Benchmark Study Based on 2P2IDB

Article Cite This: J. Phys. Chem. B 2018, 122, 2544−2555

pubs.acs.org/JPCB

Benchmark Study Based on 2P2IDB to Gain Insights into the Discovery of Small-Molecule PPI Inhibitors Zhe Wang,† Yu Kang,† Dan Li,† Huiyong Sun,† Xiaowu Dong,† Xiaojun Yao,‡ Lei Xu,§ Shan Chang,§ Youyong Li,∥ and Tingjun Hou*,† †

College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau (SAR), China § Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China ∥ Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China ‡

S Supporting Information *

ABSTRACT: Protein−protein interactions (PPIs) have been regarded as novel and highly promising drug targets in drug discovery. Numerous new experimental techniques and computational approaches have been developed to assist the design of PPI modulators during the past two decades. However, identification and optimization of small-molecule inhibitors targeting PPIs is still a particularly challenging task due to the “undruggable” profiles of PPI interfaces. Nowadays, in silico screening, especially docking-based virtual screening, has emerged as an effective method to complement experimental high-throughput screening in identifying novel and potent small-molecule PPI inhibitors. Here, on the basis of the 2P2IDB database, we explored the structural features of the known small-molecule PPI inhibitors and analyzed the characteristics of the PPI binding pockets. More importantly, we evaluated the sampling power and screening power of six popular docking programs for PPI targets. Our results indicate that the chlorinated conjugate group and amidelike linkage are two types of privileged fragments of PPI inhibitors; the average druggability of the binding sites of the PPI targets in 2P2IDB is slightly worse than that of traditional ones; both academic and commercial docking programs exhibit an acceptable accuracy on pose prediction for PPI inhibitors, but their screening powers for identifying PPI inhibitors are still not satisfactory. It is expected that our work can provide valuable guidance on the construction of PPI-focused library, the determination of druggable PPI binding pocket, and the selection of docking program for the screening of small-molecule PPI inhibitors. interventions would bring significantly beneficial effects.9−11 For example, blockage of the interaction between programmed cell death protein 1 (PD1) and its ligand PD-L1 by an antagonist has emerged as an effective way to combat several types of cancers;12,13 disruption of the interaction between antiand proapoptotic B-cell lymphoma-leukemia 2 (Bcl-2) proteins by an inhibitor can reactivate the apoptosis in malignant cells for cancer therapy.14,15 Therefore, it is explicable that PPIs are becoming highly attractive targets for drug discovery, even though this therapeutic target class was deemed to be essentially “undruggable” a few years ago. Obviously, the development of PPI drugs is filled with opportunities and challenges. On the one hand, with the release of more protein− protein complex crystallographic structures and different kinds

1. INTRODUCTION In the postgenomic era, the interactome, especially protein interactome, has been a pivotal research focus in current biological research due to its significance in regulating multiple vital cellular processes.1−3 The so called protein interactome is the full repertoire of protein−protein interactions (PPIs) that can occur in an organism. In living cells, only a small part of proteins exert their biological functions independently and the vast majority (more than 80%) of proteins fulfill their duties by interacting with other molecules.4 More importantly, a large number of critical cellular processes and biochemical events, including gene expression, signal transduction, and membrane transport, are achieved by corresponding PPIs.5,6 It has been widely recognized that the construction of PPI networks not only plays a key role in predicting protein functions but also provides valuable information to find “druggable” targets.7,8 More and more studies reveal that PPIs are involved in different types of disease pathways where therapeutic © 2018 American Chemical Society

Received: December 23, 2017 Revised: February 3, 2018 Published: February 8, 2018 2544

DOI: 10.1021/acs.jpcb.7b12658 J. Phys. Chem. B 2018, 122, 2544−2555

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The Journal of Physical Chemistry B of PPI structural databases, e.g., TIMBAL,16 2P2IDB,17 PrePPI,18 Structure-PPi,19 and PPI3D,20 structure-based approaches can be used to design PPI modulators rationally. On the other hand, the relatively flat and featureless binding surfaces of PPIs are one of the most challenging obstacles that still hinder the development of potent PPI drugs. Generally, PPI modulators can be divided into three major categories: (1) humanized monoclonal antibodies, (2) peptides and peptidomimetics, and (3) small molecules. Although each class has its own advantages and disadvantages, from a medicinal chemistry and current drug development perspective, the class of small-molecule modulators appears more amenable.21 Over the last two decades, numerous methodologies and strategies have been developed to design smallmolecule PPI inhibitors.22−24 Notably, in silico screening, a computational approach complementary to experimental highthroughput screening, has been broadly applied to search for small-molecule PPI inhibitors.25−27 Docking-based virtual screening (DBVS) is one of the most widely used structurebased methods that is employed against not only PPI targets but also traditional drug targets. It is noteworthy that several additional important issues, including the construction of PPIfocused virtual compound libraries and the identification of druggable binding sites, need to be considered when adopting DBVS method against PPI targets. In recent years, much attention has been paid to the handling of these issues, for example, Reynès and co-workers developed a program named PPI-HitProfiler to generate a focused chemical library enriched with putative PPI inhibitors using machine-learning methods.28 As another example, Bai et al. proposed an integrated approach using molecular fragment docking and coevolutionary analysis to estimate druggable protein−protein interfaces.29 However, relatively few studies have been reported to systematically benchmark the performances of current docking programs focusing on PPI inhibitors. In the present study, six docking programs (AutoDock Vina, PLANTS, rDock, Glide, GOLD, and Surflex-Dock) that possess good performances on small molecules against traditional targets were selected for benchmarking on the basis of the 2P2IDB database. Briefly, our work can be divided into three parts: (1) the structural features of PPI inhibitors were discussed on the basis of a comparison between 238 PPI inhibitors and 1822 FDAapproved small-molecule drugs; (2) the characteristics of PPI binding sites were analyzed according to a comparison between the PPI targets in 2P2IDB and the traditional targets in the PDBbind core set; and (3) the sampling power and screening power of the selected docking programs were evaluated systematically.

2P2IDB was used in our study. The excluded entries are listed in Table S1 in the Supporting Information. 2.2. Structure Preparation. The structures of the protein−ligand complexes were standardized using an inhouse script. Briefly, the structure preparation and molecular mechanics (MMs optimization) functions in molecular operating environment (MOE) 2014.09 (Chemical Computing Group Inc., Montreal, QC, Canada) were applied to the structure of each complex and then the prepared structure was divided into a protein and a ligand. The rotated and optimized three-dimensional (3D) structures of each ligand for docking benchmarking were generated on the basis of the crystal structure automatically by an in-house python script that has been used in our previous study.31 Briefly, the original conformation of each ligand was rotated 180° around the Z axis in three-dimensional space, followed with a structural optimization. The initial 3D conformation of each ligand for virtual screening was obtained from the two-dimensional structure in 2P2IDB using the MM function in MOE. The decoy molecules for the screening power evaluation were generated on the basis of an automated decoy generation method provided by DUD-E (http://dude.docking.org/), and their 3D structures were created using the MOE suite.32 2.3. Comparison of Ligand Structures. After removing the duplicates, a total of 238 PPI inhibitors from 2P2IDB were used for structure comparison. The structures of 1822 FDAapproved small-molecule drugs (molecular weight ≤ 2000 g/ mol) were obtained from the e-Drug3D database.33 To gain a deeper insight into the difference between PPI inhibitors and marketed drugs, the distributions of seven important molecular properties for the PPI inhibitors and marketed drugs were compared. These molecular properties, including molecular weight, number of OH and NH groups, number of O and N atoms, log P (MOE log P model unpublished. Source code in $MOE/lib/svl/quasar.svl/q_logp.svl), aqueous solubility (log S),34 topological polar surface area (TPSA),35 and number of rotatable bonds, were calculated by MOE. Furthermore, on the basis of the ECFP_6 fingerprints, a classifier was developed to ̈ distinguish PPI inhibitors from marked drugs by using the naive Bayesian classification (NBC) technique in Discovery Studio 2.5 (Accelrys Software Inc., San Diego, CA). Briefly, each molecule was categorized into a PPI-inhibitor or a non-PPIinhibitor with a label of 1 or 0 and then the ECFP_6 fingerprints were calculated as the feature variables for classification on the basis of Bayes’s theorem. Compared with other machine learning approaches, NBC can deal with largescale data, trains fast, and is tolerant of random noise. ̈ Bayesian classifier can provide the weight Moreover, the naive (or score) for each feature using a Laplacian-adjusted probability estimate and then the importance of each fragment characterized by a fingerprint can be quantitatively evaluated. The fragments with high or low scores contribute positively or negatively to the likeness of PPI inhibitors. Therefore, a classifier can be utilized to discriminate the PPI inhibitors from the traditional drugs for the purpose of highlighting the important fragments for discrimination. The similar and ̈ Bayesian classifier has detailed procedure to train a naive been described previously.36−42 2.4. Analysis of Binding Pocket. Apart from 2P2IDB, the structures of the PDBbind core set (version 2016) were included for a comparative analysis.43 To ensure that there is no overlap in these two datasets, all PPI entries (PDB entries: 3P5O, 3U5J, 4BKT, 4LZS, 4OGJ, 4W9C, 4W9H, 4W9I, 4W9L,

2. MATERIALS AND METHODS 2.1. Benchmark Data Set. 2P2IDB is a manually curated database dedicated to the structures of PPIs with known smallmolecule inhibitors.17,30 The latest version of 2P2IDB (version 2.0) contains 300 protein−ligand complexes for 26 PPIs. All of the complexes are roughly categorized into three classes: Class1, Class2, and Class3, which correspond to protein− peptide complexes, globular protein−protein complexes, and bromodomain/histone protein−protein complexes, respectively. With the purpose of avoiding possible failure in the subsequent calculations, each structure was checked manually to make sure that the crystal structure is collision free and has only a single ligand interacting with the target. Finally, a collection of 289 protein−ligand complexes chosen from 2545


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The Journal of Physical Chemistry B

Figure 1. Distributions of seven molecular properties, including molecular weight, number of OH and NH groups, number of O and N atoms, log P, log S, topological polar surface area (TPSA) and number of rotatable bonds for PPI inhibitors (orange), and FDA-approved small-molecule drugs (blue).

and 4WIV) in the PDBbind core set were excluded. All protein structures were prepared using the Protein Preparation Wizard (PrepWizard) with the default settings. To avoid bias in searching binding poses, all crystallographic water molecules were removed. Then, the SiteMap (version 3.6) module of Schrodinger suite (Schrödinger, LLC, New York, NY) was used to analyze the binding site properties for each protein.44 The binding site position of each protein was located according to the coordinates of the cocrystallized ligand. The sitebox and grid parameters were set to 5.0 and 0.35 Å, respectively. To detect the shallow binding sites, the enclosure and maxvdw parameters were set to 0.4 and 0.55 kcal/mol, respectively. In addition, the dthresh and rthresh parameters were set to 7.5 and 6 Å to guarantee that the nearby subsites and the main site were merged. The other parameters were kept as the default values. Finally, seven SiteMap properties, including SiteScore (for binding-site identification), Dscore (for classifying druggability), Volume (site volume), Exposure (amount of exposure to solvent), Enclosure (degree of enclosure by the protein), Contact (average grid contact strength with the protein), and Balance (the ratio of relative hydrophobicity and hydrophilicity of binding site) of the top 1 site for each protein were computed to characterize the binding pocket. 2.5. Molecular Docking. Six different docking programs employed in our benchmarking study can be classified into

three academic programs, including AutoDock Vina version 1.1.2,45 PLANTS version 1.2,46 and rDock version 2013.1,47 and three commercial programs, including Glide version 68015,48 GOLD version 5.2,49 and Surflex-Dock version 2.706.13302.50 The selection of these docking programs (except PLANTS) is mainly on the basis of the evaluation of the performance on 10 docking programs for a diverse set of protein−ligand complexes reported in our previous study.31 Moreover, given the recent successful applications of PLANTS in VS, we decided to include it in this benchmark.51,52 For each target, the docking site was determined from the coordinates of the cocrystallized PPI inhibitor. The maximum number of the docking conformations was set to 20 and the pose clustering distance cutoff was set to 0.5 Å for all of the tested docking programs. The other critical parameters and scoring functions used in our study for each program are described as follows. 2.5.1. AutoDock Vina. The default optimization parameters were used for conformation sampling and the poses were scored by the default scoring function. For each docking run, only single-threaded execution was requested (the cpu parameter was set to 1). 2.5.2. PLANTS. The conformations were sampled under the screen mode on the basis of the ant colony optimization algorithm and then scored using the PLANTSCHEPLP scoring function. The search_speed parameter was set to speed 1. 2546


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Figure 2. Top 10 good features from 238 PPI inhibitors (A) and 1822 FDA approved small-molecule drugs (B). The structures of PPI inhibitors and drugs are from 2P2IDB and e-Drug3D database, respectively.

2.5.3. rDock. The standard docking protocol was used to generate low-energy binding poses. Briefly, three stages of genetic algorithm search (GA1, GA2, and GA3) were initially executed, followed by low-temperature Monte Carlo and simplex minimization (MIN) stages. The docking score was calculated by the default scoring function. 2.5.4. Glide. To obtain the best balance between accuracy and speed, the conformation sampling and scoring were completed under the standard precision (SP) mode of Glide. The OPLS-2005 force field was chosen for all docking calculations. 2.5.5. GOLD. To apply optimal settings for conformation sampling, the autoscale parameter was set to 1, which indicates that 100% search efficiency was employed for each ligand. In addition, the early_termination option was turned on, which means that GOLD will terminate the docking runs on a given ligand as soon as a specified number of runs have given

essentially the same answer. Docking poses were scored using the ChemPLP scoring function. 2.5.6. Surflex-Dock. The “-pgeom” option was specified to select the built-in default parameter set choices for docking. Docking poses were ranked by the total scores of Surflex-Dock. 2.6. Virtual Screening. To reduce the computational cost, three representative PPI targets (human double minute 2 (HDM2), von Hippel-Lindau (VHL), and BRD4 bromodomain 1 (BRD4-1)) with a sufficient number of known inhibitors from three different classes were chosen for the assessment of the screening power of six docking programs. In VS, the ratio of inhibitors and decoys was set to 1:50. The detailed information about the targets and screening datasets is listed in Table S2 in the Supporting Information. To eliminate the induced fit bias, the apo structure of each target extracted from the protein−protein/protein−peptide complex in 2P2IDB (PDB entries: 4YCR for HDM2, 4AJY for VHL, and 3UVW for 2547


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The Journal of Physical Chemistry B Table 1. Average Values of Seven SiteMap Properties across the 2P2IDB Database and PDBbind Core Set source b

2P2IDB PDBbindc

SiteScore

Dscore

Volumea

Exposure

Enclosure

Contact

Balance

0.93 ± 0.08 1.01 ± 0.08

1.04 ± 0.11 1.05 ± 0.10

217.61 ± 131.33 394.12 ± 193.25

0.70 ± 0.07 0.54 ± 0.14

0.44 ± 0.06 0.63 ± 0.15

0.45 ± 0.08 0.71 ± 0.25

1.61 ± 1.08 1.30 ± 1.68

a The unit of binding pocket volume is Å3. bThe number of successfully computed proteins from 2P2IDB is 289. cThe number of successfully computed proteins from PDBbind core set is 274.

From the view point of medicinal chemistry, revealing the privileged fragments of PPI inhibitors is of great significance for the rationalization of library construction and lead optimization for PPIs. To determine the important fragments for the likeness ̈ Bayesian classifier based on the of PPI inhibitors, a naive ECFP_6 fingerprints was developed. This classifier was validated with a 5-fold cross-validation and had an ROC Score of 0.97 for the discrimination of PPI inhibitors and traditional drugs. The important fragments with favorable or unfavorable contributions to the discrimination were then highlighted by the Bayesian scores. By observing the top 10 privileged fragments of PPI inhibitors shown in Figure 2, we can find that the chloride groups appear with a relatively high frequency (fragments 1, 2, 3, 4, and 7). It is quite possible that the introduction of a chlorine substituent can increase the lipophilicity of the whole molecule, which is conducive to the enhancement of bioactivity.55 Interestingly, it can also be noticed that the amide groups (fragment 5) and amidelike linkages (fragments 9 and 10) are favorable fragments for PPI inhibitors. That is because small-molecule peptide mimetics occupy the vast majority of existing PPI inhibitors. Compared with PPI inhibitors, the privileged fragments of market drugs are relatively featureless and inexplicable. However, the fragments from existing drugs may be useful for experimental and computational chemists to design PPI inhibitors with improved drug likeness. 3.2. Characteristics of Binding Sites of PPI Targets. PPIs are usually classified as difficult or even undruggable targets owing to their extended surface areas and shallow interactions at protein−protein binding interfaces. Thus, identifying the PPI targets with druggable binding sites is a crucial step for the development of therapeutics. As listed in Table 1, the average values of the SiteMap properties computed for the 289 binding sites of the PPI targets from 2P2IDB and those computed for the 274 binding sites of the conventional drug targets from the PDBbind core set have intriguing differences (the density plots of these properties are also provided in the Supporting Information). The smaller pocket volumes, higher exposure scores and lower enclosure scores of PPI binding sites indicate that they are too narrow and flat to form a buried binding with ligands. However, the average values of SiteScore and Dscore remind us that the evaluated PPI binding sites are still druggable, although slightly worse than the traditional ones. One possible explanation for this is that the relatively higher hydrophobic feature of PPI binding sites (with a larger Balance value) relieves the shape defect and enhances their druggability. These results of our quantitative assessment can not only help us to gain a deep insight into the differences of the binding site features between prominent PPI targets and traditional drug targets but also give a reference standard for the selection of PPI binding sites. 2P2IDB is composed of three different classes of PPI targets whose interfaces have their own characteristics. Thus, a further analysis of the PPI binding sites for the three subsets of 2P2IDB was also conducted. Figure 3 shows the distributions of the

BRD4-1) was utilized for screening. For each target, the coordinates of the inhibitor were used to determine the docking site. The values of the maximum number of docking conformations and pose clustering distance cutoff were set to 10 and 2.0 Å, respectively, whereas the other parameters were the same used in docking, which have been described in the previous section. 2.7. Assessment Methods. In molecular docking, the heavy-atoms root mean square deviation (RMSD) between the docked binding pose and the native binding pose was utilized as a key criterion to evaluate the sampling power of each docking program and it is regarded as a successful docking if RMSD is less than 2.0 Å. The pose with the highest docking score (referred to as the top scored pose) and the pose that is the closest to the native binding pose (referred to as the best pose) were both analyzed. In VS, the area under curve (AUC) value of an receiver operating characteristic (ROC) plot was used to measure the screening power of each docking program. As a widely used metric, an ROC curve is a plot of true-positive rates versus false-positive rates for all compounds and the AUC is the probability of active compounds being ranked earlier than decoy compounds. The precalculated data for the ROC plots were obtained with the enrichment.py script available from the Schrödinger Script Center (https://www.schrodinger.com/ scriptcenter).

3. RESULTS AND DISCUSSION 3.1. Structural Features of Small-Molecule PPI Inhibitors. It is obvious that a reasonable PPI-focused library is a prerequisite for the identification of potential PPI inhibitors in DBVS campaigns. To facilitate the construction of highquality chemical databases enriched with putative PPI inhibitors, a proper understanding of the physicochemical and structural features of PPI inhibitors is quite necessary. As shown in Figure 1, the distributions of seven important physicochemical properties of 238 existing PPI inhibitors and 1822 FDAapproved small-molecule drugs are depicted. The results show that there is no significant difference in the numbers of OH and NH groups (Lipinski’s hydrogen bond donors), numbers of O and N atoms (Lipinski’s hydrogen bond acceptors), TPSA, and numbers of rotatable bonds between these two groups, whereas emphasis is on an average molecular weight of 460 g/mol for PPI inhibitors versus 380 g/mol for regular drugs, an average log P of 4.03 versus 2.43, and an average log S of −5.84 versus −3.93. These characteristics are coincident with the general and qualitative trends for PPI inhibitors, i.e., higher molecular weight, higher hydrophobicity, and lower solubility, which have been highlighted in other studies.53,54 Although increasing the molecular weight and hydrophobicity of compounds can maximize their potency against PPI targets, it might bring a series of unfavorable pharmacokinetic issues during further development that will eventually lead to failure. Therefore, the urgent needs for designing balanced libraries for PPI inhibitors are still ahead of us. 2548


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Figure 3. Boxplot graph of SiteMap properties of the targets from different categories in 2P2IDB. The number of successfully computed binding pockets for Class1, Class2, and Class3 are 114 (3 failures), 62 (8 failures), and 102, respectively.

3.3. Sampling Power of Current Docking Programs for PPI-Inhibitor Systems. According to the results of the sampling power benchmark on the entire 2P2IDB for all the tested programs (Figure 4A), we can find that the distributions of the success rates (a percentage of correctly docked ligands) for the top-scored poses range from about 40−50% and those for the best poses range from about 63−75%. In terms of the success rates for the top-scored poses, the performances of the docking programs follow the following rank order: GOLD (50.5%) > AutoDock Vina (47.4%) > PLANTS (46.4%) > Surflex-Dock (42.6%) > rDock (41.9%) > Glide (40.5%). By focusing the attention on the success rates for the best poses, the rank order of the performances changes significantly: Surflex-Dock (75.8%) > AutoDock Vina (74.4%) > rDock (73.0%) > Glide (67.8%) > GOLD (64.0%) > PLANTS (63.0%). As we observed, the GOLD and Surflex-Dock programs exhibit the best performances for the top-scored

seven SiteMap properties for the binding sites from different categories. The SiteScore and Dscore distributions of Class1 and Class2 are similar and lower than those of Class3, suggesting that the binding sites in Class3 are more druggable. In addition, it is easy to notice that the binding sites in Class3 are relatively bigger (higher distribution of Volume) and deeper (lower distribution of Exposure and higher distribution of Enclosure). It is noteworthy that there are several extreme cases in the distributions. For example, in Class2, there are eight binding sites (PDB entries: 4LV6, 4LUC, 4L7D, 4IFN, 4N1B, 4IQK, 4L7B, and 4L7C) with the pocket volumes over than 450 Å3; in Class1, there are five binding sites (PDB entries: 4C5D, 4LWV, 4OGN, 4LWU, and 4JSC) with the Balance values higher than 20. Overall, the binding sites in Class3 are more likely to be tight-binding sites than those in Class1 and Class2. 2549


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Figure 4. Success rates (A) and consistent rates (B) of six docking programs (AutoDock, PLANTS, rDock, Glide, GOLD, and Surflex-Dock) based on 289 complexes from 2P2IDB. 2.0 Å was used as the RMSD cutoff.

On the whole, the average success rates of all programs for the best poses are 64.0% (Class1), 73.8% (Class2), and 73.4% (Class3). In other words, the accurate predictions of the binding structures for peptide inhibitors against PPIs are relatively more challenging. The analysis of the failure cases may offer valuable information for the users and developers of docking programs in a sense. All of the failure cases are summarized in Table S3 in the Supporting Information. The binding structures of the PPI inhibitors in 18 crystal structures could not be well predicted by any of the tested docking programs. To figure out the possible reasons for these unsuccessful dockings, both of the ligand properties and binding site features of the failure cases were analyzed. Among the 18 unsuccessful docked PPI inhibitors, only 4 ligands (1 identical ligand binds to 4 different receptors) have more than 15 rotatable bonds, implying that the flexibility of a ligand may not be the dominating factor leading to the failures. On the other side, it can be found that about 78% (14/ 18) of the ligands are not neutral. For instance, the Bcl-xL inhibitor BM501 contains +2 formal charges owing to the protonation of the nitrogen atoms from the piperazine group. As depicted in Figure 6, the large conformational deviation, especially at the charged group between the docking poses and crystal structures implies that the accuracy of recent docking methodologies would be reduced significantly when handling charged systems. Another finding shows that the 18 failure cases cover nearly 70% (7/11) of the targets whose binding sites cannot be successfully recognized by SiteMap (no valid binding pocket was identified by SiteMap). In addition, other four difficult or undruggable binding sites with Dscore less than 0.8 are also among these failures. Interestingly, we also observe that there is only 1 failure case in Class3 which is the most druggable target category in 2P2IDB. Apparently, accurate predictions of the binding poses for unrecognizable or poor PPI binding sites are extremely challenging for current docking programs. 3.4. Screening Power of Current Docking Programs for PPI Targets. The ROC curves for the three representative targets from three classes in 2P2IDB are shown in Figure 7. As evidenced by the figure, there is a high degree of variability in the results of the screening powers, among not only targets but also docking programs. For HVL (Class2), acceptable early recovery results are obtained from Glide, GOLD, and PLANTS, whereas for HDM2 (Class1), only Glide docking

poses and best poses, respectively. Such results indicate that the commercial docking programs have slightly better performances than those of the academic ones on the sampling power for PPI targets. It is important to note in particular that AutoDock Vina, an open-source program designed and implemented by Dr. Oleg Trott at Scripps Research Institute (http://www.scripps.edu/), can achieve the second-best accuracy for both the top-scored poses and best poses. On the other hand, the consistent rate for each docking program was also calculated. The consistent rate is defined as SRtsp/SRbp, where SRtsp and SRbp are the success rates for the top-scored poses and best poses, respectively. To some extent, this parameter reflects the ranking power of a docking program, which is the ability to identify the near-native pose out of a set of docked decoy poses. As we can see from Figure 4B, GOLD and PLANTS achieve the highest consistent rates (78.9% for GOLD and 73.6% for PLANTS) for the PPI-inhibitor systems in 2P2IDB. Interestingly, such results agree with those reported in our previous benchmarking study (the consistent rate of GOLD is 82.5%), suggesting that the scoring function of GOLD (ChemPLP) is relatively more accurate and robust among the scoring functions used in the six evaluated docking programs.31 The widely used commercial docking program, Surflex-Dock, exhibits the lowest consistent rate (56.2%), even though it achieves the highest success rate of pose sampling. Therefore, the rescoring step after docking is still necessary to make up for the drawbacks of current docking scoring functions. In addition, the pose prediction performances of the evaluated docking programs were further dissected for each class. As illustrated in Figure 5, the performances are not immutable when the tested programs were applied to different classes. For example, on the basis of the results for the top scored poses, GOLD shows the best performance on Class2 and Class3, whereas AutoDock Vina displays a better accuracy than GOLD on Class1; Surflex-Dock shows a mediocre performance on Class1 and Class2, but becomes terrible on Class3. The average success rate for the top-scored poses of the three academic programs (AutoDock Vina, PLANTS, and rDock) and three commercial programs (Glide, GOLD, and Surflex-Dock) are 49.0 and 47.9% for Class1, 52.9 and 53.8% for Class2, and 35.6 and 34.3% for Class3, respectively, indicating that the commercial programs have no obvious advantage on pose prediction at least for the top-scored poses. 2550


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Figure 5. Cumulative distribution curves of the RMSD for each class. (A) Class1 (117 complexes), (B) Class2 (70 complexes), and (C) Class3 (102 complexes). Top-scored poses (left) and best poses (right) are both analyzed. Dotted lines indicate a 2.0 Å RMSD cutoff.

targets is beyond the scope of this study and will be discussed in our next work. As shown in Table 2, different docking programs yield variable screening powers even for the same target, indicated by the AUC values with relatively large fluctuations. For example, for VHL, the AUC values given by Glide and GOLD (0.82) are significantly higher than that given by Surflex-Dock (0.54); for HDM2, the AUC value given by Glide (0.77) is apparently higher than that given by rDock (0.41); for BRD4-1, the AUC value given by Glide (0.67) is obviously higher than that given by PLANTS (0.46). Overall, Glide achieved the best screening powers for all of the three tested targets.

yields acceptable early recovery results. For BRD4-1 (Class3), none of the evaluated docking programs show acceptable results. One possible reason for the poor screening capability of the tested docking programs for BRD4-1 may be explained by the fact that we did not keep any crystal water molecules during the VS but they play a crucial role in the binding of various inhibitors to the bromodomains.56,57 It was reported that several structurally conserved water molecules at the base of the acetyl-lysine binding pocket of BRD4-1 can offer additional hydrogen bonding potential with small molecule inhibitors.58,59 However, the influence of water molecules on the VS of PPI 2551


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Figure 6. Chemical structure of Bcl-xL inhibitor (PDB code: 3SPF) and superimposed best docked poses. The binding pocket is represented as a surface with Coulombic surface coloring (negative charge is in red; positive charge is in blue) generated using UCSF Chimera version 1.11.2.61 The ligand is depicted as sticks, and the carbon atoms from the crystal structure and docking pose are colored yellow and green, respectively.

Figure 7. ROC curves for the three targets in different classes using six docking programs. (A) HDM2 (Class1), (B) VHL (Class2), and (C) BRD41 (Class3).

Table 2. AUC Value of Six Docking Programs for the Representative Targets from Three Classes AUC target

AutoDock Vina

PLANTS

rDock

Glide

GOLD

Surflex-Dock

HDM2 VHL BRD4-1

0.50 0.60 0.63

0.63 0.79 0.46

0.41 0.59 0.64

0.77 0.82 0.67

0.56 0.82 0.51

0.42 0.54 0.55

drug targets (e.g., serine proteases, kinases, metalloenzymes, nuclear hormone receptors, and folate enzymes) in the DUD dataset with a best-practice preparation scheme. However, for the three tested PPI targets in this study, the average AUC achieved by Glide is only 0.75.60 Apart from proper structure

However, compared with conventional drug targets, the screening powers of the tested docking programs to the PPI targets deteriorate sharply. Taking Glide as an example, Repasky and co-workers reported that Glide (SP mode) can obtain an average AUC of 0.80 for 39 different conventional 2552



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preparation, the consideration of protein flexibility and solvent effect may improve the enrichment performance; one should keep in mind that it is still difficult to get desired results by adopting simply a single step or single program for the VS of PPI targets. A better strategy for the DBVS of PPI targets would be the combination of different docking tools into a single platform, which can be benefited from the advantages of different algorithms. For example, we can use Surflex-Dock to generate the binding poses of ligands and then use Glide to rescore the poses predicted by Surflex-Dock. Furthermore, using molecular docking together with other structure-based methods, such as pharmacophore modeling and molecular dynamics simulations or ligand-based approaches, may also achieve better results for screening PPI inhibitors.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected], [email protected]. Tel: +86-571-88208412. ORCID

Huiyong Sun: 0000-0002-7107-7481 Xiaojun Yao: 0000-0002-8974-0173 Youyong Li: 0000-0002-5248-2756 Tingjun Hou: 0000-0001-7227-2580 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was supported by the National Key R&D Program of China (2016YFA0501701 and 2016YFB0201700) and the National Science Foundation of China (21575128 and 81773632). We thank the National Supercomputer Center in Guangzhou (NSCC-GZ) for providing the computing resources.

4. CONCLUSIONS In this study, on the basis of the 2P2IDB database, we explored the structural features of small-molecule PPI inhibitors and analyzed the characteristics of PPI binding pockets. Then, we evaluated the performances of six docking programs for sampling and screening of PPI inhibitors. Although none of the tested docking programs has a satisfactory performance on both binding pose prediction and virtual screening for PPI targets, several useful conclusions summarized below can be obtained. (1) We found that compared with traditional drugs, PPI inhibitors possess higher molecular weight, higher hydrophobicity, and lower solubility. In addition, we further confirmed the top 10 privileged fragments (e.g., chloride group and amide group) for PPI inhibitors. (2) The average druggability of the binding sites of the PPI targets in 2P2IDB is slightly worse than that in traditional ones. The targets in Class3 are relatively more druggable than those in Class1 and Class2. (3) Surflex-Dock exhibits the best pose sampling power for the best poses with a success rate of 75.8%, whereas GOLD owns the best pose ranking power, with a consistent rate of 78.9%. The charged ligand and poor binding site are two main factors for the docking failure of PPI inhibitors. (4) Although Glide shows a relatively better screening power for some PPI targets (HDM2 and VHL), the DBVS for PPI targets is still a big challenge for current docking programs. More rational and more accurate integrated strategies need to be developed for improving the performance of the VS for PPI targets.

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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.jpcb.7b12658. Failure cases of each docking program (XLSX) Density plots of the seven SiteMap properties for the 2P2IDB database and PDBbind core set (Figure S1); cumulative distribution curves of RMSD for the entire database (Figure S2); detailed information about excluded entries for benchmark (Table S1); detailed information about targets and libraries for screening power evaluation (Table S2) (PDF) 2553


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Benchmark Study Based on 2P2IDB

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