Virtual Screening for β-Secretase (BACE1) Inhibitors Reveals the

May 4, 2005 - Application of Free Energy Perturbation for the Design of BACE1 Inhibitors. Myriam Ciordia .... A Method for Induced-Fit Docking, Scorin...
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J. Med. Chem. 2005, 48, 3749-3755

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Virtual Screening for β-Secretase (BACE1) Inhibitors Reveals the Importance of Protonation States at Asp32 and Asp228 Tı´mea Polga´r and Gyo¨rgy M. Keseru¨* CADD&HTS, Gedeon Richter Ltd, PO Box 27, H-1475 Budapest, Hungary Received October 28, 2004

A comparative virtual screen for β-secretase (BACE1) inhibitors using different docking methods (FlexX and FlexX-Pharm), scoring functions (Dock, Gold, Chem, PMF, FlexX), protonation states (default and calculated), and protein conformations (apo and ligand bound) has been performed. Apo and ligand bound conformations of BACE1 were both found to be suitable for virtual screening. Assigning calculated protonation states to catalytic Asp32 and Asp228 residues resulted in significant improvement of enrichment factors as calculated at 1% of the ranked database. Using 1FKN we obtained no enrichment by FlexX/D-Score that was improved to 36 when considering calculated protonation states. We also show that combining calculated protonation states with pharmacophore constraints using FlexX-Pharm/D-Score improved enrichment further to 41. Enrichments reported in this study suggest our screening protocol will be effective in the virtual screening of large compound libraries for BACE1 inhibitors. Introduction Alzheimer’s disease (AD) is characterized neuropathologically by the presence of amyloid β-peptide (Aβ)containing plaques and neurofibrillary tangles composed of abnormal τ-protein.1 Several forms of Aβ are produced from the amyloid precursor protein (APP); however, the Aβ42 (10% of all Aβ produced) seems to be the major pathogenic form and the most important component of amyloid plaques.2 The amyloid hypothesis of AD3 suggests that Aβ accumulation is an underlying cause of the disease. APP is processed along both the major R- and the minor β-secretase pathways that results in proteolytic fragments, which are further processed by γ-secretase. Contrary to the nonpathogenic products of R-secretase, the β-secretase pathway produces pathogenic Aβ peptides. It has been demonstrated that β-secretase (BACE1) is the rate-limiting enzyme activity in the production of Aβ2, suggesting a potentially beneficial effect of β-secretase inhibitors in AD. The availability of several X-ray structures of the target, BACE1, has enabled significant progress to be made in the discovery of BACE inhibitors.4 Crystal structures of BACE/inhibitor complexes have revealed significant information about the binding sites and the nature of protein-ligand interactions, which in turn has prompted both academic and industrial research groups to design new peptidomimetic inhibitors.5,6 The first BACE structure has been reported by Tang et al., who cocrystallized the protein with a nanomolar peptide inhibitor OM99-2.7 This structure enabled the authors to discover a number of peptidomimetic inhibitors.8 The identification of small-molecule, druglike inhibitors, however, has proved to be a challenge. The BACE inhibitors first identified were mainly hydroxyalkyl peptidomimetics and statine derivatives, until Takeda disclosed the first nonpeptidomimetic BACE * Corresponding author. Phone: +36-431-4605. Fax:+36-1-4326002. E-mail: [email protected].

inhibitors9 in 2001. To date the most detailed account of nonpeptidomimetic inhibitors was published by Vertex describing the first 3D pharmacophore map of BACE that could also guide the design and optimization of inhibitors.10 Although structure-based design and pharmacophore modeling can facilitate the identification of potent BACE inhibitors, the penetration profile of these compounds is equally important. BACE and APP are both endocytosed into endosomes for cleavage. Endosomes are likely to be the major site for β-secretase processing because of the acidic pH optimum of the enzyme activity. Consequently, orally active BACE inhibitors should penetrate across at least four biological barriers: (i) intestinal membrane when absorbed, (ii) blood-brain barrier when entering the brain, (iii) cell membrane when reaching the site of action, and (iv) endosomal membrane when binding to BACE. The extended binding site of BACE, on one hand, drives structure-based design to large molecules that, on the other hand, are suboptimal when crossing biological barriers. In fact, most of the disclosed BACE inhibitors are high molecular weight and hydrophobic molecules (MWav ) 592.50, SD ) 91.04, clogPav ) 4.76, SD ) 2.18 as calculated for compounds available in the Prous Integrity Patent database11). The need for highly active and permeable compounds prompted us to design a powerful virtual screening protocol suitable for identifying potential inhibitors from virtual databases. Herein we have undertaken a comparative enrichment study of protonation states and protein conformations using high-throughput docking by FlexX12 and docking under pharmacophore constraints by FlexXPharm13 utilizing the Vertex pharmacophore. To the best of our knowledge, this is the first validated virtual screening protocol published for BACE inhibitors. Results and Discussion Comparison of X-ray Structures. To date (October 2004), five X-ray structures of BACE are publicly

10.1021/jm049133b CCC: $30.25 © 2005 American Chemical Society Published on Web 05/04/2005

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Figure 1. Comparison of residues 68-74 (FLAP, left) and 9-14 (10s, right). Residues involved in pharmacophore contacts (Gly34, Asp28, Asp228, Asp230) are colored by atom type (red, oxygen; white, hydrogen; blue, nitrogen). 1FKN (containing OM99-2 inhibitor), yellow; 1W51 (containing compound 1 inhibitor), green; 1W50, magenta; 1SGZ, red. Table 1. Currently Available X-ray Structures of BACE PDB code 1FKN 1M4H 1SGZ

resolution crystallized (Å) at pH 1.9 2.1 2.0

7.4 6.5 6.5

PDB code 1W50 1W51

resolution crystallized (Å) at pH 1.75 2.55

6.6 6.6

available (Table 1). 1FKN and 1M4H8 (PDB ID codes) contain peptidomimetic inhibitors OM99-2 and OM003, respectively. 1SGZ (PDB ID code) is the apo form of BACE that has an open conformation. 1W50 (apoenzyme) and 1W51 (complexed with a nonpeptidomimetic inhibitor) (PDB ID codes) structures have been released during the preparation of this paper; thus we analyzed but did not use these structures for virtual screening. Structures crystallized with peptidomimetic inhibitors (1FKN and 1M4H) are very similar. The overall rootmean-square distance for all protein atoms (rmsd) between 1FKN and 1M4H is 0.66 Å. Fitting 1W51 to 1FKN (overall rmsd is 1.41 Å) revealed that the conformations of proteins are similar if an inhibitor, either peptidomimetic or nonpeptidomimetic, is bound. Ligand-free structures (1SGZ, 1W50) show no significant differences either. The weighted rmsd for all the atoms of apo proteins is 1.48 Å. Although we did not observe any major difference within the group of ligand bound (1FKN, 1M4H, 1W51) and ligand-free structures (1W50, 1SGZ), there are key differences between these structural groups in residues 68-74 that adopt a β-hairpin conformation over the active site. These residues have an open conformation in apo structures (1SGZ, 1W50) with maximal CR displacement of about 7 Å upon ligand binding. Binding of inhibitors induces significant rearrangements centered at residues 68-74, resulting in a closed structure, such as 1FKN, 1M4H, and 1W51 (Figure 1). Detailed comparison of available structures suggests that BACE can adopt at least two major conformations. Structures 1SGZ and 1W50 represent the open form, while 1FKN, 1M4H, and 1W51 are ligand bound, closed

conformation. Our virtual screening experiments involved conformations from both groups: 1SGZ was selected to represent the open conformation and 1FKN was used for the closed conformation. pKa Calculations. Previous studies on the protonation states of catalytic residues in BACE generated ambiguous results.14,15 The linear scaling quantum approach (LSQA)14 suggests that the monoprotonated form (Asp228, Asp32-) is the most stable protonation state in the presence of a bound ligand. In the absence of an inhibitor these authors argued that the double deprotonated (Asp228-, Asp32-) state should be preferred. The LSQA study demonstrated that the protonation state changes upon ligand binding, but its dependence on pH, although mentioned, was not considered. The protonation state of catalytic Asp residues in ligand-bound 1FKN was investigated by molecular dynamics.15 In this structure Asp228 was found to be ionized and Asp32 was neutral (Asp228-, Asp32), which contradicts the results from LSQA. Since neither molecular dynamics nor LSQA considers the influence of pH and the effect of other titratable residues, the reliability of these approaches might be questioned. These limitations and also the contradictory results suggest that preferred protonation states of BACE are far from being settled. Since apparent pKa values have not been reported, we calculated pKa dissociation constants of all titratable residues in 1FKN, with and without OM99-2 inhibitor and 1SGZ apo structure. Protonation and deprotonation is one of the major events in numerous enzymatic mechanisms, a problem that, in most cases, cannot be treated by protein X-ray crystallography. Although H-bonding networks can be explored by the analysis of heavy atom distances, the lack of hydrogen positions prevents exploration of more detailed mechanisms, i.e., the identification of proton donors and acceptors. pKa calculations provide, however, a suitable and straightforward way of calculating the effect of protein environment on titratable groups that

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Table 2. pKa Values Calculated by ZAP for Residues Showing Protonation States Other than Default in 1FKN and 1SGZ residue

1FKN

1SGZ

residue

1FKN

1SGZ

Asp32 His45 His49 Asp83 His145 Asp180

9.9 0.2 6.4 8.7 6.8 7.7

10.3 10.3 10.3 8.7 12.4 7.4

His181 Asp228 Asp311 His360 His362

1.9 6.1 6.3 12.2 4.4

5.0 6.0 6.6 13.6 13.6

enables us to estimate the protonation state of residues involved in the enzymatic mechanism. In fact, pKa calculations are widely used in studies of enzyme mechanisms.16-21 The applied methods are mostly based on the finite-difference solution of the Poisson-Boltzmann equation.22 Resultant electrostatic energies are used to calculate pKa values as a measure of the electrostatic free energy difference between neutral and charged states of a given titratable group. Calculation of this energy difference requires three terms: (i) Born desolvation energy associated with moving the neutral and charged form of the group from water to the protein environment; (ii) background interaction energy between the neutral and charged form of the group with permanent protein dipoles; and (iii) pairwise interaction energy between titratable groups, including charged-charged, charged-neutral, neutral-charged, and neutral-neutral interactions. Although both Born and background energies can be approximated as pH-independent, the pH-dependence of pairwise interaction energies should be taken into account. Evaluation of interaction energies requires fractional charges. Unfortunately, accurate calculation of charges at large numbers of titratable groups by Boltzmann summation is rather time-consuming. Therefore, fractional charges are usually calculated by Monte Carlo protocols23 or the “cluster approach” introduced by Gilson.24 Energies of all possible protonation states are used to calculate titration curves enumerating fractional charges at each pH for each residue. Occupancies at protonation sites were estimated by Boltzmann summation.25 Fractional charges and consequently apparent pKa values are strongly dependent on the structure. Although the pH optimum of BACE activity was found to be about pH 4.5,26,27 the available structural information restricted our protonation state study to the pH of crystallization. Crystals of BACE were grown at pH 7.4 (1FKN), pH 6.5 (1SGZ, 1M4H), and pH 6.6 (1W51, 1W50); consequently, we used 1FKN protonated at pH 7.4 and 1SGZ protonated at pH 6.5 for docking experiments. pKa values calculated for titration sites with altered protonation state relative to their default are collected in Table 2. Apart from the catalytic Asp residues, active sites defined for docking calculations did not contain these residues. In fact, most of these residues are located on the protein surface, so their protonation states have limited influence on ligand binding. The protonation state of catalytic Asp residues, however, could have a major impact on the biological function. We propose that at low pH the diprotonated form (Asp32, Asp228) is more accessible, but at pH 6.5 and 7.4swhere structural information is availables protonation cannot be handled without ambiguity. Ti-

tration curves calculated for both empty structures indicate (Figure 2) that at pH 6.5 and 7.4 only the Asp228 residue is ionized (Asp32, Asp228-). This result contradicts the findings of the previous LSQA study, suggesting a double deprotonated (Asp32-, Asp228-) active site. In 1FKN containing OM99-2 inhibitor, the Asp32 residue is ionized and the Asp228 residue is neutral (Asp32-, Asp228), in accordance with LSQA results. Both the LSQA and our Poisson-Boltzmann study indicated that the protonation state of catalytic Asp residues might change upon ligand binding. Since we are docking into ligand-free structures, pKa values for 1FKN complexed with OM99-2 were not investigated further. The protonation state of Asp32, Asp228- was used in docking calculations when indicated by the suffix “pk” in Figure 3. Docking with FlexX and FlexX-Pharm. A comparative enrichment study of docking algorithms FlexX and FlexX-Pharm was performed for 1FKN and 1SGZ crystal structures. Our first goal was to explore the impact of pharmacophore constraints on docking performance and check the ability of algorithms to handle various conformations of the same protein. Comparative structural analysis of available BACE structures showed flexible chains near the active site (residues ∼68-74 and 9-14). The impact of different chain conformations was investigated using 1FKN and 1SGZ in virtual screening. The catalytic Asp residues of BACE can adopt multiple protonation states, and the influence of protonation states of Asp32 and Asp228 on docking experiments was also studied. The main point in our scoring scheme was to handle pose extraction and ranking separately. Five scoring functions (FlexX, Dock, Chem, Gold, and PMF) were used for both pose extraction and ranking, resulting in 25 different scoring combinations in total. The Dock score28 considers both electrostatic and hydrophobic contributions to the binding energy, and a distancedependent dielectric attenuates charge-charge and other polar interactions. The Gold29,30 score focuses on H-bonding interactions, so it generally performs well if there are significant polar interactions. Since there are a significant number of H-bond donors and acceptors within the active sites, the Dock and Gold scores were expected to rank ligands the best. Because the Dock score considers a hydrophobic term as well, it can outperform the Gold score and can be expected to give the most accurate approximation of binding energy. As ChemScore31 is a robust function that is based on a diverse training set of 82 receptor-ligand complexes, it performs well in most cases. For the two docking algorithms (FlexX and FlexXPharm) that were applied, the two protonation states (calculated and default) that were considered and the 25 different scoring combinations resulted in 100 enrichment factors for each protein conformation calculated at 1% of the ranked database. All enrichment factors are available in the Supporting Information. Docking at Default Protonation States. Upon docking with FlexX and setting default protonation states, the 1SGZ crystal structure seemed to provide preferable enrichment factors compared to 1FKN. Using the apo conformation, 1SGZ, enrichments were found to be dependent on ranking rather than pose extraction.

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Figure 2. Titration curves depicting the fraction protonated, f(HA), as a function of pH for Asp32 (open circles) and Asp228 (black squares) in 1FKN without OM99-2 inhibitor and 1SGZ.

Although the quality of extracted poses was similar for each scoring function, the Dock and Gold scores were the best for ranking poses. The ligand-bound 1FKN showed a somewhat different picture; the PMF, Gold, and FlexX scores provided the most reliable poses that were best ranked by Dock and Gold. FlexX-Pharm constricted the number of possible poses and increased the inactive drop-out rate. Introduction of pharmacophore constraints by FlexX-Pharm therefore improved enrichment factors for both structures (Figure 3). When docking into 1SGZ we found that ranking by Dock score was outstanding, independent of the score used for pose extraction. Enrichment factors obtained with all the other scores were improved slightly or remained constant relative to the corresponding FlexX run. Docking into 1FKN revealed Dock, Gold, and Chem scores to be suitable for ranking. In contrast to the FlexX run, the Chem score was found to be useful for pose extraction in this case. It is interesting to note that docking experiments performed with FlexX-Pharm resulted in better enrichment factors for 1FKN than for 1SGZ. Compared to the corresponding FlexX run, the improvement was significant for the 1FKN but not for the 1SGZ crystal structure. Docking with FlexX-Pharm facilitates the study

of functionally interesting interactions. Since 1FKN adopts a ligand binding conformation, inhibitors can fit into the preformed active site that allowed the formation of relevant interactions. The more open active site of 1SGZ, however, may preclude the formation of vital interactions. Additionally, we found that false positive molecules placed outside the active site of 1FKN were filtered out by FlexX-Pharm. Because of the larger and more open cavity of 1SGZ, fewer false positive compounds were docked on the outer surface of the active site by FlexX, and consequently, fewer compounds were dropped out by FlexX-Pharm. Accordingly, it was concluded that structure 1FKN is a better target for virtual screening by FlexX-Pharm and Dock score. Docking at Calculated Protonation States. The effects of protonation states were investigated in an enrichment study using both FlexX and FlexX-Pharm algorithms. Docking into 1SGZ by FlexX at calculated protonation states yielded similar enrichment to that obtained by FlexX-Pharm with default protonation. Dock and Gold scores were found to be the best for ranking, while scoring functions used for pose extraction showed only limited effect in enrichment. In the case of 1FKN we observed similar trends; consideration of calculated protonation states has an effect comparable

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Figure 3. Enrichment factors obtained with FlexX and FlexX-Pharm (Pharm) for 1FKN and 1SGZ (panels A and B, respectively) as calculated at 1% of the ranked database. Suffix pk indicates when calculated protonation states were considered.

to that of pharmacophore constraints. Extracted poses were ranked well by Dock, Gold, and Chem scores for 1FKN. When applying the calculated protonation states, 1FKN gave better enrichment factors again, which underlines the importance of the preformed active site available in this ligand bound conformation. Our most interesting finding was that improvements in enrichment factors using FlexX with calculated protonation states are comparable to those produced with FlexXPharm applying default protonation states. This observation could be explained by the implicit protonation information encoded in pharmacophore constraints applied in FlexX-Pharm. Screening with FlexX-Pharm and applying calculated protonation states improved enrichment factors significantly for 1SGZ, while only slight improvement was observed for 1FKN. Dock, Gold and Chem scoring functions were by far the best for 1FKN, and in the case of 1SGZ, only the Dock score gave results comparable to that of 1FKN. These results show that the quasire-

dundant information of protonation and pharmacophore constraints prevents further improvement in enrichment factors using the ligand-bound 1FKN. On the other hand, however, enrichment factors were improved for the apo conformation (1SGZ), where protonation information helped to identify important protein-ligand interactions while pharmacophore constraints were used to reduce false poses. In summary, we obtained comparable enrichment factors for two protein conformations by putting emphasis on protonation and pharmacophore constraints. For ranking extracted poses, the exact placement of the given pose was found to be crucial. FlexX often finds solutions on the outer surface of the active site and therefore could not provide sufficiently accurate poses. In fact, these solutions reduced our enrichment. Pharmacophore constraints can solve this problem and could improve the enrichment significantly. Protonation states of catalytic residues played an important role in positioning and scoring ligands. 1FKN is a ligand-bound conformation, and therefore, ligands could find proper

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poses easier than in the open structure (1SGZ). In 1SGZ, however, pharmacophore constraints and proper protonation could recover the results of 1FKN given by FlexX-Pharm (default protonation) or FlexX (calculated protonation).

stability, precision, and speed when compared to conventional methods. Since the ionization state of each titratable group is dependent on dielectric mass and fixed charges of the protein and also that of other ionizable residues, the calculation of pKa values is a self-consistent problem that requires the computation of all pairwise interactions. In ZAP this task is solved by running a focusing macro over each ionizable residue and storing the interaction matrix. The partition function of possible ionization states is evaluated by a Monte Carlo approach as implemented by ZAP. pKa calculations were performed on crystal structures of 1FKN with and without OM99-2 and 1SGZ. Solvent molecules were removed by SYBYL 6.9.235 and hydrogen atoms were then added by ZAP. Calculation of the interaction matrix involved the inner and external dielectric of 2.0 and 80.0, respectively. Titration curves were calculated by Monte Carlo optimization for each ionizable residue in the resolution of pH 0.1. pKa values were obtained as pK1/2 of the corresponding titration curves. Protein Preparation. Enrichment studies were performed on both ligand-bound4 (1FKN) and apo36 (1SGZ) structures of β-secretase. Recently released X-ray structures37 (1W50, 1W51) were used for structural comparison. Protonation states in 1FKN and 1SGZ were determined at the corresponding pH of crystallization (pH 7.4 and 6.5, respectively) using the titration curves calculated by ZAP. Protonation scheme of the catalytic aspartate residues involved two different settings: Asp32 and Asp228 deprotonated (default protonation), and Asp32 protonated and Asp228 ionized (calculated protonation). The active site of 1FKN was defined as the collection of residues within 6.5 Å of the bound inhibitor. The bound inhibitor was not included in the docking run. The active site of 1SGZ included the same residues as 1FKN. Docking Protocol. Virtual screening experiments were performed by FlexX 1.13.2 and FlexX-Pharm. Standard parameters were used as implemented in the SYBYL 6.9.2 package.35 A maximum of 30 docking solutions (poses) for each docked molecule was scored and saved for further analysis. All stored poses were rescored using the CScore module of SYBYL 6.9.2 comprising five different scoring functions, including Dock, Chem, FlexX, PMF, and Gold. Pharmacophore constraints applied in FlexX-Pharm involved optional interaction constraints for -COOAsp32, -COOAsp228, -COGly34, and -COGly230 groups (see Figure 1) derived from the pharmacophore hypothesis of Vertex.9 Accepted poses fulfilled at least two of these constraints simultaneously.

Conclusions The most striking and useful results from this work is that calculated protonation states and pharmacophore constraints show much promise as a method for obtaining consistent hit rates across diverse conformations of targets and reducing the number of false positives. The results indicate that FlexX-Pharm outperforms FlexX alone in most cases, and it is more reliable in predicting accurate poses. As the poses are more accurate, the scoring is more sophisticated; thus, enrichment can be expected to be improved. Our results demonstrate that correct poses, matching interaction constraints, could significantly increase the enrichment. Crucial interactions that significantly contribute to the final value of the scores are formed at a higher rate by FlexX-Pharm than by FlexX. A weak point of FlexX is that this algorithm often places ligands outside the active sites, and these misdocked compounds increase the rate of false positives/negatives. Virtual screening for two different conformations of BACE resulted in equivocal enrichment factors without considering pharmacophore constraints and the appropriate protonation states. If protonation states are not considered or are not correct, the definitions of H-bond donors/acceptors are changed, which limits protein-ligand interactions, and ligands could be misdocked and wrongly scored. By increasing the drop-out rate of inactive compounds by pharmacophore constraints and assigning the appropriate protonation states to catalytic Asp residues, we were able to make a distinct improvement in enrichment for each conformation of the target. Computational Methods Preparation of the Screening Library. Our screening library includes a subset of the World Drug Index (WDI) as inactive molecules that were specifically designated to reduce artificial enrichment.32 WDI was first filtered in order to eliminate compounds having molecular weight lower than 200 and greater than 800, log P larger than 7, and rotatable bonds more than 15. The remaining 37 843 WDI compounds were subjected to diverse selection based on 2D UNITY fingerprints. Dissimilarity selection performed by the Selector module of Sybyl resulted in 9950 compounds with a maximum Tanimoto index of 0.69 that was defined as an inactive set. Our active set was complied by the diverse selection of 50 BACE inhibitors from the total of 218 compounds available in the Prous Integrity Drugs & Biologics database.11 The final screening library was comprised of both active and inactive sets, which were stored as a Sybyl SLN list and converted to Sybyl mol2 format by means of Concord. This library of 10 000 compounds has an active content of 0.5% that mimics real-life screening situations. Calculation of Protein pKa Values. Ionization states of BACE residues were calculated by ZAP, a finite-difference Poisson-Boltzmann solver available from OpenEye Scientific Software, Inc.33 ZAP solves the Poisson-Boltzmann equation numerically using a grid-based representation in which the potential at each grid point is calculated. In contrast to other methods, ZAP uses a dielectric function based on atomcentered Gaussians.34 The method therefore describes the solvent interface more realistically, which results in greater

Acknowledgment. Authors are grateful to Zolta´n Kova´ri for his technical assistance and Tony Ainsworth for critically reading the manuscript. Support of local and European Tripos representatives is gratefully acknowledged. Supporting Information Available: Figures summarizing all of the calculated enrichment factors for 1SGZ and 1FKN. This material is available free of charge via the Internet at http://pubs.acs.org.

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