D3Pockets: A Method and Web Server for ... - ACS Publications

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Cite This: J. Chem. Inf. Model. XXXX, XXX, XXX−XXX

D3Pockets: A Method and Web Server for Systematic Analysis of Protein Pocket Dynamics Zhaoqiang Chen,†,‡,⊥ Xinben Zhang,†,⊥ Cheng Peng,†,‡ Jinan Wang,† Zhijian Xu,*,†,‡ Kaixian Chen,†,‡,§ Jiye Shi,*,∥ and Weiliang Zhu*,†,‡,§

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CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China ‡ University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China § Open Studio for Druggability Research of Marine Natural Products, Pilot National Laboratory for Marine Science and Technology (Qingdao), 1 Wenhai Road, Aoshanwei, Jimo, Qingdao 266237, China ∥ UCB Biopharma SPRL, Chemin du Foriest, Braine-l’ Alleud B-1420, Belgium S Supporting Information *

ABSTRACT: The intrinsic dynamic properties of the ligand-binding pockets of proteins are important for the protein function mechanism and thus are useful to drug discovery and development. Few methods are available to study the dynamic properties, such as pocket stability, continuity, and correlation. In this work, we develop a method and web server, namely, D3Pockets, for exploring the dynamic properties of the protein pocket based on either molecular dynamics (MD) simulation trajectories or conformational ensembles. Application of D3Pockets on five target proteins as examples, namely, HIV-1 protease, BACE1, L-ABP, GPX4, and GR, uncovers more information on the dynamic properties of the ligand-binding pockets, which should be helpful to understanding protein function mechanism and drug design. The D3Pockets web server is available at http://www. d3pharma.com/D3Pocket/index.php.

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constructed to predict the druggability of the cavities.21−25 These models can be classified into three categories: (1) single descriptor (e.g., LIGSITE8), (2) scoring function (e.g., SiteMap,26 Fpocket,17 DrugPred,27 DoGSiterScorer22), and (3) classification algorithm in machine learning (e.g., Random Forests,28,29 Support Vector Machine22,30).31 Both the geometrical and physicochemical properties of the protein pocket may be affected by protein movement. Stank et al. introduces five different classes of protein pocket dynamic properties.32 These dynamic properties of the pocket should be considered in SBDD. Also, various MD simulation approaches, such as normal-mode analysis,33 tCONCOORD,34 FRODA,35 and Langevin-RIP,36 also have been developed for exploring the conformational change of target proteins. However, only a few computational tools are available for analyzing protein pocket dynamics: EPOSBP37 and Epock38 for tracking some pocket properties along the MD trajectory (as volume), MDpocket39 for tracking small molecule binding sites and gas migration pathways on MD trajectories or conformational ensembles, TRAPP40 for automated detection of transient regions of binding pockets in ensembles of protein

INTRODUCTION Identification of druggable ligand-binding pockets of target proteins is critically important, especially for structure-based drug design (SBDD).1,2 Molecular docking is an essential technology of virtual screening, which can accelerate the identification of hit compounds.3−5 The first step of molecular docking is identification of a target and its binding pocket. The ligand-binding pocket is usually located in the concave surface of the protein to accommodate the ligand forming more favorable contacts with the target protein. Accordingly, a variety of computational methods have been developed for detecting the cavities on the protein surface in recent decades.6 These methods can be classified into five categories: (1) gridbased method (e.g., POCKET,7 LIGSITE,8 PocketPicker,9 HINT10), (2) inscribed sphere-based method (e.g., SURFNET11 ), (3) sphere roll-based method (e.g., PASS,12 PHECOM,13 ROLL14), (4) surface-based method (e.g., MSPOCKET15), (5) tessellation-based (Voronoi Diagram) method (e.g., CAST,16 FPocket17), and some mixed geometric methods (grid-and-sphere method, e.g., KVFinder,18 CavVis19). The surface on the protein usually has multiple cavities. Small molecules that can bind to the cavities have potentials to adjust the function of the protein.20 Various models have been © XXXX American Chemical Society

Received: April 22, 2019 Published: July 2, 2019 A

DOI: 10.1021/acs.jcim.9b00332 J. Chem. Inf. Model. XXXX, XXX, XXX−XXX

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Journal of Chemical Information and Modeling

Figure 1. Stability of pockets in HIV-1 protease (A) and BACE1 (B).

conformations, POVME 3.041 for analysis of flexible protein cavities, Pocketron42 for analyzing pocket crosstalk and revealing hidden allosteric networks along microsecond-long MD simulations. CorrSite43 and AllositePro44 could predict allosteric sites based on the static structure using a Gaussian network model and elastic network model, respectively. To the best of our knowledge, neither method nor web server are currently available for systematically exploring protein pocket dynamics, e.g., pocket stability, continuity, and correlation, based on either MD simulation trajectory or conformational ensembles with large-scale conformational change. In this study, we develop a method and web server, namely, D3Pockets, for detecting and analyzing the dynamic properties of the protein pockets, viz., pocket stability, continuity, and correlation. Five target proteins, including human immunodeficiency virus (HIV-1) protease, β-secretase 1 (BACE1), L-arbinose binding protein (L-ABP), glutathione peroxidase 4 (GPX4), and glucocorticoid receptor (GR), are selected as examples to shed more light on dynamic properties of protein pockets calculated by D3Pockets.

conformations; N is the total number of the counted conformations. 2. Pocket Continuity. D3Pockets could trace the change of the pocket, including appearance, disappearance, merging, and volume change. The continuity is defined by eq 3 PC = {Pi|Pi ∩ Pref > 0}, i = 1, 2 ···, n

where P ref is the studied pocket in the first frame (conformation); Pi is the ith pocket that is spatially overlapped with Pref; n is the number of all pockets throughout MD simulation; PC is the ensembles of pockets Pi. 3. Pocket Correlation. All potential binding pockets that appear in an MD trajectory are clustered based on residues. Then, the coexist matrix and the correlation matrix are calculated with eqs 4 and 5, respectively. Ci, j = Ci ∩ Cj ρi , j =

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MATERIALS AND METHODS To reflect the dynamic properties of protein pockets, three descriptors, namely, pocket stability, pocket continuity, and pocket correlation, are designed in this study. The whole working procedure of D3Pockets is illustrated in Figure S1 of the Supporting Information. 1. Pocket Stability. The stability of the potential binding pocket is calculated with eqs 1 and 2. PS = {S1 , S2 , ···, Si , ···, Sm}

(1)

Si = n/N

(2)

(3)

(4)

cov(Vi , Vj) σViσVj

(5)

where Ci and Cj are the conformation sets of the protein corresponding to the ith and jth cluster pockets, respectively. Vi and Vj are the volume sets of the ith and jth cluster pockets in the conformations Ci,j, respectively. The covariance of Vi and Vj is defined as cov(Vi, Vj), and the variance of V is defined as σ. The positive correlation coefficient (with +1 as largest value) stands for positive correlation between two pockets, and negative correlation coefficient (with −1 as the least value) stands for negative correlation between two pockets.

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RESULTS AND DISCUSSION Stability. The target proteins HIV-1 protease and BACE1 are selected as two examples to demonstrate pocket stability. As displayed in Figure 1, D3Pockets colors the grid points that compose a pocket. The more red the points are, the more

where PS is the stability of the pocket; Si is the stability of the grid composed of the pocket; m is the number of grids in the pocket; n is the number of occurrences of the ith grid in the B


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Figure 2. Pocket continuity analysis of HIV-1 protease and L-ABP protein. (A) Change of Pocket 1 in HIV-1 protease. (B) Residues around the gap between Pockets 1-1 and 1-2 in HIV-1 protease. (C) Pockets in crystal structure and 2225th conformation of L-ABP. Original pockets are shown in surface mode; the pocket in the 2225th conformation is shown in mesh mode. (D) Change of residues around the gap among Pockets 1, 3, and 4 in L-ABP.

D3Pockets could be used as a key feature to locate the prior site for SBDD. Continuity. HIV-1 protease and L-ABP are selected as examples to demonstrate pocket continuity. For the protease, the ligand-binding pocket becomes smaller when the protein structure changes from open to closed conformations. Impressively, the pocket splits into two subpockets, Pockets 1-1 and 1-2, at 4305th frame onward (Figure 2A). The residues around the gap between Pockets 1-1 and 1-2 consisted mainly of Ile50, Ile50′, Asp25, and Asp25′ (Figure 2B). The residues Ile50 and Ile50′ are located in the two flap region. The active site of the homodimeric HIV-1 protease include six amino acids, triad Asp25-Thr26-Gly27 in each monomer.46 Among them, Asp25 and Asp25′ lie on the bottom of the cavity and interact directly with substrates to hydrolyze an amide bond. Many inhibitors of HIV-1 protease are designed to form hydrogen bonding interactions with Asp25, Asp25′, Ile50, and Ile50’. It is found that all FDA approved drugs targeting HIV-1 protease form hydrogen bonds with Asp25 and Asp25′; Ile50 and Ile50′ form a hydrogen bond network with inhibitors via an essential water except Tipranavir. The oxygen atom of ester groups in Tipranavir occupies the

frequent the points in the pocket are observed throughout the MD trajectory; the more blue the points are, the less frequent the points in the pocket are observed throughout the MD trajectory. Therefore, the subpocket region composed of the red points is more stable than other regions. For HIV-1 protease, four pockets are observed on the protein surface (Figure 1A). Pockets 2, 3, and 4 are unstable and labeled as the color blue. Pocket 1, which the intrinsic ligand binds to, is more stable than Pockets 2, 3, and 4. In detail, the top of Pocket 1 is unstable (blue). The middle of it is metastable (green), and the bottom is stable (red). The result is consistent with the motion of the HIV-1 protease with its flaps spontaneously opening and closing in MD simulations.45 The top region of Pocket 1 disappears as the flaps close. The ligands in the HIV-1 protease are found to mainly bind to the stable region during MD simulation, the bottom of Pocket 1. For BACE1, three pockets are detected on the protein surface (Figure 1B). Pocket 1, which the ligand interacts with, is more stable than other pockets. In detail, the top left corner of Pocket 1 is unstable for the large-scale motion of the flap region. The ligands in BACE1 mainly bind to the stable region of the Pocket 1. Accordingly, pocket stability calculated by C


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Figure 3. Correlation of different pockets in GPX4 (A) and GR (B). Red lines indicate the positive correlation between residues, and blue lines indicate the negative correlation calculated by the DCC method.

suggest that a pocket with strong correlation with a substratebinding pocket could be applied to design allosteric compounds. For GR, more than six different pockets are detected throughout the MD simulation (Figure S4B, Supporting Information). Pocket 1 is the substrate pocket. A negative volume correlation (−0.84) between Pockets 1 and 4 of GR exists throughout the MD trajectory, revealing that when Pocket 1 (intrinsic ligand binding site) gets bigger in its volume, Pocket 4 gets smaller (Figure 3B). The volumes of Pockets 1 and 4 vary from 91 to 273 Å3 and 224 to 92 Å3, respectively. DCC analysis indicates that Pockets 1 and 4 show a correlation. Pocket 4 is a cofactor binding pocket, which could bind nuclear receptor coactivator 2.49 Therefore, we suggest that a pocket showing a correlation with a substrate pocket may act as a functional pocket.

essential water to directly interact with Ile50 and Ile50′ via hydrogen bonds (Figure S2, Supporting Information). For L-ABP, five pockets are detected on the protein surface (Figure 2C). Pocket 1, the intrinsic ligand binding pocket, is classified as the enclosed pocket (classification of protein pockets is illustrated in Figure S3 of the Supporting Information). We trace the change of the pockets, and find that Pockets 1, 3, and 4 are merged together to form a large binding pocket from time to time (Figure 2C), which clearly reveals the pathway for the ligand entering in and out of Pocket 1. The residues around the gaps among Pockets 1, 3, and 4 are Lys10, Gln11, Glu14, Asn177, and Asn205 (Figure 2D). The distance between Asn205 and Glu14 varies from 3.31 to 11.34 Å. The distance between Glu14 and Gln11 varies from 3.92 to 12.18 Å. The distance between Asn177 and Gln11 varies from 3.42 to 16.64 Å. The distance between Lys10 and Gln11 varies from 3.84 to 12.02 Å. The shape of “Gln11-Lys10” converts from a “U” to an “L” shape. These changes make the gates among Pockets 1, 3, and 4 convert from closed to open states (Figure 2D), which provide a pathway for the ligand entering into or leaving out of Pocket 1. Correlation. GPX4 and GR are selected as examples to demonstrate pocket correlationship. For GPX4, more than five different pockets are detected throughout the MD simulation trajectory (Figure S4A, Supporting Information). Pocket 1 is the substrate pocket. Pockets 2, 3, and 4 are near Pocket 1, and Pocket 5 is far from Pocket 1. A positive volume correlation (0.55) between Pockets 1 and 5 of GPX4 exists throughout the MD trajectory, revealing that when Pocket 1 (intrinsic ligand binding site) gets bigger in its volume, Pocket 5 gets bigger as well (Figure 3A). The volumes of Pockets 1 and 5 vary from 132 to 287 Å3 and 98 to 300 Å3, respectively. In addition, dynamic cross correlation (DCC) analysis47 indicates that the residues in Pockets 1 and 5 show a correlation (Figure 3A). Indeed, Li et al. identified Pocket 5 as a potential allosteric site.48 There are no correlations among the other pockets. We

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CONCLUSION

In this study, we present a new method and its web server, namely, D3Pockets, to study the dynamic properties of potential ligand binding pockets of drug targets based on MD simulation trajectory or conformational ensembles. With D3Pockets, the stability, continuity, and correlation of protein pockets could be studied and visualized with PyMOL. With five proteins (HIV-1, BACE-1, L-ABP, GPX4, and GR) as examples, we have analyzed pocket stability, continuity, and correlationship of protein pockets to demonstrate the importance of pocket dynamic properties in SBDD. Therefore, D3Pockets should have various potential applications for the identification of orthosteric and allosteric pockets and a stable subpocket for hit identification, lead optimization, multipocket-based drug design, and so on. D


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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.9b00332. Protein data set for application, D3Pockets program features, classification of protein pockets, nine FDA approved drugs targeting HIV-1 protease, pockets of GPX4 and GR appearing during MD trajectory, pocket dynamic properties of five selected targets (Figures S5− S7), and demonstration of D3Pockets application (Figure S8, Demos 1−4) (PDF)

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected] (Z. Xu). *E-mail: [email protected] (J. Shi). *E-mail: [email protected] (W. Zhu). ORCID

Zhijian Xu: 0000-0002-3063-8473 Weiliang Zhu: 0000-0001-6699-5299 Author Contributions ⊥

Z. Chen and X. Zhang contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Key Research and Development Program (2016YFA0502301), National Natural Science Foundation of China (81573350, 81872797), and National Science & Technology Major Project “Key New Drug Creation and Manufacturing Program”, China (2018ZX09711002). The simulations were partially run at the TianHe 2 supercomputer in Guangzhou, supported by the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (second phase) under Grant No. nsfc2015_447.

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