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Discovery of Covalent Ligands via Non-Covalent Docking by Dissecting Covalent Docking Based on a “Steric-Clashes Alleviating Receptor (SCAR)” Strategy Yuanbao Ai, Lingling Yu, Xiao Tan, Xiaoying Chai, and Sen Liu J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.6b00334 • Publication Date (Web): 13 Jul 2016 Downloaded from http://pubs.acs.org on July 14, 2016

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Journal of Chemical Information and Modeling

Discovery of Covalent Ligands via Non-Covalent Docking by Dissecting Covalent Docking Based on a “Steric-Clashes Alleviating Receptor (SCAR)” Strategy Yuanbao Ai1,2,#, Lingling Yu1,2,#, Xiao Tan1,2,#, Xiaoying Chai1,2, Sen Liu1,2,*

1

Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, China Three Gorges

University, Yichang 443002, China 2

College of Medical Science, China Three Gorges University, Yichang 443002, China

Abstract Covalent ligands modulating protein activities/signals have attracted unprecedented attention in recent years, but the insufficient understanding of their advantages in the early days of drug discovery has hindered their rational discovery and development. This also left us inadequate knowledge on the rational design of covalent ligands, e.g., how to balance the contribution from the covalent group and the non-covalent group respectively. In this work, we dissected the non-covalent docking from covalent docking by creating SCARs (steric-clashes alleviating receptors). We showed that the SCAR method outperformed those specifically developed but more complicated covalent docking protocols. We furthermore provided a “proof-of-principle” example by implementing this method in the first highthroughput screening and discovery of novel covalent inhibitors of S-adenosylmethionine

ACS Paragon Plus Environment

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decarboxylase. This work demonstrated that non-covalent groups play a pre-determinate role in the design of covalent ligands, and would be of great value in accelerating the discovery and development of covalent ligands.

Introduction Chemical ligands binding to protein targets have been extremely valuable either as clinical drugs curing 1-3

diseases or as protein function modulators interrogating biological systems

. Thus far, many

computational tools have been developed to help the screening and design of protein-binding ligands 4, such as AutoDock5, Dock 6, GOLD7, FlexX8, and Glide 9. However, these programs were mainly created for docking non-covalent ligands, largely limited by the concern of the off-target effects of covalent ligands and the fact that the main stream of rational drug design relies on non-covalent interaction mechanism between protein and ligands 3,10,11. Nonetheless, the recent years have witnessed the resurgence of the screening and design of covalent ligands due to the retrospective finding that onethird of approved drugs, accidentally, act through covalent mechanisms

12

. Consequently, several

computational tools, represented by CovalentDock 13, CovDock 14, CovDock-VS

15

, DOCKovalent

16

,

and DOCKTITE 17, have been developed specifically to fulfill this goal. Another widely used program, AutoDock4, recently added a covalent docking functionality as well

18

. Nonetheless, only

DOCKovalent has been used in guiding the forward discovery of new covalent ligands 16.

Although these protocols introduced new methods into covalent ligand docking, the covalent docking problem is still far away from being solved. The programs mentioned above were based on their noncovalent counterparts, while explicitly and complicatedly taking the covalent bonds formed between the ligand and the receptor into account at different stages. For instance, CovalentDock modified the


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scoring function and the autogrid calculation of AutoDock 13. DOCKTITE did a non-realistic receptor sidechain disconnection and added additional constraints to the Molecular Operating Environment (MOE) protocol 17. CovDock

14

and CovDock-VS

15

introduced mutations on the protein (the reactive

residue was firstly mutated to Ala, and then mutated back later) with extensive conformational sampling and covalent bond formation calculation. DOCKovalent, based on DOCK, limited a ligand’s sampling space by constraining the reactive group of the ligand close to the reactive residue of the protein with carefully pre-selected distances and angles, and also needed the modification of the scoring function to ignore the ligand’s electrophilic atom

16

. All of these modifications needed highly

professional skills, were complicated, case-dependent, and therefore, could be quite tricking. As a result, these modifications could be discouraging to new users, especially when applied to new targets. Meanwhile, limited by the relatively small number of the available training data of covalent ligandreceptor complex structures, the parameterizing and the explicit consideration of the formation of covalent bonds could inappropriately distort the results 16,19.

In fact, although the binding mechanism of covalent ligands seems quite different from that of noncovalent ligands due to the formation of covalent bonds, it is widely accepted that most covalent ligands should first have an initial, non-covalently docked conformation, which then orientates the covalent bonding group (the “warhead” group) of the ligand close to the reactive residues/atoms of the receptor

3,10,20

. Therefore, taking enzyme(E)-inhibitor(I) interaction as an example, the binding

mechanism of covalent ligands could be considered as the two separate steps shown in the chemical reaction scheme [1]: First, the formation of the non-covalent complex EI; Second, the formation of the covalent complex E-I 3,10,20,21. The kinact/Ki ratio represents the potency of a covalent enzyme inhibitor;


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therefore, both the non-covalent binding affinity (denoted by Ki) and the covalent bonding activity (denoted by kinact) make contributions to the binding of a covalent ligand 3,19,21.

∙ − +

[1]

Indeed, all of the aforementioned covalent docking protocols were trying to consider and balance the calculation of the contributions from the non-covalent binding and the warhead bonding. However, due to the explicit consideration of the covalent bonds in those protocols, an unanswered but important question is, to what extent their (the non-covalent binding group and the warhead bonding) respective contribution is for the binding of a covalent ligand, or should be in the context of covalent ligand design 20? For covalent ligands used in biological systems such as drugs, although the safety range of the reaction activity of the applicable “warhead” groups (the covalent bonding groups) has not been established very well, it should be quite confined with small variations so as not to be too high or too low to be both effective and specific

3,21

. As a result, the contributions of the covalent warheads in

different covalent drugs could be comparable, and the major difference might be from the non-covalent binding groups. More importantly, the covalent warheads must be correctly positioned firstly through non-covalent binding, and left enough time to react. Theoretically then, even for covalent ligands, the first non-covalent binding step might be determinant for receptor binding, warhead orientating, and covalent bonding. Although this hypothesis has been directly or indirectly applied in the design and optimization practices of covalent drugs previously 20, it has not been systematically studied in docking studies.


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To evaluate the covalent binding of a ligand with a protein target, a useful strategy is to mutate the covalent-binding residue in the protein. Here, based on our previous work 22, we borrowed this strategy to establish a parameter-free “steric-clashes alleviating receptor (SCAR)” method for covalent docking, in which the steric clashes between covalently bonding atoms were fully or partially eliminated by in silico protein mutagenesis. We used this strategy to dissect the non-covalent docking step from the covalent bonding process, and applied it in the high-throughput in silico screening of covalent inhibitors. We showed that this method is easy to implement, highly effective and widely applicable. Based on this strategy, we screened and identified new covalent AdoMetDC inhibitors with novel molecular scaffolds. This work theoretically demonstrated that the non-covalent binding step is predeterminant for the binding of covalent ligands, as well quite efficient in orientating the warheads of covalent ligands. It also showcased that by incorporating protein design strategies, computational drug screening and design protocols could be more efficacious.

Results 1. The SCAR method re-captured the crystal conformations of covalent AdoMetDC inhibitors In our previous work Vina

28

22

, we noticed that, when using the non-covalent docking program AutoDock

to dock a covalent inhibitor, the main difference between the docking conformation and the

crystal conformation was in the warhead group, which was majorly affected by the steric clashes between the covalent-bonding atoms. In the active form of AdoMetDC, the Glu67-Ser68 peptide bond is auto-cleaved, and then Ser68 is converted to a pyruvoyl group, which can form a Schiff base with covalent inhibitors (Fig. 1a & 1b). Therefore, in non-covalent docking, it was impossible that the covalent atom (N in this case) in the ligand could overlap with the atom O3 of the pyruvoyl group (Pyr68-O3 in this case) (Fig. 1b & 1c). In order to eliminate this steric clash, we fully removed Pyr68


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to make a “steric-clashes alleviating receptor (SCAR)” (Fig. 1d), which was then energetically optimized and repacked in Rosetta

25

(Text S1). Besides, we supposed that Ser69-OG and Ser69-N

(Fig. 1d), at least partially, could provide compensating force field for hydrogen bonding and electrostatic interaction that could exist between Pyr68-O3/Pyr68-N and the covalent NH2 group of the ligand before the formation of the covalent bond, and this force field would help orientate the ligand.

First, to test the usability of the SCAR AdoMetDC, we did the docking test with known AdoMetDC inhibitors (Table 1, and Fig. S1a & S1b) using AutoDock Vina with non-covalent docking settings (Text S2). Both the protein and the ligands were then prepared in MGLTools (Text S3). Surprisingly, the docking protocol successfully re-captured the crystal conformations of all of these inhibitors, including both the covalent and the non-covalent inhibitors (Fig. 1e, and Table 1). In our docking process, up to twenty conformations were outputted for each inhibitor. We noticed that in these docking conformations, a conformation highly similar (evaluated by the RMSD values of nonhydrogen atoms) to the crystal conformation was captured and ranked in top 5 by docking score. For all inhibitors, the best docking conformations had a RMSD value less 1.0 Å, except the inhibitors in 1I7B (1.25 Å), 3DZ2 (1.2 Å), and 3DZ7 (1.21 Å). Although these three results could be considered highly accurate for in silico docking, we noticed that the major variance compared to the crystal conformations was in the covalent groups (warheads). For the inhibitor in 1I7B, the terminal methyl formate group in the docking conformation was placed in the position of Pyr68, which could be due to the caveat of the SCAR method and/or the limitation of the docking protocol. For the inhibitors in 3DZ2 and 3DZ7, the crystal structures did not have covalent bonds between Pyr68 and the inhibitors, but their docking conformations were positioned to be suitable for covalent bonding as the others. It should be pointed out that, although covalent bonds were not noticed in the crystal structures for many


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inhibitors listed in Table 1, those inhibitors were rationally designed to be covalent inhibitors (except MGBG, SAM486A, M2T, and N8M) 32, so the reason could be that the warheads in these inhibitors are not active enough. We also docked another two reported AdoMetDC inhibitors without crystal structures reported thus far (Table 1), and found that these two inhibitors had similar binding conformations with the other inhibitors, with the terminal -NH2 groups positioned well to form the Schiff base (Fig. 1f). Therefore, this docking analysis showed that the warhead group (-NH2) in a covalent AdoMetDC inhibitor could be correctly placed at the position of Pyr68-O3, which provides the possibility for the formation of the Schiff base.

Since this analysis showed that the non-covalent interaction alone is able to position covalent ligands well, we asked how much the non-covalent interaction contributes to the binding affinity of a covalent ligand. So we calculated the correlation between the experimental IC50 values (log(IC50 nM)) and the non-covalent docking scores of the best re-captured conformations (Table 1, and Fig. 1g). Because MGBG (the inhibitor in 1I7C) is very different from the other inhibitors structurally, it was excluded from the linear fitting analysis (Fig. S1b, S1c & S1d). When only the data of non-covalent inhibitors (as seen in crystal structures) were used, the Pearson’s r was 0.97 (Fig. 1g). When the covalent inhibitors (as seen in crystal structures) were included, the Pearson's r was 0.72 (Fig. 1h). These correlationships indicated that the non-covalent interaction is likely pre-determinant in the recruiting and the initial binding of both the non-covalent and covalent inhibitors, but the covalent bonding largely increased the affinity of covalent inhibitors, since two of the three covalent inhibitors had obviously weaker predicted affinities (higher docking scores). We should point out that errors could exist in the imperfection of scoring functions, and/or the experimental IC50 values, which are time-


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dependent for covalent inhibitors and highly controlled by the experimental details 17. Meanwhile, the kinact/Ki ratio could be a better indicator of the potency of a covalent ligand 3,17,19,21.

From this test, we concluded that the SCAR method might be very powerful in covalent docking, since it could improve the in silico docking accuracy of covalent inhibitors while keeping the docking accuracy of non-covalent inhibitors. Our analysis also confirmed that the non-covalent docking step is very important for the positioning of not only the non-covalent binding groups, but also of the covalent groups. The affinity fitting result further showed that the non-covalent interaction could have a dominant contribution in the binding of many, if not all, covalent ligands.

2. The SCAR method is widely applicable to the prediction of the binding conformations of the other covalent inhibitors To see if the SCAR method is also generally applicable to other covalent-docking examples, we chose to test it with a dataset containing 76 protein/covalent-ligand complexes (Table S1) used in CovalentDock 13, CovDock 14, and DOCKTITE 17. In this dataset, the covalent residues in the receptors are either Ser or Cys in the middle of a protein chain, and the ligand warheads are β-lactam, acrylamide, or vinylketone. Therefore, these covalent residues could not be fully removed as in the AdoMetDC case to eliminate atom clashes. To making SCAR proteins for these, we chose to only eliminate the sidechains by mutating the covalent residues to Gly, since the ligands approach the covalent residue from the terminus of the sidechain in these cases. The mutated proteins (SCARs) were either directly used for docking test, or fully repacked and minimized in Rosetta before docking. During the repacking and minimization of a receptor, the ligand was not included to exclude its constraints, although this


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could increase the docking difficulty due to the freely relaxation of the residue sidechains surrounding the ligand.

As mentioned in the Introduction section, the previous covalent docking protocols limited the searching space of a ligand, either implicitly or explicitly, via forced covalent bonding between the ligand and the covalent residue in the receptor. This constraint might introduce too much artificial interference in the docking step and/or the conformation-ranking step. To avoid this, in our docking study, we did a ligand-based alignment for all of these complexes first, and then defined a universe conformation-searching grid box in MGLTools

5

for all ligands (Fig. 2a). The grid dimensions were

much bigger than any of these ligands, so due to the imperfections in conformational searching and scoring functions, it could increase the docking difficulty of the ligands (especially for smaller ones) and lower the ranking of the correct conformations. However, this step would provide more objective evaluations on the docking results.

To compare with previous covalent docking protocols, we used the RMSD values of the non-hydrogen atoms of the ligands between the crystal conformations and the docking conformations as a docking quality indicator. If the ligand had missing atoms in crystal structures, only the existing non-hydrogen atoms were used for RMSD calculation. Similar as in the ref

13

, the β-lactam ring in those ligands

containing it were modeled as the broken-ring, final-product form if applicable. For alternative conformations in crystal structures, the conformer A was used. It was well known that the starting 3-D conformation of a ligand largely affects the docking result 33, so to compensate the caveat of different conformation generating programs, three 3-D starting conformation sets were used if available, which were: (i) converted from the crystal conformations, (ii) downloaded from the Chemical Components


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Database

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, or (iii) generated using RDKit

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from 2-D structures. The last two sets were

computationally generated 3-D conformations, and all three sets were allowed to rotate on rotable bonds, so that no pre-existing structural information was used, except that these different sets could help to avoid local energy minimums in the limited conformation searching trials. Both the final docking files of the receptors and the ligands were automatically prepared with the MGLTools 5.

Our result showed that the SCAR method was generally better than the aforementioned covalentdocking programs (Table 2). As mentioned above, we did not add any constraints on the ligand or the receptor, and defined a large conformation-searching space, so the ranking of the best conformation could be decreased largely. Therefore, we filtered the docking results by only considering those conformations in which the covalent atom of the ligand was 1.0 Å or less from its actual position in the crystal structure. Not surprisingly, this “post-docking constraint” significantly improved the result of the top pose (Table 2). We should note that this “post-docking constraint” did not compensate the difficulty in the conformation searching step, and therefore should not be taken as a full optimization. In another recent work, London et al.

16

presented a docking protocol (DOCKovalent) that has been

successfully used for novel covalent ligand screening. They also tested their protocol on the lactams in the same dataset. Compared to their protocol, the SCAR method was much better too (Table S2).

For the ligands with a top-pose RMSD larger than 2.0 Å, we noticed that the following two causes were common: (i) The major difference was in the groups far away from the covalent group and largely exposed, such as in 1CEF, 1KVM, 1W8Y, 2EX9, 3A3I, 3DWZ, 3IQA, and 3N8S (Fig. 2b); (ii) The binding region, along with the defined searching space, was too large, such as in 1FCO, 1FR6, 1I5Q,


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1LL5, and 3M6B (Fig. 2c). These two causes affected the quality of the top poses, but were likely attributed to the imperfection of the non-covalent docking/scoring functions.

To further test the compatibility of this method with computational protocols, we fully repacked and minimized the SCARs (ligand-free) in Rosetta before docking. This preparation of crystal structures from PDB is normally necessary for computational software, since crystal structures might contain clashed atoms, structural errors, and/or software-unfriendly rotamers. However, this process generally increases the docking difficulty, especially when the ligand-free structure is used for repacking. Our result showed that this structural optimization slightly deteriorated the high-resolution (RMSD ≤ 1 Å) docking conformations, but the overall result was still good (Fig. 2d, Table 2, and Table S1 & S2).

This comprehensive docking analysis demonstrated that even without considering the contribution of the covalent warheads, the non-covalent docking could predict the binding conformation of covalent inhibitors. This result therefore theoretically verified the hypothesis that the non-covalent binding step is pre-determinant in the binding of covalent ligands, as well as indicated that the non-covalent docking could be the first step in the binding process of most covalent ligands.

3. The SCAR method was successfully used in the discovery of novel covalent AdoMetDC inhibitors AdoMetDC is a critical enzyme in the polyamine pathway, and has been an important drug target in clinic. Although the 1st generation AdoMetDC inhibitor (MGBG) and the 2nd generation AdoMetDC inhibitor (SAM486A) are non-covalent, the 3rd generation inhibitors are all designed to be covalent inhibitors by forming the Schiff base with Pyr68 (Fig. 1a). The covalent inhibitors are becoming


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attractive because compared to covalent inhibitors, non-covalent inhibitors were more obviously counteracted by the increased expression of AdoMetDC

32

. However, all the current 3rd generation

AdoMetDC inhibitors are substrate analogs (Fig. S1a & S1b) and, by then, not very fruitful, possibly due to the fact that the widely existed adenosine group is deficient in specificity. Therefore, we hoped to look for new covalent AdoMetDC inhibitors with novel molecular scaffolds. To that end, we made up a high-throughput screening protocol (Fig. 3a) based on the SCAR method.

Since the SCAR method does not consider the activity of the warhead, we pre-selected the ligands for docking by dissecting the ligand structure into two segments: the warhead group for covalent bonding, and the rest moiety for non-covalent binding. Similar to DOCKovalent and DOCKTITE, we first defined the warhead groups based on the terminal groups of the known AdoMetDC inhibitors (Table 1). The potential warheads of non-covalent inhibitors (as seen in crystal structures) were also included to expand the hit possibility in case they are reactive with different non-covalent backbones. Next, including the warhead groups and the non-covalent groups, the molecular scaffolds were defined based on drugability, including the molecular weight, polarity, rotable bonds, and hydrogen donors/acceptors (Text S4). Then, similar to London et al.

16

, the SMILES expressions of the warheads were used to

screen the ZINC library according to the durgability rules (Text S4). Finally, 494 molecules were selected for docking using the SCAR AdoMetDC prepared from 3DZ5 (PDB ID). The docking results were subject to a further conformation filter to see if there exists an optimal conformation with: (i) a NH2 group in the warhead group being 1.5 Å or less from the position of the covalent atom (N) of the ligand in the original crystal structure, and 5.0 Å or less from Ser69N; and (ii) a docking score 35,000,000)

MolPort-002-918-828

AdoMet AdoMetDC + AdoMet AdoMetDC + AdoMet + MGBG (20 µM) AdoMetDC + AdoMet + MCULE-3265041117 (2 µM) AdoMetDC + AdoMet + MCULE-3626300541 (4 µM) AdoMetDC + AdoMet + MCULE-4717492978 (2 µM)

MolPort-002-917-336 MolPort-002-914-366 MolPort-002-913-843 MolPort-001-759-360 MGBG

Non-covalent docking (494)

DMSO

0

20

40

60

80

100 120

Activity (%)

c.

e. DMSO

MGBG

0.20

0 min 5 min 10 min

0.10

0.00

Expert evaluation (93)

0.10 0.05

7

8

9

0.00

10

7

8

Time (min)

0 min 5 min 10 min

AU

0.15 0.10

0.20

0 min 5 min 10 min

0.15

0.00

8

0.00

10

AU

0.10 0.05 7

8

10

160

30800

40

40500 30700 0

8

9

10

Alpha Chain

80 60 MCULE-3265041117 MGBG MCULE-4717492978 MCULE-3626300541 DMSO

20 0

0 min

5 min

Whole protein

AdoMetDC AdoMetDC + MCULE-3265041117 (237 Da) AdoMetDC + MCULE-3626300541 (265 Da) AdoMetDC + MCULE-4717492978 (218 Da)

100

40

40400

10 min

Time (min)

AdoMetDC

40000 20000 0 29000

30000

31000

32000

Mass (m/z)

33000

25000 20000 30654 15000 10000 5000 0 29000 30000

30891

31000

32000

33000

60000 40000

30654

30919

20000 0 29000


Mass (m/z)

AdoMetDC + MCULE-4717492978 (218 Da)


Intensity

30654

Intensity

60000


30000

31000

32000

Mass (m/z)

Intensity

f. Intensity

9

Percenage of peak area (%)

0 min 5 min 10 min

0.15

40600

155

Time (min)

0.20

0.00

30900

30600 7

Time (min)

MCULE-4717492978

179

203

0.10 0.05

0.05

Experimental screening (33)

10

MCULE-3626300541

AU

0.20

Purchase inquiry (78)

9

Time (min)

MCULE-3265041117

40700

Mass (m/z)

0.05

31000

0 min 5 min 10 min

0.15

Mass (m/z)

AU

0.15

0.20

AU

Conformation ﬁlter (171)

33000

10000 8000 6000 4000 2000 0 29000

30872 30654

30000

31000

32000

Mass (m/z)

33000

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a.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

MCULE-3265041117

M8M OH

HO H2N

N

N

NH2

N O

NH

H 2N

H 2N

NH

N

MCULE-4717492978

Cl

O

O

N

O

MCULE-3626300541

O

H N

H N O

O

H 2N

H N O O

S

Ser69

Glu67 Phe7

His243 Thr245

Glu247

b.

c.

Category B MCULE-7674667984 MCULE-8085757434 MolPort-002-181-159 MolPort-002-922-434 MolPort-002-916-916 MolPort-002-916-575 MolPort-002-914-625 MolPort-002-914-317 MolPort-002-708-250 MGBG DMSO

0

20

40

60

80

100 120

Activity (%)

Category C

d.

MCULE-3746545028

εHOMO εLUMO Hardness Chemical Electrophilic Ionization (η) potential (μ) ity index (ω) potential (IP) (eV) (eV)

MolPort-002-914-438 MolPort-002-868-773

MCULE-32 65041117 MCULE-47 17492978 MCULE-36 26300541

MolPort-002-345-107 MolPort-001-775-519 MGBG DMSO

0

20

40

60

80

Activity (%)

100

120

8

Electron Electrone aﬃnity (EA) gativity (χ)

-6.37

-1.69

4.68

4.03

1.73

6.37

1.69

4.03

-6.15

-1.74

4.41

3.95

1.77

6.15

1.74

3.95

-6.12

-1.31

4.82

3.71

1.43

6.12

1.31

3.71

3.27

0.99

5.96

0.57

3.27


-5.96

-0.57

5.39

Discovery of Covalent Ligands via Noncovalent ... - ACS Publications

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