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Molecular Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors Gerard Martinez-Rosell, Matt J. Harvey, and Gianni De Fabritiis J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.7b00625 • Publication Date (Web): 26 Feb 2018 Downloaded from http://pubs.acs.org on March 1, 2018

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Journal of Chemical Information and Modeling

Molecular Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors Gerard Martinez-Rosell,† Matt J. Harvey,‡ and Gianni De Fabritiis∗,¶ †Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C/ Doctor Aiguader 88, 08003 Barcelona, Spain ‡Acellera, Barcelona Biomedical Research Park (PRBB), C/Doctor Aiguader 88, 08003, Barcelona, Spain ¶Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, Barcelona 08010, Spain E-mail: [email protected]

Abstract

Markov state model (MSM) framework.

Fragment-based drug discovery (FBDD) has become a mainstream approach in drug design because it allows the reduction of the chemical space and screening libraries while identifying fragments with high protein-ligand efficiency interactions that can later be grown into drug-like leads. In this work, we leverage high-throughput molecular dynamics (MD) simulations to screen a library of 129 fragments for a total of 5.85ms against the CXCL12 monomer, a chemokine involved in inflammation and diseases such as cancer. Our in silico binding assay was able to recover binding poses, affinities and kinetics for the selected library and was able to predict 8 millimolaraffinity fragments with ligand efficiencies higher than 0.3. All the fragment hits present a similar chemical structure, with a hydrophobic core and a positively-charged group, and bind to either sY7 or H1S68 pockets, where they share pharmacophoric properties with experimentally-resolved natural binders. This work presents a large-scale screening assay using an exclusive combination of thousands of short MD adaptive simulations analyzed with a

Introduction Fragment-based drug discovery (FBDD) started to get popular in early 2000s as an alternative to high-throughput screening 1 (HTS) or virtual screening 2 (VS) of drug-like molecules. This tendency has continued until our days to the point that FBDD has become a mainstream technique and the driver technology of more than 30 drug candidates. 3 The main characteristic that differentiates FBDD from a typical drug-like HTS approach is the size of the ligands employed in the screening phase. In particular, fragments are usually defined as having less than 20 non-hydrogen (or “heavy”) atoms while drug-like molecules can go up to 30 heavy atoms or more 3 and can be understood as a combination of two or more fragments. 4 Additionally, fragments have less functionality and are correspondingly weaker than most drug-like hits in HTS, with binding affinities in the range of mM-30µM. 5 Therefore, while the objective of a HTS technique is to find directly a drug-like lead, the approach used in FBDD is to discover small millimolar-binding fragments

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with high ligand efficiency 6,7 (LE) that can later be extended or linked together to form a drug-sized lead. 8 Several advantages characterize FBDD. First, the smaller size of the ligands reduces the accessible chemical space. A study calculated that each heavy atom adds roughly one order of magnitude to the number of possible chemical combinations. 9 This implies that the chemical space of drug-like molecules is many orders of magnitude bigger than the fragment chemical space. A practical consequence of this fact is that a fragment library usually consists of only 1,000-5,000 compounds 10 while a drug-like library usually comprises between 0.5 and 3 million compounds. 1 Second, fragment libraries have been reported to yield higher hit rates than HTS. 11,12 The rationale behind this observation is that, as molecules grow, there is more probability that a chemical group causing an unfavorable interaction is included in the molecule and that the introduction of this group ruins completely the affinity for the target. Conversely, fragments, due to their small size, establish less interactions with the target and should be able to bind to a greater number of sites. Moreover, the quality of interactions between a fragment and a protein is usually high, as supported by the conservation of the binding mode as the fragments are grown into larger molecules. 13,14 These characteristics make FBDD specially appealing to tackle difficult targets such as allosteric sites or proteinprotein interaction interfaces (PPIs). Several experimental techniques are routinely used in FBDD. 3 These have to be sensitive enough to detect low affinity fragments and, when possible, shed light on the binding mode of the ligand to enable structure-based drug design (SBDD). The most predominant ones are surface plasmon resonance (SPR), nuclear magnetic resonance (NMR) spectroscopy and Xray crystallography, of which only the last two yield structural hints of the binding mode. On the other hand, computational methods such as docking or pharmacophoric screening have been sparsely used in the literature. 15 However, in general, the applicability of in silico FBDD has been quite limited to date, par-

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tially due to promiscuity of fragments binding mode 16,17 and docking limited ability to correctly describe protein conformational plasticity 18,19 and to score fragments. 15 Molecular dynamics (MD) simulation has been proposed as a computational alternative able to characterize the binding of fragments with atomic detail and femtosecond time resolution. Early work on the benzamidine-trypsin system 20 or the development of methods to map fragment binding hotspots such as SILCs 21,22 or MDmix, 23 exemplify the potential of the technique to recover fragment occupancies and even binding affinities. Additionally, recent validation work on factor Xa 24 and on FKBP 25 further support the ability of MD to correctly rank affinities and kinetics for a set of fragments given a protein system. We present here an MD-driven fragment screening study using a library of 129 compounds and a total simulation time of 5.85ms against the chemokine CXCL12. We obtained the compound library by filtering the ZINC 26 database using a combination of chemical property filters, docking to MD-sampled protein conformations and scaffold filter. Then, for each fragment, we built systems containing the protein and one ligand embedded in a water box and simulated them using an adaptive sampling scheme to explore the binding space efficiently. CXCL12 (stromal cell-derived factor-1/SDF1) is a chemokine, a small dimerizable soluble protein that stimulates chemotactic cell migration via activation of a G-protein coupled receptor (GPCR). 27 Its structure consists of a C-terminal α-helix, three anti-parallel β-sheets and an N-terminal flexible loop (Fig. 1D). CXCL12 and its receptor CXCR4 are particularly well studied and their participation in physiological processes 28 (e.g. embriogenesis, wound healing, stem cell homing) as well as morbid processes (e.g. autoimmune diseases, 29 cancer, 30–32 HIV 33,34 ) is known. In particular, the significant role of the CXCR4/CXCL12 axis in metastasis, tumor survival and tumor angiogenesis has raised the interest in developing targeted drug therapies. 30 The selection of CXCL12 as the test system responds to: (a) the small size of the protein


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been resolved bound to heparin 40 (Fig. 1C). The binding of all these residues to CXCL12 involve the formation of small pockets and therefore reveal potential binding hot spots that can be leveraged to design and dock specific inhibitors. Consistently with this hypothesis, recent studies report small molecules binding to sY21, 36,41,42 sY12 43 and I4/I6 43 binding pockets. In order to characterize the electrostatic properties of the CXCL12 protein surface, we ran Adaptive Poisson-Boltzmann Solver 44 (APBS) to study the charge distribution over the protein surface. The overlap of the binding hot spots previously described with a generally positively-charged surface (Fig. 1A) helped us to take the decision of selecting a negativelycharged compound library for screening. Furthermore, in order to study the dynamics of the CXCL12 monomer and generate protein conformations for docking, we produced an ensemble of 57.6 microseconds of simulation data of the Results and discussion protein alone solvated in a box of water. Using as metric the dihedral angles of the backCXCL12 surface characterization bone, we built a Markov State Model (MSM) While most attempts to block the CXCR4/CXCL12 and clustered the data into 8 macrostates. A representative from each macrostate was picked axis have been focused on inhibiting the recep(Fig. S2) and further used in docking. Intor CXCR4, 35 which presents a clear drugterestingly, one of the macrostates (Fig. S2.1) gable cavity where the chemokine CXCL12 displays a transient pocket involving CXCL12 docks, targeting CXCL12 has traditionally first and second beta-sheets that was already deemed “undruggable” due to its shallow surdescribed in another study. 45 face. 36 However, recent studies have shown that (89-140 residues depending on the isoform and 32,000 atoms in a complete solvated proteinligand system), which allows a reduction of the computational cost, (b) the difficulty of the target, which is known to present a very “soft” surface, and (c) its involvement in an array of morbid processes, which makes it very clinically relevant. Even though CXCL12 monomer is known to be a difficult target in terms of druggability due to the shallowness of its surface, we were able to characterize the binding mode and affinity of at least 8 hit fragments (∼6% of the total) with high predicted ligand efficiency (LEpred > 0.3). These fragments bound repeatedly to two small cavities (Fig. 1F) detected experimentally, namely sY7 and H1S68, and the pharmacophoric properties of their binding modes are consistent with experimentally-resolved natural binders.

CXCL12 surface is not completely flat. In fact, scientists have learned about CXCL12 druggability by studying the interaction between the chemokine and its receptor. This proteinprotein interface has been resolved via NMR in several cases, some of which are displayed in Figure 1. From the inspection of these structures and mutation studies, we learned that the CXCL12-CXCR4 interaction and affinity is mediated by key CXCR4 residues, 37–39 some of the most important being tyrosines 7, 12, 21 (Fig. 1B and 1E) and isoleucines 4 and 6 (Fig. 1E). The o-sulfation of the aforementioned tyrosines (Fig. 1B) in the Golgi apparatus seem to selectively enhance the affinity of CXCR4 for CXCL12. 39 Furthermore, CXCL12 has also

Compound library selection The compounds used for the in silico binding assay were selected following a stepwise filtering protocol (Fig. 2) down to the final list of 129 fragments. The initial library was obtained by selecting all ZINC 26 database ligands with “on stock” availability. We used the structures provided by default ZINC, whose protonation state is set at pH 7. Subsequently, we selected only negatively-charged ligands, with a number of torsions equal or smaller than 2, absence of halogen atoms and with number of heavy atoms bigger than 6 and smaller than 17. This particular selection allowed us to obtain rather small fragments without halogen


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Figure 1: A. APBS analysis for the CXCL12 monomer (chain A of PDB 4UAI 42 ). Positivelycharged surface is colored in blue and negatively-charged in red. B. CXCL12 monomer (chain A of PDB 2K05 38 ) depicted in grey surface bound to CXCR4 receptor chains (colored in green and yellow) with sulfo-tyrosines depicted in VDW-style. C. CXCL12 monomer (chain A of PDB 2NWG 46 ) represented in grey surface and heparin (H1S) residues highlighted in VDW-style. D. CXCL12 monomer (chain A of PDB 4UAI 42 ) depicted in cartoon-style and colored by secondary structure. E. CXCL12 monomer (chain A of PDB 2N55 47 ) displayed as grey surface and CXCR4 chain in transparent green cartoon. Highlighted residues of CXCR4 in VDW-style correspond to key CXCR4-CXCL12 interaction residues: tyrosines 12 and 21 and isoleucines 4 and 6. F. CXCL12 monomer (chain A of PDB 4UAI 42 ) with in silico screening hits superposed in different colors. 10 representative frames per hit are shown in line-style smoothed with a moving average window of 3.


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atoms, for which reliable parameters are hard to obtain, and with a limited number of torsions in order to reduce the computational expense related to the parameterization procedure (see Methods). Furthermore, the negative charge of the ligands was chosen to fit the positively-charged CXCL12 surface, as suggested by applying the APBS analysis. Afterwards, we docked with AutoDock VINA 48 with default parameters the remaining 2442 fragments to all the surface (grid box as big as the protein) of the each of the 8 CXCL12 conformations obtained as explained in the previous section. Only fragments yielding any binding pose with score lower than -5 kcal/mol were kept. In order to ensure chemical diversity, the remaining 1380 fragments were clustered using the SHED scaffold filter 49 and 129 random fragments were picked from the 389 different clusters obtained by SHED for subsequent in silico binding assay.

ries of short MD simulations, in our case 70-nslong, of the protein and a single ligand molecule solvated in a water box following an adaptive sampling scheme. This scheme consists in periodically respawning simulations taking into consideration previous simulations to enhance the sampling of unexplored areas based on a Markov State Model (MSM). The effectiveness of this adaptive sampling scheme was recently assessed for the system benzamidine-trypsin 50 where it was shown to reduce the amount of sampling time necessary for statistics convergence in an order of magnitude. A minimum of 29 µs and an average of 45 µs of aggregated simulation time was produced for each system following this scheme. A final analysis using MSMs allowed us to define a bulk state (i.e. unbound) and a number of sink states (i.e. bound) and associated equilibrium probabilities, as well as binding free energies by applying the Boltzmann distribution (see Methods). Predicted ligand efficiencies (LEpred ) were obtained by dividing the binding free energy by the number of heavy atoms (Fig. 3) and those ligands with LEpred higher than 0.3 were labelled as hits. 8 fragments were found to yield ligand efficiency higher than 0.3. Note that although 0.3 is an arbitrary LEpred threshold to define a hit, it is a widespread rule of thumb in FBDD. 7 Chemical structure, kinetic and affinity data for all the fragment library can be found in the supplementary material. Additionally, MSMs allowed us to calculate kinetic rates (kon and koff ) between bulk and sink states (Table 1). All ligands had kon of 107 -108 s-1 M-1 and koff of 105 -106 M-1 . Note that the predicted dissociation constant (Kd ), a common measure of protein-ligand binding affinity, can be calculated as koff divided by kon or directly from the probability of bulk and bound states using the Boltzmann distribution. Interestingly, results may differ depending on whether kinetic rates or equilibrium probabilities are used. This explains the differences between the predicted Kd of the ligands shown in Figure 3C, calculated from the kinetic rates, and the predicted Kd shown Table 1, calculated from the equilibrium probabilities. However, in the present study we give priority to the

Figure 2: Step-wise filtering protocol employed to select the 129 fragments for further in silico binding assay starting from the ZINC database library.

In silico binding assay and hit discovery For each of the 129 ligands we ran what we call an “in silico binding assay”. Essentially, this method consists in the production of a se-


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Figure 3: A. Distribution of heavy atoms number for the in silico screening library. B. Kernel distribution plot for ligand efficiencies with the hit threshold (0.3) defined in discontinuous red line. C. Log-log plot for kon and koff for the screened fragment library with hits marked in red.


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Figure 4: Representative frames of the bound macrostates (1-8 left) for each of the fragment hits (1-8 right). Fragments are represented in thick licorice. Residues around the fragment are represented in thin licorice. Hydrogen-bonds established with each respective arginine are represented in discontinuous black lines. A and B show the minimum distance from Arg20 to fragment 1 and Arg41 to fragment 5, respectively, for 5 representative simulations each where we can observe binding and unbinding events of the fragments with the respective pockets.


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binding affinity calculated from the equilibrium probabilities as the kinetic rates usually bear a higher standard deviation. It is interesting to note that in FBDD the absolute binding free energy is not as relevant as the binding free energy relative to the number of heavy atoms (i.e. ligand efficiency) to categorize a fragment as a good hit. This is the reason why we can find fragments with lower absolute binding free energy but they are not considered hits because, in relative terms, the free energy contribution per atom is not higher than 0.3, which indicates that the protein-fragment interactions are, on average, of a poorer quality. For each of the fragment hits, we selected a representative frame of the bound macrostate (Fig. 4 1-8). The analysis of their binding mode lead us to a number of conclusions: (a) first, all of the hits contain a hydrophobic core and a negatively-charged group, either a carboxyl or a sulfate; (b) second, all of the hits bind to either sY7 (fragments 1-3) or H1S68 cavities (fragments 4-8); (c) third, all the hits have a similar binding mode, with the hydrophobic core buried in the CXCL12 surface and with the negatively-charged group pointing outside; (d) the negatively-charged groups of all the hits establish an electrostatic interaction with an arginine residue (Arg41 for the H1S68 pocket and Arg20 for the sY7 pocket). The chemical properties and binding mode of the hits support the initial hypothesis that CXCL12 has a positively-charged surface, represented by arginines 41 and 20, and that negatively-charged residues should effectively bind to it. Furthermore, the pharmacophoric properties of the hits are consistent with experimentally resolved ligand-like moieties. In particular, (a) the sulfo-tyrosine 7 in the PDB 2K05, 38 which contains a hydrophobic benzene core and a negatively-charged o-sulfate group interacting with Arg20 (Fig. 5A), and (b) the heparin molecule H1S68 in PDB 2NWG, 46 whose sulfate interacts with Arg41 and this interaction seems to stabilize the opening of the hydrophobic pocket that the fragments 48 leverage to dock their respective hydrophobic cores (Fig. 5B). Note, however, that in both pockets, the respective arginines had to undergo

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a lateral chain flip in order to establish a double hydrogen bond with the carboxyl/sulfate group of the fragments.

Figure 5: Experimentally-resolved pockets and ligand-like moieties that hold analogy with the predicted hits. The discontinuous line in green outlines the cavity that docks the hydrophobic part of the fragments. The discontinuous line in red outlines the occupancy of the negatively-charged part of the fragments. A. PDB 2K05 38 with a close detail of the sulfotyrosine 7 (sY7) binding in the sY7 pocket. B. PDB 2NWG 46 with a close detail of the heparin 68 (H1S68) interacting with Arg41 and promoting the opening of the H1S68 pocket.

Conclusions FBDD offers an efficient alternative to HTS by allowing the use of reduced screening libraries that span more effectively across the chemical space and present a higher hit rate. In this study, we applied high-throughput MD simulation in a FBDD context and demonstrated its ability to screen a library of 129 fragments by predicting their binding poses, kinetics and binding affinities using MSM analysis technology. In particular, our in silico binding assay applied to the CXCL12 chemokine was able to predict at least 8 millimolar-affinity


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Table 1: Description of the fragment hits. Predicted binding free energy, ligand efficiency (LEpred ), binding kinetics (kon and koff ), predicted dissociation constant (Kd ) calculated from equilibrium probability and total simulation length for each fragment is shown. ID column shows the identifiers used throughout the text. ChemSpider ID column shows the unique identifier in the popular ChemSpider 51 database. ID

ChemSpider ID

∆G0 (kcal/mol)

LEpred (kcal/mol)

kon (s-1 M-1 )

koff (M-1 )

Kd (mM)

Sim. length (µs)

1

81494

-2.9 ± 0.19

0.322 ± 0.021

1.18e+09 ± 6.90e+08

2.14e+07 ± 2.32e+06

8.08 ± 2.33

45.92

2

5537302

-2.96 ± 0.11

0.329 ± 0.012

1.15e+09 ± 6.74e+08

4.71e+07 ± 2.12e+06

7.09 ± 1.42

40.32

3

134073

-3.31 ± 0.06

0.331 ± 0.006

6.60e+08 ± 3.91e+07

4.87e+06 ± 2.29e+05

3.89 ± 0.37

78.21

4

68076

-2.12 ± 0.04

0.353 ± 0.007

7.07e+08 ± 1.03e+08

4.09e+07 ± 4.75e+06

28.86 ± 2.06

40.6

5

71271

-2.4 ± 0.19

0.343 ± 0.027

4.96e+08 ± 2.40e+07

3.20e+07 ± 1.16e+06

18.93 ± 7.3

40.23

6

472468

-2.89 ± 0.07

0.321 ± 0.007

1.50e+09 ± 3.36e+07

2.26e+07 ± 1.04e+06

7.87 ± 0.88

40.79

7

5363628

-2.31 ± 0.38

0.33 ± 0.055

3.85e+08 ± 1.63e+08

3.51e+07 ± 5.44e+06

24.4 ± 11.58

42.84

8

10037905

-2.72 ± 0.19

0.302 ± 0.021

4.61e+08 ± 2.26e+08

2.69e+07 ± 6.26e+06

10.96 ± 3.35

79.99

Methods

fragments with high predicted ligand efficiency (LEpred > 0.3) specifically binding to sY7 and H1S68 pockets. Interestingly, all fragment hits share similar chemical properties, a hydrophobic core and a carboxyl/sulfate group, and a similar binding mode, with the hydrophobic part buried and the negatively-charged group interacting with an Arginine (Arg41 for the H1S68 pocket and Arg20 for the sY7 pocket). The pharmacophoric properties of the fragments are consistent with the experimentallyresolved binding mode of natural binders: the sulfated tyrosine 7 of the CXCR4 receptor for the sY7 pocket and the sulfate group from heparin for the H1S68 pocket. Furthermore, the relative proximity between sY7 and H1S68 pockets supports the feasibility of a fragment linking and/or fragment extension strategy. The current study paves the way for the use of MD in early screening phase of compounds in FBDD. While at the current stage it is impractical to run this type of in silico assay due to computational restrictions, the evolution of software implementations such as ACEMD and the increase of GPU computational power 52 may make the computation affordable enough to run full fragment set screenings (in the order of thousands of compounds) in the coming years and eventually introduce MD-based methods such as the present in silico binding assay in mainstream drug discovery pipelines.

Electrostatic and conformational characterization of CXCL12 APBS 44 as implemented in VMD 53 was used for the electrostatic characterization. Protein conformational analysis (Fig. 6.1) was produced by running and analyzing MD simulations of the CXCL12 monomer solvated in a water box. The system was built with HTMD 54 using the chain A from structure 4UAI 42 obtained from PDB 55,56 database and adding counterions until neutralization of the total charge. 4 40-ns long equilibrations were performed locally using the software ACEMD 57 and following the protocol described elsewhere. 58 Charmm22* 59,60 forcefield was used throughout. 100ns-long production runs were performed using the NVT ensemble in the distributed network GPUGRID 61 using ACEMD. Up to a total of 57.6 microseconds of simulations were produced using an adaptive sampling scheme 50 based on protein self-contacts metric. This adaptive sampling scheme consists in the periodic building of a MSM and respawning from undersampled states in order to optimize the exploration of the conformational landscape. Final analysis was made by building a MSM using backbone dihedrals as metric, tICA 62 dimensionality reduction down to 3 dimensions, 200 microstates clustering using Kmeans algorithm implemented in scikit-learn 63 and 60ns lag time.


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Microstates were clustered into 8 macrostates using PCCA 64 and 1 representative from each macrostate was picked for subsequent docking. Both adaptive sampling and analysis was performed using HTMD. 54

In silico binding assay Given a protein and a ligand, an in silico binding assay consists in: Building and equilibration The system was created by placing the CXCL12 protein (chain A of PDB 4UAI 42 ) in the center of a water box and the fragment in a random location around the protein. The system was then neutralized and equilibrated as specified in the previous section (fig. 6.3) using HTMD. 54 Charmm22* 59,60 force-field was used. Ligand parameters were obtained using the GAAMP 65 parameterization tool, which includes a computationally expensive quantum mechanics (QM) dihedral scan using Gaussian 66 and a QM data fitting procedure to obtain charmm-compatible parameters. Adaptive Sampling 70-ns-long simulations were launched in 2 independent batches for a number of epochs up to a total average of 54 µs (Fig. 6.4). Each of the aforementioned epochs consists on the analysis of previous simulations by building a MSM (Fig. 6.5) and the re-spawning of simulations from under-sampled states in order to optimize the exploration of the protein-ligand contact space (Fig. 6.6). MSMs were built using contact maps between protein alpha carbons and ligand heavy atoms, 5 tICA dimensions and the rest default parameters used in HTMD. 54 The computational work has been performed using ACEMD 57 in the GPUGRID 61 distributed network, which aggregates hundreds of GPUs with diverse computational performance offered by independent volunteers. The computation spanned along a wall-clock time of around 6 months, which corresponds to an

Figure 6: Graphical description of the pipeline used in the current study. First (1) we ran MD simulations of the chain A of PDB 4UAI 42 to explore the conformational space. Second (2), we docked the filtered ZINC library against 8 representative protein conformations. Then (3), we built, equilibrated and ran MD simulations of CXCL12 in the presence of 1 fragment molecule for each fragment of the final compound library. We ran an adaptive scheme for a number of epochs, consisting on (5) running a MSM analysis on previous simulations and (6) re-spawning simulations from unexplored areas of the protein-ligand binding space. Finally (7), we ran a final MSM analysis on the total ensemble of the simulations produced to obtain binding poses, kinetics and binding affinities.


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1 supplementary.pdf : Supplementary material 2 fragments.mol : Fragments used in the screening 3 fragments.xlsx : Results of the screening This material is available free of charge via the Internet at http://pubs.acs.org/.

approximate amount of 380,000 GPU/hours (based on a GTX1080 performance). Final analysis Final analysis (Fig. 6.7) was performed using HTMD. 54 Contact maps between the alpha carbons of the protein and the heavy atoms of the ligand were calculated for each frame of all the simulations. Contact maps were clustered into 1000 microstates using Kmeans clustering algorithm. 5 independent MSMs with random 75% of the total simulation data (i.e. bootstraps) were built using 3 tICA dimensions and a lag-time of 10ns (implied timescales available in Figure S1). Finally, microstates were joint into 5 macrostates using PCCA and the equilibrium distribution was calculated. The bulk (i.e. the unbound state) was automatically assigned to the macrostate with the least number of protein-ligand contacts and the sink (i.e. bound state) was automatically assigned to be the macrostate with the highest equilibrium population excluding the bulk state. Means and standard deviations for each of the measurables (∆G 0 , LEpred , kon , koff , Kd ) were calculated from the 5 bootstraps. Results for all the ligands can be found in SI3. The equilibrium binding free energy was calculated using the Boltzmann distribution: P sink , (1) ∆G = −K B T ln P bulk c

References (1) Macarron, R.; Banks, M. N.; Bojanic, D.; Burns, D. J.; Cirovic, D. A.; Garyantes, T.; Green, D. V. S.; Hertzberg, R. P.; Janzen, W. P.; Paslay, J. W.; Schopfer, U.; Sittampalam, G. S. Impact of High-Throughput Screening in Biomedical Research. Nat. Rev. Drug Discov. 2011, 10, 188–195. (2) Cheng, T.; Li, Q.; Zhou, Z.; Wang, Y.; Bryant, S. H. Structure-Based Virtual Screening for Drug Discovery: a ProblemCentric Review. AAPS J. 2012, 14, 133– 141. (3) Erlanson, D. A.; Fesik, S. W.; Hubbard, R. E.; Jahnke, W.; Jhoti, H. Twenty Years On: the Impact of Fragments on Drug Discovery. Nat. Rev. Drug Discov. 2016, 15, 605–619. (4) Ursu, O.; Rayan, A.; Goldblum, A.; Oprea, T. I. Understanding DrugLikeness. WIREs Comput Mol Sci 2011, 1, 760–781.

where ∆G is the Gibbs free energy, KB is the Boltzmann constant in kcal/(mol·K), T is the temperature (300K), Psink is the equilibrium probability of the sink or bound state, Pbulk is the equilibrium probability of the bulk or unbound state and c is the concentration of the fragment.

(5) Rees, D. C.; Congreve, M.; Murray, C. W.; Carr, R. Fragment-Based Lead Discovery. Nat Rev Drug Discov 2004, 3, 660–672. (6) Hopkins, A. L.; Groom, C. R.; Alex, A. Ligand Efficiency: a Useful Metric for Lead Selection. Drug Discov. Today 2004, 9, 430–431.

Acknowledgement GDF acknowledges support from MINECO (BIO2017-82628-P) and FEDER. The authors acknowledge funding from ACCIO (RDIS14-1-0002) and from Acellera Ltd. Supporting

Information

(7) Murray, C. W.; Rees, D. C. The Rise of Fragment-Based Drug Discovery. Nat. Chem. 2009, 1, 187–192. (8) Ferenczy, G. G.; Keseru, G. M. How are Fragments Optimized? A Retrospective Analysis of 145 Fragment Optimizations. J. Med. Chem. 2013, 56, 2478–2486.

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A. APBS analysis for the CXCL12 monomer (chain A of PDB 4UAI). Positively-charged surface is colored in blue and negatively-charged in red. B. CXCL12 monomer (chain A of PDB 2K05) depicted in grey surface bound to CXCR4 receptor chains (colored in green and yellow) with sulfo-tyrosines depicted in VDW-style. C. CXCL12 monomer (chain A of PDB 2NWG) represented in grey surface and heparin (H1S) residues highlighted in VDW-style. D. CXCL12 monomer (chain A of PDB 4UAI) depicted in cartoon-style and colored by secondary structure. E. CXCL12 monomer (chain A of PDB 2N55) displayed as grey surface and CXCR4 chain in transparent green cartoon. Highlighted residues of CXCR4 in VDW-style correspond to key CXCR4CXCL12 interaction residues: tyrosines 12 and 21 and isoleucines 4 and 6. F. CXCL12 monomer (chain A of PDB 4UAI) with in silico screening hits superposed in different colors. 10 representative frames per hit are shown in line-style smoothed with a moving average window of 3. 1381x653mm (46 x 46 DPI)



Representative frames of the bound macrostates (1-8 left) for each of the fragment hits (1-8 right). Fragments are represented in thick licorice. Residues around the fragment are represented in thin licorice. Hydrogen-bonds established with each respective arginine are represented in discontinuous black lines. A and B show the minimum distance from Arg20 to fragment 1 and Arg41 to fragment 5, respectively, for 5 representative simulations each where we can observe binding and unbinding events of the fragments with the respective pockets. 711x610mm (57 x 57 DPI)


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Experimentally-resolved pockets and ligand-like moieties that hold analogy with the predicted hits. The discontinuous line in green outlines the cavity that docks the hydrophobic part of the fragments. The discontinuous line in red outlines the occupancy of the negatively-charged part of the fragments. A. PDB 2K05 with a close detail of the sulfo-tyrosine 7 (sY7) binding in the sY7 pocket. B. PDB 2NWG with a close detail of the heparin 68 (H1S68) interacting with Arg41 and promoting the opening of the H1S68 pocket. 584x569mm (46 x 46 DPI)



Graphical description of the pipeline used in the current study. First (1) we ran MD simulations of the chain A of PDB 4UAI to explore the conformational space. Second (2), we docked the filtered ZINC library against 8 representative protein conformations. Then (3), we built, equilibrated and ran MD simulations of CXCL12 in the presence of 1 fragment molecule for each fragment of the final compound library. We ran an adaptive scheme for a number of epochs, consisting on (5) running a MSM analysis on previous simulations and (6) re-spawning simulations from unexplored areas of the protein-ligand binding space. Finally (7), we ran a final MSM analysis on the total ensemble of the simulations produced to obtain binding poses, kinetics and binding affinities. 307x680mm (57 x 57 DPI)


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Caption : A. Distribution of heavy atoms number for the in silico screening library. B. Kernel distribution plot for ligand efficiencies with the hit threshold (0.3) defined in discontinuous red line. C. Log-log plot for kon and koff for the screened fragment library with hits marked in red. 357x229mm (172 x 172 DPI)



Step-wise filtering protocol employed to select the 129 fragments for further in silico binding assay starting from the ZINC database library. 780x657mm (40 x 40 DPI)


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Graphic TOC 82x31mm (300 x 300 DPI)


Molecular-Simulation-Driven Fragment Screening ... - ACS Publications

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