Molecular docking aims at predicting the correct association between a ligand and a macromolecule, also called receptor, often a protein, to interact with. Such a calculation is performed through a search algorithm and an energy scoring function to first explore the free energy landscape and then to find the best ligand poses 9,10. In this study, the program Gold 57 was used for predicting the interactions of potential HDACis. This method was selected according to previous studies 58-64.
Whereas the standard Gold genetic algorithm was used as a search algorithm for retrieving the possible docking poses, a rescoring strategy was employed for evaluating their interactions with the target enzymes. Docking poses, first ranked according to the GoldScore, were then rescored according to the ChemPLP score 9,65. This was done to enhance the performance of docking prediction: meaningful results were obtained while redocking TSA and SAHA in the HDAC6 catalytic site, retrieving the interactions described in the literature 55. This method was then applied for the selection of novel potential HDACi from aurone-based and other chemical libraries that were then tested in vitro for their biological activity. A number of false-positives and false-negatives was obtained. The case of the aurone 84 gathered problems in the clustering threshold used for treating molecular docking data, and single point concentration issues. First of all, a RMSD cut-off of 2 Å is usually applied to determine docking poses population, as also suggested in the literature 66. In this case, by applying those standard rules, the best-ranked pose belonged to 85% of the docking solutions that converged to a unique zinc chelation mode in HDAC6. By lowering the RMSD threshold (1.8 Å), three groups of docking solutions were formed (Fig. 15). Among them, only one group actually chelated the zinc ion of HDAC6 whereas the other two were relatively far from the catalytic metal for possible coordination (38%, 47%, and 15%, respectively). Secondly, the percentage of HDAC6 inhibition of the aurone 84 at 100 µM was 44.9 ± 6.8% whereas the IC50 exceeded 300 µM. Single point testing can be tricky depending on where the tested concentration actually locates on the IC50 curve.
Figure 15. Docking of aurone 84 in the HDAC6 catalytic pocket: representation of the three clusters of docking solutions obtained with a RMSD cut-off of 1.8 Å. HDAC6 is partially shown as grey ribbons, its chelating triad labelled and shown as grey sticks. The zinc ion is represented as a light blue ball. Aurone 84’s docking solutions are represented in sticks: 38% cluster in green, 47%
cluster in yellow, and 15% in light orange. Zinc coordination distances for each docking cluster are also reported. Figure generated with MOE 2012.
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Compound 84 was selected from the aurone VS campaign according to the rule “the best-ranked solution must belong to the most populated cluster of docking solutions (RMSD cut-off 2Å)”, hence being a matter of modeling and statistics. Docking scores quantify the degree of interaction of a ligand within the so-defined docking binding site: the highest the score is, the best the interactions are expected.
It is also true that the biggest is the size of the investigated molecule the highest are the chances of interactions with the receptor. Docking scores should be normalized according to molecule’s size. This specific bias is known and recognized as a limiting factor in VS campaigns, and yet few studies address the problem. To overcome this problem, scores may be normalized by the square or cubic root of the number of heavy atoms (non-hydrogen atoms) 7,67-69.
Moreover, the chance to have an accurate binding mode prediction may also depend on a meaningful parameterization of the zinc ion. In the program Gold, the zinc coordination in HDAC2 and HDAC6 was set as pentacoordinate (two Asp, and one His were coordinated with the catalytic zinc ion) through a trigonal bipyramid geometry, allowing a bidentate binding mode whenever possible. This coordination scheme was only an approximation: the prediction was subject to errors because of both the nature of the coordination bond and the zinc electronic configuration (d10). It cannot be properly modelled through solely electrostatic and Van der Waals terms. Indeed, zinc coordination is very flexible and its bond length varies depending on the interacting heteroatom (from 1.5 to 2.5 Å) 70,71. Moreover, the electronic configuration is responsible for a less well-defined geometry than other transition metals, displaying geometries from tetrahedral to octahedral (coordination number ranging from 4 to 6) with no regular scheme 72. In a nutshell, the contradictions encountered between molecular docking results and enzymatic data may come from an inadequate parameterization of zinc interactions.
Till now, homology models of HDAC6 have been employed to realize molecular docking calculations for the discovery of selective HDAC6 inhibitors such as Tubastatin A 53. Very recently, Hai and co-workers have solved by crystallography the structure of the the human HDAC6 catalytic domain 2 (PDB code 5EDU 73). If we compare this structure with the HDAC6 model developed by Butler et al.53, also used in our study, it appears that, over 317 residues, the two structures differ from 1.8 Å (RMSD calculation based on Cα atoms). More specifically, the two active sites were relatively similar, with a RMSD of 1.6 Å for 19 Cα atoms around the catalytic zinc ion. Although those results seem encouraging, some residues adopt diverse side chain and main chain conformations, hence modifying the topology of the HDAC6 catalytic site. The crystallographic structure of HDAC6 has a much more constricted pocket that the one obtained through homology modeling (Vmodel = 406.5 Å3 versus Vcrystal = 135.7 Å3, calculated via CASTp server 56) (Fig. 16A, 16B). This difference is of particular importance in the docking calculations performed in this study as the homology model of HDAC6 was considered as a rigid body. Indeed, this technique cannot produce induced-fit movements. Notably, His side chains belonging to the HDAC6 binding site display different orientations (Fig. 16C-E). These residues were demonstrated to be crucial for HDAC6-selective inhibition 73. As a consequence, side chains conformations and absence of flexibility may also explain the relatively low success of our VS campaign. Moreover thanks to the recent crystallographic information, a specific water molecule was found to be positioned next to the zinc ion to assist the catalytic mechanism73. Pan-HDAC inhibitors have demonstrated the capability to displace such a water molecule in order to interact with the zinc ion in a bidentate manner, whereas HDAC6-selective inhibitors interacted with the metal as monodentate ligands while hydrogen-bonding with the water molecule 73. Although the zinc ion was in both cases pentacoordinated (the coordination number used in our docking studies), the presence of this water molecule may be an important parameter for a correct binding prediction.
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A B
C D E
Figure 16. Comparison between the HDAC6 homology model from Butler et al. 53 with HDAC6 crystallographic structure. HDAC6 homology model is shown as yellow ribbons, its residues as yellow sticks, and the zinc ion as a light blue ball; HDAC6 crystallographic structure (PDB code 5EDU 73) is shown as light blue ribbons, its residues as light blue sticks, and the zinc ion as a blue ball. Figures generated with MOE 2012. (A) Superimposition of both structures. (B) Zoom on both superimposed active sites. Molecular surface is drawn, in yellow, 4.5 Å around the residues characterizing the homology model of HDAC6. Residues from HDAC6 x-ray protruding from this surface highlight a narrower channel. Zoom on the His residues known to influence ligand binding:
His651from the chelating triad (C); His610 and His611 from the bottom of the pocket (D); His 499 and His500 from the channel rim (E).
If zinc parameterization is still a challenge in computational chemistry, both protein flexibility and solvent influence can be modeled and investigated through molecular dynamics simulations. This aspect will be developed in a next chapter.
Hit validation by means of in vitro bio-assays was a key checkpoint of our approach, contributing to the identification of limiting factors of the computer-aided methods. Nevertheless, in vitro techniques can also be affected by interferences. A number of compounds suffered from direct solubility issues or slow precipitation, and/or from fluorescence artefact being either quenchers or auto-fluorescents hence not compatible with a fluorogenic assay. Although fluorescence properties cannot be
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calculated but directly observed when testing, next studies should comprise solubility predictions to, at least, limit compounds rejection if still using fluorescence as a method to detect HDAC activity. The fluorogenic assay is very easy to handle and only requires a pH-meter, incubator and a fluorimeter as equipment. Nevertheless, when new scaffolds failed to be characterized in terms of HDAC activity because of fluorescence artefact, mass spectrometry methods can be employed, hence allowing not to give up on such scaffolds 74-78.
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