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Suppression of Tumor Growth and Metastases by Targeted Intervention in Urokinase Activity with Cyclic Peptides Dong Wang, Yongshuai Yang, Longguang X. Jiang, Yu Wang, Jinyu Li, Peter A. Andreasen, Zhuo Chen, Mingdong Huang, and Peng Xu J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.8b01908 • Publication Date (Web): 01 Feb 2019 Downloaded from http://pubs.acs.org on February 3, 2019

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Journal of Medicinal Chemistry

Suppression of Tumor Growth and Metastases by Targeted Intervention in Urokinase Activity with Cyclic Peptides

Dong Wang1,2,#, Yongshuai Yang1,2,3,#, Longguang Jiang4, Yu Wang3, Jinyu Li4, Peter A. Andreasen5, Zhuo Chen1, Mingdong Huang1,4*, Peng Xu1*

1

State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of

Matter, Chinese Academy of Sciences,155 West Yangqiao Road, Fuzhou, Fujian,China,350002 2

University of Chinese Academy of Sciences, No.19 (A) Yuquan Road, Shijingshan District,

Beijing, China,100049 3

College of Life Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002,

P.R. China 4 College

5

of Chemistry, Fuzhou University, Fuzhou, Fujian, 350116, P.R. China

Department of Molecular Biology and Genetics, Aarhus University, Aarhus C, 8000, Denmark

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ABSTRACT Urokinase-type plasminogen activator (uPA) is a diagnostic marker for breast and prostate cancers recommended by American Society for Clinical Oncology and German Breast Cancer Society. Inhibition of uPA was proposed as an efficient strategy for cancer treatments. In this study, we report peptide-based uPA inhibitors with high potency and specificity comparable to monoclonal antibodies. We revealed the binding and inhibitory mechanisms by combining crystallography, molecular dynamic simulation, and other biophysical and biochemical approaches. Besides, we showed that our peptides efficiently inhibited the invasion of cancer cells via intervening with the processes of the degradation of extracellular matrices. Furthermore, our peptides significantly suppressed the tumor growth and the cancer metastases in tumor-bearing mice. This study demonstrates that these uPA peptides are highly potent anti-cancer agents, and reveals the mechanistic insights of these uPA inhibitors, which can be useful for developing other serine protease inhibitors.

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INTRODUCTION Cancer metastasis is the pathological process of the spreading of cancer cells from the primary sites to distant sites.1 The metastatic property critically contributes to the high mortality, the poor diagnosis, and the low cure rate of cancer in clinics.2, 3 Proteolytic enzymes are widely involved in the processes of cancer development by performing the extracellular and intracellular proteolysis. The extracellular proteolysis directly corresponds to the metastatic processes of cancer cells, including the degradation of the basement membranes (BM) and extracellular matrices (ECM), and the invasion of cancer cells into lymphatic and blood vessels.4, 5 Plasminogen activation is the process of the conversion of plasminogen to plasmin in various physiological and pathological settings, including tissue remodeling, fibrinolysis, and cancer progression.6,

7

Plasminogen activation system includes plasmin, tissue- and urokinase-type

plasminogen activators (tPA and uPA), plasminogen activator inhibitor -1 and -2 (PAI-1 and PAI-2), α2-antiplasmin, and a membrane anchored uPA receptor. The plasminogen activation in the extracellular space is mainly triggered by uPA, which is strictly regulated by its physiological inhibitor, PAI-1, in normal conditions.8 The elevated expression levels of uPA and PAI-1 in breast cancers corresponds to the poor prognosis and the metastasis of cancer9,

10,

and such

measurements were recommended by the American Society for Clinical Oncology11 and the German Breast Cancer Society12 to determine the risk of breast cancer metastasis. The proteolytic function of uPA closely associates many carcinogenic pathways. The aberrant uPA activity in cancer tissues leads to the over-generation of plasmin, which has broad specificity and degrades ECM and BM by itself or by triggering the cascades of matrix metalloproteases (MMPs).13 The degradation of ECM and BM facilitates cancer cells detaching from the primary sites and invading 3 / 45

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the surround tissues. In addition, plasmin activates multiple growth factors, like TGFβ, promoting cancer proliferation.14 There are also evidences showing that uPA and PAI-1 facilitate cancer cells escaping from the apoptotic pathway and promote the survival of cancer cells.15 Moreover, previous studies showed that the uPA-triggered plasminogen activation relates to the angiogenesis in cancer tissues.16 Thus, uPA plays the critical roles in the processes of cancer proliferation and metastases. In addition to the cancer processes, uPA has also been related to the pathology of rheumatoid arthritis (RA).17, 18 Upregualtion of uPA expression was observed in the synovial membranes of RA patients, and the expression level was linked with the severity.19 uPA-deficient mice demonstrated amelioration of macroscopic and histological inflammatory joint disease in multiple collagen-induced arthritis (CIA) models. 20, 21 The proliferation and invasion of synoviocytes from RA patients were suppressed by monoclonal antibody of uPA.22 A peptide-based synthetic uPA inhibitor down regulated the incidence of arthritis by 50% in a uPA-induced arthritis mice model.23 Thus, uPA inhibitors are of great interest as the therapeutics in the treatments for rheumatoid arthritis. The development of uPA inhibitors can be dated from the mid-1960’s.24 In the following 50 years, numerous inhibitors targeting uPA have been developed in the forms of monoclonal antibodies, small molecule compounds, polypeptides, etc.25 Katz et al. developed a halogenated small-molecule inhibitor which showed a Ki value of 11 nM for a uPA variant.26 Giranda and his colleague developed a series of 2-naphthamidine analogues, the best of which inhibited uPA with the Ki value of 0.62 nM.27 Frederickson et al. reported developed a series of orally available uPA inhibitors based on the (R)-mexiletine based on the structure-based rational design. The best 4 / 45

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inhibitor of this series showed the Ki value of 72 nM.28 The only successful clinical use of uPA inhibitors thus far is Mesupron®, the prodrug of an uPA inhibitor WX-UK1 (Ki = 0.65 μM), which is now in Phase II clinical trial for the therapies of pancreatic and breast cancers in combination with chemotherapy of Gemzar (gemcitabine).29 However, similar to many other small-molecule uPA inhibitors, WX-UK1 is not specific as it showed comparable Ki values to other homologous proteases, including plasmin (1.46 μM), thrombin (0.5 μM), coagulation factor Xa (0.98 μM), tPA (2.51 μM), and matriptase (0.37 μM).30 The off-target effects of enzyme inhibitors can cause systemic toxicities. One famous example is the failure of the clinical trial of the broad-spectrum MMPs inhibitors.31 Members of MMPs have varied physiological and pathological functions, including pro-cancer and anti-cancer effects dependent on contexts.32 Non-specific inhibition of multiple MMPs did not achieve anti-cancer effects in the clinical trials, and also caused systemic toxicities. Thus, specificity is an important factor for the development of uPA inhibitors. Notably, all small-molecule inhibitors developed up to date targets human uPA. No specific small molecular inhibitors to murine uPA is reported. Murine uPA is quite different than human uPA structurally, which post a major challenge for the clinical translation of uPA targeting strategy because mouse model is typically used in preclinical studies. Small-molecule inhibitors normally lack of sufficient specificity. Although monoclonal antibodies are highly potent and specific, the high expense and the poor pharmacokinetics limit the clinical applications of antibodies.33 Peptides that consist of 10-20 amino acids are of great interest as drug candidates for combining the advantages of small molecules (low cost and high stability) and antibody drugs (high potency and specificity).34 Compared to small-molecule inhibitors, the higher specificity of peptide-based inhibitors seems due to the much larger interaction surfaces 5 / 45

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with the target proteins. In our previous study, we reported a 10-mer cyclic peptide inhibitor of murine uPA (muPA), mupain-1 (Ac-CPAYSRYLDC-amide), with a Ki value of 550 ± 80 nM, which was originally isolated from a phage-display library using the bait of muPA.35 Next, by screening a peptide library varying peptide sequence around residues 8 and 9, we generated mupain-1-IG (Ac-CPAYSRYIGC-amide) with much improved potency (Ki = 20 ± 4 nM) (Figure 1).36 Mupain-1-IG has Ile and Gly in the residues 8 and 9 in contrast to the Leu8 and Asp9 of mupain-1. Thus, we named mupain-1 and mupain-1-IG as LD-1 and IG-1, respectively, in the following text. In this study, we further improved the inhibitory potencies of the peptide inhibitors and generated IG-2 by exposing the N-terminus amine and C-terminus carbonyl groups (Figure 1). IG-2 (NH2-CPAYSRYIGC-COOH) inhibited muPA with a Ki value of 6.8 ± 1.1 nM, 81-fold better than that of the original LD-1. We next determined the crystal structures of IG-1 and IG-2 in complexes with muPA, and further performed all-atom molecular dynamics (MD) simulations to investigate the dynamics of the peptide-protease interactions. Together with other biophysical approaches, we revealed the binding and inhibitory mechanisms of these peptide inhibitors. In addition, we evaluated the effects of these peptides on the processes of invasion, migration, and growth of cancer cells in vitro. Furthermore, we demonstrated that the peptides suppressed the growth and metastases of colon cancer in vivo using a subcutaneously implanted model and a lung carcinoma metastatic model.

RESULTS Inhibitory potency and specificity of peptide inhibitors 6 / 45

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We

previously

reported

that

LD-1

(Ac-CPAYSRYLDC-amide)

and

IG-1

(Ac-CPAYSRYIGC-amide) inhibited muPA with the Ki values of 550 ± 80 nM and 20 ± 4 nM, respectively. In this study, we removed the chemical modification of the IG-1 peptide to free up the corresponding amine and carbonyl termini to obtain IG-2 with the sequence of NH2-CPAYSRYIGC-COOH. The IG-2 peptide demonstrated improved inhibitory potency with a Ki value of 6.81 ± 1.1 nM (Table 1). In addition to the high inhibitory potency, IG-2 demonstrated excellent specificity comparable to that of monoclonal antibodies. We determined the Ki values of the three peptides inhibiting other 13 murine and human serine proteases (Table 1). Although these peptide inhibitors inhibited muPA with the Ki values in the nM range, they did not show clear inhibition (Ki > 25,000 nM) against other proteases in the plasminogen activation system (tPA and plasmin) and the coagulation system (thrombin, activated coagulation factor XI, and activated protein C), indicating high specificity of IG-2.

Specificity of IG-2 revealed by crystallographic study To understand the binding and inhibitory mechanisms of these peptide inhibitors, we determined the crystal structures of IG-1 and IG-2 in complexes with the catalytic domain (serine protease domain, SPD) of muPA (muPA-SPD), respectively. The two peptides demonstrated mostly identical conformations when bound to uPA (RSMD = 0.167 Å) (Figure 2A), although IG-2 (Ki = 6.81 ± 1.1 nM) showed 3-fold higher potency than IG-1 (Ki = 20 ± 4 nM). We used the crystal structure of the IG-2:muPA complex as the example to delineate the binding mechanism. In this structure, IG-2 blocked the substrate-binding site of uPA and formed extensive interactions with uPA surface (Figure 2B). The backbone conformation of the 10-mer peptide IG-2 was 7 / 45

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constrained by the four intramolecular hydrogen bonds. IG-2 bound to uPA mostly through its residues 4-7 (numbering shown in Figure 1). In contrast, the residues 1-3 and 8-10 oriented away from the uPA surface and had few interactions with uPA. The only positively charged residue, Arg6, was the P1 residue, which inserted deeply into the S1 pocket, forming stable hydrogen bonds and salt bridge network with uPA. In addition, the carboxyl group at C-terminus formed a salt bridge with the amine side chain of Lys192 of uPA (chymotrypsin numbering). Serine proteases in the S1 clan show structural similarity in the catalytic domains, and have preference to cleave the peptide bonds at the C-terminus of positive residues (lysine or arginine). 37

The S1 pockets of serine proteases are highly identical, only differ in residue 190.38

Small-molecule competitive inhibitors normally target the S1 specific pocket and have limited contacts with other regions. This is the main reason why small-molecule uPA inhibitors suffer from the low specificity. In contrast, our peptides made extensive contacts not only with the S1 pocket but also with the solvent-exposed loops, which are highly varied in sequences and structures among different serine proteases, and thus are important sites to render specificity of inhibitors.

High potency of IG-2 revealed by MD simulations Our crystal structures clearly revealed the binding modes of peptide inhibitors to uPA, but failed to explain the following two questions: 1) IG-2 (Ki = 6.81 ± 1.1 nM) and IG-1 (Ki = 20 ± 4 nM) bound to uPA with identical conformation, but IG-2 demonstrated 3-fold higher inhibitory potency to uPA; 2) IG-1 (Ac-CPAYSRYIGC-amide, Ki = 20 ± 4 nM) showed 27-fold enhanced affinity to uPA compared to that of LD-1 (Ac-CPAYSRYLDC-amide, Ki = 550 ± 80 nM), 8 / 45

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although they only differ at residues 8 and 9 (the italic and underlined residues). To answer these questions, we performed thorough (1200 ns long) MD simulations based on the structures of the peptide:uPA complexes to investigate the dynamics of the peptide-enzyme interactions. The plots of the time-dependent root-mean-square deviation (RMSD) values of the peptides showed that IG-1 and IG-2 reached equilibrium at ~700 ns, while the values of LD-1 varied significantly throughout the whole 1200 ns MD simulation (Figure 2C), indicating the conformation of the latter peptide in the complex may be less stable than the others. Moreover, the RMSD values of IG-2 (SD = 0.12 Å, Ki = 6.81 ± 1.1 nM) fluctuates more than those of IG-1 (SD = 0.20 Å, Ki = 20 ± 4 nM) and LD-1 (SD = 0.28 Å, Ki = 550 ± 80 nM) do, consistent with the tightest binding of IG-2. The differences between the MD-representative structures and the crystal structures of IG-1:uPA or IG-2:uPA complexes mainly located on the residues 9 and 10 (Table S2 and S3), but not the uPA binding residues (residue 4-7). In contrast, the MD-representative structure of LD-1 demonstrated conformation similar to the crystal structures of IG-1 and IG-2 including the residues 9 and 10 (Figure S8). By aligning the crystal structure and the representative MD structure of IG-2:muPA complex, we found that the Cα atoms of Gly9 and Cys10 shifted by 3.8 Å and 2.7 Å, respectively, while other residues remained unchanged (Figure 2D). Similarly, the representative MD structures of LD-1, IG-1, and IG-2 showed mostly identical conformation at residues 1-8, while differed only at residues 9 and 10. In the representative MD structures of the LD-1:uPA complex, the Cα atom of the residue 9 showed a 3.6 Å shift from to IG-1:uPA complex, which suggests that the carboxyl side chain of Asp9 obstructs peptide from forming the low-energy conformation (Figure 2E). Besides, the side-chain carboxyl group of Asp9 on LD-1 9 / 45

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was solvent-exposed and had no contribution to the peptide:uPA interaction. In contrast, Gly9, with lower steric hindrance and higher flexibility, facilitated IG-1 searching and reaching a strong binding conformation, explaining why IG-1 (Ac-CPAYSRYIGC-amide, Ki = 20 ± 4 nM) showed higher affinity to uPA than LD-1 (Ac-CPAYSRYLDC-amide, Ki = 550 ± 80 nM) did. The MD dynamic study also showed that the Cα atom of Cys10 shifted by 2.4 Å between the IG-1:uPA and the IG-2:uPA complexes (Figure 2F). This shift induced additional salt bridges between IG-2 and the amine side chain of Lys192 in uPA (Figure 2F), strengthening the IG-2:uPA interaction. In addition, the Lys192 appears to be important for the proteolytic activity of muPA, as the catalytic activity of muPA was significantly reduced once the Lys192 was mutated to alanine (Figure S9). Thus, the MD simulation, together with the crystal structure studies, explained why IG-2 demonstrated 3-fold higher affinity in contrast to IG-1.

Further validation of uPA inhibition by ITC and SPR methods The thermodynamic and kinetic parameters of the peptide-uPA binding were determined with the isothermal titration calorimetry (ITC) and surface plasmon resonance (SPR), respectively (Table 2 and 3). The KD values determined by ITC and SPR were mostly consistent with the Ki values determined via the chromogenic assays, providing further validation of our data. ITC data (Table 3 and Figure S10) showed that all peptides bound to uPA with an entropic penalty, suggesting the reduced flexibility of peptides upon binding to uPA. The lower KD values corresponded to the higher ΔH values, which were partially compensated by the entropic penalty, indicating that the peptide:uPA binding is enthalpy-driven rather than the entropy-driven. SPR data (Table 2) showed that the higher affinity of IG-1, compared to that of LD-1, was due to the 10 / 45

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higher association rate (kon) and the lower dissociation rate (koff). However, the improved affinity going from IG-1 to IG-2 was solely due to the lower dissociation rate, which was consistent with the tighter binding of IG-2 due to the extra salt bridges between the C-terminus in IG-2 and the amine side chain of Lys192 in uPA indicated by the MD structures. Additionally, we calculated the thermodynamic parameters of these complexes using the molecular mechanics/generalized Born surface area (MM/GBSA) method based on the equilibrium MD trajectories. Notably, the binding free energies (∆Gbind) of the peptide:uPA complexes determined by ITC, SPR, and MM/GBSA were linearly dependent (Table S5 and Figure 2F), indicating the high reliability of our MD simulations performed here. We further decomposed the calculated ∆Gbind into different energy components (Table S6). The results showed that, in agreement with above structural analysis, the solute electrostatic interactions (∆Eele) were pivotal for the binding of the peptides to uPA, and their contributions in IG-2:uPA complex was much stronger than the others possibly due to the formation of additional salt bridge between the C-terminus in IG-2 and the amine side chain Lys192 in uPA. In addition, both van der Waals interactions (∆EvdW) and the nonpolar part of solvation free energy (∆Gnonpolar) contributed considerably to the binding of all three peptides, while the polar part of the latter (∆Gpolar) was energetically unfavorable to the binding affinities of the complexes. Our study is a nice example demonstrating the agreement between experimental data and structural analysis.

Peptide inhibitors suppressed cancer invasion in vitro The aberrant proteolytic activity of uPA on the surfaces of cancer cells closely associates with the metastases of cancer cells. We demonstrated that our peptide inhibitors inhibited the 11 / 45

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intrinsic peri-cellular uPA activity in a murine colon cancer cell line, CT-26 (Figure S11). The invasion of this cell line was next evaluated through the Transwell® invasion assays (Figure 3 and S12).

For

negative

control,

we

generated

an

inactive

uPA

inhibitor,

IG-2-R6A

(NH2-CPAYSAYIGC-COOH) by substituting the P1 Arg6 with an alanine, because the Arg6 is the P1 residue and provided greatest contribution to the peptide:uPA binding based on the crystal structures39. IG-2-R6A completely lost the inhibitory potency against uPA (Ki > 25,000 nM). All three peptides, LD-1 (Ki = 550 ± 80 nM), IG-1 (Ki = 20 ± 4 nM), and IG-2 (Ki = 6.81 ± 1.1 nM), suppressed the invasion of CT-26 cells in a dose-dependent manner. Consistent with their lower Ki values, IG-1 (IC50 = 5.02 ± 0.86 2M) and IG-2 (IC50 = 3.28 ± 0.51 μM) suppressed CT-26 invasion to a greater extent than LD-1 (IC50 = 39.4 ± 5.8 μM). Both IG-1 and IG-2 demonstrated comparable suppression of cancer invasion (Figure 3A-3C). As a negative control, IG-2-R6A showed non-measurable inhibition of CT-26 invasion even at a high concentration of 100 μM (Figure 3D). In another set of experiments, we evaluated the influence of these peptides on the invasion of a human breast cancer cell line, MCF-7, which has negligible expression of human uPA.40 All of the three active peptides and the inactive IG-2-R6A did not affect the invasion of MCF-7 cells at the concentration of 100μM (Figure 3E). Thus, our uPA inhibitors efficiently suppressed the invasion of cancer cells by intervening with the uPA activity. We also tested the influence of these peptides on the migration of CT-26 cells using the wound healing model (Figure 3 and S13). The inactive IG-2-R6A showed no inhibition of cell migration at the highest concentration of 100 μM (Figure 3F). LD-1, IG-1 and IG-2 inhibited the cell migration mildly (Figure 3 G-I). Consistent with the Ki values, IG-1 and IG-2 showed better inhibition than LD-1 did. At the high concentrations (33 or 100 μM), IG-1 and IG-2 only inhibited 12 / 45

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around 30% migration. In contrast, at the same concentrations, IG-1 and IG-2 inhibited 70 – 80 % invasion of CT-26 cells (Figure 3B and 3C). In addition, all the peptides did not show cytotoxicity to CT-26 cells even at the high concentrations of 100 μM (Figure S14). Thus, the suppression of the invasion of CT-26 cells was mainly through inhibiting the degradation of BM and ECM, but not intervening with the cellular migration or growth processes.

Evaluation of anti-cancer and anti-metastatic efficacies in vivo To estimate the anti-cancer and anti-metastatic efficacies of peptide inhibitors in vivo, based on CT-26 cells and balb/c mice, we established a subcutaneously grafted model to evaluate the suppression of tumor growth and a lung metastatic model to evaluate the suppression of tumor metastases. Before we performed the in vivo experiments, we evaluated the stability of IG-2 against the proteolytic degradation by plasma proteases by determining the inhibition of the uPA activity after incubation with 75% murine plasma for 0-24 h (Figure S15). IG-2 showed the identical inhibition at different time points up to 8hr: 0 hr (IC50 = 10.1 ± 0.6 nM), 2 hr (IC50 = 11.9 ± 0.4 nM), and 8 hr (IC50 = 11.3 ± 0.6 nM). However, after incubating with plasma for 24 h, the inhibitory potency was obviously declined (IC50 = 70.7 ± 3 nM), indicating that IG-2 was stable within 8 h while were partially degraded after 24 hr. .

Peptide inhibitors suppressed tumor growth in vivo The anti-tumor efficacy of these peptide inhibitors was then evaluated in a subcutaneously implanted cancer model in vivo. CT-26 cells were subcutaneously implanted to the right flanks of Balb/c mice. When tumors reached the size of around 150~200 mm3, the mice were treated daily 13 / 45

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with saline, 12.3 mg/kg LD-1, 1.13, or 11.3 mg/kg IG-2 through intraperitoneal injection for 15 days. On Day 16, the mice were sacrificed and the tumors were resected and weighted (Figure 4A). Both LD-1 and IG-2 demonstrated obvious inhibition of the tumor growth in contrast to the saline-treated group (Figure 4B). Mice treated with 12.3 mg/kg LD-1, 1.13 mg/kg and 11.3 mg/kg IG-2 IG-2 showed 36.4 %, 36.1%, and 56.8% reduction of tumor growth, respectively, indicating that IG-2 (Ki = 6.8 ± 1.1 nM) demonstrated higher anti-cancer efficacy than LD-1 (Ki = 500 ± 80 nM) did, which was consistent with the much higher inhibitory potency of IG-2.

Peptide inhibitors suppressed cancer metastasis in vivo The anti-metastatic efficacy of these uPA inhibitors was evaluated in a lung metastatic model in vivo. CT-26 cells, stably transfected with green fluorescence protein (GFP) gene, were intravenously implanted in male Balb/c mice. The cancer-grafted mice were then intraperitoneally treated with saline, 12.3 mg/kg LD-1, 1.13 or 11.3 mg/kg IG-2 daily after implantation. On Day 15, the metastatic lungs were resected for histological examination after the mice were sacrificed. A group of mice without tumor implantation was set up as the normal group for comparison and background subtraction. The metastases of CT-26 cells in each group were quantitively evaluated through the following four indicators (Figure 4C-F): 1) the amount of the genomic DNA of the metastatic CT-26 cells; 2) the fluorescence signal of the GFP (ex488, em520) derived from the metastatic CT-26 cells; 3) the numbers of tumor nodules on the pleural surfaces; and 4) the weights of the metastatic lungs. The genomic DNA of the metastatic CT-26 cells in the lungs from CT-26-infused mice was quantitatively determined through quantitative real-time PCR (qPCR, for detailed procedures, 14 / 45

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please refer to the Experimental Section). The treated groups demonstrated significantly reduced metastatic CT-26 DNA in lungs compared to the saline treated group (Figure 4C). Groups treated with 12.3 mg/kg LD-1, and 1.13 mg/kg IG-2 demonstrated mild reduced cancer metastases with no statistical significance (P > 0.05). 11.3 mg/kg IG-2 caused 49.8 % inhibition of cancer metastases (P = 0.010). The metastatic lungs showed enhanced GFP fluorescent signals compared to the normal lungs, demonstrating the presence of the metastatic CT-26 cells (Figure 4I). Treatments with peptides efficiently reduced the GFP fluorescence, indicating the decline of tumor metastases (Figure 4D). 12.3 mg/kg LD-1, and 1.13 mg/kg IG-2 demonstrated mild reduced the GFP fluorescence with no statistical significance (P > 0.05). 11.3 mg/kg IG-2 caused 50% decline of GFP fluorescence per lung (P = 0.011). The lungs from CT26-infused mice presented apparent metastatic tumor nodules on surface, in contrast to the smooth surface of lungs from the normal group (Figure 4H). The groups treated with LD-1 and IG-2 demonstrated much fewer metastatic tumor nodules on surface (Figure 4E). 12.3 mg/kg LD-1, and 1.13 mg/kg IG-2 demonstrated mild reduced nodules numbers with no statistical significance (P > 0.05). 11.3 mg/kg IG-2 caused 46.5 % decline of nodules numbers per lung (P = 0.032). The metastatic lungs were much heavier than the normal lungs because of the hyperplasia. The peptide-treated groups demonstrated clearly reduced hyperplasia in contrast to the saline-treated group (Figure 4F). The hyperplastic weights were calculated by the weights of lungs subtracting the weights of the normal lungs. 12.3 mg/kg LD-1, and 1.13 mg/kg IG-2 demonstrated mild reduced hyperplastic weights with no statistical significance (P > 0.05). 11.3 mg/kg IG-2 15 / 45

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caused 51.7 % decline of hyperplastic weights (P = 0.013). Thus, all of the four indicators demonstrated that our peptide inhibitors efficiently reduced the cancer metastases in the cancer-grafted mice. IG-2, at the dosage of 11.3 mg/kg, reduced around 50% cancer metastases, which was consistent according to all of the four indicators. Furthermore, IG-2 (Ki = 6.8 ± 1.1 nM) demonstrated better anti-metastatic efficacy than LD-1 (Ki = 550 ± 80 nM) did in all cases, in line with its much higher inhibitory potency. The histological analysis qualitatively demonstrated the same results (Figure 4J): Both LD-1 and IG-2 efficiently suppressed the metastases of CT-26 cells into the lungs, while IG-2 presented better anti-metastatic efficacy. Interestingly, during the 15 days, the mice with and without treatments showed no significant difference in the body weights (Figure S16). In addition, IG-2 prolonged the survival time of the CT-26 implanted mice, but no statistical significance was observed (Figure 4G).

DISCUSSION Urokinase-type plasminogen activator (uPA) is an important cancer-related enzyme and has been identified as the level-of-evidence-1 (LOE-1) biomarker for breast and prostate cancer in clinics. uPA inhibitors are also considered as potent therapeutics for the treatment of rheumatoid arthritis (RA). Mesupron®, the prodrug of a non-specific uPA inhibitor, WX-UK1, showed promising results of enhancing the survival rates of patients in Phase II clinical trial.29 In the following studies, uPA inhibitors with higher potency and specificity were developed as the candidates of anti-cancer agents.

41-43

In contrast to small-molecule inhibitors, peptide-based

inhibitors show higher potency and specificity. Wakselman et. al. reported a monocyclic 16 / 45

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hexapeptide uPA inhibitor with the Ki value of 41 nM.44 Umezawa et. al. developed a linear tetrapeptide inhibiting uPA with the IC50 value of 5.8 μM and demonstrated the inhibition of the invasion of cancer cells in vitro.45 Heinis’s group generated bicyclic peptide inhibitors of uPA with Ki values in nM range with phage-display screening and chemical modification.46-48 However, these peptide-based inhibitors have not been evaluated in animal models. CJ-463 (benzylsulfonyl-D-Ser-Ser-4-amidinobenzylamide), a peptide-based uPA inhibitor, demonstrated excellent antitumor effects in vivo.49, 50 Thus, we compare the inhibitory potency, specificity, and antitumor efficacies of WX-UK1, CJ-463, and our IG-2 in this study. The catalytic domains of serine proteases show high conservation in the region of the S1 pocket. In contrast, the regions outside the S1 pockets are much more variable, providing potential sites for engineering specificity.38 IG-2 demonstrated much higher selectivity than WX-UK130 and CJ-463 did.49 Because IG-2 formed 940 Å2 contact area with uPA (PDB code: 6A8N), which was much larger than those of WX-UK1 (619 Å2, PDB code: 1VJA) and CJ-463 did (582 Å2, PDB code: 1F92) (Figure S17). More importantly, the contact area outside the structurally identical S1 pocket of IG-2:uPA complex (638 Å2) is even larger than those of WX-UK1:uPA (316 Å2) and CJ-463:uPA (275 Å2) complexes, which is a likely reason why IG-2 showed higher specificity to uPA. Based on two CT-26-mice cancer models, we demonstrated the excellent antitumor and anti-metastatic efficacy of our peptide-based uPA inhibitors. In the lung cancer metastatic model, qPCR quantification showed that IG-2 suppressed 49.1% metastases with the daily dosage of 11.3 mg/kg (Figure 4B). In a similar Lewis lung carcinoma (LLC) lung metastatic model, CJ-463 exhibited approximate 41.5% reduced tumor nodules numbers.50 However, the blood 17 / 45

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concentration of CJ-463 (1.01 mM, 100 mg/kg) was much higher than the 0.1 mM (11.3 mg/kg) of IG-2 in our situation. In addition, CJ-463 was dosed twice daily in contrast to the once dosage of IG-2. Data showed that our IG-2 achieved higher anti-metastatic efficacy with lower dosage than CJ-463 did. Unlike the anti-metastatic effects, the mechanism of the uPA inhibitors in suppressing tumor growth was not well established. In an orthotopical breast cancer model, WX-UK1 showed 53% inhibition of tumor growth at the daily dosage of 1.0 mg/kg, respectively.30 In a subcutaneous lung cancer model, CJ-463 inhibited 43% tumor growth at the daily dosage of 200 mg/kg.50 In our current subcutaneous colon cancer model, IG-2 demonstrated 56.8% inhibition of tumor growth at the daily dosage of 11.3 mg/kg (Figure 4B). WX-UK1 exhibited the best antitumor efficacy even though it has the lowest potency (Ki = 0.1 μM). One possible explanation could be that WX-UK1 is not specific to uPA, as it also showed comparable Ki values of inhibiting other cancer-related proteases, including tissue kallikreins, plasmin, and matriptase.30 Many studies have proven these proteases are also potent targets for cancer treatments.51, 52 Crystallography is the most important and frequently used approach to reveal the details of peptide-protein interactions. However, crystallography does not provide dynamic information in the protein-protein interactions. Because of the highly flexible peptides, the dynamic factors also play critical roles in the binding of peptides to the target proteins. In addition, the processes of crystallization are always carried out under the rigorous conditions, e.g., high concentration of salts or precipitants, which might lead to the conformational selection in some cases. In this study, crystal structures revealed the binding information of uPA and peptide inhibitors, but cannot explain some critical questions which are relevant to the dynamic effects. We thus used the MD 18 / 45

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simulation to induce the dynamic factors and to study the peptide-protein interactions in physiological conditions. Several critical interactions that were not observed in crystal structures were found in the MD simulation, which indicated the important clues for explaining the unsolved questions. Besides, the results of the MD simulation were also consistent with the experimental data. Thus, our current study demonstrates the importance of studying the mechanism of peptide-based drugs by combining in silico and experimental approaches.

CONCLUSION In conclusion, we developed highly potent and highly specific peptidic inhibitors of urokinase-type plasminogen activator (uPA) as potent anti-cancer and anti-metastatic agents. The most potent peptide, IG-2, inhibited uPA with a Ki value of 6.7 nM and with the specificity comparable to monoclonal antibodies. By combining the crystallography, MD simulation, SPR, and ITC analysis, we revealed the inhibitory mechanism of these peptides in the molecular level. Next, we demonstrated that the peptide inhibitors suppressed the invasion of murine colon cancer cells, CT-26, in vitro, by inhibiting the process of degrading ECM rather than intervening with the cell growth or migration processes. In addition, in a subcutaneously implanted colon cancer model, we demonstrated the high efficacy of suppressing tumor growth in vivo. Furthermore, in a lung metastatic model, peptide inhibitors exhibited excellent anti-metastatic efficacy at a relatively low dosage. Our current study not only provides a new type of uPA inhibitors, but also a potent anti-metastatic agent for the cancer treatments.

EXPERIMENTAL SECTION 19 / 45

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General chemicals were purchased from Sigma-Aldrich (St. Louis, USA), Iris Biotech GmbH (Marktredwitz, Germany), or Rapp Polymere GmbH (Tübingen, Germany) and used without further purification unless otherwise stated. Proteases used for the determination of Ki and KD values were purchased from Molecular Innovation (Novi, MI, USA), Roche Applied Science (Penzburg, Germany), Maxygen (California, USA), and ProSpec-Tany Technogene Ltd. (New Jersey, USA) and used without further purification unless otherwise stated. The peptides were synthesized by Wuxi PharmaTech (Shanghai, China). The purity and quality of peptides was estimated with HPLC and mass spectra provided by the vendors (Figure S1-S4). The purity of all peptides was >95% according to the analytical HPLC chromatography. The purity of the recombinant protein was >95% according to the sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) and size exclusion chromatography (Figure S5). Crystallization and Structure Determination The molecular clone, expression procedures of the catalytic domain of murine uPA (muPA-SPD) can be referred to in the Additional Experimental section in the Supporting Information. The characterization of muPA-SPD is indicated in Figure S5. All of the three peptides, LD-1, IG-1, and IG-2, showed lower inhibitory potency against muPA-SPD than that against muPA (Figure S6). IG-1 and IG-2 in complexes with muPA-SPD were crystalized from sitting drops, composed of 0.5 μL of the mix and 0.5 μL of the reservoir solution. The reservoir solution was 80 mM Tris-HCl 8.5, 1.6 M NaH2PO4 supplemented with 20% Glycerol. Crystals grew within 7 days at 25 °C. The reservoir solutions with 25% (v/v) glycerol were used as cryoprotectants used for data collection.X-ray diffraction data were collected at 100 K BL19U1/BL18U1 beamline in Shanghai Synchrotron Radiation Facility (SSRF, China). The 20 / 45

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crystal structures were solved by molecular replacement using Molrep software of the CCP4 program suite53 and the published structure of LD-1:huPA complex (PDB code: 4X1Q)54 as the search model. The Fo-Fc electron density map revealed clear extra electron density for peptides at the active site of muPA-SPD, and was modeled by the peptide sequences. The models were then manually adjusted by COOT

55

and refined by REFMAC. After the final cycles of refinement

using REFMAC of CCP4, the models have reasonable statistics and geometries (Table S1). The structures of muPA-SPD in complexes with IG-1 and IG-2 have been deposited to the RCSB PDB database with the codes of 6A8G and 6A8N, respectively. Molecular Dynamic simulation The starting structures for the MD simulations of the IG-1 and IG-2 in complexes with muPA-SPD were obtained from the corresponding crystal structures resolved here. The one for the LD-1:muPA-SPD complex was built based on the IG-1:muPA-SPD crystal structure. Each starting structure was inserted into a box of explicit water and 150 mM NaCl with edge lengths of 80×80×80 Å3. The protonation states of residues were assigned according to the corresponding pKa values calculated by using the H++ webserver 56. Sodium ions were added to counterbalance the charge of the complexes. All systems were underwent MD simulations with AMBER ff99SB-ILDN force field

57-59

and general AMBER force field (GAFF)

code61on in-house GPU clusters. The Åqvist potential

62

60

and TIP3P model

using the Amber16 63

were used for the

ions and for the water molecules, respectively. All bond lengths were constrained by LINCS algorithm 64. Periodic boundary conditions were applied. Electrostatic interactions were calculated using the Particle Mesh-Ewald (PME) method

65,

and van der Waals and Coulomb interactions

were truncated at 10 Å. Each system underwent 1000 steps of steepest-descent energy 21 / 45

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minimization with 1000 kJ·mol−1·Å−2 harmonic position restraints on the protein, followed by 2500 steps of steepest-descent and 2500 steps of conjugate-gradient minimization without restraints. The system was then gradually heated from 0 K up to 298 K in 20 steps of 2 ns. After that, 1200 ns-long MD simulations were carried out in the NPT ensemble for each system. Temperature and pressure controls were achieved by Nosé-Hoover thermostat66 and Berendsen barostat

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with a frequency of 2.0 ps, respectively. The most representative structure for each

complex was identified by the cluster analysis over the equilibrated trajectories (after 700 ns). To assess the convergence of the simulated trajectory we considered their projections on the top essential dynamical spaces obtained from a standard covariance analysis. Following Hess’s criterion 68, these projections were next compared with those expected for a random reference. The observed negligible overlap (i.e. cosine content close to 0, data not shown) confirms a posteriori adequate sampling of the complex conformations around the equilibrium position. Hydrogen bond interactions were defined to be present if the atomic distance between the acceptor and donor atoms is below 3.5 Å and the angle among the hydrogen-donor-acceptor atoms is below 30o. Hydrophobic interactions were defined to be present if the center-of-mass distance between side chains are smaller than 4.5 Å.69 The time-dependent root-mean-square deviation (RMSD) values of the backbone atoms of peptides and uPA were plotted against time in Figure S7. The potential polar interactions of the peptides and uPA residues in crystal structures and MD simulations of peptide-uPA complexes were listed in Table S2-S4. Cell invasion experiments in vitro The influence of the peptides on the invasion of CT-26 cells was evaluated by using the Transwell® invasion experiment. The preparation of BD Matrigel-coated chamber was in 22 / 45

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accordance to the manual instruction provided by the vendor. Briefly, 100 µl 0.2-0.3 mg/ml BD Matrigel (BD Biosciences, Franklin Lakes, NJ, USA) was coated the top chamber of a Corning chamber (polycarbonate filter with 8-µm pore size inserts, Corning Pharmingen, San Diego, CA) per well for 2 h. 2×105 cells in the serum-free DMEM media containing LD-1, IG-1, and IG-2 at the concentrations of 0-10 μM were seeded into the top chamber. Complete medium containing 10% serum was placed in the lower chamber. After 24 h, cells that migrated to the underside of the membrane were stained using 0.1% crystal violet solution. The assay was performed with three replicates for each group. The invaded cells were photographed through a bright-field microscope (Leica Microsystems CMS, GmbH, Germany) and eluted using a 33% acetic acid/water (v/v). The absorbance at 570 nm was determined using a microplate reader. The standardized cell invasion rate was calculated by setting the A570 of non-treated groups as 100%. CT-26-based lung metastatic model We established a CT-26 lung metastatic model based on male Balb/c mice to evaluate the antimetastatic efficacies of the peptides in vivo. Balb/c mice with weights around 20 g were divided into 5 groups with 6 mice per group. CT-26 cell line containing the GFP genes was purchased from Perkin-Elmer (Waltham, MA, USA). 200 µl cell suspension containing 106 cells mixed with saline, 12.3 mg/kg LD-1, 1.13 and 11.3 mg/kg IG-2 were intravenously injected. One group without tumor implantation was set as the normal group for comparison. Mice were treated with saline, LD-1 (12.3 mg/kg), and IG-2 (1.13 and 11.3 mg/kg) via intraperitoneal injection daily from Day 1-Day 15. On Day 16, mice were anesthetized and sacrificed. The lungs were resected and weighed immediately after sacrifice. The GFP fluorescence of the metastatic lungs was imaged by an Amersham™ Imager 600 fluorescent analyzer (GE Healthcare Bio-Sciences AB, 23 / 45

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USA) with a 488 nm laser diode for excitation. The fluorescence signal was quantified by collecting the fluorescence signals within an area of 20×20 mm2. The tumor nodules on the surface

of

lungs

were

counted

after

soaking

with

formaldehyde.

Formalin-fixed,

paraffin-embedded lung tissue sections were routinely stained with H&E. The quantitative real-time polymerase chain reaction (qPCR) analysis The amount of the genomic gene of the metastatic GFP-containing CT-26 cells in the lungs was quantified through the quantitative real-time polymerase chain reaction (qPCR). The qPCR analysis targeted for the amplification of a sequence of a section of GFP-DNA, which does not exist in the tissues of mice. The amplified sequence is 287 bp, with the primers of 5'-CAGTGCTTCAGCCGCTAC-3' (forward) and 5'-TTCACCTTGATGCCGTTC-3' (reverse). For the purpose of detecting the metastatic cells in the lungs, the standard curve was plotted by the fluorescence of the SYBR Green (ex497, em520) versus the genomic DNA of CT-26 in vitro cultures added to the mouse genomic DNA extracted from normal lungs at the ratios ranged 0.135%-100% with the consisting total DNA concentration of 200 ng. The total DNA was isolated from the metastatic lungs with and without treatments after homogenization. The polymerase chain reaction analysis of the tested samples and standards containing the total DNA of 200 ng was performed on a QuantStudio 5 Real-Time PCR System (Thermo Fisher Scientific, MA, US) by using Bio-Rad iQ SYBR Green Supermix reagents with the program of 95 oC, 10 min, 45 × (95 oC,

oC

15s, 56 oC, 30s, 72 oC, 30s). Fluorescence data are acquired at the end of each cycle (during 72 extension phase).

ASSOCIATED CONTENTS 24 / 45

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Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: XXX. Additional experimental section, HPLC chromatographs and mass spectra of peptides, characterization of the recombinant proteins, the raw enzymological data, the crystallographic parameters, potential polar interactions between peptides and uPA in crystal structures and MD simulations, other supplementary data of MD simulations, raw ITC data, stability of peptides against plasma proteases, daily changes of body weights of cancer-grafted mice with and without treatments, and other supporting data. PDB-formatted coordinates for computational models (MD-derived LD-1-muPA.pdb, MD-derived IG-1-muPA.pdb, and MD-derived IG-2-muPA.pdb). Molecular-formula strings (CSV). Accession codes The PDB codes for the structures of IG-1 and IG-2 in complexes with muPA-SPD are 6A8G and 6A8N, respectively. Authors will release the atomic coordinates and experimental data upon article publication. AUTHOR INFORMATION *Corresponding

authors

Mingdong Huang: Email: [email protected], Tel: +86-591-63173094. Peng Xu: Email: [email protected], Tel: +86-591-63173089. ORCID: Longguang Jiang: 0000-0002-4734-3778 25 / 45

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Jinyu Li: 0000-0002-8220-049X Peter A. Andreasen: 0000-0003-4794-4212 Zhuo Chen: 0000-0002-6351-8689 Mingdong Huang: 0000-0002-1377-6786 Peng Xu: 0000-0003-3968-7279 Author contributions: #

These authors contributed equally to this work.

Notes The authors declare no competing financial interest. ACKNOWLEDGEMENT This work was financially supported by grants from Ministry of Science and Technology (2017YFE0103200); the National Natural Science Foundation of China (21708043, 21603033, 81572944, U1405229); Natural Science Foundation of Fujian Province (2018J05031, 2018J01729); and Fuzhou University Testing Fund of precious apparatus (2017T010). The authors gratefully acknowledge the computing time granted by the Fujian Supercomputing Center. ABBREVIATIONS USED uPA: urokinase-type plasminogen activator, MD: molecular dynamics, tPA: tissue-type plasminogen activator, PAI-1 and PAI-2: plasminogen activator inhibitor -1 and -2, MMPs: matrix metalloproteases, LOE-1: level-of-evidence 1, muPA and huPA: murine and human uPA, fXIa: activated coagulation factor XI, aPC: activated protein C, SPD: serine protease domain, RMSD: root-mean-square deviation, SD: standard deviation, ITC: isothermal titration calorimetry, SPR: surface plasmon resonance, MM/GBSA: molecular mechanics/generalized Born surface area, 26 / 45

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GFP: green fluorescent protein, qPCR: quantitative real-time polymerase chain reaction, ECM: extracellular membrane, BM: base membrane.

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Andreasen, P. A. Selection of high-affinity peptidic serine protease inhibitors with increased binding entropy from a back-flip library of peptide-protease fusions. J Mol Biol 2015, 427, 3110-3122. 37. Hedstrom, L. Serine protease mechanism and specificity. Chem Rev 2002, 102, 4501-4524. 38. Xu, P.; Andreasen, P. A.; Huang, M. Structural principles in the development of cyclic peptidic enzyme inhibitors. Int J Biol Sci 2017, 13, 1222-1233. 39. Xu, P.; Xu, M.; Jiang, L.; Yang, Q.; Luo, Z.; Dauter, Z.; Huang, M.; Andreasen, P. A. Design of specific serine protease inhibitors based on a versatile peptide scaffold: conversion of a urokinase inhibitor to a plasma kallikrein inhibitor. J Med Chem 2015, 58, 8868-8876. 40. Trivanovic, D.; Jaukovic, A.; Krstic, J.; Nikolic, S.; Okic Djordjevic, I.; Kukolj, T.; Obradovic, H.; Mojsilovic, S.; Ilic, V.; Santibanez, J. F.; Bugarski, D. Inflammatory cytokines prime adipose tissue mesenchymal stem cells to enhance malignancy of MCF-7 breast cancer cells via transforming growth factor-beta1. IUBMB Life 2016, 68, 190-200. 41. Jiang, L.; Botkjaer, K. A.; Andersen, L. M.; Yuan, C.; Andreasen, P. A.; Huang, M. Rezymogenation of active urokinase induced by an inhibitory antibody. Biochem J 2013, 449, 161-166. 42. Jiang, L.; Oldenburg, E.; Kromann-Hansen, T.; Xu, P.; Jensen, J. K.; Andreasen, P. A.; Huang, M. D. Cleavage of peptidic inhibitors by target protease is caused by peptide conformational transition. BBA-Gen Subjects 2018, 1862, 2017-2023. 43. Jiang, L.; Zhang, X.; Zhou, Y.; Chen, Y.; Luo, Z.; Li, J.; Yuan, C.; Huang, M. Halogen bonding for the design of inhibitors by targeting the S1 pocket of serine proteases. RSC Adv 2018, 8, 28189-28197. 44. Wakselman, M.; Xie, J.; Mazaleyrat, J. P.; Boggetto, N.; Vilain, A. C.; Montagne, J. J.; Reboud-Ravaux, M. New mechanism-based inactivators of trypsin-like proteinases. Selective 31 / 45

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inactivation of urokinase by functionalized cyclopeptides incorporating a sulfoniomethyl-substituted m-aminobenzoic acid residue. J Med Chem 1993, 36, 1539-1547. 45. Kawada, M.; Umezawa, K. Suppression of in-vitro invasion of human fibrosarcoma cells by a leupeptin analog inhibiting the urokinase-plasmin system. Biochem Bioph Res Co 1995, 209, 25-30. 46. Angelini, A.; Cendron, L.; Chen, S. Y.; Touati, J.; Winter, G.; Zanotti, G.; Heinis, C. Bicyclic peptide inhibitor reveals large contact interface with a protease target. ACS Chem Biol 2012, 7, 817-821. 47. Chen, S. Y.; Rebollo, I. R.; Buth, S. A.; Morales-Sanfrutos, J.; Touati, J.; Leiman, P. G.; Heinis, C. Bicyclic Peptide ligands pulled out of cysteine-rich peptide libraries. J Am Chem Soc 2013, 135, 6562-6569. 48. Chen, S. Y.; Gopalakrishnan, R.; Schaer, T.; Marger, F.; Hovius, R.; Bertrand, D.; Pojer, F.; Heinis, C. Dithiol amino acids can structurally shape and enhance the ligand-binding properties of polypeptides. Nat Chem 2014, 6, 1009-1016. 49. Schweinitz, A.; Steinmetzer, T.; Banke, I. J.; Arlt, M. J. E.; Sturzebecher, A.; Schuster, O.; Geissler, A.; Giersiefen, H.; Zeslawska, E.; Jacob, U.; Kruger, A.; Sturzebecher, J. Design of novel and selective inhibitors of urokinase-type plasminogen activator with improved pharmacokinetic properties for use as antimetastatic agents. J Biol Chem 2004, 279, 33613-33622. 50. Henneke, I.; Greschus, S.; Savai, R.; Korfei, M.; Markart, P.; Mahavadi, P.; Schermuly, R. T.; Wygrecka, M.; Sturzebecher, J.; Seeger, W.; Gunther, A.; Ruppert, C. Inhibition of urokinase activity reduces primary tumor growth and metastasis formation in a murine lung carcinoma model. Am J Resp Crit Care 2010, 181, 611-619. 51. Galkin, A. V.; Mullen, L.; Fox, W. D.; Brown, J.; Duncan, D.; Moreno, O.; Madison, E. L.; Agus, 32 / 45

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D. B. CVS-3983, a selective matriptase inhibitor, suppresses the growth of androgen independent prostate tumor xenografts. Prostate 2004, 61, 228-235. 52. Steinmetzer, T.; Schweinitz, A.; Sturzebecher, A.; Donnecke, D.; Uhland, K.; Schuster, O.; Steinmetzer, P.; Muller, F.; Friedrich, R.; Than, M. E.; Bode, W.; Sturzebecher, J. Secondary amides of sulfonylated 3-amidinophenylalanine. New potent and selective inhibitors of matriptase. J Med Chem 2006, 49, 4116-4126. 53. Collaborative computational project, N. The CCP4 suite: programs for protein crystallography. Acta Crystallogr D Biol Crystallogr 1994, 50, 760-763. 54. Zhao, B. Y.; Xu, P.; Jiang, L. G.; Paaske, B.; Kromann-Hansen, T.; Jensen, J. K.; Sorensen, H. P.; Liu, Z.; Nielsen, J. T.; Christensen, A.; Hosseini, M.; Sorensen, K. K.; Nielsen, N. C.; Jensen, K. J.; Huang, M. D.; Andreasen, P. A. A Cyclic peptidic serine protease inhibitor: increasing affinity by increasing peptide flexibility. Plos One 2014, 9, e115872, 1-27. 55. Emsley, P.; Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr D Biol Crystallogr 2004, 60, 2126-2132. 56. Gordon, J. C.; Myers, J. B.; Folta, T.; Shoja, V.; Heath, L. S.; Onufriev, A. H++: a server for estimating pKas and adding missing hydrogens to macromolecules. Nucleic Acids Res 2005, 33, W368-W371. 57. Lindorff-Larsen, K.; Piana, S.; Palmo, K.; Maragakis, P.; Klepeis, J. L.; Dror, R. O.; Shaw, D. E. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 2010, 78, 1950-1958. 58. Walker, R. C.; de Souza, M. M.; Mercer, I. P.; Gould, I. R.; Klug, D. R. Large and fast relaxations inside a protein: Calculation and measurement of reorganization energies in alcohol dehydrogenase. J 33 / 45

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Phys Chem B 2002, 106, 11658-11665. 59. Craft, J. W., Jr.; Legge, G. B. An AMBER/DYANA/MOLMOL phosphorylated amino acid library set and incorporation into NMR structure calculations. J Biomol NMR 2005, 33, 15-24. 60. Wang, J.; 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. 61. Case, D. A.; Cheatham, T. E., 3rd; Darden, T.; Gohlke, H.; Luo, R.; Merz, K. M., Jr.; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R. J. The amber biomolecular simulation programs. J Comput Chem 2005, 26, 1668-1688. 62. Aqvist, J. Ion water interaction potentials derived from free-energy perturbation simulations. J Phys Chem 1990, 94, 8021-8024. 63. Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of simple potential functions for simulating liquid water. J Chem Phys 1983, 79, 926-935. 64. Hess, B.; Bekker, H.; Berendsen, H. J. C.; Fraaije, J. LINCS: A linear constraint solver for molecular simulations. J Comput Chem 1997, 18, 1463-1472. 65. Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An N⋅ log (N) method for Ewald sums in large systems. J Chem Phys 1993, 98, 10089-10092. 66. Hoover, W. G. Canonical dynamics: Equilibrium phase-space distributions. Phys Rev A 1985, 31, 1695-1697. 67. Berendsen, H. J. C.; Postma, J. P. M.; Vangunsteren, W. F.; Dinola, A.; Haak, J. R. Molecular-dynamics with coupling to an external bath. J Chem Phys 1984, 81, 3684-3690. 68. Hess, B. Convergence of sampling in protein simulations. Phys Rev E 2002, 65, 031910-1-031910-10. 34 / 45

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69. Kumar, A.; Cocco, E.; Atzori, L.; Marrosu, M. G.; Pieroni, E. Structural and dynamical insights on HLA-DR2 complexes that confer susceptibility to multiple sclerosis in Sardinia: a molecular dynamics simulation study. PLoS One 2013, 8, e59711, 1-13

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Figure 1: Peptide-based uPA inhibitors used in this study. The conserved and varied residues were colored in black and blue, respectively.

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Table 1: The Ki values of uPA inhibitors for the inhibition of human and murine serine proteases. All data have been determined for over three times. h- and m- indicate the human and murine proteases, respectively. PK indicates plasma kallikrein, tPA indicates tissue-type plasminogen activator, fXIa indicates activated coagulation factor XI, aPC indicates activated protein C. SPD stands for the serine protease domain. Data with asterisk were from our previous studies.35, 36 Ki values / nM

Protease LD-1

IG-1

IG-2

m-uPA

550±80*

20±4*

6.8±1.1

m-uPA-SPD

1769±88

246±46

15.3±0.5

h-uPA

(15±2)×103*

903±285*

340±60

h-PK

> 25,000*

>25,000*

>25,000

m-PK

> 25,000*

>25,000*

>25,000

h-tPA

> 25,000*

> 25,000*

> 25,000

m-tPA

> 25,000*

> 25,000*

> 25,000

h-plasmin

> 25,000*

>25,000*

>25,000

m-plasmin

> 25,000*

>25,000*

>25,000

h-thrombin

> 25,000*

>25,000*

>25,000

m-thrombin

> 25,000*

>25,000*

>25,000

m-trypsin

(8.3±0.3)×103

(1.6±0.6)×103

(2.1±0.1)×103

h-matriptase

> 25,000

>25,000

>25,000

h-fXIa

> 25,000

>25,000

>25,000

h-aPC

> 25,000

>25,000

>25,000

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Figure 2: Crystal structures and MD-simulation of peptide inhibitors in complexes with uPA. A. The overall crystal structures of IG-1 (green sticks) and IG-2 (magenta sticks) in complexes with uPA (wheat surface) (PDB codes: 6A8G and 6A8N). B. Schematic representation of polar interactions between IG-2 (black) and uPA (blue). The polar interactions of intramolecular and intermolecular polar interactions were presented in black and blue dash lines, respectively. C. Time-dependent RMSD of the backbone atoms of peptide inhibitors with respect to the starting conformation during the 1200 ns-MD simulation D. The alignment of the backbones of IG-2 in the crystal structure (magenta cartoon) and the MD simulation (cyan cartoon). E and F. The alignment of the backbones of LD-1 (cyan cartoon), IG-1 (green cartoon), and IG-2 (magenta cartoon) in the MD simulation-derived structures. The Cα atoms were presented as spheres. The distances between the atoms were presented as black dash lines. uPA were presented as the wheat surface. The critical residues were presented as sticks. G. Correlations of the binding free energy of peptide-uPA complexes calculated from MD trajectories (ΔGMD) with the ones determined by experiments (ΔGEXP). The correlation coefficients (R2) between ΔGMD and ΔGEXP determined by ITC (green line) and SPR (blue line) are 0.97 and 0.99, respectively.

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Table 2: Kinectic parameters of peptide inhibitors binding to muPA determined by SPR. Data with asterisk were from our previous studies. 35, 36 peptide

kon (M-1s-1), x10-5

koff (s-1), x102

KD (nM)

LD-1

0.91 ± 0.25 (3)*

3.64 ± 0.07 (3)*

398 ± 46 (3)*

IG-1

4.48 ± 0.76 (3)*

0.64 ± 0.07 (3)*

14.7 ± 1.8 (3)*

IG-2

2.73 ± 0.13 (3)

0.066±0.019 (3)

2.40 ± 0.61 (3)

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Table 3: The thermodynamic parameters of peptide inhibitors binding to muPA determined by ITC. Data with asterisk were from our previous studies.35, 36 peptide

N

KD (nM)

∆G (kJ/mol)

∆H (kJ/mol)

T∆S (kJ/mol)

LD-1

0.83±0.1 (3)*

354±132 (3)*

-37.0±0.9 (3)*

-49.6±6.3 (3)*

-12.6±7.2 (3)*

IG-1

0.78±0.03 (3)

42.1±3.35 (3)

-42.1±0.20 (3)

-79.7±1.4 (3)

-37.6±1.6 (3)

IG-2

1.1±0.06 (3)

10.6±1.4 (3)

-45.5±0.03 (3)

-91.4±15.8 (3)

-45.9±15.7 (3)

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Figure 3: The uPA inhibitors suppressed the invasion and migration of CT-26 cells in vitro. A-C: LD-1 (A), IG-1 (B), and IG-2 (C) suppressed the invasion of the murine colon cancer cell line, CT-26, in a dose-dependent manner (0.1 - 100 μM). The IC50 values were calculated accordingly. D: The inactive peptide, IG-2-R6A, demonstrated non-measurable inhibition of cancer invasion at the concentration of 100 μM. E: All peptides showed no inhibition of the invasion of a human breast cancer cell line, MCF-7, which is a uPA-negative cell line. F: IG-2-R6A did not inhibit the migration of CT-26 cells at the concentration of 100 μM. G-I: LD-1 (G), IG-1 (H), and IG-2 (I) slightly inhibited the migration of CT-26 cells. The values were represented as mean ± SEM.*P < 0.05,

**P

< 0.01, and

***

P < 0.001 vs. the vehicle (non-treated) group. The graphics of cell

invasion and migration were demonstrated in Figure S11 and S12.

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Figure 4: The anti-tumor and anti-metastatic efficacies of uPA inhibitors in vivo. A-B: The suppression of the tumor growth in balb/c mice subcutaneously implanted with CT-26 cells at the daily dosage of saline, 12.3 mg/kg LD-1 or 1.13-11.3 mg/kg IG-2 for 15 days. Representative graphics (A) and tumor weights (B) of resected tumors with and without treatments. C-J: Peptide inhibitors suppressed cancer metastases in a lung metastatic mice model. The cancer-grafted mice were treated with saline, 12.3 mg/kg LD-1, 1.13 mg/kg, or 11.3 mg/kg IG-2. C-F: Quantification of the lung metastases using the genomic DNA of the metastatic cancer cells (C), the GFP fluorescence of the metastatic cancer cells per lung (D), the nodules numbers on surface per lung (E), and the hyperplastic weights of the metastatic lungs in contrast to the normal lungs (F). G: The survival curves of CT-26-grafted mice with the treatments of saline (red), 12.3 mg/kg LD-1 (green), and 11.3 mg/kg IG-2 (blue), respectively. H-I: The representative graphics (H) and fluorescent images (I) of normal lungs and metastatic lungs with and without treatments. J: The graphics of the H&E-stained histological sections of the normal lungs and metastatic lungs with and without the treatments. The values were represented as mean ± SEM.

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