Structure-based design of novel combinatorially

Molecular BioSystems PAPER

Cite this: Mol. BioSyst., 2017, 13, 1406

Structure-based design of novel combinatorially generated NBTIs as potential DNA gyrase inhibitors against various Staphylococcus aureus mutant strains† Anja Kolaricab and Nikola Minovski

*a

Although intercalating agents such as quinolones have had proven therapeutic success as antibacterial agents for more than 40 years, new forms of quinolone-based resistance in bacteria are continually emerging. To alleviate this problem, a new class of antibacterials is urgently needed; recently, novel bacterial topoisomerase inhibitors (NBTIs) have been found to be particularly important. Based on 67 experimentally evaluated NBTIs against wild-type (WT) DNA gyrase originating from Staphylococcus aureus, a predictive QSAR model was initially constructed and validated and was later used for in silico prediction of biological activities for an in house designed compound library of 548 novel drug-like NBTI combinatorial analogs. To evaluate the influence of gyrA alterations on NBTI resistance, various mutant homology models were constructed; meanwhile, their resistance profiles were assessed and validated relative to that of WT enzyme by structure-based virtual screening (VS) of known NBTIs. Received 20th March 2017, Accepted 23rd May 2017

Surprisingly, the M121K mutant model was recognized as the most selective due to an additional

DOI: 10.1039/c7mb00168a

the NBTI right-hand site (RHS) fragment; this finding was additionally supported by VS of our

established cation–p interaction between K121-NH3+ (not found in the WT) and the aromatic moiety of combinatorially generated NBTIs. Moreover, we identified several attractive, synthetically feasible RHS

rsc.li/molecular-biosystems

building blocks that may enable the development of new NBTIs.

1. Introduction Increasing bacterial resistance is a global threat that is diminishing the effectiveness of antibiotics. The World Health Organization (WHO) is continually reporting not only an increasing number of bacteria-related deaths, but also on the precautions a

Department of Cheminformatics, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia. E-mail: [email protected]; Fax: +386 1 4760 300; Tel: +386 1 4760 383 b Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Ljubljana, Asˇkercˇeva 7, 1000 Ljubljana, Slovenia † Electronic supplementary information (ESI) available: Spatial comparison of natively presented NBTI (GSK299423) conformation and its calculated dock poses (Fig. S1); the screening chemical libraries NBTIexp (Table S1) and NBTIcombi (Table S2); 10-fold re-modeling of the predictive NBTIs-based QSAR model (Table S3); the results obtained after level 1 Boolean-based (T/F (true/false) clustering (geometry properties assessment) of the combinatorially-generated drug-like NBTI binding poses (NBTIcombi) for the constructed gyrA mutant models (Table S4); the results obtained after level 2 Boolean-based (T/F (true/false)) clustering (score based clustering) of the (T)-signed combinatorially-generated drug-like NBTI binding poses (NBTIcombi) from level 1 for the constructed gyrA mutant models (Table S5); the results obtained after the level 3 Boolean-based (T/F (true/false)) clustering (activitybased clustering) of the (T)-signed combinatorially-generated drug-like NBTI binding poses from level 2 for the constructed gyrA mutant models (Table S6). See DOI: 10.1039/c7mb00168a

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as well as the financial burden related to the uncontrolled antibiotics-based treatment of bacterial resistance. Persistent bacterial infections with growing resistance to currently known antibiotics caused by multidrug-resistant strains, such as methicillin-resistant Staphylococcus aureus (MRSA), Streptococcus pneumoniae, Escherichia coli, and non-typhoidal Salmonella (NTS), pose an enormous problem to health care by requiring treatments with more expensive and toxic drugs. It has been found that people with MRSA have approximately 64% higher mortality compared to people with non-resistant forms of infection.1 Therefore, the discovery of novel, efficient antibacterial agents for combating bacterial resistance is urgently needed. Bacterial topoisomerases, such as DNA gyrase and its paralogous form topoisomerase IV, have been well-validated targets for antibacterial chemotherapy for more than forty years.2,3 Despite their high level of structural similarity, these heart-shaped heterotetrameric enzymes, i.e., A2B2 (in DNA gyrase) and C2E2 (in topoisomerase IV), are implicated in different intracellular functions; while topoisomerase IV is responsible for DNA decatenation activity, the correct spatial DNA topology is maintained exclusively by DNA gyrase through cleavage and resealing of double-stranded bacterial DNA followed by introduction of negative supercoils.3–5 Consequently, disturbance of the native

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Fig. 1 Staphylococcus aureus DNA gyrase enzyme in complex with a DNA molecule and an NBTI ligand (GSK299423),11 intercalated between the central DNAbp (top view). Both DNA gyrase subunits are colored differently (gyrA (green) and gyrB (magenta)); the bacterial DNA is depicted in solid orange, while the NBTI ligand (stick representation) is represented in native atomic colors. (A) Close view of the crucial interactions between the NBTI ligand and key amino acid residues from both gyrA subunits (HB, hydrogen bonding interactions; HI, hydrophobic interactions). (B) Established structure– activity relationship (SAR) knowledge of the available NBTIs used in our study. Molecular figures were generated with PyMol.66

bacterial DNA topology by intercalating small ligand molecules between DNA base pairs (DNAbp) and establishing a stable ligand-DNA–DNA gyrase covalent complex (Fig. 1A) followed by bacterial replication/transcription disruption and bacterial cell death has been found to be a promising antibacterial mechanism.5,6 For several decades, quinolone antibacterials, particularly their 6-fluoro derivatives, 6-fluoroquinolones (6-FQs), have been the most recognizable and successful intercalating agents for antibacterial chemotherapy.7,8 Unfortunately, daily reports associated with various quinolone-caused ‘‘acquired resistances’’ are becoming considerably frequent, pinpointing the decreased or even total ineffectiveness of current drugs in antibacterial treatments;9,10 this imposes an urgent need for the development of novel intercalating agents lacking cross-resistance to 6-FQs. Recently, a new class of non-fluoroquinolone antibacterials named novel bacterial topoisomerase inhibitors (NBTIs) was discovered.11–14 NBTIs are a class of intercalating DNA gyrase inhibitors; unlike 6-FQs, inhibition of bacterial DNA gyrase by this class is not associated with stabilization of double strand breaks, but with single strand breaks.11 As depicted in Fig. 1B, these inhibitors are assembled from three parts: an aromatic bicyclic left-hand side (LHS), a six-to-eight membered cyclic/bicyclic linker, and an aromatic heterocyclic right-hand side (RHS). These bind in the middle of the two active sites, with the upper LHS moiety strongly stacked between central base pairs of the stretched DNA and the lower RHS moiety perpendicularly oriented, occupying a deep non-catalytic hydrophobic pocket formed by both gyrA subunits (Fig. 1A).11 It has been found that the linker plays an important role in the correct spatial positioning of the LHS and RHS moieties. Due to its planar aromatic nature, the LHS building block is stably stacked between central DNAbp by a network of p–p interactions; hence, the direction of the RHS moiety and, consequently, the binding of NBTIs to the gyrA key amino acid residues, which is crucial for biological activity, seems to be directly correlated to the geometry

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of the linker fragment.15 This has resulted in extensive NBTI structure–activity relationship (SAR) optimization protocols; various linker variants have been introduced recently, including aminopiperidine, tetrahydroindazole, oxabicyclooctane, and tetrahydropyrane (Fig. 1B).13,14,16–18 Important SAR observations indicate the importance of substitution of the LHS with a halogen or cyano at position 2 and a methoxy group at position 7; these were demonstrated to be necessary for optimal antibacterial activity and an optimal spectrum.19 Hydroxylation of the linker chain is relevant for optimal physico-chemical properties connected with appropriate oral bioavailability, while the basic nitrogen on the linker chain improves the affinity.17 Unfortunately, some of these substructural alterations are directly correlated to increased hERG toxicity followed by occurrence of prolonged QT intervals that may trigger a life-threatening torsades de pointes (TdP) arrhythmia,20,21 resulting in discontinuation of some NBTIs in clinical trials.22 Because 6-FQs resistance is most commonly caused by alteration of amino acid residues constituting the so-called quinoloneresistance determining region (QRDR) in DNA gyrase,9 Black and co-workers12 explored the possibility of a similar scenario of spontaneous mutations induced by NBTIs that could affect their affinities. They demonstrated that the most frequent spontaneous single point mutations, such as D83G or D83N harbored on the a4-helix as well as M121K situated on the b3–b4 loop of both gyrA subunits, can induce significant decreases in activity that can progress to bacterial resistance.12 The latter appears to be especially significant because M121K can strongly affect the properties of D83 or even cause a loss of its conformational freedom, most likely through alteration of the pKa of the D83 carboxylate group in the close spatial vicinity of the K121 protonated nitrogen.12,15 The present study aims to investigate the potential influence of the most frequently appearing spontaneous bacterial DNA gyrase alterations on the binding and affinity of NBTIs as well as to design and identify novel NBTI hits as future promising bacterial DNA gyrase inhibitors. Based on recently revealed

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experimental findings,11,12 five S. aureus gyrA mutant homology models (D83Gmod, D83Nmod, M121Kmod, D83G + M121Kmod, and D83N + M121Kmod) were constructed, while their validation and NBTI resistance profiles were estimated by structure-based virtual screening (VS) of known, experimentally confirmed NBTIs relative to the wild-type (WT) enzyme. With the intention of identifying novel NBTI analogs, all constructed S. aureus gyrA mutant homology models together with the WT enzyme were additionally subjected to a robust structurebased VS assessment of an in house compiled combinatorial library comprising 548 RHS-assembled drug-like NBTI analogs whose biological activity values were predicted utilizing a pre-constructed and validated predictive quantitative structure– activity relationship (QSAR) model. Moreover, this study identified several novel, synthetically attainable building-blocks which may be of valuable importance in the ongoing design and optimization of innovative NBTIs for combating bacterial resistance.

2. Methods 2.1. Construction of a predictive NBTI quantitative structure–activity relationship model 2.1.1. Dataset. A dataset of 67 NBTIs (hereafter named NBTIexp) with in vitro experimentally determined biological activity values against S. aureus DNA gyrase enzyme (IC50,exp [mM] expressed as pIC50,exp = log(IC50,exp), of which 52 were active (IC50,exp r 2.0 mM) and 15 were inactive (IC50,exp 4 2.0 mM)); available as ESI,† Table S1) was initially compiled from the literature.13,14,17,18 Prior to modeling, all chemical structures comprising the NBTIexp library were sketched using ChemBioDraw Ultra23 and energetically minimized utilizing LigandScout’s integrated Merck Molecular Force Field 94 (MMFF94) module.24,25 2.1.2. Calculation of molecular descriptors. For the purpose of constructing a predictive QSAR model, all chemical structures from the NBTIexp library were initially encoded in numerical form (molecular descriptors). The DRAGON software tool26 was used to calculate a starting pool of 1664 molecular descriptors which was pre-processed by eliminating the variables with null values and constant values as well as highly inter-correlated descriptors. This resulted in a total of 1353 molecular descriptors that served as independent variables for QSAR model development. 2.1.3. QSAR model development and validation. Prior to modeling, the entire dataset was randomly divided into training and prediction sets in percentage ratios of 80% (54 compounds) and 20% (13 compounds), ensuring that NBTIexp compounds with minimal and maximal in vitro biological activities were included in the training set. This ensures that the established QSAR model covers the entire biological activity range and that prediction set values are situated within the model applicability domain. The QSARINS modeling platform27 was employed to construct a predictive QSAR model by means of multiple linear regression (MLR). The model was constructed solely on training set objects

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in a genetic algorithm (GA)-based iterative fashion to select those variables (molecular descriptors) from the starting pool of 1353 molecular descriptors which best correlate with the modeled endpoint (biological activity). During the modeling, the model was internally validated by the cross-validation leave-one-out (QLOO2) procedure (1):28 Ntr  2 P yi;exp  yi;pred

QLOO 2 ¼ 1  i¼1 Ntr  P

yi;exp  yi;exp

2

(1)

i¼1

where Ntr is the total number of training set objects, yi,exp and yi,pred denote the experimental and predicted biological activity values for the training set compounds, respectively, and y% i,exp represents the mean response values for the training set. As part of the internal validation, 10-fold Y-scrambling tests were performed; this method was substantiated on re-construction of the QSAR model by iterative shuffling (randomizing) the values composing the vector of the dependent variables (biological activities), while the original data matrix (GA-selected molecular descriptors) remained unchanged. If the obtained randomized QSAR models are statistically inferior to the original model (lower R2 and Q2 values), the established QSAR model is sensitive to the biological data used and is not a result of chance correlation.27,29 Additionally, as part of the model validation and, most importantly, to ascertain the model quality (particularly in terms of the repeatability of GA-selected independent variables that best correlate with the modeled biological activity), up to 10 repetitions of the entire QSAR modeling procedure were performed using different dataset divisions (division ratios of 80% and 20% for the training and prediction sets, respectively, using different training and prediction set objects in each modeling step), while the GA modeling settings were left intact. Lastly, the predictive power of the established QSAR model was assessed by a comprehensive external validation of the previously excluded prediction set compounds not used during the model development. In this way, five external validation parameters were calculated whose critical lower thresholds (QFn[F1,F2,F3]2 Z 0.70 (2 to 4),30,31 CCCEXT Z 0.85 (5),32,33 and rm2  0:50 ð6Þ34) determine the acceptance of a QSAR model as externally predictive. All mathematical and statistical details supporting these external validation criteria are broadly elaborated in the literature.30–34 N ext P 2

QF1 ¼ 1 

i¼1 N ext P

ðyi  y^i Þ2 (2) ðyi  ytr Þ2

i¼1

N ext P

ðyi  y^i Þ2

QF2 2 ¼ 1  Ni¼1 ext P ðyi  yext Þ2

(3)

i¼1

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N ext P 2

QF3 ¼ 1 

i¼1 Ntr P

ðyi  y^i Þ2



ðyi  yext Þ2

Next 

(4) Ntr

i¼1

CCCEXT ¼ N ext P i¼1

  ðyi  yÞ y^i  y^

2

N ext P

2

i¼1 N ext P

ðyi  yÞ þ



2  2 y^i  y^ þ Next y  y^

(5)

i¼1

 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rm2 ¼ r2 1  r2  r02 ;

0

rm2 ¼

rm2 þ rm 2 2

(6)

where the QFn[F1,F2,F3]2 parameters designate similar expressions as the QLOO2 parameter (1), although they were calculated in this case using the predictions of the prediction set (as denoted by the ‘‘ext’’ index);30,31 CCC stands for concordance correlation coefficient,32,33 while rm2 represents a recently devised QSAR external validation metric similar to the squared correlation coefficient.34 Thus, this derived and validated QSAR model was further employed for biological activity prediction of novel, virtually generated NBTI combinatorial compounds. 2.1.4. QSAR model applicability domain. The applicability domain (AD) of the established predictive QSAR model was defined and assessed using the well-known leverage approach.35,36 Because this method is grounded on the graphical assessment of the leverage values (hii: hat matrix diagonal elements obtained from the original descriptor matrix describing the QSAR model) as a function of the standardized cross-validated residuals, it was found to be particularly useful not only for identification of structurally-influential outliers (e.g., NBTIexp compounds that are structurally diverse compared to the remaining training set chemicals, i.e., the model), but also for detection of response outliers (e.g., NBTIexp compounds poorly predicted by the QSAR model). Put differently, the predictions by the QSAR model can be assigned as unreliable for those compounds whose hii values are greater than the critical leverage value (h* = 3(p + 1)/n, where p is the total number of descriptors on which the QSAR model is based, while n is the total number of training set compounds), i.e., pure structurally influential outliers. Similarly, the compounds for which the calculated standardized residual values are greater than 3s units can be referred to as response outliers. 2.2. Virtual combinatorial library design of novel RHS-assembled NBTIs Virtual combinatorial synthesis is a popular approach in modern drug discovery which enables inter alia formation of novel structural analogs based on existing ligands with confirmed in vitro activity on the biological target of interest. The design of novel ligand analogs for the target under investigation by SARbased scaffold alterations is a widely accepted strategy in modern drug discovery.37 Following the original synthetic pathways of currently known NBTIs18 and well-established NBTIs SAR knowledge,13,14,16–18 we opted to define four generic NBTI virtual synthetic schemes by taking into account only the crucial synthetic steps (Fig. 2).

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Because the stabilities and affinities of NBTI ligands are directly connected to the planar aromatic nature of the LHS fragment by establishing a comprehensive network of LHSDNAbp p–p stacking interactions and the spatial geometry of the linker moiety,15 respectively, we decided to preserve the LHSlinker moiety (Fig. 2). Put differently, all RHS fragment-clipping operations were performed at a pre-defined linker position according to the NBTI SAR.15 This resulted in defining a total of 18 NBTI combinatorial variants (covering our 67 ligands comprising the NBTIexp library) which could be classified in three major categories according to their linker moieties: oxabicyclooctane-based (OXBC), tetrahydroindazole-based (THIND), and tetrahydropyrane-based (THPYR) combinatorial NBTIs (Fig. 2). The compilation of RHS building blocks was accomplished using the iScienceSearch web-based platform38 set in minimum 80% structural-similarity-search mode to known RHS fragments from the NBTIexp library; this initially yielded 1 206 219 fragments. Because the crucial synthetic step in our virtual combinatorial reactions is clipping of the RHS fragments to the LHS-linker moiety through reductive amination or amide bond formation, substructural RHS fragments containing carbonyl or carboxyl moieties were selected (Fig. 2). For this purpose, an in house two-level Pipeline Pilot protocol39 was devised, which includes: (1) Thorough pre-filtering of the initial fragment library (e.g., carbonyl/carboxyl substructural filtering, removal of duplicate fragment entities, salt removal, Ro3 filtering) and selection of RHS building blocks. Here, the acronym Ro3 refers to the well-known ‘‘Rule-of-three’’ fragment-likeness filtering set (MW r 300 and nHBD, nHBA, nRB r 3), where MW is molecular weight, while nHBD, nHBA, and nRB indicate the number of hydrogen bond donors, number of hydrogen bond acceptors, and number of rotatable bonds, respectively.40 (2) Combinatorial enumeration (virtual combinatorial generation) and construction of NBTI combinatorial analogs. In this way, a total of 430 and 247 RHS fragments containing carbonyl and carboxyl moieties, respectively, were selected and directly used in the second level – combinatorial enumeration and construction of 11371 NBTI combinatorial analogs. The selection of drug-like NBTI combinatorial compounds was achieved by implementation of a combined Lipinski/Veber drug-likeness rule set,41,42 which resulted in a list of a total of 6468 drug-like NBTI combinatorial analogs containing OXBC (371), THIND (3433 based on carbonyl and 1017 based on carboxyl RHS moieties, respectively), and the THPYR (1647) linker. These NBTI combinatorial compounds served as an external dataset for prediction of their biological activity values (IC50,pred-combi [mM]) through implementation of our previously constructed and validated QSAR model. The QSAR-driven prioritization, i.e., selection of NBTI combinatorial compounds for further assessment, was performed utilizing an Insubria graph, where the AD plot of the QSAR model represents pIC50,pred as a function of the descriptorsderived hat values.43 Those combinatorial compounds situated within the AD boundaries defined by the minimum/maximum predicted biological activities for training set objects and the critical hat value (h* = 3(p + 1)/n, where p denotes the total

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Fig. 2 Virtual synthetic schemes for the combinatorial enumeration of novel, RHS-assembled combinatorial NBTI analogs, representing one-step coupling of the LHS-linker moiety with the carbonyl or carboxyl groups of selected RHS building blocks. Three different linkers were included: (A) oxabicyclooctane (OXBC), (B) tetrahydropyrane (THPYR), and (C and D) tetrahydroindazole (THIND).

number of descriptors used for constructing the QSAR model and n represents the total number of training set compounds) were considered to be reliable. In this way, a focused compound library comprised of a total of 548 NBTI combinatorial analogs was built (hereafter named NBTIcombi; available as ESI,† Table S2) containing 10 OXBC, 152 THIND, and 386 THPYR NBTI analogs, which were further used for structure-based calculations. 2.3. In silico mutagenesis and construction of S. aureus gyrA mutant homology models Following the scenario of 6-FQs ‘‘acquired resistance’’,9,10 Black and co-workers12 in their seminal work for the first time investigated the possibility that NBTI-induced spontaneous mutations within the DNA gyrase NBTI binding pocket could significantly affect their affinities. To evaluate the potential influence of the most frequently occurring gyrA alterations on NBTI resistance,12 we opted to construct all possible gyrA mutant variants (D83G, D83N, M121K, D83G + M121K, and D83N + M121K). For this purpose, the available crystal structure of DNA gyrase enzyme in complex with a DNA molecule and an intercalated

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NBTI ligand (GSK299423) originating from S. aureus WT strain (PBD ID: 2XCS)11 served as a template for performing single- and double-point in silico mutagenesis experiments within the NBTI binding pocket (Fig. 1A). Discovery Studio’s integrated MODELER mutagenesis engine44–46 was employed for in silico amino acid alterations followed by side-chain CHARMm energy minimization and refinement,47,48 while the double-stranded DNA molecule and the co-crystallized NBTI ligand (GSK299423) were left intact. Thus, the constructed and refined S. aureus gyrA mutant homology models (D83Gmod, D83Nmod, M121Kmod, D83G + M121Kmod, and D83N + M121Kmod) together with the WT enzyme (Fig. 3) were further used in structure-based calculations. 2.4.

Structure-based calculations

Structure-based (molecular docking) calculations on both NBTI compound libraries (NBTIexp and NBTIcombi, respectively) within the NBTIs binding pocket of S. aureus DNA gyrase WT enzyme11 and its previously constructed gyrA mutant homology variants (D83Gmod, D83Nmod, M121Kmod, D83G + M121Kmod, and D83N + M121Kmod) were performed utilizing Pipeline

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Fig. 3 Close view of the co-crystallized NBTI ligand (GSK299423)11 nested within the S. aureus DNA gyrase NBTIs binding pocket assembled by both gyrA subunits of (A) the WT enzyme (PDB ID: 2XCS)11 and our constructed mutant homology models: (B) D83Gmod, (C) D83Nmod, (D) M121Kmod, (E) D83G + M121Kmod, and (F) D83N + M121Kmod.12 The in silico substituted amino acid residues for each separate mutant model (B–F) are represented in solid red (stick representation); the gyrA subunits are represented in solid green (cartoon representation); bacterial DNA is depicted in solid orange, while the intercalated NBTI ligand is represented in native atomic colors (stick representation).

Pilot’s GOLD docking interface.49 The experimental coordinates of the co-crystallized NBTI ligand (GSK299423) were used to define the binding site (cavity radius of 16.0 Å) that covers all surrounding key amino acid residues.11 All structure-based calculations were accomplished using the same settings and technical parameters (population size = 100, selection pressure = 1.1, number of operations = 100.000, number of islands = 5, niche size = 2, migrate = 10, mutate = 95, cross-over = 95) by running 10-fold genetic algorithm (GA) iterative sampling per ligand molecule. The GoldScore Fitness (GSF) function was used as a scoring function to quantify the binding affinity of the NBTIs.49 The quality of all performed structure-based settings was estimated by initial 3-fold re-docking experiments of the natively present co-crystallized NBTI ligand (GSK299423), i.e., reproduction of its spatial conformation and orientation. As decisive criteria for the performed ligand reproductions, the heavy-atoms root-mean-square deviation (RMSD r 2.0 Å) value between each calculated docking solution and co-crystallized ligand conformation was calculated.50 2.5. Assessment of discriminatory performance of S. aureus gyrA mutant homology models It is widely accepted that the foundation of structure-based modeling for the VS of ligand molecules into a defined protein binding site and, consequently, the identification of high-quality

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hits is grounded on the construction of a protein homology model which is capable of correctly discriminating a set of known, active compounds among a pool of inactive molecules.51,52 Bearing this in mind, an appropriate assessment of the discriminatory performance of our constructed S. aureus gyrA mutant homology models (D83Gmod, D83Nmod, M121Kmod, D83G + M121Kmod, and D83N + M121Kmod) was performed.51 The receiver operating characteristics (ROC) methodology,51,53 a recommended approach for solving binary classification problems (e.g., distinction between known active and inactive molecules), was applied. This was achieved by a so-called enrichment of the library of active NBTIs (in our case, 52 NBTIexp ligands with IC50,exp r 2.0 mM) by a set of decoy (inactive) molecules, i.e., compounds that physico-chemically match our known active NBTIs but at the same time are topologically distinct.54 The unavailability of NBTI decoys for our biological target in the directory of useful decoys (DUD)54 necessitated the in house development of a library of 500 artificial decoys randomly selected from the ZINC database55 by a method similar to that described in our previous work.56 The vROCS engine57,58 was implemented to evaluate and quantify the VS performance of each assembled S. aureus gyrA mutant homology model (D83Gmod, D83Nmod, M121Kmod, D83G + M121Kmod, and D83N + M121Kmod) relative to that of the experimentally solved WT enzyme.11 The in depth similarity screening of the

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compounds comprising both libraries (actives and decoys) against re-docking derived NBTI ligand poses (GSK299423)11 with a minimum calculated RMSD (Å) value used as template structures (queries) resulted in the generation of ROC curves (for the WT enzyme and each gyrA mutant model, separately). These data were further used to calculate the areas under the ROC curves (ROC AUC) and early enrichment parameters (at 0.5%, 1.0%, and 2.0% using confidence boundaries of 95%), which allowed the estimation of a resistance profile for each gyrA mutant homology model. 2.6. Boolean-based clustering for the selection of novel NBTI combinatorial hits As stated previously, the GSF function has been used as a main scoring function for the binding affinity quantification of NBTIs.49 However, it is known that the selection of dockingderived hits utilizing only the highest calculated docking score is not always a satisfactory approach, mainly as a result of the inability of some scoring functions to correctly appraise the ligand binding mode.59,60 To alleviate this issue, our recently devised Boolean-based [T/F (true/false)] clustering method52,56 was employed for thorough in-pocket analysis of all combinatorial NBTI docking solutions (NBTIcombi). This approach is substantially grounded on three consecutive VS levels:  Level 1: geometry properties assessment (examination of the spatial orientation and conformation for each dockingderived combinatorial NBTI ligand pose relative to that of the natively present co-crystallized ligand conformation and building a cluster of (T)-signed dock poses).  Level 2: score-based clustering ((T)-signing of the previously identified Level 1 (T)-signed combinatorial NBTI poses with GSF Z 70 and constructing a cluster of highly scored (T)-signed combinatorial NBTIs).  Level 3: activity-based clustering ((T)-signing of the previously distilled Level 2 (T)-signed highly-scored combinatorial

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NBTIs with predicted biological activity IC50,pred-combi r 1.0 mM and assembling a new cluster of (T)-signed most ‘‘active’’ NBTI combinatorial hits). All (T)-signed combinatorial NBTI analogs extracted at the end of this screening strategy (selected novel NBTI combinatorial hits) were further screened utilizing a PAINS (pan assay interference compounds) filter in order to confirm the absence of known problematic substructures.61 All selected NBTI combinatorial hits extracted by this method were finally subjected to a detailed assessment of the combinatorially attached RHS fragments in relation to their putative biological activity.

3. Results and discussion 3.1. NBTIs QSAR model validation and applicability domain assessment A predictive QSAR model was established for a set of 67 NBTIs (NBTIexp) with experimentally determined in vitro biological activities IC50,exp (mM) against S. aureus DNA gyrase enzyme. Starting from a training set of 54 randomly selected NBTIs and a pool of 1353 calculated molecular descriptors, a six-parameter predictive QSAR model was constructed in a GA-based iterative fashion; meanwhile, its internal validation was carried out by the cross-validation leave-one-out procedure (R2 = 0.8047, QLOO2 = 0.7538; Fig. 4A). The results indicate that the biological activities for our series of NBTIs (expressed as pIC50,exp) can be correlated with the six most important molecular descriptors: J, BEHv3, RDF135u, RDF155m, RDF075p, and R3e (Table 1). As demonstrated in Table 1, J parameter belongs to the class of pure topological descriptors clearly describing the importance of NBTI topology, i.e., the shape of the NBTIs scaffold for correct orientation and nesting of the entire ligand within the NBTI binding pocket.15 The BEHv3 parameter indicates the influence of the atomic van der Waals volumes of the NBTIs,

Fig. 4 Graphical representation of (A) experimental vs. predicted pIC50 values obtained by the QSAR model for the dataset of 67 NBTIs (NBTIexp) and (B) the QSAR model AD plot calculated by the utilized leverage approach. Training set objects are represented as solid blue rectangles, while objects comprising the prediction set are depicted as solid red circles. The compounds determined as potential outliers are marked accordingly.

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Paper Table 1

Molecular BioSystems Detailed characterization of the molecular descriptors used for the construction of the predictive QSAR model

ID

Mol. desc.

Definition

Class

1 2 3 4 5 6

J BEHv3 RDF135u RDF155m RDF075p R3e

Balaban distance connectivity index Largest eigenvalue n. 3 of Burden matrix weighted by van der Waals volume Radial distribution function – 135/unweighted Radial distribution function – 155/weighted by mass Radial distribution function – 075/weighted by polarizability R autocorrelation of lag 3/weighted by Sanderson electronegativity

Topological descriptor Burden eigenvalue RDF descriptor RDF descriptor RDF descriptor GETAWAY descriptor

contributing to the overall ligand accommodation within the deep hydrophobic pocket of the enzyme.11 On the other hand, RDF135u, RDF155m, and RDF075p are parameters from the radial distribution function (RDF) class of molecular descriptors weighted by atomic masses and atomic polarizabilities, respectively, indicating the importance of the polarizability62,63 of some NBTI substituents, such as the LHS planar aromatic moiety for ligand intercalation and stabilization between DNAbp through establishing a network of strong p–p interactions. The R3e parameter is related to the atomic electronegativities, which in turn pinpoints the positive effect of electron withdrawing groups (such as those present in the lower NBTI RHS fragment) for increased ligand potency.14 In order to ensure the suitability of the established QSAR model for its further application as an efficient device for the prediction of biological activity values for a series of compounds not used during the model development, a decent model external validation was performed.30–34 For this purpose, our established QSAR model in the first instance was challenged for its ability to predict the biological activity values for a set of 13 previously excluded NBTIexp compounds (REXT2 = 0.7657, QF12 = 0.7602, QF22 = 0.7196, QF32 = 0.7356, CCCEXT = 0.8570, and rm2 ¼ 0:6563). These results clearly show that all five QSAR model’s derived parameters are in the boundaries of the proposed external validation criteria (QFn[F1,F2,F3]2 Z 0.70, CCCEXT Z 0.85, and rm2  0:50) as suggested by Chirico and Gramatica.33 In addition to the internal validation, our QSAR model was further challenged by 10-fold Y-scrambling trials utilizing a different shuffling of the dependent variables (the biological activity data) for each separate scrambling test,27,29 while the matrix of independent variables (molecular descriptors) was left intact. As expected, this validation method yielded significantly poorer values for the predictivity of the model (RYscr 2 ¼ 0:1133, where RYscr 2 refers to the average predictive squared correlation coefficient obtained by averaging the RYscr2 values obtained by 10 Y-scrambling operations, compared to R2 = 0.8047 obtained by unscrambled data); this result undoubtedly shows that the established QSAR model is indeed sensitive to the biological data used and is not obtained by chance.29 Moreover, the additionally performed 10-fold re-modeling procedure utilizing the same GA settings and different training/prediction set objects situated within the 80% training and 20% prediction set ratio, respectively, resulted in a list of good and comparable QSAR models to the selected one, particularly in terms of the repeatability of the GA-selected independent variables that best

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correlate with the modeled biological activity (see ESI,† Table S3). These results not only account for the quality and stability of the model (as demonstrated by the highest R2 and QLOO2 parameters; R2 = 0.8047, QLOO2 = 0.7538) but also pinpoint the significance of the selected molecular descriptors for proper encoding of the NBTI structural space (see Table 1 and ESI,† Table S3). The AD analysis for our constructed QSAR model shows a very good data distribution as depicted by the AD plot (standardized residuals vs. leverages; see Fig. 4B). According to the calculated critical hat value (h* = 0.389), only two training set compounds (ID2 and ID45) can be identified as pure structurally influential outliers (hID2 = 0.4101 and hID45 = 0.3902, respectively), i.e., objects that lay outside of the AD of the model (see ESI,† Table S1). The structural analysis of these compounds confirmed a slight structural diversity compared to the remaining training set molecules, i.e., an extra hydroxyl group in ID2 attached to the oxabicyclooctane linker moiety (not found in its sole ID1 structural analogue) and RHSsubstituted vicinal nitro and methyl groups, respectively, in ID45 (not found in the tetrahydroindazole-based NBTIs). The single outlier originating from the prediction set is the compound ID42. Apparently, the model undeniably poorly predicted this compound, as it is situated outside of the AD boundaries of the model defined by 3s units for the calculated standardized residuals (Fig. 4B). Thus validated, the established NBTIs QSAR model was utilized for biological activity prediction and subsequent prioritization of novel, combinatorially generated NBTIs (NBTIcombi) for structure-based calculations and VS. 3.1.1. QSAR-driven prioritization of combinatorially generated NBTIs. As stated previously, QSAR-driven prioritization of novel NBTI ligands was performed on a combinatorial library of total 6468 drug-like NBTIs whose biological activity values (pIC50,predcombi) were estimated by employing our validated NBTIs QSAR model. As depicted in Fig. 5, this was achieved by selecting those combinatorial NBTIs situated within the AD framework (compounds with reliable biological activity predictions) defined by the critical hat value (h* = 0.389) and minimum/maximum predicted biological activity values for training set objects, derived from the QSAR model. Detailed inspection of AD-selected NBTI combinatorial compounds showed co-existence of all the NBTI structural classes, such as OXBC (81), THIND (1079), and THPYR (1046) NBTI combinatorial analogs. However, taking the biological activity of known active NBTIexp ligands into account (IC50,exp r 2.0 mM), we further screened AD-determined combinatorial NBTIs and distilled 548 hypothetically active NBTIs with estimated biological activity values below the aforementioned IC50 threshold.

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Fig. 5 AD plot for the constructed QSAR model (Insubria graph) for prioritization of NBTI combinatorial analogs. The AD area of reliable pIC50,pred-combi predictions for the combinatorial compounds is defined by the critical hat value (h* = 0.389) and the minimum/maximum pIC50,exp values for the training set compounds. The training set objects are represented as solid blue rectangles, while the NBTI combinatorial compounds from all three structural classes, OXBC, THIND, and THPYR, are depicted as solid pink triangles, solid green diamonds, and solid orange stars, respectively.

In this way, a focused drug-like NBTI combinatorial library (NBTIcombi; available as ESI,† Table S2) was obtained and further used for structure-based VS and selection of novel NBTI hits. 3.2. Estimation of the influence of NBTIs on the resistance profiles of S. aureus gyrA mutant homology models It is widely accepted that the VS efficiency of the structurebased modeling concept for identification of high-quality hits employing virtually constructed protein homology models is directly dependent on properly performed validations.50,51 For this purpose, a primal validation of experimentally solved S. aureus DNA gyrase WT enzyme and its derived gyrA mutant homology models (D83Gmod, D83Nmod, M121Kmod, D83G + M121Kmod, and D83N + M121Kmod) was carried out by structure-based reproduction of the spatial orientation and conformation of the co-crystallized NBTI ligand (GSK299423).11 As outlined in Table 2, satisfactory performance was observed for WT enzyme and the modeled gyrA mutant variants. This is supported by the calculated RMSD values below 2.0 Å50 for re-docking reproduced co-crystallized ligand (see ESI,† Fig. S1); a result that conspicuously display their suitability for VS purpose.

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Table 2 RMSD values in Angstrom units (Å) computed by heavy-atom alignment between the co-crystallized NBTI ligand conformation (GSK299423)11 and its calculated docking solutions obtained by re-docking within the binding pocket of S. aureus DNA gyrase WT enzyme and its derived gyrA mutant homology models (D83Gmod, D83Nmod, M121Kmod, D83G + M121Kmod, and D83N + M121Kmod). Docking solutions with minimum calculated RMSD values are depicted in bold

Docking solutions Dock pose 1 (Å) Dock pose 2 (Å) Dock pose 3 (Å) WT

0.9136

1.4135

1.4614

Model D83Gmod D83Nmod M121Kmod D83G + M121Kmod D83N + M121Kmod

1.2566 1.1421 1.2402 1.2346 1.3123

1.0394 1.4206 0.9854 1.3533 1.2119

1.3332 1.7128 1.3557 0.7626 1.4180

To attentively validate our gyrA mutant models as well as to estimate their resistance profiles, a series of ROC validation trials were performed. Each constructed gyrA mutant homology model was thoroughly assessed by quantification of its discriminatory performance (evaluation of the capability of the

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Molecular BioSystems

Table 3 Statistical parameters describing the discriminatory performance of S. aureus gyrA mutant models (D83Gmod, D83Nmod, M121Kmod, D83G + M121Kmod, and D83N + M121Kmod) relative to the WT enzyme: ROC AUC (area under the ROC curve), EF (enrichment factor, early enrichment parameters at 0.5%, 1.0% and 2.0% using a 95% confidence interval) and their corresponding p-values (probability of obtaining better VS performance for a given model, assuming that the null hypothesis is true)

ROC AUC

EF (0.5%)

EF (1.0%)

EF (2.0%)

WT

0.96 [0.94, 0.98]

118.67 [87.81, 151.85]

63.03 [49.18, 77.78]

32.82 [26.27, 39.6]

D83Gmod p D83Nmod p M121Kmod p D83G + M121Kmod p D83N + M121Kmod p

0.82 0.00 0.94 0.13 0.98 0.90 0.87 0.00 0.95 0.30

32.73 0.00 101.96 0.27 140.25 0.84 67.88 0.02 131.12 0.70

20.01 0.00 58.31 0.34 72.46 0.83 40.07 0.02 69.87 0.77

12.06 0.00 30.91 0.36 38.24 0.87 24.23 0.07 36.43 0.79

[0.77, 0.87] [0.90, 0.97] [0.96, 0.99] [0.82, 0.91] [0.93, 0.98]

[12.24, 60.90] [40.00, 139.29] [112.3, 167.35] [37.21, 105.26] [88.89, 160.71]

model to correctly identify known active NBTIs in a wave of decoy molecules). Table 3 summarizes the ROC validation outcome for each gyrA mutant homology model relative to that of the WT enzyme, computed from the generated ROC curves (Fig. 6). As demonstrated in Table 3, the mutant homology model M121Kmod showed the best discriminatory performance in identifying true active NBTI compounds relative to the WT enzyme and other mutant homology models (D83Gmod, D83Nmod, D83G + M121Kmod and D83N + M121Kmod), which is also supported by its highest calculated ROC AUC value (ROC AUC[M121Kmod] = 0.98); meanwhile, D83Gmod was identified as the poorest model (ROC AUC[D83Gmod] = 0.82). The latter also applies in the case of the double-point D83G + M121Kmod mutant model (ROC AUC[D83G+M121Kmod] = 0.87), clearly

Fig. 6 ROC plot illustrating the discriminatory performance of our constructed S. aureus gyrA mutant homology models (Table 3) expressed as the fraction of active/decoy molecules found by the model relative to the WT enzyme. The black diagonal line (line of no discrimination) presents randomly distributed data (RDD = 0.5).

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[8.96, 33.33] [40.35, 73.91] [57.89, 85.96] [24.32, 56.00] [55.10, 82.61]

[6.00, 19.44] [24.5, 37.93] [31.7, 44.57] [16.04, 33.3] [29.4, 42.39]

ascribing the negative influence of the altered G83 residue alone (D83Gmod) or in the presence of the K121 mutation (D83G + M121Kmod); these results are firmly supported by the calculated p-values for the selective probabilities of the models as well as their early enrichment parameters (Table 3). Interestingly, the unaltered WT enzyme showed slightly inferior capability (moderate discriminatory performance) for identifying true active NBTIs, comparable to those of the D83Nmod and D83N + M121Kmod mutant models (Table 3). In summary, the lowest resistance degree can be ascribed to the model M121Kmod (the model with the highest capability of identifying true active NBTIs), while the D83Gmod was identified as the model with the highest resistance degree (model with the lowest capability of identifying true active NBTIs). With the intent to clarify and understand the outcome of the performed ROC validation, we carried out a detailed inspection of the intermolecular interactions established between 52 active NBTIexp ligands and our gyrA mutant homology models (Fig. 7). This analysis revealed an additional cation–p interaction formed between mutated K121-NH3+ and the planar aromatic/ heteroaromatic moieties of the RHS fragments (not found in WT, D83Gmod, and D83Nmod; Fig. 7A–C), which in turn can probably enhance the binding of the NBTI ligand to a level comparable to the strength of a hydrogen bond.64 Interestingly, double-point mutant homology models (D83G + M121Kmod and D83N + M121Kmod) showed somewhat poorer discriminatory performance than M121Kmod, despite the ability of K121 to form cation–p interactions (see Fig. 6 and Table 3). However, a detailed inspection of the NBTIexp ligand interactions identified a decreased ability of the mutated G83 residue in the D83Gmod mutant homology model to form the key hydrogen bonding interaction with the basic nitrogen of the linker, which in turn weakens the binding of NBTI ligand in the binding pocket and, consequently, its affinity.11,17 This basic nitrogen is a hydrogen bond donor; however, according to our observations, G83 is too short and consequently too spatially far away to establish any interaction with the ligand (Fig. 7B and E). In contrast to D83 (aspartic acid), N83 (asparagine) possess a different functionality (amide group instead of carboxyl); however,

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Fig. 7 Key NBTIs-gyrA intermolecular interactions revealed by interaction analysis of 52 active NBTIexp ligands within the NBTI binding pockets of the (A) WT enzyme, (B) D83Gmod, (C) D83Nmod, (D) M121Kmod, (E) D83G + M121Kmod, and (F) D83N + M121Kmod mutant homology models. The mutated amino acid residues in each separate mutant model are represented in red, while the unaffected residues are represented in native atomic colors (stick representation). Hydrogen bonding interactions are depicted as dashed green lines, hydrophobic interactions as dashed pink lines, and cation–p interactions as dashed red lines. For clarity, the co-crystallized NBTI ligand (GSK299423)11 was employed.

it is still capable of establishing a hydrogen-bonding interaction with the basic nitrogen of the NBTIs linker (Fig. 7C and F). However, hydrogen-bonding distance measurements revealed a slightly stronger hydrogen bond formed between the basic nitrogen of the NBTIs linker and the carboxylate oxygen (d = 1.94 Å; mostly electrostatic interaction) of the unaltered D83 residue (Fig. 7A and D) compared to the distance of the hydrogen bond (d = 2.56 Å; pure electrostatic interaction) established between the same NBTI functionality and the N83 carbonyl group (Fig. 7C and F).65 On the basis of these findings, we believe that the best discriminatory performance of M121Kmod mutant variant (compared to the WT enzyme and other mutant homology models) may be a consequence of strengthened binding and stabilization of the NBTI ligand due to the additional cation–p interaction formed between the K121-NH3+ moiety and the p-electron cloud of the NBTI RHS fragment as well as the key hydrogen bonding interaction established between the D83 residue and the basic nitrogen of the NBTI linker.11,12 It should be stressed, however, that the additionally identified cation–p interaction in this study should not necessarily be taken as a decisive factor strongly related to any increased or decreased

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biological affinity of NBTI ligands in the studied gyrA mutants without subsequent in vitro experimental evaluations. 3.3. Boolean-based clustering of combinatorial NBTIs for selection of novel hits With the intention to identify novel NBTI hits as highly promising bacterial DNA gyrase inhibitors as well as to propose some new NBTI SAR guidelines, our S. aureus DNA gyrase WT enzyme and its derived mutant homology models (D83Gmod, D83Nmod, M121Kmod, D83G + M121Kmod, and D83N + M121Kmod) were further challenged by in depth VS utilizing the molecular docking data obtained by structure-based calculations of our previously assembled drug-like NBTIcombi library comprised of 548 NBTI analogs. As described previously, our recently devised Booleanbased (T/F) clustering method52,56 was employed for thorough visual assessment of all 548 calculated NBTIcombi docking solutions within the NBTI binding pocket (of the WT enzyme and our constructed gyrA mutant homology models), while the natively present co-crystallized NBTI ligand conformation (GSK299423)11 was again used for comparative purposes. Following our Boolean-based (T/F) VS strategy, the spatial

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examination (level 1: geometry properties assessment; see ESI,† Table S4) of the NBTIcombi calculated docking solutions identified a total of 210 (T)-signed compounds as correctly positioned relative to the co-crystallized ligand conformation within the NBTI binding pocket of the M121Kmod model. The WT enzyme resulted in a somewhat poorer outcome of the 143 (T)-signed compounds, while our remaining gyrA mutant models identified significantly smaller numbers of compounds, i.e., 107, 97, 76, and 49 (T)-signed NBTIcombi analogs for the D83Nmod, D83G + M121Kmod, D83N + M121Kmod, and D83Gmod models, respectively; these findings are almost congruent with our previously performed ROC assessment. All (T)-signed NBTIcombi solutions identified in the framework of the first level Boolean-based (T/F) clustering scheme were collected as separate clusters and used in the second Booleanbased VS level (level 2: score-based clustering) by taking into account their docking-derived GSF values.49 In this filtering routine, a pre-defined GSF threshold (GSF Z 70) was implemented for segregation of all top-scored NBTIcombi clusters, while only highly scored NBTIcombi docking solutions from each top-scored cluster were selected (see ESI,† Table S5). As demonstrated, this resulted in a comparable outcome to that obtained from the previous VS level, which once again corroborates our previously attained validation; totals of 200, 129, 95, 86, 65, and 42 NBTIcombi compounds for the M121Kmod, WT, D83Nmod, D83G + M121Kmod, D83N + M121Kmod, and D83Gmod models, respectively, were identified as highly-scored hits (see ESI,† Table S5) which entered the final stage of our Boolean-based VS procedure (level 3: activity-based clustering; see ESI,† Table S6). For this purpose, an IC50,pred-combi r 1.0 mM cutoff was utilized for selection of the most ‘‘active’’ NBTIcombi hits (as predicted by the previously applied QSAR model), which resulted in the identification of 107, 74, 55, 47, 36, and 28 combinatorial NBTIs for the M121Kmod, WT, D83Nmod, D83G + M121Kmod, D83N + M121Kmod, and D83Gmod models, respectively (see ESI,† Table S6). The cross-screening visual assessment of the NBTIcombi hits distilled at the end of the performed Boolean-based clustering identified only six NBTIcombi hits common to all investigated protein targets as promising DNA gyrase inhibitors against all investigated S. aureus mutant strains (Table 4). Interestingly, the structural inspection of the selected hits clearly showed that they all belong to the THPYR class of NBTIs – a result that is firmly supported by the relatively high in vitro measured biological activity of known THPYR-based NBTIs (see ESI,† Table S1). Moreover, the PAINS filter confirmed the absence of known problematic substructures. The structural analysis of combinatorially clipped RHS fragments in our identified NBTIcombi hits revealed some similarities among all six compounds (Table 4). Namely, the connection between the THPYR linker moiety and the RHS building block is made through the amine, which according to the established NBTI SAR guidelines is of pivotal importance for their improved affinity;17 meanwhile, all the RHS building blocks are composed of five-membered aromatic heterocyclic systems, including pyrazole connected with a phenyl ring

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Molecular BioSystems Table 4 The most representative NBTIcombi hits identified by Booleanbased VS as potential novel inhibitors against all investigated S. aureus DNA gyrase protein targets (WT and its derived gyrA mutant variants), together with their predicted biological activity values (IC50,pred-combi) by the established QSAR model. Novel combinatorially attached RHS fragments are represented by bold bonds

ID

Chemical structure

IC50,pred-combi [mM]

1

0.8188

2

0.4776

3

0.2446

4

0.5253

5

0.1072

6

0.4956

(compounds 3 and 4) and imidazole fused with a benzene ring in a bicyclic RHS fragment (compounds 1, 2, and 5), or connected with a pyridine moiety, as in the case of compound 6 (Table 4).

4. Conclusions In this study, our purpose was to explore the potential influence of the most frequently occurring spontaneous bacterial DNA gyrase mutations on the binding and affinity of NBTIs. Based on recently disclosed experimental findings related to NBTIs-induced gyrA alterations,11,12 a series of single- and

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double-point S. aureus gyrA mutant homology models were constructed (D83Gmod, D83Nmod, M121Kmod, D83G + M121Kmod, and D83N + M121Kmod), while the estimation of their resistance profiles was performed by structure-based VS of known active NBTIs relative to the WT enzyme. Surprisingly, and somewhat paradoxically, the model M121Kmod showed the best performance over the WT and other mutant homology models in identifying true active NBTIs, while the D83Gmod mutant model was identified as the poorest model regardless of the appearance of the G83 alteration alone (D83G) or in the presence of the K121 mutation (D83G + M121K); this result clearly emphasizes the negative influence of the altered G83 residue on the affinity of NBTIs. The analysis of gyrA-NBTIs intermolecular interactions revealed an additional cation–p interaction formed between the RHS planar aromatic/heteroaromatic moieties of the NBTIs and K121-NH3+ (not found in WT, D83Gmod, and D83Nmod); comparable to the strength of a hydrogen bond,64 this additional cation–p interaction seems to be a pivotal reason for the superior discriminatory power (in identifying true active NBTIs) of M121Kmod. Interestingly, M121K-comprised double-point mutant models, such as D83G + M121Kmod and D83N + M121Kmod, showed slightly poorer discriminatory performance compared to that of M121Kmod, regardless of the ability of K121 to form cation–p interactions. Thorough ligand–protein interaction analysis revealed that compared to the natively present D83 residue in the WT enzyme (a key attachment point that is crucial for increased NBTI affinity), its altered counterpart G83 is apparently too short and consequently spatially far away from the basic nitrogen of the NBTI linker to establish any hydrogen bonding interactions, probably resulting in a significant decrease in NBTI affinity.11,17 In contrast to G83 and, most importantly, the unaltered D83 residue, N83 is still somewhat capable of stabilizing NBTI ligands through hydrogen bonding interactions, although with significantly lower affinity.65 To re-confirm our findings and to identify some novel NBTI hits, we further challenged our constructed S. aureus gyrA mutant homology models in a robust virtual cross-screening campaign utilizing structure-based sampling of in-house constructed drug-like RHS-assembled NBTI combinatorial analogs whose biological activity values were predicted using a predevised and validated QSAR model. This strategy resulted in the selection of six novel NBTI combinatorial hits that were identified as properly nested within the NBTI binding pocket of all investigated S. aureus gyrA mutant models. Interestingly, the identified NBTI combinatorial hits belong to the tetrahydropyrane class of NBTIs18 – a result strongly corroborated by the relatively high in vitro determined biological activities of existing NBTIs from the same structural class. Moreover, the structural analysis of the de novo-introduced RHS buildingblocks revealed some structural similarities among all the selected NBTI hits, mainly five-membered N-heterocyclic systems, such as pyrazole- or imidazole-based fragments, whose heteroaromatic properties strongly underline the existing NBTI SAR guidelines for good accommodation and stabilization of the NBTI RHS fragment within the deep hydrophobic binding pocket.11

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We are confident that the results obtained in the framework of this study will provide valuable guidance in ongoing NBTI hit-to-lead protocols for the design and optimization of more effective NBTI antibacterials for combating bacterial resistance.

Acknowledgements The authors thank the Agency of Research of R. Slovenia (ARRS) for financial support through Grant P1-0017. We are sincerely grateful to Dr Marjana Novic for valuable insights, discussion and her continuing support of this research.

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