IFPTarget: A Customized Virtual Target Identification Method Based on

Article pubs.acs.org/jcim

IFPTarget: A Customized Virtual Target Identification Method Based on Protein−Ligand Interaction Fingerprinting Analyses Guo-Bo Li,*,† Zhu-Jun Yu,† Sha Liu,† Lu-Yi Huang,‡ Ling-Ling Yang,§ Christopher T. Lohans,∥ and Sheng-Yong Yang‡ †

Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan 610041, China ‡ Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Sichuan 610041, China § College of Food and Bioengineering, Xihua University, Sichuan 610039, China ∥ Department of Chemistry, University of Oxford, 12 Mansfield Road, Oxford, OX1 3TA, United Kingdom S Supporting Information *

ABSTRACT: Small-molecule target identification is an important and challenging task for chemical biology and drug discovery. Structure-based virtual target identification has been widely used, which infers and prioritizes potential protein targets for the molecule of interest (MOI) principally via a scoring function. However, current “universal” scoring functions may not always accurately identify targets to which the MOI binds from the retrieved target database, in part due to a lack of consideration of the important binding features for an individual target. Here, we present IFPTarget, a customized virtual target identification method, which uses an interaction fingerprinting (IFP) method for target-specific interaction analyses and a comprehensive index (Cvalue) for target ranking. Evaluation results indicate that the IFP method enables substantially improved binding pose prediction, and Cvalue has an excellent performance in target ranking for the test set. When applied to screen against our established target library that contains 11,863 protein structures covering 2842 unique targets, IFPTarget could retrieve known targets within the top-ranked list and identified new potential targets for chemically diverse drugs. IFPTarget prediction led to the identification of the metallo-βlactamase VIM-2 as a target for quercetin as validated by enzymatic inhibition assays. This study provides a new in silico target identification tool and will aid future efforts to develop new target-customized methods for target identification.

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INTRODUCTION

Currently established virtual target identification methods can be categorized into ligand-based and structure-based approaches. The former typically infers potential targets for the molecule of interest (MOI) by comparing its structure with a database of compounds whose targets are known. For example, Keiser et al. developed a similarity ensemble approach (SEA),12,13 which works by quantitatively grouping related protein targets based on the similarity of 2D fingerprints describing the structures of their ligands.12,13 The SEA method successfully identified new drug-target associations,13 demonstrating the effectiveness of ligand-based methods. In addition to 2D similarity methods, recently established 3D similarity methods have been used for target screening, such as ReverseScreen3D,14 Gaussian Ensemble Screening,15 and ChemMapper.16 Although promising, these methods likely have limited predictive ability, in large part due to a lack of

Target identification plays an important role in modern chemical biology and drug discovery. It not only enables understanding of the mechanism of action of bioactive molecules or drugs but also helps characterize interactions with unintended targets, explaining side effects or adverse reactions.1,2 For an existing drug, it is possible to find new targets via target identification, which may open up additional medical applications.3,4 Target identification is also useful in efforts to discover drugs that interact with multiple therapeutically synergic targets relevant to a particular disease.5 Although direct biochemical and genetic interaction methods are relatively well established for target identification,1,6−8 such experimental methods are usually expensive, time consuming, and are often limited by the availability of protein targets or appropriate assays. Therefore, in recent years, considerable efforts have been made to develop virtual target identification approaches, which could substantially improve the efficiency of target identification.9−11 © 2017 American Chemical Society

Received: April 21, 2017 Published: June 29, 2017 1640

DOI: 10.1021/acs.jcim.7b00225 J. Chem. Inf. Model. 2017, 57, 1640−1651

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Journal of Chemical Information and Modeling

binding affinity and active data (e.g., Ki, Kd, and IC50) for each protein−ligand complex structure from the PDBbind database. Binding Site Library. To establish the binding site library for reverse docking using Autodock Vina,31 all of the collected protein−ligand complex structures were first prepared by adding Gasteiger-Marsili charges and polar hydrogens to protein and ligand using the Raccoon script,32 assigning proper charges to bound metal ions using an in-house Perl script (error messages appeared when using Raccoon to assign GasteigerMarsili charges to metal ions), defining the rotatable bonds for ligands using Raccoon, and saving in the pdbqt format. The binding site for each target was defined as a rectangular grid, which encompasses all of the protein residues around the cocrystallized ligand. The binding site information for Autodock Vina, including the grid center, grid size, and number of docking poses, was automatically generated using in-house scripts and saved in configuration files (conf format). The prepared protein structures and binding site information were stored in the binding site library for later use. Reference IFP Library. An interaction fingerprints (IFP) model ideally represents the set of important interactions governing the binding of a ligand to its target. Such a model may be used to search for compounds that have similar binding modes as the ligand. For each collected protein−ligand complex structure, we established an IFP model (hereafter called the reference IFP) encompassing eight important types of protein−ligand interactions, including hydrogen-bond donors (D), hydrogen-bond acceptors (A), positive charges (P), negative charges (N), face-to-face π−π stacking (F), edgeto-face π−π stacking (E), hydrophobic interactions (H), and metal-binding interactions (M). The definition of these interaction types is similar to that used in our previous method.25−27 Considering that electrostatic interactions are the most important features for ligand binding, we defined the weights for the P, N, and M features as 2, while the other features (A, D, and H) were given a weight of 1 in the IFP model. In each IFP model, the calculated important interaction features are organized in columns, and the residues involved in the binding site are organized into rows. The fingerprint bit “1” denotes that the corresponding binding site residue provides a specific type of interaction, whereas “0” denotes that no such interaction is present. All IFP models were automatically generated using an in-house program called IFP-Analyses.25 Two examples of the IFP models are depicted as follows. The IFP model shown in Figure 1a was generated from the crystal structure of heat shock protein 90 (HSP90) complexed with a tricyclic inhibitor (PDB ID: 2YKC).33 The interactions observed from this structure involve Tyr139 and Thr184 acting as hydrogen-bonding donors, Asn51 and Leu103 acting as hydrogen-bonding acceptors, face-to-face π−π stacking interactions from Phe138, edge-to-face π−π stacking interactions from Trp162, and hydrophobic interactions from Met98, Leu107, Phe138, Tyr139, and Trp162. Similarly, the IFP model (Figure 1b) generated from the crystal structure of leukotriene A4 hydrolase (LTA4H) with bound inhibitor RB3040 (PDB ID: 3B7R)34 represents an interaction pattern involving Gly268, Gly269, Tyr383, and Arg563 acting as hydrogen-bonding donors, Glu271 and Tyr378 acting as hydrogen-bonding acceptors, positively charged interactions from Arg563 and Lys565, negatively charged interactions from Glu271 and Glu318, hydrophobic interactions from Val292 and

consideration of key receptor information important for ligand binding. Structure-based target identification methods predict potential targets for the MOI based on information regarding both ligand and receptor. These methods are likely more desirable, particularly with the increasing number of protein−ligand complex structures available in the Protein Data Bank (PDB). Current structure-based methods include INVDOCK,17 TarFisDock,18 idTarget,19 VinaMPI,20 VTS,21 iRAISE,22 and ACTP.23 These methods usually employ a “universal” scoring function to predict binding affinity and rank all retrieved targets from an established target library for the MOI. However, it is difficult for many of these methods to correctly identify proteins to which the MOI binds from a large set of retrieved targets, as they do not specifically consider the important binding features for an individual target. We previously proposed a strategy termed CTarPred which combined molecular docking and pharmacophore models (which at least partially represent the target-specific binding features) for target prediction, which showed improved target prediction ability.24 Herein, we report a new customized virtual target identification method termed IFPTarget, in which a protein− ligand interaction fingerprinting (IFP) method was used to analyze the target-specific binding features. The IFP method involves eight types of protein−ligand interactions (including hydrogen-bond donors, hydrogen-bond acceptors, positive charges, negative charges, face-to-face π−π stacking, edge-toface π−π stacking, hydrophobic, and metal-binding interactions) as defined in our previous work.25−27 In IFPTarget, we also integrated the ID-Score method26 for binding affinity prediction, which was derived from a set of protein−ligand interaction descriptors and a machine learning method. In addition, we introduced a comprehensive index (Cvalue) integrating molecular docking, interaction fingerprinting analyses, and binding affinity prediction for target ranking. We comprehensively evaluated the performance of IFPTarget and the embedded methods (e.g., IFP method) using the highquality PDBbind core set,28,29 followed by performance assessment of IFPTarget to retrieve both known targets and new potential targets for several drugs from our established target database containing 11,863 protein structures covering 2842 unique protein targets. Application of IFPTarget led to the identification of the metallo-β-lactamase (MBL) VIM-2 as a target for quercetin.

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MATERIALS AND METHODS Compilation of Comprehensive Target Library. To establish and assess the virtual target identification method IFPTarget, we first created a comprehensive target library containing target information, binding site, and reference interaction fingerprints (IFP). We began by collecting potential drug targets for which protein−ligand complex structures are available from the PDBbind database,28,29 the Therapeutic Target Database (TTD)30 and the Protein Data Bank (PDB) as described in our previous work.24 As much as possible, multiple complex crystal structures were collected for the selected protein targets; all of these structures were collected from the PDBbind database28,29 as they are suitable for direct use. We then used in-house Perl scripts to extract information including short/full target name, biochemical type, resolution, and publication date for each target from the corresponding PDB file (downloaded from the PDB database) and collected 1641


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Figure 1. Two examples of reference IFP models generated from the complex structures of (a) HSP90 with a tricyclic inhibitor (PDB ID: 2YKC)33 and (b) LTA4H with RB3040 (PDB ID: 3B7R).34 The two reference IFP models are shown in the box, representing interaction patterns involving eight types of important protein−ligand interactions, including D (hydrogen-bonding donor), A (hydrogenbonding acceptor), P (positively charged feature), N (negatively charged feature), F (face-to-face π−π stacking interaction), E (edge-toface π−π stacking interaction), H (hydrophobic interaction), and M (metal-binding interaction). The fingerprint bit “1” indicates the presence of a specific type of interactions, whereas “0” denotes that there is no such interactions.

Figure 2. Workflow of IFPTarget. IFPTarget starts by reading target information, then executes molecular docking, IFP analyses, and binding affinity prediction. Finally, IFPTarget ranks the targets based on their Cvalues (for details, see Materials and Methods).

3. Generating IFP Models for Docking Poses: For each docking pose, an IFP model is generated using the same method as was used for the reference IFP models. 4. Calculating IFP Similarity: The similarity score (IFPscore) between the docking pose IFP and the reference IFP models for each target is calculated using formula I

His295, and metal-binding interactions from the catalytically important zinc ion. Ultimately, the target library includes 243 NMR structures and 11,620 crystal structures. Of these crystal structures, 323 structures are of a resolution >3 Å, 1730 structures are between 2.5 and 3 Å, and 9567 structures are at least 2.5 Å. These 11,863 protein structures cover 2842 potential drug targets, corresponding to the preparation of 11,863 binding site files and the generation of 11,863 reference IFP models. Details of IFPTarget Method. The IFPTarget method was developed to predict potential targets for the MOI by integrating molecular docking, interaction fingerprinting analyses, and binding affinity prediction method. IFPTarget was compiled using the C/C++ programming language. Figure 2 schematically depicts the workflow of IFPTarget, which involves the following steps: (1) reading target information, (2) executing molecular docking, (3) generating IFP models for docking poses, (4) calculating IFP similarity, (5) rescoring, and (6) target ranking. These steps are described in more detail below. 1. Reading Target Information: The target information, including PDB code, resolution, publication date, biochemical type, short/full target name, and ligand binding/activity data, is selected as input for IFPTarget. 2. Executing Molecular Docking: The small molecule of interest (converted to pdbqt format as described above) is called to dock to each target binding site in the target library by Autodock Vina. For each target, the 10 topranked docking poses as determined by Vinascore were retained for subsequent IFP analyses.

IFPscore =

∑ Ci × Wi ∑ Di × Wi + ∑ R i × Wi − ∑ Ci × Wi

+

∑ Ci × Wi ∑ R i × Wi

2 (I)

where Di is the sum of bits in the docking pose IFP model, Ri is the sum of bits in the reference IFP model, Ci is the sum of the bits in common between the docking pose IFP and the reference IFP model, and Wi is the corresponding weight for each interaction type. For each target, only the docking pose with the highest IFPscore is retained for subsequent binding affinity prediction. 5. Rescoring: The docking poses filtered by IFPscore are rescored using the ID-Score method, which is different from the Autodock Vinascore and therefore may capture different binding information. 6. Target Ranking: With the aim of integrating the complementary advantages of IFPscore, Vinascore, and IDscore, we introduced a comprehensive index (Cvalue) for the final target ranking. Cvalue is calculated using formula II: ⎛ Vinascore − μ2 ⎞ ⎛ IFPscore − μ1 ⎞ Cvalue = ⎜ ⎟ ⎟ × w1 + ⎜ σ1 σ2 ⎝ ⎠ ⎠ ⎝ ⎛ IDscore − μ3 ⎞ × w2 + ⎜ ⎟ × w3 σ3 ⎝ ⎠ 1642

(II)


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Journal of Chemical Information and Modeling where μ1, μ2, and μ3 are the mean IFPscore, Vinascore, and IDscore values for all the tested cases, respectively; σ1, σ2, and σ3 are the standard deviations of the IFPscore, Vinascore, and IDscore values across the tested cases, respectively; w1, w2, and w3 are the weights for IFPscore, Vinascore, and IDscore, respectively. Weight w1 was set as 2 due to the excellent performance of IFPscore as observed in the subsequent evaluation results (see Performance Evaluation), and w2 and w3 were set as 1. Validation Metrics. To examine the ability of Autodock Vina and IFP analyses to predict and rank binding poses, respectively, each co-crystallized ligand in the test set was docked to the corresponding protein using Autodock Vina, followed by IFP analyses on the generated docking poses. The PDBbind core set28,29 was selected as the test set, which is composed of 195 protein−ligand complex crystal structures (≤2.5 Å resolution). Three types of root-mean-square deviation (RMSD) values were calculated: RMSDBestPose (corresponding to the docking pose that is closest to the native binding pose observed by crystallographic analyses), RMSDVinascorePose (corresponding to the docking pose with the best Vinascore value), and RMSDIFPscorePose (corresponding to the docking pose with the best IFPscore value). Cross-target prediction was then performed on the test set using IFPTarget to examine its target prediction ability. The target ranking order for each ligand in the test set was calculated using the four scoring methods involved in IFPTarget, i.e. IFPscore, Vinascore, IDscore, and Cvalue. The target prediction ability of IFPTarget was further examined by screening against the established target library of 11,863 crystal structures corresponding to 2842 unique targets. The percent rank, which is the rank order of a known target of the MOI divided by the total number of retrieved targets, was used to compare the performance of IFPTarget to other virtual target identification methods. Inhibition Assays. Recombinant VIM-2 was produced in Escherichia coli, and assays were carried out as described previously.25,35 The IC50 value of quercetin against VIM-2 was first determined using 5 μM FC5 as a fluorescent substrate.35 Further assays were carried out with increasing concentrations of FC5 (10, 20, and 40 μM) to determine the corresponding IC50 values with the aim of determining whether quercetin acts a competitive inhibitor. All determinations were performed in triplicates. The IC50 values were obtained using GraphPad Prism (La Jolla, CA).

Figure 3. Profile of the comprehensive target library. (a) Distribution of potential drug targets according to their biochemical types, indicating that enzymes, in particular, hydrolases and transferases, are the most important potential drug targets in this library. (b) Database redundancy. Each bar represents the number of targets for which there are a certain number of crystal structures in the target library. (c) Distribution of the number of IFP features across all of the reference IFP modes, which reflects the diversity of protein targets and their interaction modes.

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RESULTS AND DISCUSSION Profile of Target Database. The current target database contains a total of 2842 unique protein targets. According to the biochemical classification, these targets cover 10 different biochemical types, including enzymes, factors and regulators, binding proteins, transport proteins, receptors, signaling proteins, structural proteins, viral proteins, ion channels, and others (Figure 3a). The selected protein targets are primarily enzymes (1820 enzymes, Figure 3a), which comprise hydrolases (42.7%), transferases (33.8%), lyases (11.4%), oxidoreductases (7.4%), isomerases (4.6%), and synthases (1.1%), reflecting that enzymes, in particular, hydrolases and transferases, are the most important potential drug targets. Although G-protein coupled receptors and other receptors are also important drug targets, fewer crystal structures are available, resulting in a low number of receptor targets (2.2%) in the current target database. With consideration of protein flexibility and promiscuity (i.e., a protein target may bind to structurally

diverse ligands), we collected as many protein−ligand complex structures for a single protein target as possible. For approximately half of the collected potential drug targets (i.e., 1389 targets, Figure 3b), we included at least two crystal structures with structurally different ligands in the target library. There are more than 168 targets (Figure 3b) for which >10 crystal structures are described. Some of these have >100 structures, such as beta-secretase 1 (270 entries), HIV-1 protease (248 entries), cyclin-dependent kinase 2 (235 entries), carbonic anhydrase II (232 entries), and thrombin (169 entries). Notably, the multiple complex crystal structures obtained for these proteins may demonstrate protein flexibility related to “induced-fit” effects due to ligand binding, which is an important factor affecting the target prediction results. Additionally, the target database includes the established reference IFP models. Each model represents a protein−ligand 1643


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Journal of Chemical Information and Modeling interaction pattern, as described in the Materials and Methods section, and is used for subsequent target prediction (Figure 2). By analyzing all of the reference IFP models, we observed that at least one hydrogen-bonding donor (D), hydrogen-bonding acceptor (A), and/or hydrophobic (H) feature is present in >70% reference IFP models (Figure S1), indicating that these three features are the most common interaction types. In contrast, the positively charged (P), negatively charged (N), and metal-binding (M) features, which are also important features for ligand binding, exist in 23.7%, 23.15%, and 5.9% of reference IFP models, respectively (Figure S1). The face-to-face (F) and edge-to-face (E) π−π stacking interactions occur in 9.2% and 6.9% of models, respectively (Figure S1), indicating the importance of aromatic interactions for ligand binding and recognition. Notably, we observed similarity between the distribution of IFP feature numbers (across all the reference IFP modes) and the normal frequency distribution (Figure 3c), reflecting that the target library contains diverse interaction modes. Taken together, our comprehensive target database includes a large number of potential drug targets with diverse target-customized interaction patterns, which are used for the subsequent development and assessment of the IFPTarget method and for actual virtual target screening studies. Development of IFPTarget. IFPTarget was developed as an automated tool for customized virtual target identification involving a target-specific interaction fingerprinting method and a comprehensive index-based target ranking method. Here, we briefly describe the workflow of IFPTarget (for further details, see Materials and Methods). IFPTarget starts by inputting information from the comprehensive target database (Figure 2). Then, it invokes Autodock Vina to sequentially execute molecular docking simulations for the MOI with each predefined binding site in the target database. The resulting docking poses are submitted for fingerprinting analyses to generate the docking pose IFP models. The resulting docking pose IFP models for each target are subsequently compared with the corresponding reference IFP model, and the similarity (IFPscore) is computed by formula I. The binding affinity for the top-ranked docking pose for each target is further predicted using the ID-Score method.26 All of the targets are then ranked according to a comprehensive index (Cvalue) as computed by formula II, which considers fingerprint similarity and predicted binding affinity. Then, IFPTarget will give a list of the topranked target “hits” for the MOI. Performance Evaluation. We began by assessing the performance of Autodock Vina in a binding pose prediction and of interaction fingerprinting (IFP) analyses in docking pose ranking, which are the most important components of IFPTarget (Figure 2). We selected the PDBbind core set (version 2013) compiled by Wang et al.28,29 as the test set, which comprises 195 protein−ligand complex crystal structures (≤2.5 Å resolution, Table S1) that were selected for their diversity of structures and binding data. Thus, this test set should be very suitable for the performance evaluation. We first examined the performance of Autodock Vina on binding pose prediction for the test set. By comparing all of the generated docking poses with the native binding pose for each protein−ligand complex, we observed that 165 of 195 complexes in the test set are predicted with an RMSDBestPose ≤ 2.0 Å (success rate of 84.6%, Table 1); this level of prediction is regarded to be successful as indicated in other studies.28,36 The mean ± SD of the RMSDBestPose values among the test set is 1.32 ± 1.24 Å (Supporting Information, Table S1), indicating

Table 1. Ligand Complexity and Success Rate of Prediction for Test Set Number of targets with RMSD ≤ 2.0 Å (success rate)a Number of ligand rotatable bonds

Number of targets

RMSDBestPose

RMSDVinascorePose

RMSDIFPscorePose

≤10 >10 total

165 30 195

147 (89.1%) 18 (60%) 165 (84.6%)

113 (68.5%) 11 (36.7%) 124 (63.6%)

134 (81.2%) 16 (53.3%) 150 (76.9%)

The docking pose with a RMSD ≤ 2.0 Å when compared to the experimentally observed binding pose is regarded as a successful prediction, as indicated in previous studies.28,36 Success rate (i.e., number of targets with RMSD ≤ 2.0 Å divided by the total number of targets) is given in parentheses.

a

that Autodock Vina is relatively reliable in reproducing experimentally observed binding modes. Notably, we observed that the accuracy of the Autodock Vina prediction weakens for more flexible ligands (Figure 4a); the success rate is 89.1% for ligands with ≤10 rotatable bonds but decreases to 60% for ligands with >10 rotatable bonds (Table 1). Also, the performance of Autodock Vina appears to be associated with ligand molecular weight (Figure 4b). Still, these results revealed that Autodock Vina is a promising tool in binding pose prediction, particularly for structurally uncomplicated compounds. We then examined the prediction ability of the Autodock Vina scoring function, Vinascore, for ranking docking binding poses. Analyses of the RMSD of the best docking pose predicted by Vinascore and the native binding pose (i.e., RMSD VinascorePose ) revealed that the mean ± SD of RMSDVinascorePose values is 2.46 ± 2.67 Å (Table S1), far larger than that of RMSDBestPose (1.32 ± 1.24 Å, Table S1); only 124 of 195 complexes are predicted with RMSDVinascorePose ≤ 2.0 Å (success rate of 63.6%, Table 1). For Vinascore, the prediction success rate was observed to decrease with increasing ligand complexity, with a rate of 36.7% for ligands with more than 10 rotatable bonds (Table 1). These results indicate that Vinascore only has a moderate prediction ability for prioritizing docking poses, which may be the main factor limiting the predictive ability of Autodock Vina in virtual screening. We next assessed the potential of the established IFP method to prioritize docking poses. For this purpose, we generated the IFP models for all of the Vina docking poses from the test set and computed the similarity (IFPscore) between the docking pose IFP and the reference IFP models using formula I (see Materials and Methods). By analyzing RMSD IFPscorePose (corresponding to the best IFPscore docking pose), we found that the mean ± SD of RMSDIFPscorePose values among the test set is 1.61 ± 1.62 Å (Table S1), lower than that of RMSDVinascorePose (2.46 ± 2.67 Å, Table S1) and close to RMSDBestPose (1.32 ± 1.32 Å, Table S1). Compared with Vinascore, IFPscore has a success rate of 81.2% and 53.3% for ligands with ≤10 and >10 rotatable bonds, respectively (Figure 4c and Table 1); performance is still impacted by ligand molecular weight (Figure 4d). Interestingly, the performance of IFPscore was observed to be not impacted by the number of IFP features and the target types (Figure 4e and 4f), implying that IFPscore can be applied to a broad range of biological target types. These results indicated that the IFPscore target1644


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Figure 4. Performance of IFPscore was observed to be related to ligand flexibility and molecular weight but not to the number of IFP features and the types of targets for the test set. (a, b) Comparison analyses of RMSDBestPose with ligand rotatable bonds and molecular weights, indicating that the prediction ability of Autodock Vina declines as the ligand flexibility and molecular weight increases. (c) Comparison of RMSDVinascorePose and RMSDIFPscorePose, revealing that IFPscore has a better performance in prioritizing docking binding poses than Vinascore. (d) Performance of IFPscore appears to decline according to an increase in ligand molecular weight. (e, f) Performance of IFPscore is likely not associated with the number of IFP features and the target types.

Table 2. Retrieval of 12 Protein Targets of 4OH-Tamoxifen Using IFPTarget, CTarPred, TarFisDock, and PharmMappera Rank order/Number of retrieved targets/Percent rank (%) Target name

Cvalue

IFPTarget

CTarPred

TarFisDock

PharmMapper

Estrogen-related receptor gamma Estrogen receptor alpha Estrogen receptor beta 17β-Hydroxysteroid dehydrogenase Cyclooxygenase-2 Glutathione S-transferase 3α-Hydroxysteroid dehydrogenase Dihydrofolate reductase Calmodulin Protein kinase c-θ type Human fibroblast collagenase Alcohol dehydrogenase

16.63 14.97 14.25 14.04 12.94 12.71 12.58 12.11 11.94 11.43 10.41 10.15

1/11863/0.008 33/11863/0.28 59/11863/0.50 74/11863/0.62 164/11863/1.38 193/11863/1.62 216/11863/1.82 298/11863/2.51 331/11863/2.79 459/11863/3.87 775/11863/6.53 867/11863/7.31

−/1481/− 1/1481/0.07 13/1481/0.88 3/1481/0.20 102/1481/6.89 35/1481/2.36 34/1481/2.30 100/1481/6.75 23/1481/1.55 158/1481/10.67 222/1481/14.99 84/1481/5.67

−/698/− −/698/− −/698/− 27/698/3.87 −/698/− 12/698/1.72 −/698/− 4/698/0.57 −/698/− −/698/− 21/698/3.01 −/698/−

1/7302/0.01 −/7302/− −/7302/− 18/7302/0.25 124/7302/1.70 49/7302/0.67 168/7302/2.30 29/7302/0.40 297/7302/4.07 222/7302/3.04 136/7302/1.86 817/7302/11.19

a

The rank orders of the protein targets of 4OH-tamoxifen by TarFisDock, PharmMapper, and CTarPred are from refs 18, 37, and 24, respectively. Percent rank = (Rank order/Number of retrieved targets) × 100.

customized fingerprinting method performs better than Vinascore for prioritizing docking poses. We subsequently assessed the target prediction ability of IFPTarget using Cvalue, which integrates both molecular docking and interaction fingerprinting analyses (Figure 2), by performing cross-target prediction for all ligands with all targets in the test set. The ranking of the true target for each ligand was predicted by four scoring methods involved in IFPTarget (IFPscore, Vinascore, IDscore, and Cvalue). We observed that the mean ± SD of the ranking orders by IFPscore, Vinascore, and IDscore are 1.63 ± 2.77, 14.81 ± 22.79, and 21.05 ± 21.01

(Table S2), respectively. Notably, IFPscore appears to assign the highest rank to the true target for most ligands in the test set (Table S2), reflecting that the target-customized interaction fingerprinting strategy (IFPscore) performs markedly better than “universal” scoring functions (Vinascore and IDscore). We also observed that IFPscore, Vinascore, and IDscore resulted in predicted different target profiles for the ligands in the test set (Figure S2a−c), indicating that these scoring strategies may capture different protein−ligand interaction information, suggesting that the integration of these scoring methods may improve their prediction capability. As expected, the 1645


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Figure 5. Potential target “hits” for sunitinib, sorafenib, and ABT-702. Views of the predicted binding poses of sunitinib compared with the crystal structures of (a) PDE4B complexed with the inhibitor 5HcPP (PDB ID: 3O57),38 (b) CatS complexed with inhibitor 7 (PDB ID: 4P6E),52 and (c) RadA complexed with the inhibitor LmTrp (PDB ID: 4B2L).39 Views of the predicted binding poses of sorafenib compared with the crystal structures of (d) BC1 complexed with azoxystrobin (PDB ID: 1SQB),53 (e) ACE complexed with the inhibitor bis-tacrine (PDB ID: 2CMF),54 and (f) PAP14 complexed with the inhibitor A16 (PDB ID: 4F1Q).42 Views of the predicted binding poses of ABT-702 compared with the crystal structures of (g) ChiA complexed with chelerythrine (PDB ID: 3ARW),55 (h) PDE10A complexed with inhibitor 16 (PDB ID: 3QPP),56 and (i) BRD4 complexed with a triazolopyrimidine ligand (PDB ID: 4MEQ).43

(see Materials and Methods) is useful for the purposes of this study. From Table 2, we observed that, according to Cvalue, four of the 12 known targets of tamoxifen (including estrogen-related receptor gamma, estrogen receptor alpha, estrogen receptor beta, and 17β-hydroxysteroid dehydrogenase) are ranked within the top 1% of the retrieved 11,863 entries. All 12 known targets are within the top 10% of the database (Table 2); on average, the percent rank for these targets is 2.43%. By comparison, TarFisDock,18 which is a molecule docking-based target prediction method with a target database of 698 protein structures, retrieves only one of the 12 known targets of 4OHtamoxifen in the top 1% of the retrieved database and four targets within the top 10%. PharmMapper,37 which uses a pharmacophore-based approach for target prediction against a database comprising 7302 pharmacophore models, is able to retrieve four known targets of 4OH-tamoxifen within the top 1% and a further five targets within the top 10% of the database. Obviously, IFPTarget has a performance comparable to PharmMapper. Using CTarPred,24 a method combining molecular docking- and pharmacophore-based target prediction proposed by us, we observed that it could retrieve three known targets of 4OH-tamoxifen within the top 1% of the ranked database (including 1481 entries) and nine targets within the top 10% (Table 2). The mean percent rank from CTarPred is 4.76%, which is larger than that from IFPTarget (2.43%, Table 2). Overall, IFPTarget has a target prediction ability superior to that of CTarPred for 4OH-tamoxifen. Target Prediction for Multiple Kinase Inhibitors. We then used IFPTarget to retrieve the potential targets for multi-kinase

comprehensive index Cvalue, as calculated by formula II, which integrate IFPscore, Vinascore, and IDscore, resulted in low ranking orders (1.56 ± 1.55, Table S2) for the test set, which has a performance comparable to IFPscore (but with a lower standard deviation; 1.63 ± 2.77), and is significantly better than Vinascore (14.81 ± 22.79) and IDscore (21.06 ± 21.01) alone. Compared with IFPscore, Cvalue is more likely to differentiate the true target for the tested ligands from other targets (Figure S2d). These overall evaluation results revealed the potential of IFPTarget (Cvalue) to prioritize targets from the test set, supporting the proposal that it is possible to improve prediction ability through a combination of molecular docking and IFP analyses in virtual target identification. In the following sections, we further examine the target prediction capability of IFPTarget for the multi-target drug 4OH-tamoxifen, the multi-kinase inhibitors (including sorafenib, sunitinib, and ABT-702), and the natural product quercetin through screening against our established target database containing 11,863 crystal structures covering 2842 unique targets (Figure 3). Target Prediction for 4OH-tamoxifen. 4OH-tamoxifen is the active metabolite of tamoxifen, which interacts with at least 12 protein targets (Table 2) and has been used to assess performance for many virtual target identification methods such as TarFisDock,18 PharmMapper,37 and CTarPred.24 Although IFPTarget and these other methods (i.e., TarFisDock, PharmMapper, and CTarPred) employed dissimilar strategies and screened against different target databases, we think that a general comparison between these methods by percent rank 1646


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Figure 6. Experimental results revealed that VIM-2 MBL is a target of quercetin. (a) Superposition of the predicted binding pose of quercetin with the crystal structure of VIM-2 complexed with a mercaptocarboxylate inhibitor (PDB ID: 2YZ3).49 (b) The inhibitory potency (IC50) curve of quercetin with VIM-2. (c) The inhibitory potency of quercetin with VIM-2 decreased with increasing concentrations of substrate FC5, suggesting that quercetin is a competitive inhibitor.

inhibitors from the established target database. These inhibitors included sunitinib, sorafenib, and ABT-702 as they are well characterized, structurally diverse, and have different kinase profiles. The top 1% target “hits” identified by IFPTarget and their predicted scores are given in the Supporting Information, Table S3. We observed that IFPTarget was able to rank well-known kinase targets of sunitinib, such as mast/stem cell growth factor receptor KIT (rank 2/3) and tyrosine-protein kinase receptor RET (rank 9), in the top 10 of the retrieved database (Table S3), and a further 24 kinase entries for sunitinib, such as auroraA kinase, Janus kinase JAK2, mitogen-activated transferase JNK1, and casein kinase II, were ranked within the top 1% (Table S3). In addition, we observed that IFPTarget may be able to retrieve potential non-kinase targets which bind to inhibitors/ligands chemically different from sunitinib but with similar binding modes. For example, for phosphodiesterase 4B (PDE4B) (rank 1, Table S3), sunitinib was predicted to have π−π stacking interactions with Phe446 and hydrophobic interactions with Trp406 and Tyr233 (Figure 5a), similar to the crystallographically observed binding mode of the PDE4B inhibitor, 5-heterocycle pyrazolopyridine (5HcPP).38 Similarly, sunitinib was observed to have similar binding modes as inhibitors of cathepsin S (Figure 5b) and RadA (Figure 5c). Notably, compared to the L-methylester tryptophan ligand of RadA (PDB ID: 4B2L),39 sunitinib likely has additional hydrogen-bonding interactions with Gln217 and electrostatic interactions with Glu224 (Figure 6c). For sorafenib, the well-known target vascular endothelial growth factor receptor 2 (VEGFR2)40,41 was ranked at number 6 by IFPTarget (Table S3), and a total of 29 kinase entries were ranked within the top 1% of the retrieved database (Table S3). By comparing the predicted binding poses of sorafenib with the complex crystal structures of the top-ranked targets, we

observed that sorafenib has a similar binding mode as structurally different inhibitors/ligands to targets including cytochrome BC1, acetylcholinesterase, and poly[ADP-ribose]polymerase 14 (PAP14) (Figure 5d−f). For instance, sorafenib was observed to make hydrogen-bonding interaction with Gly1602, Ser1640, Tyr1646, and Tyr1640, and π−π stacking interactions with Tyr1633 of PAP14, which is similar to the binding mode of the inhibitor A16 with PAP14 (PDB ID: 4F1Q).42 For multiple kinase inhibitor ABT-702, which is structurally different from sunitinib and sorafenib, IFPTarget was able to retrieve 34 kinase entries within the top 1% of the target database (Table S3). It also identified potential nonkinase targets for ABT-702, such as Chitinase A, PDE10A, and BRD4 (Figure 5g−i). For example, ABT-702 was predicted to have a similar binding mode as the BRD4 ligand 5-methyltriazolopyrimidine (PDB ID: 4MEQ);43 both form hydrogenbonding interactions with Asn140 of BRD4 and the structural water W1 (Figure 5i). Taken together, these results validated the target prediction ability of IFPTarget and revealed its potential to identify potential kinase and non-kinase targets for these kinase inhibitors. Target Prediction and Experimental Validations for Quercetin. We next used IFPTarget to predict the potential targets for the natural product quercetin, which is a flavonol found in many fruits, vegetables, and Chinese herbal medicine.44 From the predicted results, we observed that 21 kinase entries were ranked within the top 1% of the retrieved target database (Table S4), which is consistent with experimental evidence that quercetin is a nonspecific kinase inhibitor.45 Among the top 1% predicted candidates, there are nine known non-kinase targets of quercetin (data from the BindingDB database46), namely, tankyrase 2 (rank 7), (3R)hydroxyacyl−acyl carrier protein dehydratase (rank 14), carbonic anhydrase I (rank 19), carbonic anhydrase II (rank 1647


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Journal of Chemical Information and Modeling Table 3. Structure-Based Methods for Target Identification Method

a

Data source

Number of targets

Number of structures

Docking engine

Pose-ranking method

Target-ranking method

INVDOCK TarFisDock idTarget VTS

PDB PDTD sc-PDB PDB

− 371 2091 343

− 698 − 1451

INVDOCK DOCK 4.0 MEDock GLIDE

ΔELP DOCK 4.0 score AutoDock 4 score GLIDE Gscore

VinaMPI

DUD

−

−

Vinascore

CTarPred

TargetDB

1105

1481

Autodock Vina GOLD

iRAISE

sc-PDB

2879

7915

−

ACTP IFPTarget

PDB PDBbind

86 2842

615 11863

Libdock Autodock Vina

Goldscore/ Chemscore Triangle descriptor similarity Libdock score IFPscore

Parallela?

Online service?

ΔELP Einter Z-score Boltzmannweighted average Bj Vinascore

NM NM Yes NM

No Yes Yes No

Chen et al.17 Li et al.18 Wang et al.19 Santiago et al.21

Yes

No

Ellingson et al.20

C-value

NM

No

Li et al.24

Scoring cascade

NM

No

Schomburg et al.22

Libdock score Cvalue

NM Yes

Yes No

Xie et al.23 This work

Ref

Not mentioned.

scoring functions used. Complementary to molecular docking, IFPscore is able to prioritize docking poses for most of the molecules tested (Table 1 and Table S1), which works by comparing the interaction modes of docking poses with the reference binding modes obtained from protein−ligand complex crystal structures. The second factor could be the use of the comprehensive index Cvalue for target ranking. Essentially, different scoring methods may capture different aspects of protein−ligand binding interactions. As revealed by other studies, the integration of complementary methods improves prediction ability.50,51 Cvalue integrates Vinascore and IDscore with the complementary method IFPscore, thereby leading to improved performance as observed in the validation results (Figure 4, Figure S2, and Tables S2−S4). In addition, the inclusion of multiple crystal structures for a single target in the target library could be another important factor. The consideration of multiple crystal structures could better represent protein flexibility, especially with respect to “inducedfit” effects due to ligand binding, and hence may result in different reference IFP models leading to better binding pose prioritization and target ranking. The use of such a target library may improve the quality of IFPTarget, including the prediction capability and the applicability to chemically different MOIs. Compared with other structure-based methods for target identification (Table 3), IFPTarget was developed on the basis of Autodock Vina, an open source docking program which supports parallel computing on multi-core processors. This makes it suitable to predict a great number of protein targets (Table 3) and thereby extends the potential applications of such an approach. Notably, IFPTarget uses an interaction fingerprint-based, target-customized method for docking pose sorting, different from other methods that use “universal” scoring functions as implemented in the docking engines (Table 3). In the target-ranking phase, several different strategies were developed. For example, in the INVDOCK,17 VinaMPI,20 and ACTP23 methods, the docking scoring functions were adopted for target ranking, while in idTarget19 and VTS,21 Z-scores and Boltzmann-weighted averages (Table 3) were used. IFPTarget adopted the comprehensive index Cvalue, integrating different scoring methods, which is likely to improve performance for target ranking. IFPTarget can be

21), glycogen phosphorylase b (rank 67), glyoxalase I (rank 73), beta-secretase 1 (rank 80), epoxide hydrolase B (rank 104), and estrogen receptor beta (rank 105) (Table S4). Particularly, we noticed that the metallo-β-lactamase VIM-2, a clinically relevant target for antibacterial resistance,25,47,48 was ranked as number 8 from the retrieved database by IFPTarget (Table S4). From the IFPTarget prediction results, we observed that quercetin is likely to have a similar binding mode as a mercaptocarboxylate (MPC) inhibitor49 of VIM-2 (Figure 6a); both form hydrophobic interactions with Phe61, Tyr67, and His263, face-to-face π−π stacking interactions with Tyr67, and metal-binding interactions with the active site zinc ions (IFPscore = 0.96, Cvalue = 15.59, Figure 6a). In order to examine whether VIM-2 is a target of quercetin, we tested whether quercetin inhibited the VIM-2-catalyzed hydrolysis of the fluorogenic substrate FC5 (for details, see Materials and Methods).35 The results revealed that quercetin has a good inhibitory activity against VIM-2 with an IC50 value of 9.56 ± 1.90 μM when using 5 μM FC5 (Figure 6b). We also observed that the inhibition potency of quercetin to VIM-2 decreased when the concentration of FC5 was increased (Figure 6c); when treated with 5, 10, 20, and 40 μM FC5, the inhibitory activities (IC50) of quercetin are 9.56 ± 1.90, 15.8 ± 2.51, 33.2 ± 4.82, and 55.4 ± 6.68 μM, respectively, suggesting that quercetin is a competitive inhibitor of VIM-2, consistent with the prediction that quercetin binds in the active site of VIM-2 (Figure 6a). These results revealed that VIM-2 is indeed a target of quercetin, validating the effectiveness of IFPTarget. Factors Contributing to Effectiveness of IFPTarget. The overall evaluation results reveal that IFPTarget has a good prediction ability in ranking known targets of the drugs tested from a large target database, and it has potential to identify new possible targets for ligands which are structurally different to the query drug molecules but have similar binding modes. Application of IFPTarget led to the identification of VIM-2 as a target for quercetin. Several factors may contribute to the effectiveness of IFPTarget. The first factor could be the adoption of the protein−ligand interaction fingerprinting method to analyze and prioritize the docking poses. It is a common perspective that molecular docking enables the generation of correct binding poses for small molecules36 but always fails to prioritize docking poses due to limitations associated with the “universal” 1648



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provided for academic and noncommercial use, although a web server for IFPTarget is not available at present. As crystal structures are not always available for important drug targets (e.g., G-protein coupled receptors), this may limit the use of IFPTarget to identify a comprehensive target profile for MOIs. Still, the current target database includes 2842 unique targets, providing plentiful information for target profiling analyses. More importantly, the results reveal that IFPTarget enables the identification of new potential targets for ligands which are structurally different from the MOIs, which may be particularly useful in compensating for the limitation of molecule similarity methods such as SEA12 and ReverseScreen3D.14 IFPTarget may also be useful in the identification of new hit compounds for polypharmacology.

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ABBREVIATIONS MOI, molecule of interest; IFP, interaction fingerprinting; SEA, similarity ensemble approach; PDB, protein data bank; RMSD, root-mean-square deviation; PDE4B, phosphodiesterase 4B; VEGFR2, vascular endothelial growth factor receptor 2; PAP14, poly[ADP-ribose]polymerase 14; 5HcPP, 5-heterocycle pyrazolopyridine; CatS, cathepsin S; LmTrp, L-methylester tryptophan; ACE, acetylcholinesterase; ChiA, Chitinase A; PDE10A, phosphodiesterase 10A

CONCLUSION In summary, this study describes the development of IFPTarget, a customized virtual target identification method. It was established based on a large comprehensive target database containing 11,863 entries which cover 2842 unique targets. Our evaluation indicates that IFPTarget has good prediction ability for the benchmark test set, and it could retrieve the known targets of the tested drugs at the top of the ranking list. The results also reveals the potential of IFPTarget to identify new possible targets for ligands which are structurally different from the MOIs. IFPTarget prediction led to the identification of the metallo-β-lactamase VIM-2 as a target for quercetin, at least partially validating the effectiveness of IFPTarget by wet experiment. This study is expected to provide a new useful in silico tool for target identification and will aid future efforts to develop new customized virtual target identification approaches.

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REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jcim.7b00225. Figures of distribution of the interaction fingerprint types and heat map representations of ranking orders by IFPscore, Vinascore, IDscore, and Cvalue. Tables of RMSD values with ligand molecular weight, rotatable bonds and IFP features, ranking orders by four scoring methods, and target hits predicted by IFPTarget for sunitinib, sorafenib, ABT-702, and quercetin. (PDF)

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ACKNOWLEDGMENTS

We are grateful to Professor Christopher J. Schofield (University of Oxford, U.K.) for the gift of the VIM-2 plasmid. This work was supported by the National Natural Science Foundation of China (Grant No. 81502989), Scientific Research Foundation of Sichuan University (Grant No. 20822041A4193), and China Postdoctoral Science Foundation Funded Project (Grant No. 2015M570789).

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Guo-Bo Li: 0000-0002-4915-6677 Christopher T. Lohans: 0000-0003-1094-0349 Sheng-Yong Yang: 0000-0001-5147-3746 Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes

The authors declare no competing financial interest. 1649


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