Identification and Preliminary SAR Analysis of Novel Type-I Inhibitors

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Identification and Preliminary SAR Analysis of Novel Type-I Inhibitors of TIE-2 via Structure-Based Virtual Screening and Biological Evaluation in in vitro Models Peichen Pan, Sheng Tian, Huiyong Sun, Xiaotian Kong, Wenfang Zhou, Dan Li, Youyong Li, and Tingjun Hou J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.5b00576 • Publication Date (Web): 30 Nov 2015 Downloaded from http://pubs.acs.org on December 8, 2015

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Journal of Chemical Information and Modeling

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Identification and Preliminary SAR Analysis of Novel Type-I

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Inhibitors of TIE-2 via Structure-Based Virtual Screening and

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Biological Evaluation in in vitro Models

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Peichen Pana, Sheng Tianb, Huiyong Suna, Xiaotian Kongb, Wenfang Zhoua, Dan Lia,

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Youyong Lic, Tingjun Houa,b

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College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China

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State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058, China

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c

Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China.

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Corresponding authors:

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Tingjun Hou

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E-mail: [email protected]

or

[email protected]

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Keywords：：Angiogenesis, TIE-2, Type-I inhibitor, Structure-based virtual screening,

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Substructure search

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ACS Paragon Plus Environment


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Abstract

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Angiopoietin (ANG) ligands and their downstream TIE receptors have been validated

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as the second vascular signaling system involving vessel remodeling and maturation.

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Among them, ANG/TIE-2 signaling pathway is involved in numerous life-threatening

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diseases and has become an attractive potential therapeutic target. Several

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large-molecule inhibitors targeting ANG/TIE-2 axis have recently entered clinical

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phase for the therapy of various solid tumors, but selective small-molecule inhibitors

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of TIE-2 is still quite limited. In the present work, structure-based virtual screening

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was performed to search for type-I inhibitors of TIE-2. Of the only 41 compounds

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selected by our strategy, 8 molecules with the concentration of 25 µg/mL exhibit over

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50% inhibitory rate against TIE-2 in in vitro enzymatic activity assay, and the IC50

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values of 2 hits are lower than 1 µM. Further optimization and SAR analysis based on

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compound TP-S1-30 and 31 were carried out by using substructure searching strategy,

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leading to the discovery of several sub-100nM inhibitors. Among them, the most

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potent compound, TP-S1-68, showed an inhibitory IC50 of 0.149 µM. These novel

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inhibitors of TIE-2 discovered in this study and the analogs of the active core

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scaffolds can serve as the starting points for further drug development.

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Introduction

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Blood supply plays a critical role in the progress of tumor growth. Blocking the

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formation of vascular system is believed to be an effective way to suppress tumor

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growth and metastasis.1 Vessels can grow in a number of different ways. Early

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formation of blood vessels occurs by a process known as vasculogenesis, where

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endothelial cells differentiate, proliferate and then assemble to a primitive vascular

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network. This primary vascular plexus is modified and extended by another process

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called angiogenesis, which includes both sprouting and non-sprouting angiogenesis.

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This emerging capillary plexus is soon remodeled to resemble a mature vasculature

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through both pruning and vessel enlargement.2 Further maturation of blood vessels is

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then accomplished where endothelial cells integrate tightly with smooth muscle cells,

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pericytes and surrounding matrix.1, 3

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Complex signaling pathways and a high degree of co-ordination among various cell

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types are involved in the formation of blood vessels. In the past decade, great progress

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has been made in identifying and characterizing physiological regulators of blood

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vessel growth. The members of the vascular endothelial cell growth factor (VEGF)

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receptor family, which includes Flt-1 (VEGF-R1), Flk-1/KDR (VEGF-R2), and Flt-4

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(VEGF-R3), are potent mediators for angiogenesis.4, 5 A number of kinase inhibitors

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targeting VEGF signaling and anti-VEGF antibodies have entered clinical trials,

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among which sunitinib (Sutent), sorafenib (Nexavar), etc. have been approved by the

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FDA for the treatment of various solid tumors.4, 6, 7 Although clinical outcomes were

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often encouraging, patients always failed to respond or became resistant to the

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currently available anti-angiogenic drugs.8-12

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Of the other factors involved in angiogenesis, TIE-2/TEK (tyrosine kinase with

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immunoglobulin and epidermal growth factor homology domains-2), a tyrosine kinase

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that is expressed principally on vascular endothelium, was found to play a critical role

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in the stabilization, maturation, remodeling and hierarchical organization of

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preliminary vessels.13-15 Interference with the ANG/TIE-2 signaling pathways has

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been shown to block or attenuate tumor-induced angiogenesis and suppress primary 4


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and metastatic tumor growth in a variety of xenograft models.16-19 Recently, it has

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become clear that combination of both first (VEGF signaling) and second generation

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(ANG/TIE-2 signaling) agents can lead to better therapeutic outcomes, which is

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superior to treatments with either agent alone.16, 20 Therefore, ANG/TIE-2 axis has

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become a very promising anti-angiogenesis target for the therapy of various tumors.

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Pharmaceutical companies and some academic institutions have been involved in

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developing ANG/TIE-2 inhibitors in recent years.21-26 Several large molecular

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antibodies

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REGN-910 and AMG-780)27-29 targeting ANG ligands (ANG-1 and/or ANG-2) and at

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least two small molecule candidates of TIE-2, namely CEP-1198121 (Phase I,

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Cephalon, Inc.) and ARRY-61424 (Phase I, Array Biopharma. Inc.), have been pushed

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into the clinical phase.

(e.g.

AMG-386/Trebananib,

MEDI-3617,

CVX-060/PF04856884,

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With the explosive increasing number of synthesized compounds from various

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chemical libraries, the demand for efficient strategies to identify novel and potent

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small inhibitors of a certain drug target is also growing. Structure-based virtual

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screening (VS) method is being widely utilized in hit identification and plays an

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important role in the preliminary stage of drug discovery.30-36 In this work, a

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docking-based VS model for discovering type-I inhibitors of TIE-2 was developed

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and evaluated by in vitro biological inhibitory activity experiments. Several active

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compounds were successfully identified from the first-round virtual screening

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campaign, where the inhibitory activities (IC50) of 2 hit compounds were lower than 1

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µM. Subsequently, preliminary optimization based on the core structures of these

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compounds were performed using substructure search methodology, and several

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sub-100nM inhibitors were then discovered. According to the results of both

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experimental tests and molecular modeling simulations, the structure-activity

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relationships (SAR) of the inhibitors were analyzed. The active compounds identified

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in this work and the detailed binding mechanism of these inhibitors to TIE-2 can

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further guide the design and discovery of more potent inhibitors of TIE-2, which

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would finally benefit to the targeted therapy of associated fatal diseases.

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Materials and Methods

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Initial Structures and Preparations for Molecular Docking. The DFG-in crystal

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structures of TIE-2/inhibitor complexes used in this study were obtained from RCSB

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Protein Data Bank,37 including 3L8P21 and 2OO838. The preparations of the protein

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structures were accomplished with the Protein Preparation wizard in Schrödinger

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9.0.39 The prediction accuracy of docking may be influenced when water molecules

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(especially the active site ones) are included in docking simulations, and the positions

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of water molecules in the binding of different inhibitors may also significantly vary.

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Thus, all crystallographic water molecules were deleted, and hydrogen atoms and

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partial charges were added to the proteins, and a restrained partial minimization

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terminated when the root-mean-square deviation (RMSD) reached a maximum value

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of 0.3 Å.

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The 3D structures of known TIE-2 inhibitors were obtained from BindingDB

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database,40 and then the type-II inhibitors and those with poor inhibitory activity

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(IC50 > 100 µM) were removed.41 The final inhibitor decoy set was generated by

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extracting 100 structurally diverse type-I inhibitors from the cleared database above

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using Find Diverse Molecule module in Discovery Studio 2.5.42 A library of

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approximately 240,000 screening compounds was downloaded from the Specs

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chemical library, and the non-inhibitor decoy set of 10,000 compounds for

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establishment and validation of screening models was randomly extracted from the

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Specs library using Discovery Studio 2.5 package.42 All the compounds used for

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docking were minimized with Macromodel in Schrödinger using the OPLS-2005

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force field. Ligand preparations were carried out with LigPrep module in Schrödinger,

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and correct protonated states were generated at pH = 7.0 ± 2.0. Default settings were

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used for the other parameters.

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Docking Study. All molecular docking calculations were carried out using the Glide

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module in Schrödinger. The grid boxes of proteins for docking were generated and

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centered on the inhibitors in the binding pockets, and the scaling factors for van der

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Waals radii was set to 1.0. The maximum partial atomic charge was set to 0.25. 6


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Subsequently, the studied compounds were docked into the binding site of TIE-2 with

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different precision modes, which includes HTVS (high throughput virtual screening),

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SP (standard precision) and/or XP (extra precision). The Epik state penalties to

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docking score were added.

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Virtual Screening Workflow. Before screening the Specs chemical library, validation

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of the performance of molecular docking based on two reported DFG-in crystal

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structures of TIE-2 in complex with type-I inhibitors (PDB entry: 2WQB and 3L8P)43,

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44

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non-inhibitor decoy set randomly extracted from Specs were docked into the two

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TIE-2 crystal structures using three different scoring modes: HTVS, SP and XP. By

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comparing the accuracy of Glide docking in distinguishing inhibitors from

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non-inhibitors based on two different TIE-2 structures, the crystal structure of 3L8P

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with better prediction accuracy was finally chosen for the following screening

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campaign. A hierarchical scheme was applied in the virtual screening of TIE-2

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inhibitors (Figure 1). In the first step, all the screened compounds were docked into

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the crystal structure of 3L8P, and the binding energies were scored and ranked by

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Glide SP mode. Subsequently, the top ranked 50,000 molecules were saved and

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submitted to the following Glide XP docking assessment. According to the ADMET

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properties of the compounds predicted by ACD/ADME package,45 the 1,000 top

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ranked molecules from Glide XP docking were then filtered by several criteria, and

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the compounds in accordance with either criteria were removed: (1) logP/logD

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(pH=7.0) > 6.0; (2) violation of Lipinski's rule of five ≥ 2;46 (3) violation of Opera's

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rules of drug-likeness ≥ 4;47 (4) functional groups with toxic, reactive, or otherwise

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undesirable moieties defined by the REOS rules48. In order to maximize the chemical

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diversity of the selected compounds for experimental testing, the remaining filtered

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compounds were clustered into 50 clusters on the basis of the Tanimoto distance

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predicted from the FCFP_4 fingerprints using Find Diverse Molecule module in

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Discovery Studio 2.5.42 50 compounds were obtained by selecting the molecule with

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the lowest docking scores in each cluster. However nine compounds were not

was first carried out. The molecules of both the inhibitor decoy set of TIE-2 and the

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available for purchasing from chemical vendor, Specs. So 41 compounds were

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eventually purchased and tested experimentally for their ability to inhibit the

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enzymatic activity of TIE-2. Furthermore, hit optimization and initial SAR analysis

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based on substructure searching of the active core structures was also carried out by

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querying the entire Specs database for structures with the same core scaffold. Then a

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second round experimental screening was also accomplished.

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MD Simulations and MM/GBSA Calculations. Molecular dynamics (MD)

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simulations and molecular mechanics/generalized born surface area (MM/GBSA)

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calculations were performed in AMBER14.49 Atomic partial charges of inhibitors

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were generated by fitting the electrostatic potentials calculated at the HF/6-31G* level

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using the RESP fitting technique after the small molecules were optimized by

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semi-empirical AM1 method in Gaussian09.50, 51 The general AMBER force field

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(gaff)52 was used for the inhibitors, and the ff99SB force field for the proteins.53

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TIP3P water molecules with a distance of 10 Å extended from any solute atoms were

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applied into the system. Particle Mesh Ewald (PME) method54 was employed to

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handle the long-range electrostatics.

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In the first stage, 500 cycles of steepest descent and 500 cycles of conjugate

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gradient minimizations were carried out with 50 kcal/mol/Å2 restraint on the backbone

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carbons. Then, 1000 cycles of steepest descent and 4000 cycles of conjugate gradient

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minimizations without any restraint were carried out. Subsequently, each system was

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heated up from 0 to 300 K in 50 ps, and 5 ns NPT (P = 1 atm and T = 300 K) MD

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simulations were then performed. The SHAKE algorithm was applied to constrain all

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bonds involving hydrogen atoms.55 The time step was set to 2.0 fs, and the

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coordinates were saved every 10 ps.

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The binding free energy (∆Gbind) of each inhibitor/TIE-2 complex was calculated

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using the Molecular Mechanics/Generalized Born Solvent Area (MM/GBSA)

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methodology.56,

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dielectric constant (εin) was set to 1. The polar part of desolvation (∆GGB) was

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computed with the modified GB model developed by Onufriev et al. (referred as

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The exterior dielectric constant was set to 80, and the solute

8


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igb=2 in Amber),58 and the non-polar part of desolvation (∆GSA) was determined

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based on the solvent accessible surface area (SASA) computed by the LCPO

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algorithm:59 ∆GSA= 0.0072 × ∆SASA. The change of the conformational entropy

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(-T∆S) was not considered due to high computational cost and low prediction

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accuracy.56, 60 In total, 100 snapshots evenly extracted from 2 to 5 ns were used to

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calculate the energy terms. For each complex, the interaction spectrum between

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inhibitor and TIE-2 on a per-residue basis was calculated by MM/GBSA

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decomposition analysis supported by the mm_pbsa module in AMBER.61-63

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Reagents and Materials for Bioassays. All reagents and anhydrous solvents were

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purchased from commercial sources. The positive control TIE-2 kinase inhibitor, also

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referenced under CAS 948557-43-5, was purchased from Merck Calbiochem

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(Darmstadt, Germany, purity ≥ 97% by HPLC).64 Each compound was dissolved in

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100% dimethyl sulfoxide (DMSO) as a 10 mM stock solution. The final DMSO

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concentration in each reaction was less than 1%. Purified recombinant human TIE-2

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protein (Catalog number: PV3628) and Z′-Lyte® kinase assay kit - ser/thr 5 peptide

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(Catalog number: PV3178) were purchased from Thermo Fisher Scientific Inc. All the

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96 test compounds were purchased from Specs, and the purity of these compounds is

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≥ 95%, which is confirmed by Specs (compound data are summarized in Table S1).

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In vitro Inhibitory Activity Assay of TIE-2. All the assays were carried out in

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384-well plate format. The 4×test compounds and 4×ATP solution were firstly

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prepared before the enzyme reaction starts. The 2×recombinant human TIE-2 protein /

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Tyr 05 peptide mixture was prepared in 50 mM HEPES (pH 7.5), 0.01% BRIJ-35, 10

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mM MgCl2, 4 mM MnCl2, 1 mM EGTA and 2 mM DTT. The final 10 µL kinase

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reaction consists of 5 µL 1×TIE-2 / Tyr 05 peptide mixture (10 ng TIE-2 and 2 µM

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Tyr 05 peptide), 2.5 µL ATP (100 µM) and 2.5 µL test compounds with desired

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concentration. The assay plate was shaked on a plate shaker for 30 seconds to mix the

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reactions thoroughly. After 1 hour kinase reaction incubation at room temperature

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(20−25°C), 5 µL of a 1:1024 dilution of development reagent A is added, and the

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assay plate was incubated for another 1 hour at room temperature. Then, the reactions 9



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were terminated by adding 5 µL of Stop Reagent to each well, and the assay plate was

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placed into a fluorescence plate reader (BioTek Synergy™ 4) to measure both the

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coumarin and fluorescein emission signals (excitation: 400 nm; emission: 445 and

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520 nm, respectively).

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Results and Discussion

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Evaluation of Virtual Screening Strategy. At present, five crystal structures of

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TIE-2 are available in RCSB Protein Data Bank, namely 2OO8, 2OSC, 2P4I, 2WQB

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and 3L8P. However, the structures of 2OO8, 2OSC and 2P4I are all DFG-out

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conformations of TIE-2, which bind to type-II inhibitors. Thus, only 2WQB and 3L8P

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with DFG-in conformations can be utilized to screen type-I inhibitors of TIE-2. In

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order to determine the best screening strategy, the docking accuracy for both 2WQB

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and 3L8P was evaluated to distinguish inhibitors from non-inhibitors using different

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Glide docking modes. Each molecule in the validation set containing inhibitor and

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non-inhibitor decoy sets was docked into the active pockets of both 2WQB and 3L8P

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using three different Glide modes (HTVS, SP and XP).

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The validation results were summarized in Figure 2. The student’s t-test was

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utilized to assess the significance of the difference between two data sets, which can

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be indicated by P-values. As shown in Figure 2, the P-values of 3L8P are obviously

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lower than those of 2WQB in all three Glide modes, indicating that the discrimination

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power of 3L8P in distinguishing inhibitors from non-inhibitors is better than that of

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2WQB. So the structure of 3L8P was employed in the virtual screening workflow. It

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was also observed that the P-values of HTVS Glide mode for both 2WQB and 3L8P

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are quite high implying a poor discrimination power. Though HTVS mode is

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extremely efficient in docking procedure, it is not used in our screening campaign due

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to its low prediction accuracy. The XP Glide mode exhibited the best discrimination

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power among all three models. However, XP mode is highly time-consuming when

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entire Specs database is docked, thus SP mode with moderate prediction accuracy was

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firstly carried out. Then, the top ranked 50,000 molecules were saved and re-docked 10


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using Glide XP mode. In this way, both prediction accuracy and computing efficiency

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were balanced. Subsequently, the top 1,000 compounds ranked by Glide XP docking

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were filtered and clustered according to their predicted ADMET properties, and 41

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compounds were finally purchased for bioassays (Figure 1).

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In vitro Biological Activity of Virtual Screening Compounds. To evaluate the in

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vitro TIE-2 enzymatic activity of the 41 candidate compounds, we developed a

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quantitative FRET-based Z’-Lyte assay as described above. Commercially available

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TIE-2 inhibitor (CAS: 948557-43-5) was included as a positive control to validate the

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screening assay. Under the same assay conditions, TIE-2 inhibitor inhibited the kinase

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activity of TIE-2 with an IC50 value of 0.31 µM, which is consistent with the reported

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data (0.25 µM). In the first step, all the obtained 41 compounds were tested for

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inhibition of TIE-2 activity at 25 µg/mL. The results were summarized and plotted in

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Figure 3. Eight compounds (TP-S1-11, 18, 22, 30, 31, 37, 40 and 41) inhibited at least

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50% of TIE-2 enzymatic activity at the concentration of 25 µg/mL, with four of them

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(TP-S1-18, 30, 31 and 41) inhibiting at least 80 % of TIE-2 activity. We can find that

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several compounds exhibited negative inhibitory activity, especially compounds

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TP-S1-5, 9 and 20 (-101.76%, -240.97% and -94.16%, respectively). This

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phenomenon may be caused by the auto-fluorescence property of the tested

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compounds, which finally affects the detection of FRET signal. The inhibitory

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activities of these compounds were actually not correctly characterized, so these

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compounds were treated as inactive. The four compounds with over 80% inhibitory

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activity were further tested to check their ability to inhibit TIE-2 activity at a lower

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concentration, and the dose-dependent effects of these compounds (the inhibitory IC50

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values) were then determined as well (Figure 4). The IC50 values for compounds

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TP-S1-18, 30, 31 and 41 are 11.71 µg/mL (27.39 µM), 0.32 µg/mL (0.89 µM), 0.16

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µg/mL (0.48 µM), 5.79 µg/mL (14.82 µM), respectively. Among them, compounds

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TP-S1-30 and TP-S1-31 are in low micro molar range.

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The novelty of these 4 active compounds was also evaluated by comparing the

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structural similarity of each inhibitor to known TIE-2 inhibitors. Based on the 11



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FCFP_4 fingerprints, the pairwise Tanimoto similarity, which is demonstrated to be a

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preferential method of choice for computing molecular similarities,65 were calculated

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using the Find Similar Molecules by Fingerprints protocol in Discovery Studio 2.5.

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The values of Tanimoto coefficients range from zero (no bits in common) to unity (all

290

bits the same). According to our results, all the 4 inhibitors exhibit low Tanimoto

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similarities to the known TIE-2 inhibitors obtained from BindingDB (TP-S1-18:

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0.138, TP-S1-30: 0.148, TP-S1-31: 0.130 and TP-S1-41: 0.159). Therefore, the 4

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inhibitors discovered from VS are structurally novel compared with the reported

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TIE-2 inhibitors.

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The active site residues Ile830, Glu832, Val838, Lys855, Leu876, Ala905, Leu971,

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Ala981 and Asp982 are involved in the binding of the best four compounds to TIE-2.

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However, the dominating residues for the binding of each inhibitor-protein complex

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are different. According to the results of the Glide XP docking simulations shown in

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Figure 5A, the key binding residues of the TP-S1-18/TIE-2 complex are Asp982,

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Lue971, Val838 and Ile830. A hydrogen bond is formed between Asp982 and a

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carbonyl group close to the benzene ring. The arene ring of isoindole in TP-S1-18 can

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form hydrophobic interactions contacts (arene-H interaction) with both Lue971 and

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Val838. Another hydrophobic contact can be observed between Ile830 and the

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benzene ring of phenylethanone, which is an obvious difference compared with the

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other inhibitors due to the extended phenylethanone structure in this region.

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Compounds TP-S1-30 and 31 share the same [1,2,4]triazolo[3,4-b][1,3,4]thiadiazole

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core structure linked with two conjugate ring group in 3- and 6-position, respectively.

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Thus the key binding patterns of compounds TP-S1-30 and 31 are quite similar.

309

Residue Lys855 in both TP-S1-30 and 31 complexes can form cation-pi interactions

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with the thiadiazole group of the core structure. Though the substitutions at 3-position

311

of the core structure are different, the same benzene ring can form similar

312

hydrophobic interaction with Val838 in both systems. However, different substitutions

313

at 3- and 6-positions of the core structure still have significant influence on the

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binding modes of TP-S1-30 and 31. A hydrogen bond interaction was formed between

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the residue Asp982 in TP-S1-31/TIE-2 complex and the substituted pyridine group at 12


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6-position, but this phenomenon was not observed in the TP-S1-30/TIE-2 complex,

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where the 3-chlorobenzene group is substituted at 6-position. So this suggests that

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incorporating a hydrogen bond acceptor in the region close to Asp982 would help to

319

form similar hydrogen bonds and therefore benefit to inhibitor binding. In addition,

320

the binding patterns of the substituents at 3-position are also different. For the

321

TP-S1-30/TIE-2 complex, the oxygen of the 1,3-benzodioxole group can form

322

hydrogen bond interaction with the backbone of Ala905. But the dioxole group is

323

replaced by a benzene group in compound TP-S1-31, which can interact with residue

324

Leu971 via hydrophobic contact. In the binding of TP-S1-41 to TIE-2, the

325

hydrophobic interactions of the residues Lys855, Val838, and Ala981 are observed.

326

Different from compounds TP-S1-18, 30 and 31, the phenylethanone group extends

327

into a different region close to Glu832, where a hydrogen bond is formed between the

328

carbonyl oxygen and the backbone of Glu832.

329

Preliminary Optimization and SAR Analysis Based on the Core Structures of

330

TP-S1-30 and 31. To obtain a preliminary understanding of the structure−activity

331

relationships, substructure searches were carried based on the Specs database.

332

According to the results from our virtual screening campaign, two most potent

333

candidate inhibitors, TP-S1-30 and TP-S1-31 (IC50 = 0.89 µM and 0.48 µM,

334

respectively), were selected and submitted to initial optimization and SAR analysis

335

using substructure search. Unfortunately, few derivatives were available in the

336

database when the frame structure of either TP-S1-30 or TP-S1-31 was taken as the

337

template

338

([1,2,4]triazolo[3,4-b][1,3,4]thiadiazole) of both compounds TP-S1-30 and 31 linked

339

with two phenyl groups at 3- and 6-position was taken as one of the starting

340

structures.

scaffold.

Thus,

the

identical

core

structure

341

Hundreds of compounds were retrieved through substructure search, and most

342

substituents located in the two phenyl groups at 3- and 6-positions of the core

343

structure. To give an explicit SAR analysis, only the compounds with substituents at

344

one side (3- or 6-position phenyl) were selected and purchased for the following 13



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

345

biological evaluation. 26 compounds listed in Table 1 mainly focus on R1

346

modifications at 6-position of the phenyl group. A preliminary enzymatic inhibitory

347

test of these compounds was first carried out. We can observe that almost half of the

348

tested compounds inhibited over 95% TIE-2 activity at the concentration of 5 µg/mL.

349

However, there were still eight compounds exhibiting low percentage inhibitory

350

activity, so the dose-dependent effects of these compounds were not determined. The

351

inhibitory IC50 values varied significantly among the determined compounds, ranging

352

from 0.169 µM to 60.95 µM, which implies that substitutions at different positions of

353

R1 have a great influence on the activity of the derivatives.

354

Compound TP-S1-44 is the most potent inhibitor among the 26 compounds in

355

Table 1 with an IC50 value of 0.169 µM, which is almost 10-fold more active than

356

compound TP-S1-42 (IC50 = 1.267 µM) without any substitution at R1 and/or R2.

357

MM/GBSA has been validated as a successful method to compare the binding

358

affinities of related inhibitors and to gain rational insights into their differences.66, 67

359

Therefore, in order to understand the difference of the binding mechanism among

360

these derivatives, the MM/GBSA binding free energy calculations and decomposition

361

analysis based on MD simulations were carried out. According to the residue-inhibitor

362

interaction spectrum of the dominating amino acids depicted in Figure 6, the

363

contributions of residues Val838 and Ile902 determine the higher binding affinity of

364

compound TP-S1-44 over TP-S1-42 and 46. Though hydrophobic contacts are

365

observed in the binding of TP-S1-42 to residues Val838 and Lue971, the interactions

366

are relatively weak. Introducing the methoxy group at 2-R1 position leads to the

367

interaction between the R1 phenyl ring and Lue876, which is not found in the binding

368

of TP-S1-42 and 46. Incorporation of strong electron withdrawing groups (2-F or

369

2-CF3) at R1 phenyl dramatically decreases compound activity. However, the effect of

370

the 3-F or 3-Cl modification at R1 phenyl is not significant. In contrast to TP-S1-44,

371

substitution of the methoxy group at 3-R1 position opposes binding. For the

372

modifications at 4-R1 position, the impact of introducing the methoxy, Cl, F or ethoxy

373

group is quite limited, but modifications with the Br, ethyl, propyl or isopropyl group

374

significantly decrease activity. The replacement of two or more positions at R1 phenyl 14


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375

is not favorable to inhibitor binding with at least 40 fold decline in activity compared

376

to TP-S1-42, which can be roughly reflected by their poor performance in docking

377

simulations (not shown).

378

Chemical modifications of moieties at R2 phenyl are listed in Table 2. We can

379

find that the compounds containing the methoxy substitution at 3-R2 position show

380

the best activity, e.g. TP-S1-73 and 85. Compound activity is significantly influenced

381

when the trifluoromethyl group is introduced to either 3-R2 or 4-R2 position

382

(compounds TP-S1-75 and 84). Interestingly, the activity of TP-S1-79 with the F

383

substituted at 4-R2 position, which is also a strong electron withdrawing group, is

384

much higher compared with TP-S1-84. In order to explore the structural mechanism

385

underlying the difference in compound activity, the binding modes of compounds

386

TP-S1-73, 79 and 84 were analyzed and the contributions of the important residues

387

for binding were also calculated and compared. As plotted in Figure 7, the R2 phenyl

388

ring of compound TP-S1-73 can form hydrophobic interactions with residues Val838

389

and Lue971. Similar interactions can also be observed in the binding of compounds

390

TP-S1-79 and 84, indicating that the decreased activity of TP-S1-84 may not be

391

caused by the fluctuations of residues Val838 and Lue971. This phenomenon is also

392

demonstrated by the energy decomposition analysis depicted in Figure 7D, where the

393

contributions of residues Val838 and Lue971 vary slightly upon binding to the three

394

compounds. By comparing the difference of the contribution of each amino acid, we

395

can observe that the impaired interactions with residues Gly831, Lys855 and Ile902

396

determine the inferior binding affinity of compound TP-S1-84. Though the inhibitory

397

activities of compounds TP-S1-73 and 79 are similar, several key interactions for

398

binding are strikingly different. The interactions of compound TP-S1-73 with residues

399

Lys855, Ile886 and Ile902 are much stronger than those of TP-S1-79. For the binding

400

of TP-S1-79, the contributions of residues Gly831, Val838 are greater. Of the other

401

molecules in Table 2, compounds TP-S1-81 and 86 also exhibit superior inhibitory

402

activities with IC50 values of 0.301 nM and 0.283 nM, respectively.

403

In addition, several derivatives of the core structure of compound TP-S1-31 with

404

the pyridine group replaced by the phenyl ring were also retrieved from the Specs 15



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

405

database. As we can see in Table 3, the IC50 values of most compounds are higher

406

than 2 µM, except for compound TP-S1-90 with Cl at the 3 position of R3 phenyl.

407

This phenomenon, on the contrary, implies the significance of the pyridine group to

408

the binding of TP-S1-31 to TIE-2, where a hydrogen bond is formed between the

409

residue Asp982 and pyridine.

410

Because of the limitation of substructure search methodology, the diversity of the

411

modified compounds is far from sufficient and needs to be further optimized.

412

According to the discussions above, some crucial interaction patterns between these

413

inhibitors and TIE-2 are determined. Most binding pocket residues of TIE-2 are

414

nonpolar, especially the regions around R1 and R2 phenyl, so the hydrophobic

415

interactions with the surrounding residues (e.g. Val838, Leu876 and Leu971) should

416

be maintained. Not only the phenyl groups but also various aromatic rings can be

417

incorporated into both 3- and 6-position of the [1,2,4]triazolo[3,4-b][1,3,4]thiadiazole

418

core structure. In spite of the hydrophobic property of the binding site, the regions

419

close to the flank of the core structure (e.g. residues Lys855 and Asp982) are

420

hydrophilic. Thus introducing moieties containing hydrogen donors or acceptors into

421

the core structure, which is not realized by substructure search, might benefit to form

422

H-bond interactions. The inhibitors identified and the information presented in this

423

work will help to guide further design and optimization of more potent inhibitors.

424

Conclusions

425

Due to the critical role of ANG/TIE-2 signaling pathway in the angiogenesis process

426

of preliminary vessels, TIE-2 inhibitors have gained significant attention and are now

427

believed to be potential preventative and therapeutic agents for a variety of

428

life-threatening diseases. Several candidate inhibitors of TIE-2 have been reported by

429

pharmaceutical companies in recent years, and some agents have entered clinical trials

430

for the treatment of a few cancer types. However, the development of selective

431

small-molecule inhibitors of TIE-2 is also quite limited. In this work, we developed a

432

structure-based virtual screening approach to discover TIE-2 inhibitors, and have

433

successfully identified several active compounds. Although most of the hit 16


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434

compounds discovered showed inhibitory activity in moderate to low micromolar

435

range, the scaffold of each inhibitor is novel and thereby represents a potential starting

436

structure for further drug development. Based on substructure search technology,

437

preliminary optimization and SAR analysis of the core structures of both compound

438

TP-S1-30 and 31 were performed, leading to the discovery of several sub-100nM

439

inhibitors. According to the binding properties of the optimized inhibitors from MD

440

simulations and MM/GBSA predictions, the relationship between inhibitor structure

441

and biological activity was analyzed at length. The detailed mechanism of action of

442

these compounds binding to TIE-2 and the information obtained from SAR analysis

443

justify further investigation to improve the inhibitory activity of these candidate

444

compounds.

445

Associated Content

446

Supporting Information

447

Supporting Information Available: ** Chemical information and 2-D structures of the

448

purchased compounds from Specs database.** This material is available free of

449

charge via the Internet at http://pubs.acs.org.

450

Acknowledgments

451

This study was supported by the National Science Foundation of China (21575128).

452

References

453 454 455 456 457 458 459 460 461 462 463 464

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2

(Vegf-R/Tie-2)

Inhibitor

11-(2-Methylpropyl)-12,

13-Dihydro-2-Methyl-8-(Pyrimidin-2-Ylamino)-4 H-Indazolo [5, 4-a] Pyrrolo [3, 4-C] Carbazol-4-One (Cep-11981): A Novel Oncology Therapeutic Agent. J. Med. Chem. 2012, 55, 903-913. 18


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553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596

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11-(2-Methylpropyl)-12,13-Dihydro-2-Methyl-8-(Pyrimidin-2-Ylamino)-4h-Indazolo[5,4-a]Pyrrolo[3,4-C ]Carbazol-4-One (Cep-11981): A Novel Oncology Therapeutic Agent. J. Med. Chem. 2012, 55, 903-913. 45. Acd/Adme Suite 5.0, Advanced Chemistry Development Inc., Toronto, Canada. 2011. 46. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J., Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings. Adv. Drug Delivery Rev. 2012, 64, Supplement, 4-17. 47. Oprea, T. I., Property Distribution of Drug-Related Chemical Databases. J. Comput.-Aided Mol. Des. 2000, 14, 251-264. 48. Walters, W. P.; Namchuk, M., Designing Screens: How to Make Your Hits a Hit. Nat. Rev. Drug Discovery 2003, 2, 259-266. 49. Case, D.; Babin, V.; Berryman, J.; Betz, R.; Cai, Q.; Cerutti, D.; Cheatham III, T.; Darden, T.; Duke, R.; Gohlke, H., Amber 14 (University of California). San Francisco 2014. 50. Frisch, M.; Trucks, G.; Schlegel, H.; Scuseria, G.; Robb, M.; Cheeseman, J.; Montgomery Jr, J.; Vreven, T.; Kudin, K.; Burant, J., Gaussian 03, Revision D. 01. Gaussian, Inc.: Wallingford, CT 2004. 51. Bayly, C. I.; Cieplak, P.; Cornell, W. D.; Kollman, P. A., A Well-Behaved Electrostatic Potential Based Method Using Charge Restraints for Deriving Atomic Charges: The Resp Model. J. Phys. Chem. 1993, 97, 20


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10269-10280. 52. Wang, J. M.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A., Development and Testing of a General Amber Force Field. J. Comput. Chem. 2004, 25, 1157-1174. 53. Duan, Y.; Wu, C.; Chowdhury, S.; Lee, M. C.; Xiong, G. M.; Zhang, W.; Yang, R.; Cieplak, P.; Luo, R.; Lee, T.; Caldwell, J.; Wang, J. M.; Kollman, P., A Point-Charge Force Field for Molecular Mechanics Simulations of Proteins Based on Condensed-Phase Quantum Mechanical Calculations. J. Comput. Chem. 2003, 24, 1999-2012. 54. Darden, T.; York, D.; Pedersen, L., Particle Mesh Ewald: An W Log (N) Method for Ewald Sums in Large Systems. J. Chem. Phys. 1993, 98, 10089-10092. 55. Ryckaert, J. P.; Ciccotti, G.; Berendsen, H. J. C., Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of< I> N-Alkanes. J. Comput. Phys. 1977, 23, 327-341. 56. Wang, J. M.; Hou, T. J.; Xu, X. J., Recent Advances in Free Energy Calculations with a Combination of Molecular Mechanics and Continuum Models. Curr. Comput.-Aided Drug Des. 2006, 2, 287-306. 57. Kollman, P. A.; Massova, I.; Reyes, C.; Kuhn, B.; Huo, S.; Chong, L.; Lee, M.; Lee, T.; Duan, Y.; Wang, W., Calculating Structures and Free Energies of Complex Molecules: Combining Molecular Mechanics and Continuum Models. Accounts Chem. Res. 2000, 33, 889-897. 58. Onufriev, A.; Bashford, D.; Case, D. A., Exploring Protein Native States and Large-Scale Conformational Changes with a Modified Generalized Born Model. Proteins: Struct., Funct., Bioinf. 2004, 55, 383-394. 59. Weiser, J.; Shenkin, P. S.; Still, W. C., Approximate Atomic Surfaces from Linear Combinations of Pairwise Overlaps (Lcpo). J. Comput. Chem. 1999, 20, 217-230. 60. Hou, T. J.; Li, Y. Y.; Wang, W., Prediction of Peptides Binding to the Pka Rii Alpha Subunit Using a Hierarchical Strategy. Bioinformatics 2011, 27, 1814-1821. 61. Hou, T.; Li, N.; Li, Y.; Wang, W., Characterization of Domain-Peptide Interaction Interface: Prediction of Sh3 Domain-Mediated Protein-Protein Interaction Network in Yeast by Generic Structure-Based Models. J. Proteome Res. 2012, 11, 2982. 62. Hou, T.; Zhang, W.; Case, D. A.; Wang, W., Characterization of Domain-Peptide Interaction Interface: A Case Study on the Amphiphysin-1 Sh3 Domain. J. Mol. Biol. 2008, 376, 1201-1214. 63. Hou, T. J.; Xu, Z.; Zhang, W.; McLaughlin, W. A.; Case, D. A.; Xu, Y.; Wang, W., Characterization of Domain-Peptide Interaction Interface. Mol. Cell. Proteomics. 2009, 8, 639-649. 64. Semones, M.; Feng, Y.; Johnson, N.; Adams, J. L.; Winkler, J.; Hansbury, M., Pyridinylimidazole Inhibitors of Tie2 Kinase. Bioorg. Med. Chem. Lett. 2007, 17, 4756-4760. 65. Willett, P., Similarity-Based Virtual Screening Using 2d Fingerprints. Drug Discovery Today 2006, 11, 1046-1053. 66. Shen, M.; Zhou, S.; Li, Y.; Pan, P.; Zhang, L.; Hou, T., Discovery and Optimization of Triazine Derivatives as Rock1 Inhibitors: Molecular Docking, Molecular Dynamics Simulations and Free Energy Calculations. Mol. BioSyst. 2013, 9, 361-374. 67. Sun, H.; Li, Y.; Tian, S.; Xu, L.; Hou, T., Assessing the Performance of Mm/Pbsa and Mm/Gbsa Methods. 4. Accuracies of Mm/Pbsa and Mm/Gbsa Methodologies Evaluated by Various Simulation Protocols Using Pdbbind Data Set. Phys. Chem. Chem. Phys. 2014, 16, 16719-16729.

639 21



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

640

Legend of the Figures

641 642

Figure 1. Workflow of the structure-based virtual screening.

643 644

Figure 2. Distributions of the glide docking scores of the inhibitor decoy and

645

non-inhibitor decoy dataset for the two crystal complexes of TIE-2. (A) PDB entry:

646

2WQB, Glide HTVS docking, (B) PDB entry: 2WQB, Glide SP docking, (C) PDB

647

entry: 2WQB, Glide XP docking, (D) PDB entry: 3L8P, Glide HTVS docking, (E)

648

PDB entry: 3L8P, Glide SP docking, (F) PDB entry: 3L8P, Glide XP docking;

649 650

Figure 3. Inhibitory activity of the 41 hit compounds from VS campaign at the

651

concentration of 25 µg/mL. All values are the average of n ≥ 2 ± standard deviation.

652

The plots of the compounds with < -100% inhibitory activity were not fully presented.

653 654

Figure 4. Dose-dependent analysis of TIE-2 inhibition by compound TP-S1-18 (A),

655

TP-S1-30 (B), TP-S1-31 (C), and TP-S1-41 (D). For determination of IC50 values, the

656

resulting inhibitory activity was plotted against the concentration of inhibitor, and the

657

data was fitted to a dose-response curve with a variable slop.

658 659

Figure 5. Schematic representation of the interactions between TIE-2 and four active

660

compounds. (A) TP-S1-18, (B) TP-S1-30, (C) TP-S1-31, and (D) TP-S1-41. All the

661

binding complexes are predicted by Glide XP docking using 3L8P crystal structure as

662

TIE-2 template.

663 664

Figure 6. 2-D schematic diagrams of the binding patterns for compound (A)

665

TP-S1-42, (B) TP-S1-43, and (C) TP-S1-44. (D) Contribution of the important

666

residues for ligand binding. All structures are average conformations generated from

667

the last 3 ns snapshots of each MD system. The averaged energetic contributions of

668

the dominating residues from MM/GBSA decomposition analysis are also plotted. 22


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669 670

Figure 7. 2-D schematic diagrams of the binding patterns for compound (A)

671

TP-S1-73, (B) TP-S1-79, and (C) TP-S1-84. (D) Contribution of the important

672

residues for ligand binding. All structures are average conformations generated from

673

the last 3 ns snapshots of each MD system. The averaged energetic contributions of

674

the dominating residues from MM/GBSA decomposition analysis are also plotted.

675 676

23



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Page 24 of 32

Table 1. TIE-2 Kinase Inhibitory Activity @ 5 µg/mL and Half-Maximal Inhibitory Concentrations (IC50) for TP-S1-42 ~ 67 Analogues with R1 Modifications

a

Compd

R1

R2

Kinase Inhibition (%) @ 5 µg/mL (±SD) a

IC50, µM (±SD) a

TP-S1-42 TP-S1-43 TP-S1-44 TP-S1-45 TP-S1-46 TP-S1-47 TP-S1-48 TP-S1-49 TP-S1-50 TP-S1-51 TP-S1-52 TP-S1-53 TP-S1-54 TP-S1-55 TP-S1-56 TP-S1-57 TP-S1-58 TP-S1-59 TP-S1-60 TP-S1-61 TP-S1-62 TP-S1-63 TP-S1-64 TP-S1-65

-H 2-F 2-OCH3 2-I 2-OCH2CH3 2-CF3 2-Cl 3-Cl 3-F 3-OCH3 4-OCH3 4-Cl 4-F 4-OCH2CH3 4-Br 4-CH2CH3 4-CH2CH2CH3 4-CH(CH3)2 3-Cl, 4-F 3, 4-OCH3 3, 4, 5-OCH3 3, 5-CH3 2, 4-CH3 2, 3, 4, 5-F

-H -H -H -H -H -H -H -H -H -H -H -H -H -H -H -H -H -H -H -H -H -H -H -H

97.33 ± 0.45 17.57 ± 4.49 99.59 ± 0.16 97.74 ± 0.34 97.74 ± 1.07 48.41 ± 2.27 97.02 ± 0.96 99.81 ± 1.04 98.45 ± 0.21 82.27 ± 1.55 98.44 ± 0.07 99.88 ± 3.03 98.93 ± 1.18 94.26 ± 0.14 16.90 ± 3.41 12.08 ± 4.35 20.62 ± 0.77 14.42 ± 0.31 45.14 ± 0.95 26.31 ± 3.90 38.83 ± 2.35 15.77 ± 4.21 42.33 ± 0.32 47.75 ± 2.33

1.267 ± 0.129 NT b 0.169 ± 0.055 2.208 ± 0.059 1.877 ± 0.066 44.36 ± 6.59 8.478 ± 0.401 4.826 ± 0.793 0.97 ± 0.093 22.93 ± 2.56 1.208 ± 0.102 0.822 ± 0.028 4.484 ± 0.38 4.388 ± 0.1 NT b NT b NT b NT b 41.62 ± 3.82 NT b NT b NT b 47.31 ± 25.22 60.95 ± 16.29

TP-S1-66

97.59 ± 1.55

0.768 ± 0.006

TP-S1-67

89.63 ± 2.24

6.739 ± 0.736

All values are the average of n ≥ 2 ± standard deviation. bNT, not tested. 24


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677 Table 2. TIE-2 Kinase Inhibitory Activity @ 5 µg/mL and Half-Maximal Inhibitory Concentrations (IC50) for TP-S1-68 ~ 87 Analogues with R2 Modifications

a

Compd

R1

R2


IC50, µM (±SD) a

TP-S1-68 TP-S1-69 TP-S1-70 TP-S1-71 TP-S1-72 TP-S1-73 TP-S1-74 TP-S1-75 TP-S1-76 TP-S1-77 TP-S1-78 TP-S1-79 TP-S1-80 TP-S1-81 TP-S1-82 TP-S1-83 TP-S1-84 TP-S1-85 TP-S1-86 TP-S1-87

-H -H -H -H -H -H -H -H -H -H -H -H -H -H -H -H -H -H -H -H

2-Cl 2-OCH3 2-Br 3-Cl 3-Br 3-OCH3 3, 3-CH3 3-CF3 3-F 3-CH3 4-Cl 4-F 4-Br 4-CH3 4-N(CH3)2 4-OCH3 4-CF3 3, 4, 5-OCH3 3, 4-OCH3 2, 4-Cl

101.62 ± 1.01 76.15 ± 0.11 6.09 ± 1.91 54.81 ± 0.06 50.12 ± 8.93 97.84 ± 0.89 48.67 ± 3.44 8.26 ± 1.04 37.05 ± 2.77 76.78 ± 3.12 34.24 ± 5.35 96.69 ± 3.22 45.98 ± 0.61 99.74 ± 4.11 91.61 ± 2.14 48.86 ± 1.46 20.03 ± 2.96 97.15 ± 4.78 98.6 ± 1.54 92.9 ± 6.09

3.650 ± 0.35 30.88 ± 1.29 NT b 26.25 ± 1.47 46.50 ± 2.14 0.177 ± 0.049 49.63 ± 10.85 NT b 55.90 ± 41.69 32.75 ± 0.39 NT b 0.436 ± 0.057 9.029 ± 1.786 0.301 ± 0.03 0.464 ± 0.04 35.72 ± 3.48 NT b 0.159 ± 0.006 0.283 ± 0.039 11.70 ± 1.52

All values are the average of n ≥ 2 ± standard deviation. bNT, not tested.

678 679 680 681

25



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Table 3. TIE-2 Kinase Inhibitory Activity @ 5 µg/mL and Half-Maximal Inhibitory Concentrations (IC50) for TP-S1-88 ~ 96 Analogues with R3 Phenyl Modifications

a

Compd

R3


IC50, µM (±SD) a

TP-S1-67 TP-S1-88 TP-S1-89 TP-S1-90 TP-S1-91 TP-S1-92 TP-S1-93 TP-S1-94 TP-S1-95 TP-S1-96

-H 2-OCH3 2-Br 3-Cl 3-F 3-CH3 4-N(CH3)2 4-OCH3 4-CH3 4-CF3

89.63 ± 2.24 87.45 ± 4.77 -169.15 ± 10.55 100.2 ± 1.59 52.21 ± 1.55 99.15 ± 0.38 94.09 ± 0.92 52.93 ± 1.9 77.64 ± 9.08 67.74 ± 1.45

6.739 ± 0.736 2.691 ± 0.161 NT b 0.149 ± 0.013 29.60 ± 2.88 2.589 ± 0.204 6.548 ± 0.246 9.393 ± 1.095 2.226 ± 0.112 30.05 ± 1.379

All values are the average of n ≥ 2 ± standard deviation. bNT, not tested.

682 683

26


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684 685

Figure 1

686 687

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

688 689

Figure 2

690 691

28


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692 693

Figure 3

694 695

696 697

Figure 4

698

29



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

699 700

Figure 5

701

30


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702 703

Figure 6

704

31



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

705 706

Figure 7

707

32


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Identification and Preliminary SAR Analysis of Novel Type-I Inhibitors

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