Recent Developments in Computational Prediction of

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Recent Developments in Computational Prediction of hERG Blockage Sichao Wanga, Youyong Lia, Lei Xua, Dan Lib and Tingjun Houa,b,* a

Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China; bCollege of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China Abstract: The blockage of the voltage dependent ion channel encoded by human ether-a-go-go related gene (hERG) may lead to drug-induced QT interval prolongation, which is a critical side-effect of non-cardiovasular therapeutic agents. Therefore, identification of potential hERG channel blockers at the early stage of drug discovery process will decrease the risk of cardiotoxicity-related attritions in the later and more expensive development stage. Computational approaches provide economic and efficient ways to evaluate the hERG liability for large-scale compound libraries. In this review, the structure of the hERG channel is briefly outlined first. Then, the latest developments in the computational predictions of hERG channel blockers and the theoretical studies on modeling hERG-blocker interactions are summarized. Finally, the challenges of developing reliable prediction models of hERG blockers, as well as the strategies for surmounting these challenges, are discussed.

Keywords: hERG, QT prolongation, ADME/T, QSAR, QSPR, homology modeling, pharmacophore modeling, molecular docking. 1. INTRODUCTION The importance of absorption, distribution, metabolism, elimination and toxicity (ADMET) for the drug discovery process is extensively recognized [1-5]. Blockage of human ether-A-go-go related gene (hERG, KCNH2 or Kv11.1) ion channel is believed to be closely related to drug-induced long QT Syndrome (LQTS), which may cause avoidable sudden cardiac death. Nowadays, the early assessment of hERG-related cardiotoxicity becomes conventional in drug discovery pipelines. Recently, many important drugs have been withdrawn from the market or severely restricted in availability as a result of their undesirable hERG-related cardiotoxicity [6-12]. Currently, several technologies for pre-clinical hERGrelated cardiotoxicity assessment are available [13], including rubidium-flux assays, radioligand binding assays, in vitro electrophysiology measurements, and fluorescence-based assays [14]. However, these in vitro experimental assays are time-consuming and costly, then limiting their use at the earlier discovery stage. Therefore, the development of reliable theoretical methods to predict hERG-related cardiotoxicity is urgent. Numerous computational models or methods have been developed to predict hERG channel blockers or model hERG-blocker interactions by quantitative structureactivity relationship (QSAR) studies, pharmacophore modeling, homology modeling, molecular dynamics (MD) simulations, free energy calculations, etc. *Address correspondence to this author at the Institute of Functional Nano & Soft Materials (FUNSOM) and Jiangsu Key Laboratory for CarbonBased Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China; Tel: +86-512-65882039; E-mails: [email protected] or [email protected] 1568-0266/13 $58.00+.00

In this review, we present the recent advances in computational studies for modeling hERG blockage. First, the structure of the hERG channel is outlined. Then, the ligandbased approaches for filtering out potential hERG channel blockers and the receptor-based studies for understanding hERG-drug interactions are summarized. Finally, the challenges of developing reliable prediction models for hERG blockage, as well as the strategies for surmounting these challenges, are discussed. 2. THE STRUCTURE OF THE hERG CHANNEL The hERG channel is a member of the Kv family of voltage-gated potassium channels. Although the crystal structure of the hERG channel is still not available, the basic 3-D topology of the structure is quite similar to the other members of the voltage-gated K+ channel family. The hERG channel is a homo-tetramer (Fig. 1), and each subunit contains six -helical transmembrane segments (S1S6). In each subunit, the S1-S4 segments form the voltagesensing transmembrane domain, while the S5 segment (Ploop) and the S6 segment form the central potassium (K+)selective pore, which is responsible for K+ conduction [1521]. The central K+-selective pore domain shows the wellpreserved K+ channel features: the pore helix and K+selectivity filter, which controls the selective movements of K+ ions (Fig. 2). The highly conserved signature sequence TV-G-Y-G positioned at the C-terminal end of the K+selectivity filter is responsible for the creation of the aqueous environment [22, 23]. The central channel cavity is below the K+-selectivity filter, and it is bound by the four S6 helices of the tetrameric © 2013 Bentham Science Publishers

2 Current Topics in Medicinal Chemistry, 2013, Vol. 13, No. 11

channel helices. The hERG channel also contains an elongated S5-P linker (turret helix). On the intracellular side of the membrane, the N-terminal region has the Per-Arnt-Sim (PAS) domain and the C-terminal region has a cyclic nucleotide-binding domain (cNBD). Extensive Ala-scanning mutagenesis of the hERG channel has been used to characterize the binding specificity of various drugs and drug-like molecules [24-35]. The mutagenesis data suggests that the residues important for the binding of hERG blockers include Tyr652, Phe656 and Ile647 in the S6 helix, Ser620, Ser624, Ser631 and Val625 in the Ploop and Ala561 in the S5-P linker [36]. Among these residues, Tyr652 and Phe656 provide the driving force for the binding of high affinity blockers. Four Phe656 residues are located at the cytoplasmatic side of the hERG channel, and four Tyr652 residues face the central cavity (Fig. 2). Similar to other voltage-gated potassium channels, the hERG channel shows three types of conformational states: closed, open, and inactivated. The activation of hERG involves the opening of an intracellular gate (from the closed to open state), whereas the inactivation involves the opening of a gate at the extracellular end of the permeation pathway (from the open to inactivated state). However, the hERG channel shows unusual gating behavior. For instance, the transitions between the closed and open states are unusually slow (typically a few milliseconds), while the transitions between the open and inactivated states are both very fast and voltage-dependent [37]. Obvious correlation between the binding affinity of many hERG blockers and the conformational states of the channel has been observed [38]. Due to the importance and uniqueness of the hERG gating behavior, an in-depth understanding of the conformational change of the channel during the cardiac action potential is quite helpful for the design of safe drugs. 3. LIGAND-BASED APPROACHES OF PREDICTING HERG BLOCKAGE In the earlier attempts to identify whether a molecule is a hERG blocker or not, a set of simple rules based on structural and functional features have been proposed [39, 40]. For example, Buyck et al. proposed a threshold of ClogP 3.7 for potent hERG blockers, which is roughly consistent with the Aronov’s observation that few of known hERG blockers show ClogP < 1 or MW < 250 [39]. The features of structural topology were also used as the rules to distinguish hERG blockers from non-blockers [39]. By analyzing the topology of ring-linker arrangements versus the potential for hERG blockade, Aronov observed that the molecules with V-shaped geometry stemming from ortho-substitution patterns show more possibility to be hERG blockers than the meta-/para-attached molecules with linear topology [39]. These simple rules are easy to be understood by medicinal and computational chemists, but they are not reliable predictors for filtering out hERG blockers. In order to give accurate prediction for hERG blockage, a wide range of quantitative structure-activity/property relationship (QSAR/QSPR) models have been developed based on a variety of statistical techniques and machine learning methods, including multiple linear regression (MLR) [41, 42], partial least square (PLS) [43-48], k-nearest neighbor algorithm

Wang et al.

(kNN) [49-51], linear discriminant analysis (LDA) [52], artificial neural networks (ANN) [45, 53], support vector machine (SVM) [49, 50, 52, 54-56], self-organizing mapping (SOM) [44, 45, 48, 57], recursive partitioning (RP) [58-60], random forest (RF) [46, 61], genetic algorithm (GA) [62, 63], naive Bayesian classification (NBC) [60, 64], etc. Moreover, pharmacophore modeling technique has been employed to develop ligand-based prediction models of hERG channel blockers [65-71]. 3.1. Experimental Data for Model Development The preparation of relevant datasets with high quality and quantity is a major step toward constructing ligand-based models with high confidence. Recently, several large datasets have been developed and released for the public. In 2008, Li et al. developed a hERG classification model based on a dataset of 495 compounds [55]. In 2010, Doddareddy and the coworkers released a dataset of more than 2600 compounds, but the data set contains a lot of duplicated compounds and further curation is necessary [52]. Recently, we reported a data set that contains 806 molecules (http://cadd.suda.edu.cn/admet) [60]. In our data set, ~60% of the data (495) were obtained from the collection reported by Li and co-workers [55], and a small external test set of 66 compounds with hERG information was extracted from the WOMBAT-PK database. In addition, we updated the data set with an extra set of 245 molecules collected from recent publications. More recently, Broccatelli et al. reported a dataset of 1173 molecules [49]. However, in Broccatelli’s dataset, only the data for 648 compounds were stemmed from the literature search, and the other data were extracted from the ChEMBL and Tox-Portal databases. Here, we should mention that various experimental techniques have been used to measure hERG blocking potency. Even for the same experimental technique, the data in the same dataset may be determined based on different cell lines. Bains and co-workers found that that compounds measured in the Xenopus laevis oocytes cell lines were ~12 fold less potent than those measured in the mammalian cell lines [62]. Therefore, it is quite possible that some available datasets may not be quite robust and reliable when the data from different experimental protocols are mixed together. So before any model building study, we must give careful consideration to how the experimental data was collected. With the aim of building more robust and reliable models, further development of data set is believed to be urgent. 3.2. QSAR/QSPR Models for Predicting hERG Blockage The representative QSAR/QSPR models are listed in (Table 1) [41-64, 66, 72-79]. These prediction models can be divided into two categories: regression models and classification models. Two excellent reviews have summarized the ligand-based prediction models for hERG blockage [39, 80]. Therefore, here, we only focus on the representative models reported in the latest five years (2008~now). In 2008, Li et al. [55] derived a binary classifier to discriminate between hERG blockers and nonblockers based on a diverse data set of 495 compounds. This model combines SVM and pharmacophore-based GRIND descriptors. The models were applied at different thresholds from 1 to 40 m.

Recent Developments in Computational Prediction of hERG Blockage

Current Topics in Medicinal Chemistry, 2013, Vol. 13, No. 11

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Fig. (1). Schematic representation of the hERG channel.

Fig. (2). (a) Structure of the S5 and S6 subunits of hERG (five important residues are shown in tube); (b) stereoview of the ribbon representation of the hERG channel viewed from the extracellular side (the four subunits are distinguished by different colors). Table 1.

The Ligand-based Models for Predicting hERG Blockage

Reference

Methods

Model

Descriptors

Data Set

Performance

Training

Test

Cavalli [66]

CoMFA

Regression

CoMFA fields

31

6

Training: r2=0.92, q2=0.77 Test: r2=0.74

Roche [45]

SOM, PCA, PLS, ANN

Classification

1258 atom types, TSAR, CATS, Volsurf and Dragon descriptors

244

72

Training: acc=93% Test: blockers: acc=71% non-blockers: acc=93%

82+13

SLRA: Training: r=0.97, Test: r=0.75 HQSAR: Training r=0.98, q=0.80 Test: r=0.90

Keserü [76]

HQSAR, Stepwise linear regression analysis (SLRA)

Regression

HQSAR, 29 Sybyl and 72 Volsurf descriptors

55


Wang et al.

(Table 1) contd….

Reference

Methods

Model

Descriptors

Data Set

Performance

70-100

22-24

Training: acc=87%-89% Test: acc=85%-90%

Bains [62]

Genetic programming

Classification

Molecular properties, molecular fragment-based descriptors and experimental parameters

Dubus [58]

RP

Classification

184 2D descriptors

160, 203, 100

33, 55


Tobita [56]

SVM

Classification

108 descriptors

73

827

Training: acc=95% Test: acc=67%-78%

Coi [72]

MLR

Regression

CODESSA descriptors

55, 64,

27, 18

Training: r2=0.77, 0.74 Test: not reported

Sun [64]

NBC

Classification

Atom types

1979

66

Training: ROC=0.87 Test: acc=87.9%

Yoshida [42]

MLR

Regression

MOE descriptors

86

18, 68

Training: r2=0.71 Test1: r2=0.66 Test2: r2=0.65

Song [46]

SVR, PLS, RF

Regression

2D fragments

71

19

Training: r2=0.82-0.91, q2=0.38-0.68 Test: r2=0.75-0.85

Ekins [59]

Kohonen and Sammon mapping, SOM, RP

Classification

150 molecular descriptors

93

35

Training: acc=86% Test: acc=95%

Wang [79]

RP, RBFNetwork, SVM

Classification


91

47

Training: acc=71.5-81.4% Test: acc=72.3-80.9%

Jia [54]

SVM

Classification

Atom-type descriptors

977

66

Training: ROC=0.92 Test: acc=94, ROC=0.91

Li [55]

SVM

Classification

GRIND descriptors

495

66, 1948

Training: acc=0.74 Test: acc=72% (test1), 73% (test2)

Chekmarev [50]

KNN, SVM, SOM

Classification

Shape signatures

83

Thai [78]

Binary QSAR

Classification

184 2D descriptors, P_VSA descriptors

240

73


56

12

Training: r2=0.84, q2=0.78 Test: r2=0.70

Leave-20-out: acc= 66%74%

Garg [75]

GFA

Correlation

156 E-state indices, information, content, topological, ADME and thermodynamic descriptors

Kramer [44]

PLS, SVR

Correlation

Quantum mechanically derived descriptors

98

15

Training: r2=0.63 Test: r2=0.62

Thai [53]

Binary QSAR, ANN

Classification

SIBAR descriptors

194

49


6

Training: r2=0.93, q2=0.69 (Almond) r2=0.95, q2=0.77 (CoMFA) Test: SDEP=0.90 (Almond)

Ermondi [43]

PLS

Correlation

GRIND descriptors, CoMFA

31

Hansen [61]

Ridge Regression, Gaussian Process, SVR, RF

Correlation

ChemAxon pharmacophoric fingerprints, MOE descriptors

563

Nisius [63]

Topomer-based approach, GA

Classification

232

Training: r2=0.25-0.55

43




5

(Table 1) contd….

Reference

Methods

Model

Descriptors

Data Set

Performance

Su [47]

PLS, GFA

Correlation Classification

MOE descriptors, 4Dfingerprints

250

876, 106

Training: r2=0.61-0.67, q2=0.57-0.63 acc=91% Test: acc=83% (test1), 77% (test2)

Doddareddy [52]

LDA, SVM

Classification

Molecular fingerprints

2389

255

Training: ROC=0.89-0.94 Test: acc=80%

Filz [74]

PASS

Classification

MNA descriptors

163

/

Training: acc=87.1%

Hidaka [57]

SOM

Classification

188 MOE descriptors

37, 1895

/

Maps

Sinha [77]

ANN

Correlation

Global descriptors

77

80


Du-Cuny [51]

kNN-QSAR

Correlation


93

54

Training: q2= 0.56 Test: r2=0.59

Durdagi [73]

Pharmacophore modeling

Correlation

Pharmacophore site

31

9

Training: r2=0.98 Test: average deviation between actual and predicted pIC50 is around 0.28

Tan [41]

Stepwise linear regression

Correlation


53

60


Su [48]

SVR, PLS

Correlation

MOE descriptors, 4Dfingerprints

546

1668, 1093

Training: r2=0.92 Test1: r2=0.83 Test2: r2=0.62 Training: acc=84.8% Test1: acc=85.0% Test2: acc=89.4% Test3: acc=75.3% Training: acc=90% Test: acc=85%

Wang [60]

Broccatelli [49]

NBC, RP

SVM, GA-kNN

Classification

Molecular fingerprints

620

120, 66, 1953

Classification

VolSurf+, DRH, MOE2D, MOL, MACCS, CDK and FLAP descriptors

545

258

The model at the threshold of 40 m illustrates the best performance for the training set (specificity=0.82, sensitivity=0.54 and global accuracy=74%). The global prediction accuracies for the external test set of 66 compounds and the PubChem data set of 1877 compounds are 72% and 73%, respectively. Obviously, the prediction accuracy of this SVM classifier is not good enough to be used as a practical tool to filter out potential hERG channel blockers. In 2009, Thai and Ecker [53] reported classification models for hERG blockage based on the similarity-based descriptors (SIBAR). First, 184 2D-MOE molecular descriptors, 86 3D-grid-based Volsurf descriptors and 50 3Dsensitive QSAR descriptors were calculated, and 11 relevant descriptors were chosen by a bivariate contingency analysis for each descriptor and the activity or property value. Then, the SIBAR approach was employed to calculate the similarity for each compound in the data set to each compound in the reference set, leading to a given number of similarity values (equal to the number of reference compounds used)

for each compound in the data set, which are assigned as SIBAR-descriptors. Finally, based on the similarity values, classification models were developed and validated. Total accuracies of 85~88% for the training set and 80~85% for the test sets were obtained by using the binary classification models based on the SIBAR descriptors. In 2010, Su et al. [47] constructed a continuous PLS QSAR model for predicting hERG blockage based on a data set of 250 structurally diverse compounds. The 4DFingerprints and MOE descriptors were used to build the models. The best continuous QSAR model optimized by the Genetic function approximation (GFA) approach has a r2 of 0.60 and a q2 of 0.56. A cutoff pIC50 value was selected to distinguish active from inactive compounds, and then the continuous QSAR predictions were transformed into binary classifications. The binary classification based on a cutoff of 40 M achieves an accuracy of 91% for the training set, an accuracy of 83% for the first external test set of 876 compounds and an accuracy of 77% for the second external test


set of 106 compounds. However, further validation of this model is necessary because the specificity (0.38) of the prediction for the second external test set is quite low. That is to say, the model cannot give reliable prediction for the inactive compounds. In 2011. Du-Cuny et al. [51] compiled a data set containing 178 hERG blockers and 351 non-blockers. And they developed regression models based on 147 molecules with known hERG inhibition activity, KNN classification models based on 498 molecules, and 3D pharmacophore models for the hERG blockers with and without basic moieties. Then, based on the developed kNN classification models and 3D pharmacophore models, a consensus model was proposed for predicting the activity of hERG blockers. A compound was determined as a blocker if it was identified as blocker by either KNN or pharmacophore models. The consensus prediction based on these two methods led to a dramatically improved sensitivity of 91.8% for hERG blockers and a slight increase of the prediction accuracy compared to pharmacophore modeling (70.3% versus 67.5%). However, the global accuracy and specificity (70.3% and 62.7%) of the consensus prediction decrease obviously compared with those (82.2% and 80.1%) of the KNN classification. Recently, we reported a set of classification models based on an extensive hERG inhibition data set of 806 molecules [60]. The NBC and RP techniques were employed to establish the classifiers for hERG blockage. Using the threshold of 30 μM, the best Bayesian classifier with 14 molecular properties and the ECFP fingerprint set demonstrates good predictivity, indicated by the a prediction accuracy of 84.8% for the training set, that of 85% for the test set I of 120 molecules randomly selected from the whole data set, that of 89.4% for the test set II of 66 compound from the WOMBAT data set, and that of 75.3% for the PubChem data set. Moreover, the important molecular fragments favorable or unfavorable for hERG blockage are highlighted by the Bayesian analysis, and they are very helpful for the design of new drugs avoiding unfavorable hERG blockage. More recently, Broccatelli et al. [49] derived classification models by using different methods, including RF, SVM and GA-kNN along with various descriptors. Twenty two classification models were generated using 545 molecules and validated through 258 external molecules. The accuracy in predicting the entire validation set (258 compounds) for the three best models ranges between 83% and 86% when they were used individually. The accuracy is 90% when they were used consensually. Then these three best models were used to predict the activities of 26 proprietary compounds tested in radioligand binding displacement (RBD). The consensus model reaches a result of accuracy = 85%, specificity = 0.64 and sensitivity =1.00. 3.3. Pharmacophore Models for Predicting hERG Blockage In 2002, Ekins et al. [68] reported the first pharmacophore model for hERG blockers, and this pharmacophore model derived from a training set of 15 compounds contains a positive ionizable center connected by four hydrophobic features. Then, other pharmacophore models were reported by Cavalli et al. [66], Pearlstein et al. [70], Peukert et al.

Wang et al.

[71], Sanguinetti et al. [81], Matyus et al. [69], Du et al. [67] and Aronov et al. [39, 65, 82]. These published pharmacophore models typically show the following common features: (1) two or three hydrophobic and/or aromatic moieties, which form hydrophobic and/or - stacking interactions with Tyr652 and Phe656 in the S6 helix; (2). a protonated nitrogen, which is important for achieving high hERG potency but not necessarily critical [41, 65]; (3). flexibility [7, 41, 56, 83-85], which makes a compound flexible when binding into the cavity. It should be noted that most pharmacophore models contain a protonated nitrogen feature. However, many hERG blockers, such as mizolastine, ondansetron and ketoconazole, do not have the basic amine motifs. Aronov and co-workers developed pharmacophore models for non-charged hERG blockers [65], and they found that two five-point pharmacophore models occurred significantly more frequently in neutral hERG blockers than in neutral hERG non-blockers. Then, Aronov et al. generated a union six-point pharmacophore model by combining two five-point pharmacophore models togother. This six-point pharmacophore model shows a good match between 21% (IC50 < 10μM) and 44% (IC50 < 30μM) of uncharged hERG blockers. 4. STRUCTURE-BASED BLOCKAGE

STUDIES

ON

hERG

A typical structure-based study on hERG blockage includes establishing a homology model of the hERG channel, molecular docking, MD simulations and/or free energy calculations for investigating drug-hERG interactions. 4.1. Homology Modeling for the hERG Channel The crystal structure of the hERG channel is not available now. Since the basic architecture of the hERG channel is believed to be similar as those of the other voltage-gated K+ channels, there are studies using the crystal structures of KcsA (closed), KvAP (open) and MthK (open) to build the homology models of the hERG channel [86-92]. A big challenge for homology modeling for the hERG channel lies on the low sequence similarity between the hERG channel and the templates [15, 84, 93-95]. The widely used templates (KcsA and MthK) have two trans-membrane domains, so most homology models only contain the hERG pore domain region (S5 and S6). Helices S1-S4 form the voltage sensor domain (VSD), and S4 with the connecting loop structure may be the key component of the voltage sensing machinery of all voltage-gated ion channels [96]. Because the whole hERG protein has six transmembrane domains, the absence of the other helices (S1-S4) in homology models should have substantial impact on the further studies based on these homology models. Moreover, most models do not have the extracellular S5-P linker, because this linker in the hERG channel is significantly longer (40 amino acids) than those in other K+ channels (typically 12~15 amino acids) [97]. Therefore, there is no good template available to construct the structure of the S5-P linker. Furthermore, the PAS domain at the N-terminal and the cNBD domain at the C-terminal located in cytoplasm are missing in all homology models due to the lack of good template structures.


4.2. Theoretical Predictions of hERG-blocker Interactions After the homology models have been developed, they can be used for molecular docking, MD simulations and free energy calculations to explore the hERG-blocker interactions. A typical structure-based study on hERG blockage based on molecular docking was reported by Farid et al. [83]. A homology model was created based on the crystal structure of bacterial KvAP channel, and then a set of known hERG blockers were docked into the pore between the extracellular entrance and the selectivity filter using the induced fit docking (IFD) protocol in Schrodinger to handle the flexibility of the hERG channel. For five sertindole analogues with IC50 values varying from 3 nM to 75 μM, the XP Glide scores show good linear correlation (r2=0.95) with the experimental IC50 values. According to the docking results, the authors suggest that the following structural features play a key role in promoting HERG blockade: (1). Attraction of the basic center by the negative field within the pore; (2). Extensive - stacking and hydrophobic interactions between radially arranged substituents and the crown shaped hydrophobic volume shaped by the multiple Tyr652 and Phe656 residues; (3). Ability of polar groups, and most notably a basic center, to interact with Ser624 and nearby polar backbone atoms; (4). Ability of ligands to assume multiple poses within the pore and to form additional hydrogen bonds with polar side chains and backbone atoms. Recently, MD simulations and free energy calculations have also been employed to study the binding of hERG blockers. In 2011, Boukharta et al. [98] used a recently developed homology model of hERG to conduct molecular modeling for a series of sertindole analogues with IC50 ranging from 3 to 137 nM. First, the studied molecules were docked into the binding site of the hERG channel with GOLD. Then, the docked complexes were put into MD simulations. Finally, the binding free energies were calculated from the MD trajectories with the linear interaction energy (LIE) method. Based on the results given by molecular docking, the following interaction patterns were observed: (1). The head group of the ligands forms polar interactions with the backbones of Leu622, Ser649, and Ser624, and hydrophobic interactions with Tyr652 and Phe656; (2). The positively charged nitrogen of the central piperidine moiety is placed near the focus of the electric field from the pore helices, where cations have been observed in crystal structures while do not form interactions with the channel; (3). The indole moiety is sequestered by two copies of Phe656 and one copy of Tyr652. Moreover, the resulting binding free energies predicted by the LIE method for the studied molecules are in excellent agreement with those derived from experiments (r2=0.60). Extensive studies show that the binding of some blockers are state-dependent, and therefore choosing the best homology model from several candidates in different conformational states is essential for achieving the best prediction accuracy of molecular docking. In 2010, Thai et al. [90] reported a molecular docking study for propafenone derivatives. Propafenone was docked into the homology models of the hERG channel in different states (open and closed). For the closed state, the aromatic rings, particularly the phenyl


7

ring of propafenone, have contacts with two Tyr652 residues. Furthermore, the hydrogen bonds between propafenone and Thr623/Ser624 have also been observed. Therefore, the protonated nitrogen atom not only interacts via cation- interactions with Phe656, as commonly postulated, but also interacts with Thr623 and/or Ser624, as proposed by Farid et al. [83] and Choe et al. [7]. For the open state, the nitrogen atom in propafenone no longer forms interactions with Thr623 and Ser624. The results highlight the importance of electrostatics in drug trapping, because, together with the movement of the S6 helices upon channel closure, the protonated nitrogen atoms tend to move up near the base of the selectivity filter. Recently, Durdagi and coworkers developed homology models of the hERG channel in the open, closed and openinactivated states using a multistep protocol [73]. The open, closed and open-inactivated state S1~S6 models were modeled based on the open state Kv1.2, the closed state Kv1.2 and the open-inactivated KcsA from Mus musculus. The S5pore linker was modeled using the loop modeling protocol in ROSETTA. The models were then refined with MD simulations. Several state-selective blockers were docked to the models of the open and inactivated states of hERG1 using the Glide induced-fit docking (IFD) protocol. The docking results based on the homology models incorporating multiple experimental constraints (salt bridges, close contacts, helix angles, and accessibility) show good correlation with the experimental pIC50 values for high- and low-affinity blockers. 5. CURRENT CHALLENGES AND FUTURE DIRECTIONS A lot of efforts have been dedicated to predict hERG blockage and uncover the binding mechanisms for hERG blockers. Currently, most in silico models do not give satisfactory predictions. How to improve the prediction accuracy of the theoretical models still remains a significant challenge. The lack of reliable and extensive experimental data is undoubtedly a major obstacle, and therefore, it is still urgent to develop high-quality hERG blocking data for the public domain. Another way to improve the prediction accuracy of hERG blockage is to develop consensus models by combining two or models together. As we mentioned above, the concept of “consensus modeling” has been introduced into the prediction of hERG blockage by Du-Cuny et al. [51] and Broccatelli et al. [49] In the near future, we plan to develop better consensus models by combining different individual prediction models. Moreover, it is worthy emphasizing that although a variety of in silico models are available for predicting hERG blockage, we still know little about the performance of these models, since different models were developed based on different datasets. More comparisons of the prediction performance of different prediction models based on the same “gold standard” dataset are urgent for guiding scientists to choose the most appropriate model in the drug discovery process. Since the crystal structure of the hERG channel is not available, the receptor-based modeling for hERG blockage based on homology models is more qualitative and descriptive rather than predictive. Building reliable homology mod-


els for the hERG channel is still a big challenge. Moreover, the hERG channel shows unusually broad poly-specificity and recognizes large numbers of structurally diverse compounds, suggesting that not all drugs interact with hERG in the same manner. In addition, it is believed that the binding pocket of the hERG channel may undergo substantial conformational change upon association with blockers. The conformational change upon blocker binding, which are crucial for blocker-hERG associations, may not be well captured by most docking-based algorithms. Therefore, how to accurately incorporate the induce-fit effect into molecular modeling and enhance the accuracy of receptor-based modeling for hERG blockage remains a big problem. Appropriate ways to overcome the challenges summarized above will be helpful for designing drugs without undesirable hERG-related cardiotoxicity. CONFLICT OF INTEREST The author(s) confirm that this article content has no conflicts of interest. ACKNOWLEDGEMENTS This study was supported by the National Science Foundation of China (20973121 and 21173156), the National Basic Research Program of China (973 program, 2012 CB932600), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). REFERENCES [1] [2] [3]

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