Dependence of results on applied LUDI parameters – Comparison with

Since any virtual screening method has to face docking and scoring as two critical issues, the calculated LUDI results were firstly analysed for their dependence on the applied docking parameters. For developing a more efficient selection strategy, all nineteen compounds were investigated.

In a first step, the calculated LUDI scores of selected compounds from the Lead-Quest^® database run were compared with the results of the combined database run with respect to the sphere radius and the flexibility of docked ligands. Additionally, the observed differences were related to the rms deviation of the docked poses^‡‡ in each LUDI run. Results for five selected compounds are presented in Table 3.2.

Compounds 2 and 3 from the Accelrys database were only included in searches us-ing a sphere radius of 8 Å. Their calculated scores were compared with respect to rotating bonds present or not (irot = 1 or 0). For compound 2, LUDI suggested a rather similar spatial position in both calculations, but the predicted inhibition con-stants differed by a factor of 35 (60 M and 1.6 M for the first and the second cal-culation, respectively). This may result from a probably high overestimation of ionic

†† In cooperation, soaking experiments for 1,3-diacetylbenzimidazole-2-thione (17) with hylSpn were accomplished by M. J. Jedrzejas, Children’s Hospital Oakland Research Institute, Oakland, USA.

‡‡ target-bound conformation and orientation of each screened ligand

interactions (six instead of four) between the molecule’s carboxylic moieties and the amino acid residues Arg468 and Arg634 of the enzyme (data not shown, see Figure 3.3). Additionally, these ionic interactions as well as hydrogen bonds are direc-tional,²⁸ i.e. they significantly depend on the spatial arrangement of the ligand and the interacting groups of the protein. By contrast, the results for compound 3 did not vary upon alteration of flexibility parameters. But also in this case, the LUDI score seems to be largely overestimated because of ionic interactions.

This problem might be tackled by changing the protonation states of the carboxylic functions on both molecules. During the automated generation of the GENFRA data-bases, the protonation states of the processed compounds may be defined by ap-plying a special parameter. This parameter could not be altered due to an error in the LUDI programme supplied by Accelrys. Unfortunalely, despite of intensive efforts to solve this issue, the Accelrys hot line could not provide a fix for this crucial error. Al-ternatively, both compounds were treated as uncharged carboxylic acids after manu-ally altering the protonation states, but then they were not retrieved by LUDI (data not shown).

Although compound 6 was docked in an identical position by LUDI (rmsd = 0.00 Å), the calculated LUDI scores were slightly dependent on the sphere radius, but not on the allowed flexibility of the ligand. Probably, a larger sphere with more and/or addi-tional, differently typed interaction sites (in this case hydrogen donor sites) led to this observed change because the LUDI scoring function is cumulative with respect to the free energy contribution from hydrogen bonds, ionic, and lipophilic interactions.²⁸ The LUDI scores of compound 12 vary in similar manner as in the case of compound 6.

The slight difference of the docking positions (rms deviation of 0.89 Å) might reflect the dependence of the scores on the absolute position of the interacting functional groups of the ligand and the protein. For compound 17, a significantly different effect can be observed. Despite a fairly similar LUDI score for all four settings, the respec-tive docking pose is strikingly different with a rms distance of 4.65 Å as depicted in Figure 3.4. This result underlines that alternative poses of the ligand might lead to similar LUDI scores and, thus, similar predicted inhibition constants. An identification of the native binding mode, either manual or by means of the docking programme, is therefore a basic requisite for the reliable prediction of binding affinities.¹¹

Several methods were described in the literature for slightly differing scenarios like 1) structure-based inhibitor design,^29,30 2) virtual screening by pharmacophore filters,²⁴ and 3) validation of scoring functions.^31,32 In the first scenario, Grädler et al. cali-brated the LUDI score starting from a proteligand complex of a substrate-like in-hibitor with known Ki value by adjusting the LUDI parameters so that the calculated pose resembles the detected binding mode of the co-crystallised ligand and that the predicted score corresponds to the measured Ki value. Following this approach, the X-ray structure of hylSpn complexed with L-ascorbic acid⁴ could suit as reference.

After superposition of the hylSpn-vitamin C structure with the hylB4755 model, vita-min C was merged into the model, and a very low LUDI score of 64 corresponding to a predicted Ki value of 230 mM was calculated (For comparison, the measured IC50 value of L-ascorbic acid on hylB4755 is 5 mM, see chapter 6.). Therefore, vitamin C could not serve as a reference since its LUDI score falls below the threshold of 300 for acceptable values and, moreover, since it could not be retrieved in several LUDI calculation attempts.

Table 3.2. Overview of the calculated LUDI Score in dependence of the applied parameter and of the resulted docking accuracy

Compound

No. LUDI Score^a rmsd

r = 5 Å

irot = 0 r = 5 Å

irot = 1 r = 8 Å

irot = 0 r = 8 Å

irot = 1 in Å

2 — — 422 579 1.44^b

3 —- — 555 555 0.00^b

6 400 400 441 441 0.00^c

12 421 421 392 392 0.89^c

17 343 343 363 363 4.65^c

aCalculated LUDI score with indicated parameters for sphere radius r and rotatable bonds on or off (irot = 1 or 0) brms deviation between the pose positions calculated with parameters r = 8 Å, rotatable bonds off (irot = 0) and parameters r = 8 Å, rotatable bonds on (irot = 1)

crms deviation between the pose positions calculated with parameters r = 5 Å, rotatable bonds off (irot = 0) and parameters r = 8 Å, rotatable bonds on (irot = 1)

Figure 3.4. Two putative binding modes of compound 17 dependent on the LUDI sphere radius (5 Å, purple, 8 Å, green) encompassing the active site of the hylB4755 model. Hydrogen bonds (yellow) be-tween both acetyl substitutents of the molecule and Arg409 and Arg634 are depicted in dashed style.

A pilot project for virtual screening of human carboanhydrase II inhibitors published by Grüneberg et al.^24,33 represents the second scenario. In their work, a hierarchical filtering strategy was applied using a 2D query for essential functional groups, sub-sequently a 3D pharmacophore query and finally a similarity scoring with FlexS³⁴ based on known potent inhibitors resulting in around 3300 of 90000 compounds at the beginning. The 100 best scoring molecules were then flexibly docked with FlexX³⁵ and ranked by FlexX score and DrugScore.³⁶

The third scenario was described by Stahl et al. who presented a detailed analysis of scoring functions for virtual screening using the programme FlexX as docking en-gine.³² A well-chosen combination of two tested scoring functions led to a new, ro-bust scoring scheme called ScreenScore, which is implemented in the latest versions of FlexX. The results obtained with ScreenScore were compared with those from the combination of other scoring functions, a method called ‘consensus scoring’.³⁷ It was suggested that consensus scoring offered more robust results than individual scoring functions.³⁸

In the present work, we attempted to combine approaches from the second and the third scenario in order to obtain a more reliable selection of compounds. In the first step, a consensus scoring of all hits from the calculation with the combined data-bases was envisaged. To date, several consensus scoring approaches like the CScore modul implemented in SYBYL³⁹ and X-Score⁴⁰ have been described in litera-ture. Since X-Score was readily available from the authors, all hits from the combined databases run were re-scored using standard parameters. The hylB4755 model and the poses of all hits were taken as calculated by LUDI in the combined database run.

The results of this re-scoring approach with X-Score are summarised in Table 3.3.

The comparison of the ranking order of both scoring functions revealed that the se-lected compounds were all resorted by the X-Score scoring function. But, in general, the predicted Ki and Kd values of most investigated compounds are rather similar, e.g. for compound 2 with Ki = 1.6 µM as LUDI and Kd = 4.4 µM as X-Score prediction.

Only compounds 11, 18 and 19 differ in their predicted Ki and Kd values, respectively, by more than one order of magnitude. Such discrepancies are certainly due to the different ‘basis sets’ of the scoring functions. On the one hand, no standardised data set of protein-ligand complexes exists for calibration and, on the other hand, the number and type of appropriate biological systems is rather limited.¹¹ Nevertheless, Table 3.3 shows that the selection of compounds by their X-Score is as effective as using the LUDI score with respect to picking of potentially active inhibitors.

In general, active compounds are more reliably predicted if a docking engine like LUDI reproduces crystallographically observed (native-like) binding modes within a certain accuracy reflected by a rms deviation below 2.0 Å.^24,32,41

Recently, in order to cover the entire conformational space of the candidate mole-cules as well as possible and then to retrieve their native-like binding geometries, flexible docking of the selected compound set followed by – at best – a consensus scoring scheme was proposed.^24,41,42 In our study, we tried to adapt a similar strategy to investigate whether it would facilitate the selection of compounds.

Table 3.3. Comparison of calculated LUDI score and X-Score for selection of proposed compounds

Compound No. LUDI score^a X-Score^b Inhibitory activity^c

rank score rank score

2 1 579 2 5.36 +

3 2 555 15 5.10 +

5 6 452 32 4.78 –

7 10 436 127 3.96 +

9 17 426 141 3.78 +

11 32 403 1 5.41 +

12 46 392 146 3.65 +

13 49 391 90 4.30 –

14 57 384 75 4.43 +

16 79 364 53 4.60 +

17 80 363 116 4.04 +

18 89 357 32 4.78 –

19 131 328 15 5.10 +

a 10 ₁₀₀

score

Ki

= −

b score

Kd =10⁻

cligand with inhibitory activity on hylB4755: +; ligand without inhibitory activity on hylB4755: –

Therefore, all proposed hits of the combined database run were subjected to an automated flexible docking procedure using FlexX with standard parameters. The results of the FlexX calculations are summarised in Table 3.4. For comparison, the poses calculated by LUDI were taken as reference since LUDI is supposed to be able to predict reliable binding modes.^30,43 A rms deviation smaller than 2 Å between FlexX and LUDI poses was taken as a criterion for the prediction of a LUDI-like binding mode.

Table 3.4. Overview of FlexX results with respect to docking score and docking accuracy compared to poses calculated by LUDI

Compound No.

LUDI-like pose^a FlexX best pose^b Inhibitory activity^c

score rmsd/Å score rmsd/Å

5 -22.59 0.97 -26.57 2.05 –

12 -14.84 3.78 -15.88 4.88 –

19 -12.87 0.41 -14.20 4.93 +

7 -12.59 1.34 -22.72 5.34 +

2 -12.06 1.61 -18.70 5.89 +

11 -12.02 0.91 -14.20 4.74 +

13 -11.77 5.32 -17.46 6.82 –

3 -11.45 1.53 -13.56 3.09 +

17 -10.08 0.95 -16.83 5.31 +

16 -9.37 1.57 -18.52 8.42 +

9 -8.72 1.14 -15.41 7.89 +

14 -7.68 1.13 -19.43 5.62 –

18 -3.56 1.85 -10.68 14.46 –

a FlexX-Score of the FlexX pose with the lowest rmsd compared to the LUDI pose from the combined database run

bFlexX pose with the best FlexX-Score among all poses, rmsd compared to the LUDI pose from the combined database run

c ligand with inhibitory activity on hylB4755: +; ligand without inhibitory activity on hylB4755: –

For each of the investigated hits, the pose with the best FlexX score and the LUDI pose are very different (column FlexX best pose). There is no correlation between the FlexX scores and inhibitory activity so that a compound selection uniquely based upon this score is not more efficient than a selection based on any other scoring function. However, when the most LUDI-like FlexX poses are considered, the predic-tion is improved. Apart from two inactive compounds (12 and 13) where FlexX did not find a LUDI-like pose and one outlier (compound 5) with no inhibitory activity but high FlexX score, this double-docking-double-scoring procedure may be able to privilege active ligands (e.g. compounds 7 and 19 vs. compounds 14 and 18) and should therefore be applied for the selection of new compounds in future work.

Im Dokument Structure-based design of hyaluronidase inhibitors (Seite 47-54)