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Docking of Macrocycles: Comparing Rigid and Flexible Docking in Glide Hiba Alogheli, Gustav Olanders, Wesley Schaal, Peter Brandt, and Anders Karlen J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.6b00443 • Publication Date (Web): 12 Jan 2017 Downloaded from http://pubs.acs.org on January 15, 2017

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Journal of Chemical Information and Modeling

Docking of Macrocycles: Comparing Rigid and Flexible Docking in Glide

Hiba Alogheli†, Gustav Olanders†, Wesley Schaal†,‡, Peter Brandt†, Anders Karlén†,* †

Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry, Uppsala University,

BMC, Box 574, SE-751 23 Uppsala, Sweden. ‡

Current address Wesley Schaal: Department of Pharmaceutical Biosciences, Pharmaceutical

Bioinformatics, Uppsala University, BMC, Box 591, SE-751 24 Uppsala, Sweden.

*

Corresponding author. Tel.: +46 18 471 42 93

E-mail address: [email protected]

ABSTRACT: In recent years, there has been an increased interest in using macrocyclic compounds for drug discovery and development. To address docking of these commonly large and flexible compounds, a screening and a validation set were assembled from the PDB consisting of 16 and 31 macrocycle containing protein complexes, respectively. The macrocycles were docked in Glide by rigid docking of pre-generated conformational ensembles produced by

ACS Paragon Plus Environment

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the macrocycle conformational sampling method (MCS) in Schrödinger Release 2015-3 or by direct Glide Flexible Docking after performing ring-templating. The two protocols were compared to rigid docking of pre-generated conformational ensembles produced by an exhaustive Monte Carlo Multiple Minimum (MCMM) conformational search and a shorter MCMM conformational search (MCMM-short). The docking accuracy was evaluated and expressed as the RMSD between the heavy atoms of the ligand as found in the X-ray structure after refinement and the poses obtained by the docking protocols. The median RMSD values for Top-Scored Poses of the screening set were 0.83, 0.80, 0.88, and 0.58 Å for MCMM, MCMMshort, MCS, and Glide Flexible Docking, respectively. There was no statistically significant difference in the performance between rigid docking of pre-generated conformations produced by the MCS and direct docking using Glide Flexible Docking. However, the flexible docking protocol was 2 times faster in docking the screening set compared to the MCS protocol. In a final study, the new Prime-MCS method was evaluated in the Schrödinger Release 2016-3. This method is faster compared to MCS, however, the conformations generated were found to be suboptimal for rigid docking. Therefore, based on timing, accuracy, and ease to set up, standard Glide Flexible Docking with prior ring-templating is recommended over current gold standard protocols using rigid docking of pre-generated conformational ensembles.

INTRODUCTION Macrocyclic scaffolds are attracting increased interest for drug discovery1 due to their structural pre-organization,2,3,4,5,6 potential for beyond rule-of-57 bioavailability,2,3,5,6,8 and novel chemical structures for generation of IP. The commonly larger size of macrocycles in combination with restrictions in conformational flexibility allow binding to less druggable


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targets displaying shallow, featureless ligand binding sites or disruption of protein-protein interactions.3,6,9,10 Despite their seemingly non-drug-like properties, as macrocycles often possess physicochemical properties beyond rule-of-5, they may show therapeutically relevant properties such as metabolic stability and permeability.2,3,5 Natural products are the main sources of the macrocyclic drugs currently available,5,11 but synthetic macrocycles are gaining in importance.5 Historically, the use of synthetic macrocycles in drug discovery has been reported in several drug target classes.2,4,5,6 In the protease area, macrocyclization strategies have shown to be successful for converting pharmaceutically unfavorable peptidic leads into clinical peptidomimetic candidates.6 Also, in the field of small molecule kinase inhibitors, macrocyclization strategies have proven successful,5 and chemistry is constantly evolving to provide new methods for macrocyclizations.12,13,14 Despite the successful application of macrocyclization approaches to tackle druggability challenges, macrocycles are underrepresented within drug discovery.2 We argue that one major reason for this being the computational challenges involved in structurebased design of macrocyclic drugs. As computational chemistry has been successfully integrated into the non-macrocyclic drug discovery process, it is important to extend the scope to include efficient computational tools also applicable to macrocyclic compounds.15,16 Efficient methods, will for example, aid in the design and prioritization of novel macrocyclic structures prior to synthesis by predicting ADMET properties and target binding modes.3 One key tool is conformational analysis of macrocycles, which has been the subject of many computational studies due to the close relationship between conformational preferences and biological function.17,18,19,20,21 However, macrocycles are challenging molecules for many of the current conformational analysis methods, since adequate ring-sampling is not easily performed.21 To address this, Watts et al. proposed a


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new conformational sampling method, Macrocycle Conformational Sampling (MCS), developed for macrocyclic compounds.19 The MCS protocol is implemented in MacroModel and uses brief MD simulations followed by minimization and normal-mode search steps. The method was benchmarked by Watts et al. using a data set of 150 macrocyclic compounds and the MCS results were compared to published results. It was found that the MCS method could identify conformations with low RMSD values to the X-ray conformer.19 The generation of conformers prior to docking has become the gold standard in structure-based modeling of macrocycles,15,16 for examples, see Poulsen et al.22 and Bowers et al.23 where they studied docking of macrocyclic inhibitors to two different targets. Therefore, a fruitful combination would be to use MCS generated conformers for subsequent docking in, e.g., structure-based drug design efforts. However, to the best of our knowledge, no systematic validation of this approach has been published hitherto and examples from literature are sparse. In the current work, docking protocols for macrocycles have been evaluated using 16 protein complexes with macrocyclic drug-like ligands. The first protocol involves the combination of the two computational techniques described above; a conformational search by MCS followed by rigid docking of the generated conformational ensembles using Glide. This protocol was benchmarked against a rigid docking of larger conformational ensembles derived from exhaustive and more time consuming Monte Carlo Multiple Minimum (MCMM) searches. The macrocycles were also docked using a default flexible docking approach in Glide, where the conformations of the ligands were sampled during the docking process using pre-generated ring conformations, with the aim to highlight the difficulty this structural class poses to flexible docking. To assess the docking protocols, the Glide Top-Scored Poses were compared to the native poses of X-ray structures in the RCSB Protein Data Bank (PDB). Further, the Best-Fit


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Pose (i.e., the pose most similar to the X-ray structure) within one score unit from the TopScored Pose was investigated as well as the Best-Fit Pose among the poses ranked. As a final step, an additional 31 macrocyclic structures extracted from the work of Watts et al.19 were used to validate the conclusions from the study of the screening set. When this manuscript had been submitted we learnt that a new tool had been introduced in the Schrodinger suite to generate macrocycle conformations (in Suite 2015-4). This method, Prime Macrocycle Conformational Sampling (Prime-MCS), uses a new approach to sample conformational space which is claimed to be quicker and to be able to reproduce the bioactive conformation similarly well as the MCS method. The method has now replaced the MCS method and we therefore decided to include it in this study to investigate how it compared to MCS in generating conformations for docking.

METHODS Unless otherwise stated, all calculations were performed within the Schrödinger Small Molecule Drug Discovery Suite (Schrödinger Release 2015-3). The Prime-MCS conformational analysis study was performed with Schrödinger Release 2016-3. Data set selection Two macrocycle data sets were compiled in this study, a screening set and a validation set. All crystal structures were downloaded from the PDB.24,25 Only structures with an overall resolution of 2.5 Å or better were considered. When available, the electron density maps of the complexes, downloaded from the Electron Density Server at Uppsala University,26,27 were analyzed to confirm that the resolution of both the protein and the ligand were acceptable. Macrocyclic


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structures bound to DNA or RNA were ignored as the aim of this study was to evaluate macrocycle docking to protein targets. Screening set. The primary inclusion criteria for this part of the data set were drug-like structures containing a macrocycle with a ring size of 12 or more atoms bound to high-quality Xray structures. Initially, for a pilot study, seven structures were manually selected from the PDB (1QY8, 1S9D, 2HFK, 2XYT, 3BXS, 3JRX, 3SU0). To create the final screening set, nine structures were added based on a more systematic search of the PDB for macrocycles registered also in the DrugBank database28 either as FDA approved or experimental drugs (see Table 1 and Table 2, and Supporting Information Table S1 and S2 for structures, descriptions, and characteristics of the screening set).


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Table 1. Structures of the macrocycles in the screening set in the tautomer/ionization states used for conformational analysis and docking. To the upper left is the name of the ligand. The PDB codes of the complex structure and the ligand code are shown to the lower left and right, respectively.

Table 2. Information about the X-ray structures in the screening set and the corresponding PDB codes.a

PDB code

PDB code ligand

Ligand name

Classification

1LD8

U49

DB08674b

Transferase

1QY8

RDI

Radicicol

Chaperone

Endoplasmin

Canis lupus

1S9D

AFB

Brefeldin A

Protein Transport/exchange Factor

ADP-Ribosylation Factor 1

Bos taurus

Target Protein farnesyltransferase alpha subunit

Organism Homo sapiens


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2E9U

A25

A780125

Transferase

Serine/threonineprotein kinase Chk1

Homo sapiens

2ESA

GDM

Geldanamycin

Chaperone

Endoplasmin

Canis lupus

2HFK

E4H

10-Deoxymethynolide

Hydrolase

Type I polyketide synthase PikAIV

Streptomyces venezuelae

2IWX

M1S

DB08153b

Chaperone

ATP-dependent molecular chaperone hsp82

Saccharomyces cerevisiae

2J9M

PY8

DB08441b

Transferase

Cell division protein kinase 2

Homo sapiens

2XYT

TC9

Tubocurarine

Receptor

soluble acetylcholine receptor

Aplysia californica

3BE9

P04

DB08338b

Transferase

Casein kinase II subunit alpha

Zea mays

3BXS

DRS

DB07679b

Hydrolase

Protease

Human immunodeficiency virus 1

3FRQ

ERY

Erythromycin A

DNA binding protein

Repressor protein MphR(A)

Escherichia coli

3JRX

S1A

Soraphen A

Ligase

Acetyl-CoA carboxylase 2

Homo sapiens

3SU0

TSV

Danoprevir

Hydrolase/hydrolase inhibitor

Genome polyprotein

Hepatitis C virus

4HUS

VIR

Virginiamycin M1

Transferase/antibiotic

Virginiamycin A acetyltransferase

Staphylococcus aureus

4NNR

FK5

Tacrolimusc

Isomerase/isomerase inhibitor

Peptidyl-prolyl cistrans isomerase FKBP2

Homo sapiens

a

c

Information extracted from the PDB unless otherwise stated. bAccession Number in the DrugBank database. Reported name in the DrugBank database.


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Validation set. To create a more extended set of macrocycle-protein complexes, structures listed in the work of Watts et al. were considered.19 This publication describes 150 macrocycles whereof 67 are also found in the PDB. Starting from these, we removed protein structures with an overall resolution above 2.5 Å, structures with poor ligand resolutions (1BZL, 2ASO, 2F3E, and 2WHW), and uncertain stereochemistry (2C7X). Whereas Watts et al. used 8 ring atoms or more as a selection criterion for macrocycles we only considered those with a ring size of 10 or more in the present study. We also excluded structures where the macrocyclic part of the ligand was predominantly solvent-exposed (1FKL, 1QPF) or where the macrocycle did not significantly overlap with the binding site, as defined by SiteMap (1QZ5, 1QZ6, 1WUA, 2ASM, 2Q0R, 2VYP). 1YXQ was excluded as this ligand is too large to be handled by LigPrep and 1A7X was excluded as this C2 symmetric binding site has partial occupancies of the non-symmetric ligand. Finally, we excluded complexes in which dimeric ligands formed extensive ligand-ligand interactions (1YND, 3KEE, 3M6G). The final validation set consisting of 31 structures is shown in Table 3 and is characterized in Table S3 and S4.


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Table 3. Structures of the macrocycles in the validation set in the tautomer/ionization states used for conformational analysis and docking. To the upper left is the name of the ligand (when given). The PDB codes of the complex structure and the ligand code are shown to the lower left and right, respectively.


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Data set preparation Preparing the X-ray structures for docking studies. The protein-ligand structures were downloaded from the PDB. For entries containing multiple subunits, monomer A was used (exception: 2XYT where chains H/I were used). Subunits (including crystal mates) adjacent to an active site and in contact with the macrocycle were also included (1LD8, 1S9D, 2XYT, 3BXS, 4HUS, and 4NNR). Protein-ligand complexes were prepared using the Protein Preparation Wizard in Maestro with default options.29 Missing side-chains were rebuilt using Prime.30 Note that none of the missing side-chain atoms were near the macrocyclic ligands. For X-ray structures with residues in alternate positions, the positions with the highest average occupancies were selected. In case of equal average occupancies, the first listed was selected. The ligand tautomer and ionization state were assigned and if a metal was present in the protein and coordinated to a ligand, metal binding states were included (1LD8 and 2C6H). The


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tautomer/ionization state of the macrocycle obtained after this step was used in the conformational analysis studies and in the docking experiments (see Table 1 and Table 3). After considering the states and optimizing the hydrogen-bonding network of the protein, water molecules that formed fewer than 3 hydrogen bonds to non-waters were removed. In two cases, 1BXO and 3BXS, the protonation state of the protein was adjusted manually to have both catalytic aspartic acids protonated (only one Asp suggested to be protonated by the Protein Preparation Wizard). Finally, the protein-ligand complexes were energy minimized in a restrained manner using the IMPREF utility in which the heavy atoms were restrained with a harmonic potential of 104.6 kJ mol-1 Å-2 and hydrogens were unrestrained (default conditions). The optimization was performed to a maximum RMSD of 0.3 Å. Throughout this work, the OPLS-2005 force field31 was used for protein preparation, ligand preparation, conformational analysis, Glide Grid generation and docking studies (see below). To make sure that the correct ligand structure was used, the structures were manually checked and compared to structures reported in the original papers. Calculation of descriptors. QikProp32 was used to calculate the following descriptors for the macrocycles in the screening and validation set: molecular weight (MW), number of hydrogen bond donors (donorHB), number of hydrogen bond acceptors (accptHB), octanol/water partition coefficient (QPlogPo/w, i.e., calculated logP), and polar surface area (PSA). For the macrocycle tubocurarine, containing a quaternary amine, Instant JChem33 was used for calculating donorHB, accptHB, logP and PSA as QikProp cannot generate descriptors for permanently charged molecules (for comparison Instant JChem calculated descriptors for all molecules can be found in the Supplementary information Table S2 and S4). The number of torsional angles sampled in the MCMM conformational search was used to describe the torsional flexibility of each ligand.


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SiteMap calculations. The macrocycle binding sites were characterized by SiteMap34 calculations using default settings.35,36 The binding sites were defined manually by selecting the bound ligand. Conformational analysis Generating starting conformations. The starting structures for the conformational analysis and Glide Flexible Docking were prepared by extracting the ligands from the prepared PDB structures in the appropriate tautomeric/ionization state. To eliminate any bias in the docking, the coordinates of the X-ray ligands were regenerated by conversion to one-dimensional SMILES codes with stereochemical information followed by reconversion to three-dimensional structures using LigPrep.37 All ligands, including those from Watts et al., were energy minimized using 5 000 truncated Newton conjugate-gradient (TNCG)38 iterations to a gradient convergence criterion of 0.001 kJ Å-1 mol-1 to get an appropriate starting geometry as input for the conformational search39 and Glide Flexible Docking studies (see Supporting Information for the final coordinates). Conformational analysis using MCMM. The Monte Carlo Multiple Minimum (MCMM)39 search method implemented in MacroModel40 was used for the generation of conformational ensembles of each macrocycle in the screening set. Throughout this work, the GB/SA continuum solvation model for water was used.41 The total number of Monte Carlo steps was set to 10 000 steps per sampled torsion angle. For torsional angle searches of ring systems, the distance tolerance for ring-closures was set to 0.5 - 2.5 Å (default) for non-macrocyclic rings and to 0.1 5.0 Å for the macrocyclic rings. Ring-closure bonds adjacent to chiral centers were avoided when possible. Torsional sampling of amides and esters was allowed in the searches (extended sampling). Each conformation found was energy minimized for up to 50 000 steps using the


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TNCG energy minimization method with a gradient convergence criterion threshold of 0.05 kJ Å-1 mol-1 using the default line searcher (MINI arg 2 = 0, not default). This choice of line search method was based on the observation that the TNCG method with default line searcher showed to be faster for macrocycles. Redundant conformers were eliminated based on a distance criterion of maximum atom deviation between any pair of corresponding heavy atoms (and O-H, S-H) of 0.5 Å. The energy window for keeping the conformers was set to 62.8 kJ mol-1 (15 kcal mol-1). Conformational analysis using MCS. The default settings were used except that torsional sampling of amides and esters was allowed. The energy window for keeping conformers was set to 41.8 kJ mol-1 (10 kcal mol-1) and redundant conformers were eliminated using an RMSD of 0.75 Å. The default settings further imply 5 000 simulation cycles and 5 000 LLMOD (largescale low-mode) search steps. Conformational analysis using a less exhaustive MCMM search (MCMM-short). A shorter version of MCMM (MCMM-short) was run using default settings with the exception of the energy minimization method, the distance tolerance for ring-closures and the energy window, which were set to the same settings and values as for the more exhaustive MCMM protocol above. The shorter MCMM search used 100 search steps per torsion angle or 1 000 steps in total, whichever was less. Conformational analysis using Prime-MCS. In the Schrödinger suite 2015-4 a new macrocycle sampling protocol integrated in Prime, Prime Macrocycle Conformational Sampling (Prime-MCS) was released. Briefly, the sampling algorithm splits the macrocycle into two pieces, which are sampled independently, using libraries of known angles. The halves are reconnected if they can be matched. The side-chains are then attached and the molecule is


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minimized (the method as described in the Prime Help section). The jobs were run from the command

line

with

the

following

statement:

“$SCHRODINGER/run

-FROM

psp

macro_sample.py --spinroot 10 -sample_peptide -specify SGB_MOD=sgbnp "input_mae_file" where the input file corresponds to the ligand stored as a .mae file (see “Generating starting conformations” above). The extended sampling mode (thorough) was used (--spinroot 10) with sampling of the peptide bonds (-sample_peptide). The standard generalized Born solvation model (-specify SGB_MOD=sgbnp) was set as it is not the default for Prime-MCS. This model is identical to the GB/SA continuum solvation model for water used in the conformational searches above. The energy window for keeping conformers was kept at the default value 418.4 kJ mol-1 (100 kcal mol-1). Comparing conformational ensembles with the minimized X-ray ligand. All conformations from the conformational analysis were compared by superimposition to the energy minimized Xray conformation (TNCG, 5 000 energy minimization iterations, 0.001 kJ Å-1 mol-1 convergence gradient) of the corresponding ligand, hence called the energy minimized X-ray ligand. This analysis was quantified by calculating the root mean square deviation (RMSD) between the coordinates of the heavy atoms of the energy-minimized X-ray ligand and the coordinates of the conformations derived from the conformational search. Docking All dockings were executed in Glide42 using SP mode43 and default settings.43,44 Grids for docking were prepared using the previously prepared protein-ligand complexes by selecting the bound ligand to define the binding site. The “Canonicalize input conformation” option was deactivated. The different docking protocols are summarized in Figure 1.


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Canonicalized input geometry of macrocycle

MCMM

MCMMshort

MCS

PrimeRing MCS templating

Rigid docking

Rigid Rigid Rigid docking docking docking

Flexible docking

Figure 1. Protocols used for docking macrocycles. The docking involves rigid docking of pregenerated conformations using conformations generated by the specified conformational sampling method. A canonicalized geometry of the macrocycles was used as input structure to the five different approaches.

Protocols for re-docking of the X-ray conformers. The re-docking scheme involved four different protocols. The first three approaches use the X-ray ligand derived from the Protein Preparation Wizard (i.e. the Restrained X-ray Ligand) (a - c) and the fourth approach uses the energy minimized X-ray ligand for comparisons (d). More specifically, these protocols correspond to: a) calculating a score for the Restrained X-ray Ligand in the active site using the X-ray ligand coordinates as the input (Score in Place); b) refining and scoring the X-ray pose of the Restrained ligand in the active site (Refine Only); c) docking the X-ray ligand conformation as found after the protein preparation step rigidly to the active site (Rigid Docking of Restrained X-ray Ligand); and d) docking the energy minimized X-ray Ligand rigidly into the active site (Rigid Docking of the energy minimized X-ray Ligand). The option "Perform post-docking minimization" was used for the latter two experiments. Rigid docking of conformational ensembles. The conformational ensembles generated from the MCMM, MCMM-short, MCS, and Prime-MCS searches were docked rigidly into the protein binding site (Ligand sampling: Rigid). In this mode, the ligand is translated and rotated relative to the binding site but no conformations of the ligand are generated during the docking step. Five


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poses for each docked conformation were further optimized using the “Perform post-docking minimization” option in which bond lengths, bond angles as well as torsion angles are optimized in the context of the protein and the pose is re-scored. One pose was saved for each docked conformation. Flexible Docking using Glide. We also evaluated how the macrocycles are handled in the more common docking mode where the ligands’ conformational, orientational and positional degrees of freedom are sampled by the docking program. The same input macrocycle conformations that were used for the conformational analysis were used as input for the flexible ligand docking. When docking ring systems flexibly with Glide, ring sampling is carried out via a library of ring templates included in the software. In the present study, ring templates were available for 34 out of the 47 macrocycles. In cases where templates were missing, the ring templating utility was used to generate ring conformations prior to flexible docking. This utility (ring_templating) is found as a separate program in the folder $SCHRODINGER/utilities/ and uses the ligand stored in a .mae file as input. The resulting output was moved to ~/.schrodinger/mmshare/mmconf_ring_templates to be used in subsequent Glide dockings. The value of the energy window for keeping ring conformations in the docking was set to 41.8 kJ mol-1 (10 kcal mol-1); the default window is 10.6 kJ mol-1 (2.5 kcal mol-1). The number of poses to write out per ligand was set to at most five. Flexible Docking of the MCS generated ensembles using Glide. To evaluate if the flexible docking results were dependent on the input conformation, several input conformations of the same macrocycle were docked. The MCS generated conformers were chosen as input structures, as they were thought to be suitable in terms of numbers and diversity. The MCS conformers


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were docked using the same settings as in the Flexible Docking protocol mentioned above. The number of poses to write out per ligand was, however, set to at most one instead of five. Evaluation of Rigid and Flexible docking accuracy. All docking poses were compared to the experimentally determined X-ray structure after Protein Preparation Wizard treatment. This analysis was quantified by calculating the root mean square deviation (RMSD) between the coordinates of the prepared X-ray ligand and the docking poses [“Calculate in place (no transformation)”]; only heavy atoms were considered. The experimentally determined pose was considered to be successfully reproduced by the docking protocol if the RMSD was below 2 Å. In this study, we analyzed the conformations with the best docking score, i.e., the "Top-Scored Pose", as well as the one closest to the X-ray structure within one score unit from the Top-Scored Pose, the "Best-Fit Pose within one score unit". We also identified the pose closest to the X-ray structure searching all poses generated “Best-Fit Pose among all poses generated”.

RESULTS AND DISCUSSION When performing rigid docking of externally generated conformers, the docking challenge can in principle be divided into three different parts: 1) generating different conformations of the ligand including one that is very similar to the conformation of the protein-bound ligand, the so called bioactive conformation (conformational analysis), 2) placing those conformations into the protein taking into account the different positions and orientations possible for each conformation and selecting the most appropriate pose (docking and rank ordering the poses of each rigid conformation), and finally, 3) rank ordering these poses based on a scoring function to get the best-fitting pose to the top of the list (scoring). Thus, an unsuccessful docking experiment can be caused by the bioactive conformation not being present in the conformational ensemble,


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by a failure to pose the bioactive conformation into the active site, or by errors in the scoring function ranking the final poses of the docked conformations. Before addressing the three points above, a more fundamental question will be discussed, namely the re-docking of the energy minimized conformation found in the crystal structure; a supposed prerequisite for successful docking. Data sets Screening set. The data set used for initial screening of the various protocols contained 16 protein structures with macrocyclic drug-like ligands. The structures of the macrocycles are shown in Table 1 whereas the PDB codes and target information are shown in Table 2. Some characteristics of the analyzed macrocycles in the screening set are summarized in Table 4 and shown in detail in Table S1. The median resolution of the crystal structures was 1.88 Å and all available electron density maps were found acceptable (13 maps available). The ring-size of the macrocycles varied between 12 and 23 (median 15) and the number of torsion angles sampled varied between 8 and 41 (median 15). This represents a substantially increased flexibility for the macrocycles in the current study compared to marketed oral drugs.45 The polarity (PSA) of the macrocycles varies between 70 Å2 and 196 Å2 with a median of 98 Å2. Validation set. To validate the conclusions from the study of the screening set, an additional data set was constructed from the Watts data set.19 Some characteristics of the 31 structures in the validation set are shown in Table 4 and in more detail in the supplementary information (see Table S2) and is presented below. The ring size of the macrocycles varied between 11 and 29 (median 16) and the number of torsion angles sampled varied between 11 and 47 (median 23). The polarity (PSA) of the macrocycles varies between 90 Å2 and 234 Å2 with a median of 154 Å2. Thus, the structures in the validation set were slightly larger, more flexible and polar


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Page 20 of 40

compared to the structures in the screening set (see Tables S1 and S2). Even though Watts et al. had regenerated the ligands from SMILES using LigPrep, some of the 31 ligand conformers were relatively similar to their corresponding X-ray conformer. Therefore, to remove any bias in the subsequent docking these ligands were, yet again, regenerated from SMILES using LigPrep (see “Generating starting conformations” above). Table 4. Median, Minimum, and Maximum values for characteristics of the screening set and the validation set.a Screening set PDB resolution (Å) Ring size #Torsion angles sampledb MWc donorHB accptHB

d

e

QPlogPo/wf g

PSA

Validation set

Median 1.88

Minimum 1.16

Maximum 2.5

Median 1.8

Minimum 0.95

Maximum 2.4

15

12

23

16

11

29

15

8

41

23

11

47

410

280

804

617

422

1041

1.8

0

5

2.8

0

9.3

7.0

4.0

20

13.6

6

26.9

2.8

1.1

5.4

3.3

-2.8

7.5

98

70

196

154

90

234

a

b

Descriptors were calculated with QikProp unless otherwise stated. Number of torsion angles sampled during the MCMM conformational searches. cMolecular weight. dNumber of hydrogen bond donors. eNumber of hydrogen bond acceptors. fCalculated octanol/water partition coefficient. gPolar surface area.

Re-docking X-ray conformers Re-docking of the ligands using the conformations found in the X-ray structures provides an assessment of the performance of the docking algorithm itself. Thus, given the ideal conformation of the bound macrocycle, will the docking algorithm be able to re-pose this into the protein X-ray structure and what is the docking score of the conformation found in the X-ray structure relative to other poses found in the re-docking? Docking scores for poses generated by the Score in Place, Refine Only and Rigid Docking methods for the macrocycles in the screening set are reported along with the corresponding RMSD values in Table 5. As the aim of this study was a strict evaluation of the performance of docking protocols for macrocycles, crystal mates in direct contact with the bound ligand were included in the preparation of docking grids. We


20

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reasoned that to evaluate how well the docking protocol can reproduce the X-ray ligand pose, the biological significance of these artificial dimers can be neglected. However, from a structurebased design perspective, in the search for new ligands, the crystal mates should not be considered. For eight of the complexes, water molecules in the active site remained in place after the protein preparation (1LD8, 1QY8, 1SD9, 2ESA, 2IWX, 3FRQ, 4HUS, and 4NNR). The docking scores calculated based on the compounds’ original positions in the X-ray structures, the Score in Place calculations, gave a baseline for the docking scores. In this mode no docking or optimization of the ligand in the binding site is performed. The docking scores and poses generated by the Refine Only calculations gave an idea of how well our docked poses would agree with the X-ray structures if the docking algorithm could place the compound correctly. In the Refine Only mode no docking is performed, but the coordinates of the Restrained X-ray Ligand are optimized within the field of the binding site. The results show that these two scores are of comparable magnitude (see Table 5). In the Rigid Docking of the Restrained X-ray Ligand, the ligand is docked to the binding site taking into account only the different positions and orientations of the ligand (no conformations are generated). The scores from this exercise were again generally found to be in the same neighborhood as the docking scores generated by Score in Place and Refine Only described above (see Table 5). Inspecting the RMSD values from the re-docking experiment in Table 5, in general, neither the Refine Only option nor the Rigid Docking of the X-ray structure conformation of the macrocycle alter the positions significantly with respect to the Restrained X-ray Ligand. By minimizing the ligands extracted from the prepared proteins, the relaxed conformations closest to the protein bound conformations could be expected. Docking of these conformations should hypothetically generate the lowest RMSD result that can be obtained from a rigid docking of a pre-generated


21


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Page 22 of 40

conformational ensemble (given that the energy minimized X-ray conformations of the ligands have been identified in the conformational analyses, see Table 6, Best-Fit conformation RMSD). If the scoring function favors the X-ray pose, this conformation should correspond to the TopScored Pose. When the results from the two columns in Table 5 showing the docking scores and RMSD values of the energy minimized X-ray ligand are compared to the more constrained calculations (Score in Place, Refine Only and Rigid Docking of the Restrained X-ray Ligand) the values are similar for most ligands. This implies that as long as the conformational sampling generates the conformation corresponding to the conformation found in the X-ray structure, a pose very similar to the X-ray structure will result from the rigid docking. Two outliers were identified in the re-docking of the energy minimized X-ray conformation of the ligands (1LD8 and 3SU0). In the case of 1LD8, a pose with 3.2 Å RMSD was produced, whereas a pose with 6.7 Å RMSD was produced for 3SU0, indicating a challenge for the subsequent rigid docking of conformers. It should be noted that the non-minimized X-ray conformers of both 1LD8 and 3SU0 were successfully re-docked with an RMSD value of 0.2 Å for both of the complexes (see Table 5).


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Table 5. Results of the re-docking study of the macrocycles in the screening set. Docking scores and the corresponding RMSD values are presented. RMSD (Å)

Docking scores Energy minimized X-ray ligandb

X-ray liganda

X-ray liganda

Energy minimized X-ray ligandb

PDB code 1LD8

Score in Placec -8.9

Refine Onlyd -9.5

Rigid Dockinge -9.6

Rigid Dockingf -8.0

Refine Onlyg 0.4

Rigid Dockingh 0.2

Rigid Dockingi 3.2

1QY8

-8.1

-8.1

-8.1

-8.1

0.3

0.3

0.3

1S9D

-12.6

-12.7

-12.5

-12.4

0.1

0.2

0.2

2E9U

-9.0

-9.0

-8.1

-7.8

0.1

0.6

1.0

2ESA

-8.3

-8.8

-8.8

-8.0

0.2

0.2

0.2

2HFK

-6.5

-6.7

-6.7

-6.6

0.2

0.2

0.3

2IWX

-7.9

-8.0

-7.9

-8.1

0.1

0.1

0.2

2J9M

-9.8

-10.0

-10.1

-10.0

0.4

0.3

0.4

2XYT

-9.9

-10.2

-10.3

-9.6

0.4

0.2

0.4

3BE9

-12.0

-12.0

-12.3

-12.2

0.1

0.2

0.1

3BXS_Aj

-10.3

-10.6

-10.6

-9.8

0.3

0.3

0.4

3BXS_Bj

-8.6

-9.0

-8.3

-8.0

0.2

0.2

0.4

3FRQ

-10.8

-10.7

-10.9

-10.9

0.3

0.1

0.6

3JRX

-9.0

-9.8

-9.7

-9.8

0.3

0.3

0.3

3SU0

-9.4

-11.1

-11.3

-1.3

0.2

0.2

6.7

4HUS

-8.4

-8.4

-8.4

-7.8

0.3

0.3

0.4

4NNR

-13.5

-14.2

-14.2

-14.2

0.3

0.3

0.4

a

Restrained X-ray ligand obtained after refinement with the Protein Preparation Wizard. bConformation resulting from energy minimization of the Restrained X-ray ligand.cDocking score received without docking or optimization of the macrocycle (Score in Place). dDocking score received after optimization of the X-ray ligand in the field of the binding site (Refine Only). eDocking score received after the ligand has been docked to the binding site without any conformational generation (Rigid Docking of Restrained X-ray Ligand). fDocking score received after Rigid Docking of energy minimized X-ray Ligand. gRMSD received after optimization of the X-ray ligand in the field of the binding site (Refine Only). hRMSD received after the ligand has been docked to the binding site without any conformational generation (Rigid Docking of Restrained X-ray Ligand). iRMSD received after Rigid Docking of energy minimized X-ray Ligand. j3BXS contains two identical macrocyclic ligands bound to separate binding sites. Each form was docked into its binding site.


23


Table 6. A summary of the conformational analysis results of the macrocycles in the screening set.

RMSD (Å)

∆E (kJ mol-1) MCMM

MCS

213

213

213

Yes

0

0.35

30

30

31

67

67

67

Yes

0

0.01

0

0.04

16

4

16

1S9D

13

15

1 700

24

495

936

-47

-47

-47

Yes

0

0.02

0.02

0.03

3

0

0

2E9U

15

11

777

52

314

976

-37

-37

-37

Yes

0

0.61

0

0.01

9

22

9

0.05 0.03

PrimeMCS

954 897

MCS

MCMMshort

81 293

MCMM

MCS

33 51

PrimeMCSf

MCMM

105 492

MCS

15 11

MCMM

16 14

code

#Torsion angles samplede

1LD8 1QY8

PDB

MCMMshort

Best-Fit conformationd

MCMMshort

Best-Fit conformationc Eminb (kJ mol-1)

MCMMshort PrimeMCS

No. of conf.a Ring size

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

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2ESA

19

20

35 841

263

468

978

-231

-231

-230

Yes

0

1.05

1.56

0.62

3

29

43

2HFK

12

12

442

29

219

977

-10

-10

-10

Yes

0

0.01

0

0.45

7

6

7

2IWX

15

14

9 477

111

588

985

34

34

34

No

0

0.5

0.31

0.3

31

19

32

2J9M

13

8

92

7

85

975

-577

-577

-557

Yes

0.77 0.58

0.77

0.77

0

21

0

2XYT

18

19

1 057

38

238

906

-95

-95

-95

No

3BE9

14

12

401

19

258

889

-194

-194

-194

No

16

14

887

59

376

949

-525

-525

-525

Yes

g

3BXS_A 3BXS_B

g

0

0.35

0.27 0.27

0

0.09

13

28

13

0.27

0.29

13

13

13

0

0.44

0

0.07

2

29

2

0

0.39

0

0.06

4

5

4

3FRQ

14

35

38 632

175

302

984

-164

-164

-156

Yes

0

0.18

0.72

0.75

37

15

52

3JRX

16

23

57 710

332

485

987

123

124

127

No

0

0.74

0.18

0.64

5

39

4

3SU0

15

27

56 675

658

609

990

-705

-703

-704

No

0.3

1.19

1.35

1.53

37

41

62

4HUS

23

26

62 672

698

459

997

1

1

2

No

0.58 0.01

0.58

3.56

22

12

22

4NNR

23

41

38 562

378

395

992

276

292

310

No

0.27 1.15

1.65

1.41

45

36

40

961

h

h

h

0.13 0.44

0.44

0.65

16

21

21

0.18

0.35

13

21

13

Average Median

16 15

19 15

19 095 1 379

183 55.5

354 345

977

NR

h

NR

NR

h

NR

NR

h

NR

h

NR

h

NR

0

0.39

a

The total number of conformations generated for each macrocycle. bEnergy of conformation identified with lowest-energy. Values in bold italics indicate searches where MCS and MCMM-short have not identified the “Global” energy minimum (assumed to be found with MCMM) with an energy difference greater than 1 kJ mol-1. c RMSD for the conformation identified with the lowest RMSD value to the energy minimized X-ray ligand. dEnergy difference between the conformation closest to the energy minimized X-ray ligand and the “Global” energy minimum (assumed to be found by MCMM). eNumber of torsion angles sampled during the MCMM conformational searches. fYes and No refer to if the lowest energy conformer found corresponds to the energy minimum found by MCMM. g3BXS contains two identical macrocycles bound to separate binding sites. The generated conformations were compared to each one of these indicated by the suffixes _A and _B. hNR = Not relevant.


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Conformational analysis to generate diverse conformations for docking The aim of the conformational analysis part of the docking protocol is to generate low-energy conformations representing potentially bioactive conformations of each macrocycle. A summary of the conformations generated from the different search methods is shown in Table 6. The number of conformers generated by MCMM is much higher as compared to the other methods; the difference varies between 3 and over 200 times as many conformers. As expected, the number of conformers correlate with the number of torsion angles sampled (see Table 6). In most cases, MCMM, MCS, and MCMM-short found the same energy minimum and conformation (see Emin in Table 6) for each macrocycle but there are also examples where MCS (3SU0 and 4NNR) and MCMM-short (3FRQ, 3JRX and 4NNR) failed to identify the lowest energy minimum (here assumed to correspond to the global energy minimum) with a difference greater than 1 kJ mol-1. Prime-MCS is a newly implemented conformational search engine designed for generating conformations for physics-based predictions of membrane permeability of macrocycles.46 In the case of Prime-MCS the energy values are not directly comparable to the values generated by the other methods. Prime-MCS uses two rounds of iterations with 65 steps per iteration, with an RMS gradient threshold of 0.01 kcal mol-1 Å-1. When fully energy minimized, the lowest energy conformers found by Prime-MCS were identical to the global energy minima except for seven cases (2IWX, 2XYT, 3BE9, 3JRX, 3SU0, 4HUS and 4NNR) where Prime-MCS failed to find the lowest energy minima. Given the thorough exploration of the conformational space and the large number of conformers that were generated by the exhaustive MCMM search this method should provide a valid benchmark for rigid docking of conformers. As previously mentioned, if the


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Page 26 of 40

conformational search would generate conformations similar to the protein bound conformation of the macrocycles, the docking program would only need to place the conformations in the correct position and score and rank them appropriately. Therefore, we wanted to make sure that the energy minimized X-ray ligand conformation, or a conformation very close to it, was identified by the search methods. As seen in Table 6, MCMM is roughly twice as good as MCS at generating conformations identical or very similar (RMSD below 0.1 Å) to the energy minimized X-ray Ligand. The RMSD of the Best-Fit conformation is equal to or below 0.1 Å for 12 of the 17 protein-ligand complexes for MCMM, five for MCS, eight for MCMM-short and six of the complexes with Prime-MCS. When inspecting the MCS conformations of the three ligands with the highest RMSD values in Table 6, i.e., 2ESA (1.05 Å), 3SU0 (1.19 Å), and 4NNR (1.15 Å), the overall superpositions to the energy minimized X-ray conformations in fact look very similar, indicating that the conformational search methods will be able to present reasonable conformations to a docking program. It should be noted that the validation set was used in the development of the MCS method which could potentially bias the results.19 The relative energy of the Best-Fit conformations had an average value of 16, 21, and 21 kJ mol-1 for the MCMM, MCMM-short and MCS search methods, respectively (see Table 6). Thus, the default energy window used by MCS (41.8 kJ mol-1) should generally be adequate for the MCMM or MCS generation of conformers for rigid docking (see Table 6 and Table S5). Evaluating the different docking protocols The docking accuracy was evaluated and expressed as the RMSD between the heavy atoms of the ligand as found in the X-ray structure after refinement by the Protein Preparation Wizard (i.e., the Restrained X-ray Ligand) and the poses obtained by the docking protocols. The RMSD values of the Top-Scored Pose and the Best-Fit Pose (i.e., the pose most similar to the Restrained


26

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X-ray Ligand) were identified and the results are summarized in Table 7 and shown in detail in Table S5. To illustrate graphically how different RMSD values are reflected in a superposition, three poses of 4NNR with different RMSD to the X-ray ligand (below 1 Å, between 1 and 2 Å, and greater than 2 Å) are shown in Figure 2.

Figure 2. Illustration of docking poses with three different RMSD values originating from the Top-Scored Poses of different docking protocols. A) MCMM Rigid Docking, 0.77 Å RMSD. B) Flexible Docking, 1.34 Å RMSD. C) MCS Rigid Docking, 2.72 Å RMSD. The X-ray ligand pose of the 4NNR complex is depicted in gray.

Since Rigid Docking of the energy minimized X-ray Ligand was found to be successful (see Table 5), and given that the conformational searches identified conformations very similar to this structure (see Table 6), the question is now if the conformers generated by MCMM and MCS can be rank-ordered correctly. Although the exact conformation corresponding to the energyminimized X-ray conformation is not always found, the post-docking minimization algorithm could potentially improve the geometry of the poses and optimize the complexes even closer to the X-ray structure. Starting with MCMM, the method used as a benchmark for rigid docking of conformational ensembles, only two structures (2ESA and 2HFK) had a Top-Scored Pose with a larger RMSD


27


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Page 28 of 40

than 2 Å compared to the X-ray structure (see Table S5). The Top-Scored Pose of 2HFK is docked in a completely different way, where it is both translated and rotated compared to the crystal structure binding mode. The Top-Scored Pose of 2ESA was docked reasonably well, however, a flip of the 1,4-benzoquinone structure fragment in the ligand is causing the high RMSD value (larger than 2 Å). Nevertheless, the docking program can produce poses very similar to the X-ray structure that are also highly ranked. For example, the Best-Fit Pose within 1 score unit from the Top-Scored Pose of 2ESA and 2HFK had an RMSD of 0.18 Å and 0.23 Å, respectively. This indicates that the combination of an exhaustive MCMM conformational search and rigid docking is a robust method for regenerating the pose of the crystal structure. The MCS search protocol used shorter computational times and also generated significantly fewer conformers. However, the method was also able to perform well in terms of reproduction of the X-ray bound pose suggesting that the conformations generated are relevant for protein binding. To challenge the exhaustive MCMM search protocol used and to see to what extent the number of conformers generated is important, we re-ran the MCMM search method using a maximum of 1 000 search steps (see “Conformational analysis using a shorter MCMM search” above) and performed rigid docking. The number of conformers generated by MCMM-short is twice the numbers produced by MCS, but the CPU time spent in conformational searching is 6 times lower (see Table 8). Despite the larger number of conformers docked compared to MCS, it is clear that MCMM-short was unable to find good conformers for docking for some of the protein complexes as reflected by the higher RMSD values, shown in Table 7. Four of the complexes showed RMSD values above 2.0 Å for the Top-Scored Poses, as compared to two when the more exhaustive MCMM search protocol was used (see Table S5). With MCMM-short, 10 ligands had RMSD values below 1.0 Å and 8 of these correspond to the macrocycles with the lowest number


28

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of varied torsion angles, i.e., 15 or less rotatable bonds. Finally, when the Prime-MCS generated conformations were docked rigidly a slightly worse Average RMSD value was obtained as compared to the MCS-based protocol. Fifteen of the MCS complexes had RMSD values below 2 Å as compared to 12 for Prime-MCS. Overall, all three methods generated reasonable conformations that could be used for rigid docking but MCS stood out slightly. Macrocycles are said to represent a challenge for flexible docking due to the extensive sampling of conformational space required.47 To investigate/demonstrate this and to set a baseline, we docked the macrocycles in the screening data set to the corresponding active sites using Flexible Docking in Glide. Surprisingly, as shown in Table 7 and Table S5, the performance of flexible docking was not inferior to the protocols based on rigid docking of conformational ensembles. When analyzing the Top-Scored Pose, all but two complexes were found with RMSD values higher than 2.0 Å (3SU0 and 4HUS) and, remarkably, 12 below 1.0 Å RMSD. For the MCS protocol, two complexes had RMSD values for the Top-Scored Poses above 2.0 Å (3SU0 and 4NNR). The median RMSD values for Top-Scored Poses were 0.83 Å, 0.80 Å, 0.88 Å, 0.83 Å and 0.58 Å for the MCMM, MCMM-short, MCS, Prime-MCS and Glide Flexible Docking protocols, respectively. The median RMSD values for the Best-Fit Poses within one score unit were 0.33, 0.36, 0.54, 0.42 and 0.50 Å, for MCMM, MCMM-short, MCS, Prime-MCS and the flexible docking, respectively. Analyzing the results further in Table 7 and Table S5 shows that the Top-Scored Pose was rarely the Best-Fit Pose with the exception of Glide Flexible Docking, which had 10 out of 17 poses with the same RMSD value (comparing RMSD values between the two columns using the same protocols). However, most of the Best-Fit Poses were found within one score unit from the Top-Scored Pose; the few exceptions where the Best-Fit Pose is not within one score unit from


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Page 30 of 40

the Top-Scored Pose are given in parentheses in Table S5. As mentioned above the overall docking performance of the MCS-based and Flexible Docking protocols were similarly good regarding the reproduction of the X-ray ligand pose with their Top-Scored Poses. In some cases, one method performed better than the other and vice versa. To examine if there is a statistically significant difference between the MCS Rigid Docking and Glide Flexible Docking methods, a ttest was applied on the RMSD values for the Top-Scored Poses generated by the two methods using a significance level of p < 0.05 (the same significance level was used throughout this study). A paired t-test with two-tailed distribution was used. A probability value (p) of 0.78 for the test indicated that there is no statistically significant difference between the two methods. A similar t-test was also applied to the RMSD values for the Top-Scored Poses generated by Prime-MCS compared to MCS. A probability value of 0.30 was obtained implying again that there is no statistically significant difference between the methods applied to the screening set. Finally, the exhaustive MCMM method for generating conformations did not prove to be significantly better that the MCS method (p = 0.71).


30

Page 31 of 40

Table 7. The screening set results of the five docking protocols expressed as RMSD values using the Restrained X-ray Ligand as reference.

Top-Scored Posee

RMSD to X-ray ligand (Å)

Best-Fit Pose within one score unitg

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


Rigid Docking (MCMM)d Rigid Docking (MCMM-short)

d

Rigid Docking (MCS)d Glide Flexible Docking

f

Rigid Docking Prime-MCS Rigid Docking (MCMM)

d

d

Rigid Docking (MCMM-short)d Rigid Docking (MCS)

d

Glide Flexible Dockingf Rigid Docking Prime-MCS

d

Average

Median

< 1 Åa

1 Å – 2 Åb

>2 Åc

1.19

0.83

10

5

2

1.81

0.80

10

3

4

1.05

0.88

11

4

2

1.13

0.58

12

3

2

1.41

0.83

12

2

3

0.42

0.33

15

2

0

1.16

0.36

12

2

3

0.79

0.54

14

1

2

1.02

0.50

13

2

2

0.72

0.42

14

2

1

a

Number of complexes where the macrocycle docked with an RMSD value less than 1 Å to the Restrained X-ray ligand. bNumber of complexes where the macrocycle docked with an RMSD value between 1 and 2 Å to the Restrained X-ray ligand. cNumber of complexes where the macrocycle docked with an RMSD value greater than 2 Å to the Restrained X-ray ligand. dRMSD obtained after rigid docking. eTop-Scored Pose suggested by the docking program. fRMSD obtained after Glide Flexible Docking. gBest-Fit Pose to the conformation in the X-ray structure within one score unit from the Top-Scored Pose.

Table 8. Computation time (minutes) used by each docking protocol for the 17 complexes in the screening set. CPU time (min)a MCMM MCMM-short MCS 139 860 Conformational search 33579 2167 59 29 Docking Total CPU time

35746

198

889

Flexible Docking 439b

PrimeMCS 229

4

199

443

428

a

The calculations were distributed over similar but not identical CPUs. bComputational time (minutes) for ring templating 8 macrocycles (1LD8, IQY8, 1S9D, 2IWX, 2J9M, 2XYT, 3BE9, 3SU0).

Validation using a larger data set To further compare the rigid docking of MCS and Prime-MCS generated conformations, and the Flexible Docking protocols, an additional 31 macrocycles were studied. As the computationally expensive MCMM method did not outperform the MCS method (vide supra), the MCMM method was not included in this larger evaluation. Table 9 and Table S6 show the


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RMSD values of the docked poses relative the X-ray structure. Again, we found that most of the Top-Scored Poses were found under the 2 Å RMSD level. The MCS Rigid Docking and Glide Flexible Docking protocols had only five and seven complexes, respectively, with RMSD values above 2 Å. However, flexible docking failed to generate any docking poses for three of the macrocycles (1NSG, 2DG4, 3I6O) which were docked successfully with the MCS protocol. For the MCS protocol, the median RMSD value for Top-Scored Poses was 0.79 Å, whereas flexible docking had a median RMSD value of 0.92 Å. The median RMSD values for the Best-Fit Poses within one score unit were 0.70 Å and 0.84 Å for MCS Rigid Docking and Flexible Docking, respectively. Again, to evaluate if there was a statistically significant difference between the protocols, a t-test was applied on the RMSD values for the Top-Scored Poses generated by the two methods using all 47 macrocycles docked. A probability value of 0.53 showed that there is no statistically significant difference between the two docking protocols. Thus, it can be concluded that the MCS and the Flexible Docking protocols are equally good at reproducing the X-ray ligand pose with their Top-Scored Poses. The flexible docking of the screening set was, however, about 2 times faster (ring templating included) than the MCS protocol, as estimated by the screening set, see Table 8.


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Table 9. The results of the MCS, Glide Flexible Docking, and Prime-MCS protocols expressed as RMSD values using the Restrained X-ray Ligands as reference for the validation set. RMSD to X-ray ligand (Å) Rigid Docking (MCS) Top-Scored Posee

Best-Fit Pose within one score unitg a

Average Median < 1 Åa 1 Å – 2 Åb >2 Åc 1.59 0.79 21 5 5

d

Glide Flexible Dockingf

1.27

0.92

15

9

7

Rigid Docking (Prime-MCS)d

4.02

1.84

7

9

15

Rigid Docking (MCS)d

1.14

0.70

25

2

4

Glide Flexible Dockingf

1.21

0.84

16

8

7

Rigid Docking (Prime-MCS)d

2.55

1.39

11

9

11

Number of complexes were the macrocycle docked with an RMSD value less than 1 Å to the Restrained X-ray

ligand. bNumber of complexes were the macrocycle docked with an RMSD value between 1 and 2 Å to the Restrained X-ray ligand. cNumber of complexes were the macrocycle docked with an RMSD value greater than 2 Å to the to the Restrained X-ray ligand or no pose generated. dRMSD obtained after rigid docking. eTop-Scored Pose suggested by the docking program. fRMSD obtained after Glide Flexible Docking. gBest-Fit Pose to the conformation in the X-ray structure within one score unit from the Top-Scored Pose.

Although Prime-MCS worked adequately for our smaller screening set (see Table 7), the evaluation using the larger set of macrocycles indicated a substantially reduced performance compared to rigid docking of MCS generated conformations or Flexible Docking (see Table 9). A paired t-test using a two-tailed distribution applied to the RMSD values for the Top-Scored Poses generated by the MCS and the Prime-MCS based protocols indicate a significant difference (p = 0.006). The performances of the three methods are shown in Figure 3 illustrating a significant deviation between the MCS- and the Prime-MCS-based methods for half of the macrocycles studied.


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Figure 3. Rank ordered RMSD values of Top-Scored Poses generated by rigid docking of conformational ensembles from MCS and Prime-MCS, and Flexible Docking illustrating the robust performance of flexible docking. This plot contains results from all 48 macrocycles studied.

Evaluating the sensitivity of Flexible docking results to the starting geometry of the macrocycle. Having shown that the Flexible Docking protocol works quite well for these complex macrocycles we wanted to evaluate how robust the docking results were with respect to the input starting conformation used.48 Therefore all MCS generated conformers in the screening set (see Table 6) were docked using the Flexible Docking protocol. Firstly we analyzed how diverse the input conformations were within each conformational analysis by superimposing each MCS generated conformation to the energy minimized X-ray ligand (see left bar “MCS search” for each complex in Figure 4). For example, out of the 33 conformations identified for 1LD8 two conformations had an RMSD below 1 Å (6%), 15 between 1 Å - 2 Å (45%) and 16 above 2 Å (48%). After flexible docking, all 33 input conformations resulted in RMSD values between 1 Å and 2 Å which is shown in the right bar “Flexible docking” for 1LD8. More precisely all the 33 conformations, irrespective of input conformation used, gave the same final docking pose (average RMSD of 1.08 ± 0.00 Å), which is identical to the Top Scored pose in Table S5. For


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most of the complexes the MCS conformational analysis generated a diverse set of input conformations when compared to the minimized X-ray structure (see Figure 4 and Table S7).

Figure 4. A graphical overview of the diversity of the MCS conformers that were used as input structures for flexible docking, measured as RMSD (Å) to the energy minimized X-ray ligand, and the corresponding docking result, measured as RMSD (Å) to the X-ray ligand (left bar for each macrocycle). The conformers and the docking poses are, dependent on their RMSD values, divided in to three different groups with RMSD values: below 1 Å (green), between 1 Å -2 Å (yellow), and greater than 2 Å (red) (right bar for each macrocycle). Encouragingly, the docked poses were in most cases insensitive to the input conformation used for docking. The standard deviation of the average RMSD value was below 0.5 Å for 15 out of the 17 macrocycles, and below 0.25 Å for 11 out of the 17 macrocycles. For 2ESA the incorrect pose would have been generated if starting from any of the two conformations that gave the poses with an RMSD value above 2 Å. In 3SU0 we arrived at poses with a wide spread in RMSD values. This is because the 2-indole side chain can adopt many different conformations in the docked poses (see Figure 5). Finally, for 4NNR the Top scored pose had an RMSD value of 1.34 Å but most of the poses ended up between 0 and 1 Å. This is again due to different conformations assumed by a side-chain, in this case a cyclohexane. Overall, Flexible Docking in Glide gave quite robust results showing that the output docking pose is not particularly dependent on the starting geometry of the ligand even though structurally and energetically diverse starting geometries were used.


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Figure 5. The 3SU0 binding site with the X-ray ligand, depicted in yellow, and two docking poses obtained from flexible docking of MCS generated conformers. The two docking poses are depicted in green (best-fit pose, 0.38 Å RMSD) and red (4.23 Å RMSD). The figure illustrates how an error in placing a side-chain correctly, in this case a 2-indole, may give rise to high RMSD values even though the macrocyclic ring is correctly docked.

CONCLUSION For macrocycles, in spite of the structural pre-organization, the available conformational space to explore is commonly substantially larger than for non-macrocyclic small molecules. Moreover, ring-sampling of the macrocyclic scaffolds will require special care making docking of macrocycles more challenging. In this work, we have addressed these questions using a data set consisting of macrocyclic molecules with the aim to identify a robust protocol for docking these molecules. The present study shows that standard ring-templating (when required) followed by Flexible Docking in Glide is adequate even for these larger and more complex structures. Additionally, Flexible Docking in Glide does not seem to rely too heavily on the input structure to produce reasonable docking poses. Thus, there does not seem to be a need for a more thorough conformational search prior to docking macrocyclic compounds. As there was no statistically significant difference between rigid docking of pre-generated conformations produced by the MCS and direct Flexible Docking in the reproduction of the X-ray crystal


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structure, either protocol can be used. However, direct flexible docking is faster and more convenient compared to rigid docking of pre-generated conformations.

Acknowledgements This work was supported by the Swedish Research Council (521-2014-6711).

Supporting information Characteristics for structures and ligands of the validation data set. Structures of all ligands in the screening set and validation set in the tautomer/ionization state used in the conformational analysis studies and docking experiments. More detailed tables of selected results. This material is available free of charge via the Internet at http://pubs.acs.org.


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For Table of Contents use only Canonicalized input geometry of macrocycle

MCMM

MCMMshort

MCS

PrimeRing MCS templating

Rigid docking

Rigid Rigid Rigid docking docking docking

Flexible docking


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Docking of Macrocycles: Comparing Rigid and ... - ACS Publications

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