Identification of new allosteric sites and modulators of AChE through computational and experimental tools

Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=ienz20 Journal of Enzyme Inhibition and Medicinal Chemistry ISSN: 1475-6366 (Print) 1475-6374 (Online) Journal homepage: http://www.tandfonline.com/loi/ienz20 Identification of new allosteric sites and modulators of AChE through computational and experimental tools Carlos Roca, Carlos Requena, Víctor Sebastián-Pérez, Sony Malhotra, Chris Radoux, Concepción Pérez, Ana Martinez, Juan Antonio Páez, Tom L. Blundell & Nuria E. Campillo To cite this article: Carlos Roca, Carlos Requena, Víctor Sebastián-Pérez, Sony Malhotra, Chris Radoux, Concepción Pérez, Ana Martinez, Juan Antonio Páez, Tom L. Blundell & Nuria E. Campillo (2018) Identification of new allosteric sites and modulators of AChE through computational and experimental tools, Journal of Enzyme Inhibition and Medicinal Chemistry, 33:1, 1034-1047, DOI: 10.1080/14756366.2018.1476502 To link to this article: https://doi.org/10.1080/14756366.2018.1476502 © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. View supplementary material Published online: 06 Jun 2018. Submit your article to this journal Article views: 100 View related articles View Crossmark data RESEARCH PAPER Identification of new allosteric sites and modulators of AChE through computational and experimental tools Carlos Roca a , Carlos Requena a ,V  ıctor Sebasti an-P erez a , Sony Malhotra b , Chris Radoux b,c , Concepci on P erez d , Ana Martinez a , Juan Antonio P aez d , Tom L. Blundell b and Nuria E. Campillo a a Centro de Investigaciones Biol ogicas (CIB-CSIC), C/Ramiro de Maeztu, Madrid, Spain; b Department of Biochemistry, University of Cambridge, Cambridge, UK; c Cambridge Crystallographic Data Centre, Cambridge, UK; d Instituto de Qu  ımica M edica (IQM-CSIC), C/Juan de la Cierva, Madrid, Spain ABSTRACT Allosteric sites on proteins are targeted for designing more selective inhibitors of enzyme activity and to discover new functions. Acetylcholinesterase (AChE), which is most widely known for the hydrolysis of the neurotransmitter acetylcholine, has a peripheral allosteric subsite responsible for amyloidosis in Alzheimer’s disease through interaction with amyloid b-peptide. However, AChE plays other non-hydrolytic functions. Here, we identify and characterise using computational tools two new allosteric sites in AChE, which have allowed us to identify allosteric inhibitors by virtual screening guided by structure-based and fragment hotspot strategies. The identified compounds were also screened for in vitro inhibition of AChE and three were observed to be active. Further experimental (kinetic) and computational (molecular dynamics) studies have been performed to verify the allosteric activity. These new compounds may be valuable pharmacological tools in the study of non-cholinergic functions of AChE. ARTICLE HISTORY Received 8 February 2018 Revised 9 April 2018 Accepted 9 May 2018 KEYWORDS AChE; allosteric sites; Alzheimer diseases; molecular dynamics; allosteric inhibitors Introduction Acetylcholinesterase (AChE) is a well-known enzyme for the hydrolysis of the neurotransmitter acetylcholine (ACh) 1–4 , being the target of the main marketed pharmacological treatment of Alzheimer’s disease (AD). However, during the past decade it has been shown that AChE also plays other non-hydrolytic functions. Several in vitro and in vivo studies in the central nervous system suggested that developmental regulation of AChE enzyme plays a role in non-cholinergic function as morphometric processes, cell differentiation and synaptogenesis along nervous system 5 .Itis well recognised that one of the non-cholinergic actions of AChE is neurite promotion, which is regulated by a dynamic equilibrium of different interactions sites of AChE 6,7 . Zeev-Ben-Mordehai et al. published that one of the non-classical roles of AChE might be as an adhesion protein involved in synaptic development and maintenance 8 . Thus, pharmacological inhibitors of AChE that block the catalytic activity of the enzyme do not necessarily interfere with other biological activities of the protein. On the contrary, since damaging effects of overexpressed AChE may be related to noncatalytic activities, these drugs may actually aggravate certain conditions by elevating the levels of catalytically inactivated AChE. To date it is well known that the active site of AChE is subdivided into several subsites: catalytic triad (CAS, Ser-Glu-His) at the base of the gorge, anionic subsite (AS), acyl-binding pocket, and peripheral anionic subsite (PAS) at the mouth of the site, being PAS an allosteric site 9 . Modulators binding PAS limit the catalytic efficiency by both ways, combining steric and electrostatic blockage of ligand trafficking through the gorge and changing the active site conformation. Several evidences suggest that the PAS, besides its role in allosteric regulation of AChE-catalysed hydrolysis, also mediates heterologous protein associations that contribute to cell recognition and adhesion processes during synaptogenesis, and to the nucleation of amyloid peptides during the onset of AD in humans and mammalian model systems 10 . Recently, Marcelo et al. suggested another possible binding site, called site B, located outside the catalytic gorge. They showed that rosmarinic acid was able to bind this site; however, its allosteric functioning is not clear 11 . The “exit doors”are another very interesting structural motif in the hydrolysis mechanism of AChE. These regions are alternative routes to the gorge for product clearance, contributing to the high catalytic activity of the AChE. Computational studies together with X-ray crystallography suggest three possible regions of AChE implicated in the removal of cleavage products of the hydrolysis of ACh, known as back door (including Trp86, Gly448, Tyr449, and Ile451 (hAChE residue numbering)). Side door (including Asp74, Thr75, Leu76, Thr83, Glu84, and Asn87) and acyl loop door (including Trp236, Arg247, and Phe297) 12–15 . There are many challenges that need to be addressed regarding the non-classical functions of AChE, such as to identify the allosteric sites, the amino-acid residues that mediate non-classical activities and the identification of allosteric inhibitors among others 16 . Taking into account the relevance of this target in neurodegenerative diseases we consider that it is of great importance to identify allosteric sites as starting point to develop CONTACT Nuria E. Campillo [email protected] Centro de Investigaciones Biol ogicas (CSIC), Ramiro de Maeztu 9, Madrid 28040, Spain These authors contributed equally to this work. Supplemental data for this article can be accessed https://doi.org/10.1080/14756366.2018.1476502 ß2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. JOURNAL OF ENZYME INHIBITION AND MEDICINAL CHEMISTRY 2018, VOL. 33, NO. 1, 1034–1047 https://doi.org/10.1080/14756366.2018.1476502 efficient modulators of AChE and the corresponding noncholinergic functions. The main goal of this work is the identification and description of new allosteric binding sites on AChE and the discovery of new allosteric inhibitors since allosterism represents one of the most common and powerful means to regulate protein function. For this purpose, we performed a search for druggable sites on the enzyme using computational approaches with the aim of identifying putative allosteric sites. Moreover, for each of these we have defined the interacting key residues by means of virtual screening of our Medicinal and Biological Chemistry (MBC) library 17 , and a plausible mechanism of action is proposed. Methods Computational studies Pocket search In order to identify different cavities on the AChE enzyme, the Fpocket software 18 , a highly scalable and open source pocketdetection software package, was used. Fpocket is freely available for download at http://www.sourceforge.net/projects/fpocket. Fpocket extracts the information from rigid structure and only it is based on geometric parameters. Fpocket is based on the concept of a a-sphere, which is defined as a sphere that contacts four atoms on its boundary and has no internal atom. For a protein, very small spheres are located inside the protein, large spheres at the exterior, and clefts and cavities correspond to spheres of intermediate radii 19 . The use of Fpocket software involves three major steps. During the first step, the whole ensemble of a-spheres is determined from the protein structure and a pre-filtered collection of spheres is returned. The second step consists of identifying clusters of spheres close together, identifying pockets, and removing no interesting clusters. The final step is the atom properties calculation from each pocket, in order to score and rank the identified pockets. Fourteen hAChE structures (PDB IDs: 1B41, 1F8U, 2X8B, 3LII, 4BDT, 4EY4, 4EY5, 4EY6, 4EY7, 4EY8, 4M0E, 4M0F, 4PQE, 5FPQ) 20–27 were downloaded from the Protein Data Bank (www.pdb.org) and subjected to pocket search using Fpocket. The PDB structures were prepared using the Maestro 28 Protein Preparation Wizard 29 for removing the water molecules, ligands, and metal ions. Upon Fpocket search, the 14 structures with embedded centres of pocket a-spheres were analysed by visual inspection to identify conserved pockets. Hotspot analysis The Fragment Hotspot maps software 30 identifies the location and quality of binding sites on the protein by first calculating atomic hotspots and then producing Fragment Hotspot maps with simple molecular probes. These maps specifically highlight fragment-binding sites and their corresponding pharmacophores. H-Bond acceptor, donor, and apolar/aromatic interactions, reported by this software, can assist medicinal chemists search for interesting interactions in order to bind or improve the binding affinities for different ligands, and suggest modifications to the molecules. For the molecular modeller, the maps complement existing virtual screening methods because they can be visually inspected to generate docking constraints or structure-based pharmacophores. With the most important interactions highlighted, existing pharmacophore methods can be used to screen for molecules capable of making these essential interactions. The maps can also be used to generate constraints for docking and hence steer the docking towards occupying the hotspot and ensuring that the important potential interactions are satisfied. Structure preparation of hAChE in its apo state (PDB ID: 4EY4) for the hotspot calculation was performed using Protein Preparation Wizard 29 , implemented in Maestro 28 . Ligands and water molecules were removed, hydrogen atoms were added and protein residues were ionised at pH ¼7. After the target preparation, Fragment Hotspot maps were calculated using the in-house script developed by Chris Radoux in Cambridge. The Hotspot Maps 30 were visualised using PyMol 31 software in order to identify the residues which could be involved in the interaction between ligand and protein. Ligand preparation The preparation of the library and the 2D-to-3D conversion was performed using the LigPrep 32 tool, a module of the Schr€ odinger software package. LigPrep allows different preparation steps of molecules such as the addition of hydrogen atoms, neutralisation of charged groups, generation of ionisation states, low-energy ring conformations, possible tautomers, followed by energy minimisation using the OPLS-2005 force field 33,34 . In order to carry out our studies, the compounds were prepared at physiological pH conditions, all of the compounds were desalted and finally the compounds were minimised as default. A total of 2499 protonation and tautomeric states were generated from 1830 compounds of MBC library using LigPrep. Virtual screening Virtual Screening of the MBC library 17 was carried out using the Glide software 35 , with the Extra Precision (XP) Glide Mode, for the site 2 and site 3 of the 4EY4 structure which was previously prepared using Protein Preparation Wizard tool. This structure was selected because of its being the only apo form amongst the serie of AChE crystallographic structures 22 ; it also has a very good resolution and excellent validation values, such as few Ramachandran outliers. The small-molecule-bound docked poses were further filtered using Maestro Pose Filter, selecting only the compounds that interact through an H-bond with the residues that are known to be critical for the ligand-target affinity in the Hotspots Maps. For site 2, the grid was centred on the site 2, ensuring that the entire cavity was included inside the box, and the molecules were ranked based on the Glide XP score. The scores for the molecules were in the range from 8.9 to 4.4 kcal/mol while rosmarinic acid was ranked with a score of 8.5 kcal/mol. His405, Glu414, and Trp532 were selected as key residues and applied as interaction filter. For site 3, a Glide XP docking was performed ensuring the entire cavity was included in the grid. The docking score for the molecules ranges from 7.7 to 1.8 kcal/mol. Glu81, Glu452, and Arg463 were selected as key residues for the interaction filtering. Ligands that are able to interact with these residues were ranked by their XP Glide Score, and visual inspection of the fitting between the Hotspot Maps and the best ranked virtual screening results was used to select molecules for further studies. Molecular dynamics Molecular dynamics (MD) was performed on an Asus 1151 h170 LVX-GTX-980Ti workstation, with an Intel Core i7–6500 K Processor (12 M Cache, 3.40 GHz) and 16 GB DDR4 2133 MHz RAM. The workstation has Nvidia GeForce GTX 980Ti available for GPU JOURNAL OF ENZYME INHIBITION AND MEDICINAL CHEMISTRY 1035 computations. MD studies were performed using AMBER14 36 with the ff14SB 37 to assess the stability of the compounds and to look for the ligand’s inhibitory mechanism. Additionally, a MD trajectory for the apo-target was developed, in order to observe the behaviour of the apo-AChE enzyme and to compare it with the ligandtarget trajectories. To calculate the ligands parameters for the MD simulation, RESP charges were calculated using Gaussian09 38 , optimising the geometry for both compounds using the method HF6–31þþ(d,p). Once the optimisation was completed, ligands were parametrised using Antechamber module 39 . Systems were solvated using TIP3P model 40 for water molecules, with a cubic box, equilibrating the system charge by adding Na þ ions. Solvated systems were first minimised for 8000 steps with the initial 4500 steps using the steepest descent algorithm. The final 3500 steps used the conjugate gradient energy minimisation with constraints applied to the protein residues as mentioned above for sites 2 and 3. This was followed by two minimisation stages of 8000 steps each, with the last 3500 using the conjugate gradient decreasing the restrains to the system. The system was equilibrated to 300 K and 1 atm, using a step protocol, applying energetic restraints of 15 kcal mol 1 Å 1 from the initial step and gradually decreasing them until its disappearing. Trajectories of 25ns were obtained in isothermal–isobaric ensembles. All bonds involving hydrogen atoms were constrained with the SHAKE algorithm 41 . A cut-off of 10Å was used for the Lennard–Jones interaction and the shortrange electrostatic interactions. Berendsen barostat 42 and Langevin thermostat were used to regulate the system pressure and temperature, respectively. Trajectories of 25 ns were computed, analysed using the Cpptraj 43 module and VMD 44 for visual inspection. Xmgrace software 45 was used to obtain the graphics of root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) of the MD simulations. One of each five frames of the trajectories were saved into a new PDB format trajectory and were taken to further analysis using TRAnsient Pockets in Proteins (TRAPP) software 46 , which allows the simulation, analysis, and visualisation of protein cavity dynamics for detection of transient sub-pockets using protein motion trajectories or ensembles of protein structures obtained either from experiments or from simulations. The catalytic pocket was also analysed using this software. Biological studies In vitro cholinesterase inhibition assays The method was adapted from Ellman et al. 47 The assay solution consisted of 0.1 M phosphate buffer pH 8, 400 mM 5,50-dithiobis(2nitrobenzoic acid) (DTNB, Ellman’s reagent), 0.05 unit/ml AChE (Sigma Chemical Co., Madrid, Spain, Cholinesterase, acetyl human recombinant), and 800mM acetylthiocholine iodide as the substrate of the enzymatic reaction. The compounds tested were added to the assay solution and the absorbance changes at 412 nm were recorded for 5 min with a UV/Vis Microplate and cuvette Spectrophotometer, Thermo Electron Type, Multiskan Spectrum. The reaction rates were compared, and the per cent inhibition due to the presence of test compounds was calculated. The IC 50 is defined as the concentration of each compound that reduces the enzymatic activity 50% with respect to that without inhibitors. All the experiments were performed in triplicate. Kinetic study of AChE inhibition To investigate the mechanism of action of the compounds on AChE, a kinetic analysis was performed. The experiments were carried out using combinations of four substrate concentrations, and three inhibitor concentrations. Double-reciprocal Lineweaver–Burk plotting of the data obtained, in which each point is mean of three different experiments, were analysed. Competitive inhibitors have the same y-intercept as uninhibited enzyme (since V max is unaffected by competitive inhibitors the inverse of V max also does not change) but there are different slopes and x-intercepts. Non-competitive inhibition produces plots with the same x-intercept as uninhibited enzyme (Km is unaffected) but different slopes and y-intercepts. Non-competitive inhibition causes different intercepts on both the yand x-axes but the same slope. Mixed inhibitors cause intersects above or below the x-axis. Results and discussion Druggable site The determination of druggable cavities in therapeutic targets is essential for structure-based drug design in order to identify binding pockets or allosteric sites and to design small-molecule ligands that bind to these with therapeutic effects. To address this goal, several computational approaches have been developed based mainly on evolutionary or structure-based algorithms 48–51 . In this work, we have used a combination of methods to get a consensus prediction of protein druggable sites. We have used the free geometry-based algorithm Fpocket 18,19 (http://fpocket.source forge.net) together with the prediction of Fragment Hotspot maps 30 to study the AChE surface with the aim to identify allosteric sites and its key residues. The first step of this consensus protocol was performed using the Fpocket software. As Fpocket is a geometry-based pocketdetection algorithm, we performed the study using a representative set of hAChE, in both apo and different complexed forms (Table 1). Fourteen different human structures were available at the moment of the study in the Protein Data Bank (PDB) (see Material and Methods section). The software detected almost 30 cavities, with some of these appearing in all AChE structures analysed, and others, which are not conserved, in fewer crystal structures. Once all cavities were analysed and clustered, taking into account the frequency that they appear together, and the druggability prediction score given by Fpocket, only four highly reproducible cavities can be considered as putative druggable binding sites (Figure 1). We observed that the four sites appear in all of Table 1. Druggable binding sites. PDB code LIGAND SITE 1 SITE 2 SITE 3 SITE 4 1B41 Fasciculin-II 1 4, 9 2 3 1F8U Fasciculin-II 1 4, 7 3 2 2X8B Fasciculin-II 1 9, 11 6 2 3LII –18,172 7 4BDT Fasciculin-II 1 4, 10 2 5 4EY4 –25,7 1 4 4EY5 Huperzine 1 4, 6 2 3 4EY6 Galantamine 1 2, 4 3 5 4EY7 Donepezil 1 4, 6 2 3 4EY8 Fasciculin-II 1 6, 10 2 3 4M0E Dihydrotanshinone-I 1 4, 7 2 6 4M0F Territrem B 1 4, 5 2 3 4PQE –1 2, 3 5, 13 4 5FPQ Sarin 1 3, 5 2 4 The numbers refer to the score given by the program Fpocket to the site in each structure (the lower score is related to better binding sites). Two or more numbers indicate that this pocket was found as two different cavities on the structure. 1036 C. ROCA ET AL. the different structures, although the position of each site varies among the structures and, in the case of site 2, Fpocket detected it as two different cavities (Table 1). The best ranked site for all structures is the site 1 (except for 4EY4, for which is the second-best cavity) that corresponds to the well-known binding site CAS/PAS. The site 2 is ranked at the top five positions in 11 out of the 14 structures analysed. Inspection of the residues of this site (Table 2) allowed us to identify Arg296 and Glu369 as possible key binding residues of the rosmarinic acid as was previously described by Marcelo et al. 11 Some residues are also shared with the acyl loop door 14 such as Trp236, Arg247, and Phe297 (Figure 2). The site 3, in 13 out of the 14 structures analysed, is ranked in the top five positions. We identified residues Val132, Tyr449, and Glu452 as residues (Table 2) that take part of the so-called back door, described as a dimple on the surface of the protein 52 . The remaining site 4 corresponds to a cavity formed mostly by polar residues such as Asp333, Glu334, His381, Glu396, and Asp400. Since no molecular or biological function can be attributed to these residues, we did not pursue the study of this cavity. However, the possibility of using site 4 for the design of new allosteric modulators of AChE remains open for future work. The known side door 53,54 was also detected by Fpocket, although this cavity was not found in most of the structures (see Supporting Information). Volume characterisation Further we noticed that there was a fluctuation in the volume of the site 2 and site 3 when a ligand is bound in catalytic gorge. To analyse these fluctuations we use Fpocket to compare a series of structures crystallised in the same conditions (4EY4, 4EY5, 4EY6, 4EY7, 4EY8). We observed that the site 2 decreases its volume when a ligand is bound to the catalytic gorge except to complex 4EY6 (Figure 3). These changes in the volume present a logical issue, because site 2 and CAS have some common residues, so ligands in the active gorge can interact and displace these residues as a consequence and hence modify the contiguous cavity. Similar changes might affect the active gorge when a ligand binds the site 2, explaining a possible allosteric mechanism. Moreover, it has been reported that the structural perturbations of the acyl loop occur when AChE is inhibited by a covalent inhibitor, presenting a narrowing of the gorge and a displacement of the Arg296 into the active site, which could present a steric barrier to the entry of oximes or affect oxime binding for AChE reactivation 55 . In our case, the hypothesis that a ligand binds to site 2 and displaces the acyl loop, thus inhibiting AChE activity, will be further studied. We next studied the influence of the ligand in complexes 4EY7 (with donepezil) and 4EY8 (with fasciculin-2) on the volume of the site 3. Interestingly, we found that in both cases the site 3 rises its volume approximately by two-fold as compared to the apo structure. This could be explained by the opening of the back door in both complexes, while the others remain closed. Figure 1. AChE structure highlighting the best cavities found by Fpocket. Table 2. Residues of allosteric sites, site 2 and site 3. Allosteric site Residues Site 2 Pro232, Asn233, Gly234, Pro235, Trp236, Thr238, Val239, Gly240, Glu243, Arg246, Arg247, Leu289, Por290, Gln291, Ser293, Arg296, Phe297, Val300, Thr311, Pro312, Glu313, Pro368, Gln369, Val370, Asp404, His405, Cys409, Pro410, Gln413, Trp532, Asn533, Leu536, Pro537, Leu540 Site 3 Glu81, Gly82, Glu84, Met85, Asn87, Asn89, Leu130, Asp131, Val132, Thr436, Leu437, Ser438, Trp439, Tyr449, Glu452, Ile457, Ser462, Arg463, Asn464 y Tyr465 JOURNAL OF ENZYME INHIBITION AND MEDICINAL CHEMISTRY 1037 These results, indicative of a volume fluctuation of allosteric sites, get with Fpocket are preliminary but suggesting an interrelation between catalytic site and allosteric ones. Key residues In order to confirm the druggability of the allosteric sites and to identify its key residues, we next performed a theoretical study of potential fragment-binding sites using Fragment Hotspot maps 30 for each site detected by Fpocket. These hotspot maps provide visual guides of the fragment-binding sites and their corresponding pharmacophores. This method reports H-Bond acceptor, donor, and non-polar/aromatic interactions, helping in the search for interesting interactions in order to identify or design efficient inhibitors. Figure 4 shows the Fragment Hotspot maps of the AChE apo structure (PDB ID 4EY4). The analysis of the hotspot was focussed on sites 2 and 3. We found that key residues Gln413 and Trp532 in site 2 can act as acceptors and His405 as H-Bond donor. These interactions represent the theoretical minimum binding features that will allow a fragment to bind this cavity. Fragment Hotspot maps for the site 3 were also analysed, demonstrating that the key residues are Glu81 and Glu452 as acceptors and Arg463 as a H-Bond donor. In summary, considering all above results we can conclude that druggable sites (sites 2 and 3) could be possible allosteric sites and targeting them might lead to the modification of AChE activity. Virtual screening With the goal of searching for allosteric modulators of the sites 2 and 3, we performed a virtual screening using our in-house MBC chemical library 17 . To validate the computational screening, docking of donepezil and rosmarinic acid for both sites (CAS/PAS and site 2) was also performed using Glide software (see Supporting Information, Figures S1 and S2). Upon validation, a virtual screening was performed using Glide with XP for both sites. Site 2 virtual screening Eleven compounds were chosen for enzymatic assay. Two of these compounds were selected based on their docking score. The rest of them were selected by an interaction-filter based on the Fragment Hotspot maps that were previously calculated. In this way, a filter of the capability of the ligands to interact through H-bond with Gln413, Trp532, and His405 was set up and the results were visually inspected. Along with the 11 compounds selected, rosmarinic acid was also sent for biological evaluation on AChE as a control 11 (Table 3). All the compounds tested showed a percentage of AChE inhibition at 10 lM greater than rosmarinic acid (the control compound in the assay), consistent with the proposed common binding site in the enzyme. Very interesting is the case of pteridine derivative SC251 with an IC 50 value of 2.7 lM. Figure 5 displays a three-dimensional view of the predicted binding pose of SC251 at site 2. SC251 interacts by a donor hydrogen bond with Gln413, an acceptor H-bond with His405 and a donor H-bond with Asn533, fitting properly with the Fragment Hotspot maps results. To confirm a possible allosteric mechanism of inhibition, we perform competitive studies of SC251 with the natural substrate, acetylcholine (ACh) (Figure 6). A non-competitive-mixed Figure 2. Amino acids involved in site 2 and site 3 found in hAChE (PDB ID: 4EY4). Site 2 residues are shown in orange and site 3 residues are shown in blue. 1038 C. ROCA ET AL. mechanism was observed suggesting that SC251 should bind to an allosteric site, in addition to the catalytic site. MD With the aim of explaining a plausible mechanism of action for the allosteric inhibitor SC251, MD studies for AChE in the apo state and AChE-SC251 complex were carried out. MD studies provide a powerful method to follow intimate details of the conformational events in different biological systems. In this study, flexibility changes or behaviour modifications in the important sites of AChE upon inhibitor binding, at the sites 2 and 3, and the side door cavity were further analysed. To explore the dynamic stability of the trajectories, RMSD values of the protein backbone based on the starting structure along the simulation time were calculated and plotted (Figure S4). After the initial adjustment, the longer term RMSD values are stable, ranging from 1.0 to 1.9 Å during the entire simulation time. Figure 3. (A) Surface representation of pockets that belong to site 2 (orange) and site 3 (blue). Differences between the volume in the absence and presence in the CAS/PAS cavity of inhibitor are highlighted (4EY4 is AChE in the apo state, 4EY7 is AChE crystallised with donepezil). (B) Plots of volume measured in some structures crystallised in the same conditions for site 2 (orange) and site 3 (blue). Figure 4. Representation of the calculated hotspots using Fragment Hotspot maps software 30 . Yellow area refers to non-polar area where the ligand should make hydrophobic interactions. Blue dots represent the area where the ligand should make a donor H-bond, and red dots where acceptor H-bond should be formed. JOURNAL OF ENZYME INHIBITION AND MEDICINAL CHEMISTRY 1039 Table 3. Experimental inhibition values of the virtual screening compounds to bind site 2. Compounds Structure IC 50 (lM) a Interactions (H-bonds) VSP2.47 >10 (35%) Asn233, His405 DA003 >10 (40%) His405, Trp532 SC274 >10 (33%) Asn233, Asn533, Trp532 AEL039 >10 (45%) Asn233, His405, Trp532 SC653 >10 (26%) Trp532, Asn233 JHD1.21 >10 (33%) Asn533, His405 MR3.61 >10 (37%) Asn233, Asn533, His405 AEL011 >10 (44%) Asn533, His405 SC251 2.76 ± 0.25 b Gln413, Asn533, His405 VP2.42 >10 (39%) Trp532 SC008 >10 (38%) His405, Trp532 Rosmarinic acid >10 (26%) Asn533, Gln413, Thr238, Pro368, Arg296 Key interactions of the compounds with AChE are display. a % of inhibition at 10 lM is indicated into parentheses. b IC 50 curve of compound SC251 (see supporting information, Figure S3). 1040 C. ROCA ET AL. Along the trajectory, the ligand SC251 fluctuates around the predicted docking poses (Figure S5). Accordingly, during the simulation, the ligand maintains some of the strong interaction as the H-bond donor with the Asn533 (during the 85% of the simulation) and the H-bond acceptor with the His405 (during the 50% of the simulation). However, the H-bond donor with Gln413 is lost in the majority of the simulation, and appears only 2% of the simulation time as it can be observed in Figure S6 (in order to clarify the final bind mode of the complex, a picture is shown in Figure S7). The RMSF of the apo and bound state were compared in order to see any differences in the fluctuations of the residues (Figure S8). The most interesting change corresponds to the residues 70–78, that belong to the omega loop, that are also implicated in the side door. We can identify rigidity of these residues when SC251 binds the site 2, as demonstrated by the decrease in fluctuations from approximately 3 Å to barely 1Å, which might suggest the impossibility of opening the side door while the ligand remains bound to the site 2. To further analyse the conformational changes between the trajectories, we used TRAPP software 46 . TRAPP is a tool that allows the analysis of the evolution of the spatial and physicochemical properties of a specified pocket in a protein during a MD simulation. We selected one out of five frames of the simulation as an input for TRAPP. We first analysed the CAS/PAS cavity of the apo AChE trajectory, which reveals that our molecular dynamic study also shows the presence of the side and back doors in at least the 50% of the trajectory, as other previous studies have already published 56 (Figure 7(A)). The acyl loop door is less flexible therefore the gate opening is more difficult to occur, as other studies have also previously reported 56 , thus we only can see its opening in 25% of the total snapshots of the simulation. Analysing the CAS/PAS cavity of the complex AChE-SC251 trajectory, some interesting differences with the apo form are highlighted. As we commented above, while the omega loop of the apo state of AChE is very flexible and allows the opening of the side door, in the AChE-SC251 trajectory this loop is much more rigid and as a result, the side door opens only in <50% of the snapshots of the trajectory. A red area appears in the back door Figure 5. Superposition of the proposed pose for SC251 at Site 2 and the hotspot calculated with Fragment Hotspot maps software. Figure. 6. Lineweaver–Burk plots representing the reciprocal of initial enzyme velocity versus the reciprocal of ACh concentration in the absence and presence of different concentrations of SC251 (1–5lM). Each point is the mean of three different experiments. JOURNAL OF ENZYME INHIBITION AND MEDICINAL CHEMISTRY 1041