally and computationally obtained structures. The high-dimensional structural data may necessitate feature extraction methods to identify manageable representative structures for subsequent analyses. We have detailed state-of-the-art computational methods to (i) identify binding sites using data on sequence, shape, energy, etc., (ii) determine the allosteric nature of these binding sites based on structural ensembles, residue networks and correlated motions and (iii) identify small molecule binders through structure- and ligand-based virtual screening. To benefit future studies for targeting Rab allosteric sites, we herein detail a refined workflow comprising multiple available computational methods, which have been successfully used alone or in combinations. This workflow is also applicable for drug discovery efforts targeting other medically important proteins. Depending on the structural dynamics of proteins of interest, researchers can select suitable strategies for allosteric drug discovery and design, from the resources of computational methods and tools enlisted in the workflow.

Unlocking latent kinetic information from label-free binding

Transient affinity binding interactions are central to life, composing the fundamental elements of biological networks including cell signaling, cell metabolism and gene regulation. Assigning a defined reaction mechanism to affinity binding interactions is critical to our understanding of the associated structure-function relationship, a cornerstone of biophysical characterization. Transient kinetics are currently measured using low throughput methods such as nuclear magnetic resonance, or stop-flow spectrometry-based techniques, which are not practical in many settings. In contrast, label-free biosensors measure reaction kinetics through direct binding, and with higher throughout, impacting life sciences with thousands of publications each year. Here we have developed a methodology enabling label-free biosensors to measure transient kinetic interactions towards providing a higher throughput approach suitable for mechanistic understanding of these processes. The methodology relies on hydrodynamic dispersion modeling of a smooth analyte gradient under conditions that maintain the quasi-steady-state boundary layer assumption. A transient peptide-protein interaction of relevance to drug discovery was analyzed thermodynamically using transition state theory and numerical simulations validated the approach over a wide range of operating conditions. The data establishes the technical feasibility of this approach to transient kinetic analyses supporting further development towards higher throughput applications in life science.

Estimation of Protein-Ligand Unbinding Kinetics Using Non-Equilibrium Targeted Molecular Dynamics Simulations

We here report on nonequilibrium targeted molecular dynamics simulations as a tool for the estimation of protein-ligand unbinding kinetics. Correlating simulations with experimental data from SPR kinetics measurements and X-ray crystallography on two small molecule compound libraries bound to the N-terminal domain of the chaperone Hsp90, we show that the mean nonequilibrium work computed in an ensemble of trajectories of enforced ligand unbinding is a promising predictor for ligand unbinding rates. We furthermore investigate the molecular basis determining unbinding rates within the compound libraries. We propose ligand conformational changes and protein-ligand nonbonded interactions to impact on unbinding rates. Ligands may remain longer at the protein if they exhibit strong electrostatic and/or van der Waals interactions with the target. In the case of ligands with a rigid chemical scaffold that exhibit longer residence times, transient electrostatic interactions with the protein appear to facilitate unbinding. Our results imply that understanding the unbinding pathway and the protein-ligand interactions along this path is crucial for the prediction of small molecule ligands with defined unbinding kinetics.

Paradigms for Precision Medicine in Epichaperome Cancer Therapy

Alterations in protein-protein interaction networks are at the core of malignant transformation but have yet to be translated into appropriate diagnostic tools. We make use of the kinetic selectivity properties of an imaging probe to visualize and measure the epichaperome, a pathologic protein-protein interaction network. We are able to assay and image epichaperome networks in cancer and their engagement by inhibitor in patients' tumors at single-lesion resolution in real time, and demonstrate that quantitative evaluation at the level of individual tumors can be used to optimize dose and schedule selection. We thus provide preclinical and clinical evidence in the use of this theranostic platform for precision medicine targeting of the aberrant properties of protein networks.

Drug-Target Residence Time Affects in Vivo Target Occupancy through Multiple Pathways

The drug discovery and development process is greatly hampered by difficulties in translating in vitro potency to in vivo efficacy. Recent studies suggest that the long-neglected drug-target residence time parameter complements classical drug affinity parameters (K _I, K _d, IC₅₀, or EC₅₀) and is a better predictor of in vivo efficacy. Compounds with a long drug-target residence time are often more efficacious in vivo. The impact, however, of the drug-target residence time on in vivo efficacy remains controversial due to difficulties in experimentally determining the in vivo target occupancy during drug treatment. To tackle this problem, an in vivo displacement assay was developed using soluble epoxide hydrolase as a biological model. In this report, we experimentally demonstrated that drug-target residence time affects the duration of in vivo drug-target binding. In addition, the drug-target residence time plays an important role in modulating the rate of drug metabolism which also affects the efficacy of the drug.

The importance of the quaternary structure to represent conformational ensembles of the major Mycobacterium tuberculosis drug target

Flexibility is a feature intimately related to protein function, since conformational changes can be used to describe environmental changes, chemical modifications, protein-protein and protein-ligand interactions. In this study, we have investigated the influence of the quaternary structure of 2-trans-enoyl-ACP (CoA) reductase or InhA, from Mycobacterium tuberculosis, to its flexibility. We carried out classical molecular dynamics simulations using monomeric and tetrameric forms to elucidate the enzyme's flexibility. Overall, we observed statistically significant differences between conformational ensembles of tertiary and quaternary structures. In addition, the enzyme's binding site is the most affected region, reinforcing the importance of the quaternary structure to evaluate the binding affinity of small molecules, as well as the effect of single point mutations to InhA protein dynamics.

Structural Dynamics of Agonist and Antagonist Binding to the Androgen Receptor

Androgen receptor (AR) is a steroid hormone nuclear receptor which upon binding its endogenous androgenic ligands (agonists), testosterone and dihydrotestosterone (DHT), alters gene transcription, producing a diverse range of biological effects. Antiandrogens, such as the pharmaceuticals bicalutamide and hydroxyflutamide, act as agonists in the absence of androgens and as antagonists in their presence or in high concentration. The atomic level mechanism of action by agonists and antagonists of AR is less well characterized. Therefore, in this study, multiple 1 μs molecular dynamics (MD), docking simulations, and perturbation-response analyses were performed to more fully explore the nature of interaction between agonist or antagonist and AR and the conformational changes induced in the AR upon interaction with different ligands. We characterized the mechanism of the ligand entry/exit and found that helix-12 and nearby structural motifs respond dynamically in that process. Modeling showed that the agonist and antagonist/agonist form a hydrogen bond with Thr877/Asn705 and that this interaction is absent for antagonists. Agonist binding to AR increases the mobility of residues at allosteric sites and coactivator binding sites, while antagonist binding decreases mobility at these important sites. A new site was also identified as a potential surface for allosteric binding. These results shed light on the effect of agonists and antagonists on the structure and dynamics of AR.

Thienopyrimidinone Derivatives That Inhibit Bacterial tRNA (Guanine37-N1)-Methyltransferase (TrmD) by Restructuring the Active Site with a Tyrosine-Flipping Mechanism

Among the >120 modified ribonucleosides in the prokaryotic epitranscriptome, many tRNA modifications are critical to bacterial survival, which makes their synthetic enzymes ideal targets for antibiotic development. Here we performed a structure-based design of inhibitors of tRNA-(N¹G37) methyltransferase, TrmD, which is an essential enzyme in many bacterial pathogens. On the basis of crystal structures of TrmDs from Pseudomonas aeruginosa and Mycobacterium tuberculosis, we synthesized a series of thienopyrimidinone derivatives with nanomolar potency against TrmD in vitro and discovered a novel active site conformational change triggered by inhibitor binding. This tyrosine-flipping mechanism is uniquely found in P. aeruginosa TrmD and renders the enzyme inaccessible to the cofactor S-adenosyl-l-methionine (SAM) and probably to the substrate tRNA. Biophysical and biochemical structure–activity relationship studies provided insights into the mechanisms underlying the potency of thienopyrimidinones as TrmD inhibitors, with several derivatives found to be active against Gram-positive and mycobacterial pathogens. These results lay a foundation for further development of TrmD inhibitors as antimicrobial agents.

Structure functional insights into calcium binding during the activation of coagulation factor XIII A

The dimeric FXIII-A₂, a pro-transglutaminase is the catalytic part of the heterotetrameric coagulation FXIII-A₂B₂ complex that upon activation by calcium binding/thrombin cleavage covalently cross-links preformed fibrin clots protecting them from premature fibrinolysis. Our study characterizes the recently disclosed three calcium binding sites of FXIII-A concerning evolution, mutual crosstalk, thermodynamic activation profile, substrate binding, and interaction with other similarly charged ions. We demonstrate unique structural aspects within FXIII-A calcium binding sites that give rise to functional differences making FXIII unique from other transglutaminases. The first calcium binding site showed an antagonistic relationship towards the other two. The thermodynamic profile of calcium/thrombin-induced FXIII-A activation explains the role of bulk solvent in transitioning its zymogenic dimeric form to an activated monomeric form. We also explain the indirect effect of solvent ion concentration on FXIII-A activation. Our study suggests FXIII-A calcium binding sites could be putative pharmacologically targetable regions.

Principal component analysis of nonequilibrium molecular dynamics simulations

Principal component analysis (PCA) represents a standard approach to identify collective variables {x_i} = x, which can be used to construct the free energy landscape ΔG(x) of a molecular system. While PCA is routinely applied to equilibrium molecular dynamics (MD) simulations, it is less obvious as to how to extend the approach to nonequilibrium simulation techniques. This includes, e.g., the definition of the statistical averages employed in PCA as well as the relation between the equilibrium free energy landscape ΔG(x) and the energy landscapes ΔG(x) obtained from nonequilibrium MD. As an example for a nonequilibrium method, "targeted MD" is considered which employs a moving distance constraint to enforce rare transitions along some biasing coordinate s. The introduced bias can be described by a weighting function P(s), which provides a direct relation between equilibrium and nonequilibrium data, and thus establishes a well-defined way to perform PCA on nonequilibrium data. While the resulting distribution P(x) and energy ΔG∝lnP will not reflect the equilibrium state of the system, the nonequilibrium energy landscape ΔG(x) may directly reveal the molecular reaction mechanism. Applied to targeted MD simulations of the unfolding of decaalanine, for example, a PCA performed on backbone dihedral angles is shown to discriminate several unfolding pathways. Although the formulation is in principle exact, its practical use depends critically on the choice of the biasing coordinate s, which should account for a naturally occurring motion between two well-defined end-states of the system.

Machine Learning Analysis of τRAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times

Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, τ, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the τRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce τ. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating τ and to decipher the molecular features leading to longer τ values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of τ for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of τ. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the τRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization.

Holo-like and Druggable Protein Conformations from Enhanced Sampling of Binding Pocket Volume and Shape

Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand-protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g., molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the apo protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables that effectively sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. We assessed the method on three challenging proteins undergoing different extents of conformational changes upon ligand binding. In all cases our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry. Moreover, ensemble docking calculations using those conformations yielded in all cases native-like poses among the top-ranked ones.