P2′ sites affect the substrate cleavage of TNF-α converting enzyme (TACE)

Molecular Immunology 62 (2014) 122–128

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The P2/P2 sites affect the substrate cleavage of TNF-␣ converting enzyme (TACE) Sen Liu a,b,∗ , Song Liu a,b , Yanlin Wang a,b , Zhaojiang Liao c a b c

Institute of Molecular Biology, China Three Gorges University, Yichang 443002, PR China College of Medical Science, China Three Gorges University, Yichang 443002, PR China College of Biological and Pharmaceutical Science, China Three Gorges University, Yichang 443002, PR China

a r t i c l e

i n f o

Article history: Received 14 April 2014 Received in revised form 28 May 2014 Accepted 29 May 2014 Keywords: TNF-␣ converting enzyme ADAM17 Peptide–protein docking Substrate recognition

a b s t r a c t Tumor necrosis factor-alpha converting enzyme (TACE) is a proteinase that releases over eighty soluble proteins from their membrane-bound forms, and it has long been an intriguing therapeutic target in auto-immune diseases, and recently, in cancers. However, a haunting question is how TACE recognizes its substrates. In this work, we applied computational and experimental methods to study the role of the P2 site and the P2 site of the substrate peptide in the substrate cleavage of TACE. In the computational complex model, the sidechains of these residues do not form key interactions with TACE, but experimentally, the mutations at these two positions largely affect the peptide cleavage efficiency in the enzymatic assay. We then showed that the P2/P2 sites could affect the efficiency of the conformation search for the correct peptide orientation, which in turn affects the substrate cleavage efficiency. Our result provides new information to the better understanding of the enzymatic mechanism of TACE, and could be useful in the design of novel TACE inhibitors. © 2014 Elsevier Ltd. All rights reserved.

1. Introduction TACE (TNF-␣ converting enzyme) is an enzyme that releases soluble TNF-␣ from its membrane-bound form (Black et al., 1997; Moss et al., 1997), and has been found to be able to release the soluble form of about another 80 proteins (Black et al., 1997; Scheller et al., 2011; Moss et al., 1997; Gooz, 2010; Stephenson and Avis, 2012). Therefore, TACE has been a highly attractive target in inflammatory diseases (Lichtenthaler, 2012; Scheller et al., 2011; Saftig and Reiss, 2011; Bahia and Silakari, 2010) and, recently, in cancer therapy (Black et al., 1997; Guinea-Viniegra et al., 2012; Moss et al., 1997; Richards et al., 2012; Scheller et al., 2011; Saftig and Reiss, 2011; Baumgart et al., 2010). TACE, also called ADAM17, is one of the most well-studied enzymes in the ADAM (a disintegrin and metalloproteinase) family. However, the mechanism of substrate recognition and processing by TACE remains elusive (Scheller et al., 2011; Gooz, 2010; Stephenson and Avis, 2012). Caescu et al. (Lichtenthaler, 2012; Caescu et al., 2009; Scheller et al., 2011; Saftig and Reiss, 2011;

∗ Corresponding author at: College of Medical Science, China Three Gorges University, Yichang 443002, PR China. Tel.: +86 717 6397179; fax: +86 717 6397179. E-mail address: [email protected] (S. Liu). http://dx.doi.org/10.1016/j.molimm.2014.05.017 0161-5890/© 2014 Elsevier Ltd. All rights reserved.

Bahia and Silakari, 2010) and Lambert et al. (2005) found that the residue identities of the substrate peptide can affect the cleavage efficiency of TACE, but Wang et al. (2002) and Hinkle et al. (2004) suggested that the position of the cleavage site relative to the transmembrane region and the first globular part of the protein is more determinant than the sequence of the cleavage site. It was even suggested that the substrate recognition and cleavage is governed by TACE’s interactions with some unfound adaptor proteins (Mohan et al., 2002). Therefore, a structural view of the complex between the substrate peptide and TACE would be very helpful for a better understanding of the mechanism (Hartmann et al., 2013), whereas an experimental complex structure is still not available. Recent advances in computational modeling has provided a good alternative to understand the molecular mechanisms of protein–peptide specificity (Smith and Kortemme, 2011; Babor et al., 2011; Kaufmann et al., 2011; Smith and Kortemme, 2010; Goldschmidt et al., 2010; Walshe et al., 2009; Grigoryan et al., 2009; Mandell and Kortemme, 2009; Kota et al., 2009; Humphris and Kortemme, 2008; Fu et al., 2007) and enzyme–peptide activity (Chaudhury and Gray, 2009; London et al., 2011). Computationally, Manzetti et al. (2003) modeled substrate-enzyme complexes for ADAM-9 and ADAM-10 to conclude that the S1 pocket and the S2/S3 region of the enzymes dominate the substrate specificity. Therefore, when an experimental complex structure is currently absent, computational modeling could be a good way to provide

S. Liu et al. / Molecular Immunology 62 (2014) 122–128

some useful clues to the molecular mechanism of the substrate recognition of TACE. In this study, we computationally modeled and optimized the complex structure of TACEcat (the catalytic domain of TACE) and a substrate peptide, based on a preliminary docking analysis by us previously (Liu, 2012). We noticed that the P2 and the P2 sites of the substrate peptide distinctly affect the docking results, and experimentally verified that the P2/P2 sites have big impacts on the substrate cleavage by TACE. Finally, we showed that the conformation search of the substrate peptide for the correct binding orientation could be the reason.

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(Shanghai) Co., Ltd.) at a 1:2 molar ratio for 2 h at 4 ◦ C in phosphatebuffered saline (PBS: 137 mM NaCl, 2.7 mM KCl, 10 mM Na2 HPO4 , 2 mM KH2 PO4 , pH 7.4). A streptavidin-coated (SA) biosensor (ForteBio, PALL) was mounted to the BLItz system and used to immobilize biotinylated TACEcat after being hydrated for 10 min in PBS. The binding buffer (10 mM Hepes, 100 mM NaCl, pH 7.4, 0.05% (v/v) Tween-20) was used to dissolve the peptide samples as well as for the real-time binding assay. The binding assay was performed at 25 ◦ C with 600 s of association and 420 s of disassociation. The concentration of the peptides was 10 ␮M, and BSA was used as the blank control, and the response signal was subtracted in the final analysis.

2. Methods 2.1. TACEcat-peptide docking

2.4. Enzymatic cleavage of the peptides

The substrate peptide was docked to the catalytic domain of TACE (TACEcat) in the protein design software Rosetta (version 3.4). The peptide docking process was similar to previously described (Liu, 2012). Briefly, a 9-residue peptide (corresponding to the P5–P4 sites of the recognizing peptide) was prepared as a linear chain and then docked to the structure of TACEcat (from PDB ID: 1BKC) using the FlexPepDocking-AbInitio protocol in Rosetta (Raveh et al., 2011). During the docking steps, the catalytic zinc was coordinated by three His residues (the residues 187, 191, and 197 in Rosetta numbering). To keep the geometry of the catalytic center, constraints were applied on the distances between the zinc atom and the NE2 atoms of the three coordinating His residues (2.4 A˚ with a variation of 0.2 A˚ respectively), as well as the hydroxyl oxygen atom of the catalytic-water-coordinating glutamate acid (the ˚ residue 188 in Rosetta numbering; 4.6 A˚ with a variation of 0.3 A). Finally, 54,000 docking models were generated for each peptide, and the 500 lowest-score models were clustered for model evaluation.

The peptides were synthesized and assayed for cleavage by TACE with LC–MS. Synthetic peptide stock solutions were prepared in Assay buffer (25 mM Tris–HCl, 2.5 ␮M ZnCl2 , 0.005% Brij-35; pH 9.0) at a concentration of 1 mM respectively. The catalytic domain of TACE (residues Arg215-Asn671) was purchased from R&D Systems, Inc., Minneapolis, USA. Both protein and peptide substrates were incubated at 500 ␮M final concentrations with TACE at 2 ng/␮L in Assay buffer at 37 ◦ C. Aliquots from each cleavage reaction were taken at 3 h after incubation, and stopped by ice incubation. The aliquots (20 ␮L/test) were analyzed by RP-HPLC on a Sinochrom ODS-BP 4.6 mm × 150 mm column (Dalian Elite Analytical Instruments Co., Ltd.) at 25 ◦ C, using a 15 min linear gradient from 0.1% TFA in water (Buffer A) to 0.1% TFA in acetonitrile (Buffer B) for each peptide.

3. Results 3.1. The peptide–protein docking analysis

2.2. Molecular dynamics optimization of the complex model The lowest-score model in the largest cluster from Rosetta was used for molecular dynamics simulation in NAMD (GPU version 1.9) (Phillips et al., 2005). Periodic water (TIP3P) box was added to wrap the complex structure with 10 A˚ of boundary distances. Ions (Na+ and Cl− ) were added to 0.15 mol/L and counteracted the net charger of the water box including the protein complex. The Charmm parameters from c35b2 c36a2 were used, and the smooth particle-mesh Ewald (PME) method was enabled. To minimize the complex model, a 3-step procedure was applied. First, 2000 steps of minimization were applied with constraints applied on the heavy atoms of the proteins, followed by another 2000 steps of minimization with constraints only applied on the C-alpha atoms. Finally, another 2000 steps of minimization were run without any constraints on the system. After the minimization step, the system was equilibrated and a 10-ns molecular dynamics run was performed, and the atom coordinates were recorded per picosecond (ps). The analysis of the molecular dynamics trajectory and models were done in VMD (version 1.9.1) (Humphrey et al., 1996). 2.3. In vitro real-time binding assay Peptides were synthesized and covalently linked to bovine serum albumin (BSA) at the C-termini (ChinaPeptides Co., Ltd, Shanghai, China) with a Cystein residue. The real-time binding assay was performed on the BLItz system (ForteBio, PALL), which is powered by BLI (bio-layer interferometry), a powerful labelfree assay technology. The catalytic domain of TACE (residues Arg215-Asn671; R&D Systems) was biotinylated using NHS-LCBiotin (succinimidyl-6-(biotinamido)hexanoate; Sangon Biotech

As described in Section 2, a nine-residue peptide from proTNF-␣ (referred as TNFtide hereafter), PLAQA|VRSS, was docked to TACEcat in Rosetta. Bertini et al. (2006) had previously done a beautiful work in which they captured several transition states of a matrix metalloproteinase, MMP-12, with its peptide product. Matrix metalloproteinases (MMPs) are a family of proteins involved in cell signaling and tissue remodeling, and the zinc-dependent active center of MMPs shows remarkable homology with that of TACE, including the conserved amino acid sequence of -HExGHxxGxxH(Murphy and Lee, 2005). Therefore, the enzymatic mechanism of TACE could be similar to that of MMPs, and indeed, some substrates can be cleaved by both TACE and MMPs, and some MMP inhibitors can also inhibit the activity of TACE (de Meijer et al., 2010; DasGupta et al., 2009; Pirard and Matter, 2006). So we reasoned that the presented models by Bertini et al. should be a good reference for TACE too. In their model of the enzyme/peptide complex, the carbonic oxygen of the P1 residue of the peptide substrate was coordinated by the zinc atom with a distance of around 2.4 A˚ between them, which is also true in the available complex structures of TACE with other molecules in PDB and other similar proteases (Lingott et al., 2009). So, in addition to the Rosetta scores of the models (Raveh et al., 2011), the following extra two rules were applied to pick out the reasonable complex model: first, the distance between the zinc atom and the carbonic oxygen of the P1 residue of the peptide is ˚ second, the P1 residue of the peptide locates in the less than 2.8 A;  S1 pocket of TACEcat, which is known well as the requirement of the activity (Black et al., 2003; Caescu et al., 2009; Lambert et al., 2005; Yang et al., 2010; DasGupta et al., 2009). The docked complex model was further optimized with molecular dynamics modeling, which produced restrain-free complex models (Fig. 1).

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Fig. 1. (A) The 100 complex models from the molecular dynamics simulation are shown in gray. The starting docked model is shown in magenta. The TACEcat chains are shown in lines, and the TNFtide chains shown in sticks. The zinc atoms are shown in spheres. (B) The close view of the TNFtide located in the binding site of TACEcat in a representative model from the molecular dynamics simulation. The TNFtide runs from left to right over the substrate-binding tunnel of TACEcat. (For interpretation of the references to color in text, the reader is referred to the web version of this article.)

Structurally, the peptide fits in the crevice running across the catalytic site of TACEcat. The peptide adopts an anti-parallel ␤sheet conformation against a nearby ␤-sheet of TACEcat, which is quite similar to the docked models in ADAM-9 and ADAM-10 (Manzetti et al., 2003). As expected, the sidechain of the P1 residue of the peptide goes into the S1 pocket deeply, which mainly determines the specificity of the substrate processing by TACE (Black et al., 2003; Caescu et al., 2009; Lambert et al., 2005; Yang et al., 2010; DasGupta et al., 2009). What interested us was the sidechain conformation of the P2 residue and the P2 residue. In the complex model, the sidechains of these two residues are largely exposed, and do not form highly favorable interactions with TACEcat. However, it has been shown that distinct residue preferences exist at these two sites from peptide library screening (Caescu et al., 2009; Lambert et al., 2005). Therefore, we were interested to know if these two sites really affect the substrate processing by TACE, and why if they do.

the dramatic difference between TENtide and the other peptides at the turning point (600 s) of association/disassociation, we propose that this difference is mainly caused by the cleavage of the bound peptide by TACEcat, but this is subjected to further investigation. We then tested the enzymatic cleavage of the peptides by TACEcat (Section 2). As shown in Fig. 3, TNFtide can be cleaved efficiently by TACEcat, but TENtide cannot, which is consistent with the reported result (Caescu et al., 2009). With one mutation in TNFtide sequence, both TNFtide4A and TNFtide7F show lower cleavage efficiency. Additionally, the other two peptides (PLAAAVFSS, named as TNFtide4A7F; PLAMAVMSS, named as TNFtide4M7M), which have mutations at both sites, show even lower activity than both singlemutation peptides. These results prove that the P2 site and the P2 site are indeed very important for the substrate recognition and cleavage by TACE.

3.2. The P2 and P2 sites affect the substrate processing by TACE 0.5

TNFtide (PLAQA−VRSS) TENtide (PRYEA−YKMG) Pep1 (PLAAA−VRSS)

0.4

Response Unit (RU)

To experimentally test the effect of the P2/P2 sites on the substrate processing of TACE, we firstly tried introducing mutations at these sites and evaluating the binding affinities of different peptides to TACEcat. In one peptide, a Gln → Ala mutation was introduced at the P2 site (PLAAAVRSS; named as TNFtide4A); in another peptide, an Arg → Phe mutation was made at the P2 position (PLAQAVFSS; named as TNFtide7F). The TNFtide peptide was used as the positive control, and a reported peptide that cannot be cleaved by TACE, TENtide (PRYEAYKMG) (Caescu et al., 2009), was used as the negative control. The peptides were synthesized and subjected to the real-time binding assay (Section 2). As shown in Fig. 2, at the same concentration, these peptides have different binding curves against the immobilized TACEcat. The negative control, TENtide, has a rather low response, but TNFtide4A and TNFtide7F have higher responses comparable to TNFtide. This result qualitatively indicates that, with one mutation at the P2 site or the P2 site, TNFtide will have changed binding behaviors against TACEcat. Noticing that

Pep2 (PLAQA−VFSS) 0.3

0.2

0.1

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400

600

800

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Time (second) Fig. 2. The binding curves of synthetic peptides to immobilized TACEcat. TNFtide is a most efficient substrate of TACE, and TENtide is a negative control peptide, which is barely cleaved by TACE (Caescu et al., 2009).

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A

B

C

D

E

F

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Fig. 3. The enzymatic cleavage of synthetic peptides by TACEcat was evaluated by LC–MS. The calculated cleavage percentage: (A) TNFtide (PLAQAVRSS), 39.08%; (B) TENtide (PRYEAYKMG), 2.67%; (C) TNFtide4A (PLAAAVRSS), 27.45%; (D) TNFtide7F (PLAQAVFSS), 30.62%; (E) TNFtide4A7F (PLAAAVFSS), 26.94%; (F) TNFtide4M7M (PLAMAVMSS), 2.31%.

3.3. Binding conformation search could play an important role Since the cleavage position is in the middle of the P1/P1 sites, meanwhile the P2/P2 residues are relatively far away and the sidechains do not form clear interactions with TACEcat in the complex model, we supposed that the activity difference could be due to the binding affinity difference of these peptides with TACEcat. Computationally, the affinities of similar peptide–protein complex structures could be compared by the interface score of the complex (Raveh et al., 2011; London et al., 2011). So we tried to see if the affinities of these peptide–TACEcat complexes are correlated with the observed activities. As shown in Fig. 4, although the interface score distributions and scales of the peptide–TACEcat docking decoys are similar, the energy funnels have distinct differences. Before docking, the extended peptide was placed around the active site of TACEcat, and the RMSD value between the peptide in the final complex model and the initial peptide is calculated to be around ˚ Compared to TNFtide, the low-score conformations of 3.0–5.0 A. ˚ that the mutated peptides shift to RMSD values larger than 5.0 A, is, far away from the binding conformation. When another peptide, PRAAAVKSP (named as TACEtide, which can be cleaved by TACE with higher cleavage efficiency than TNFtide (Caescu et al., 2009)), is docked to TACEcat, this trend is more obvious. Therefore, the conformation search behavior of the peptide could be correlated to its cleavage efficiency. When the peptide is harder to find the correct binding conformation, the relative binding rate is slower, and the cleavage efficiency is lower. 4. Discussion TACE is one of the most well studied enzymes in the ADAM family, and has been found to be responsible for the releasing of around 80 proteins from their membrane-bound forms, such as TNF-␣,

TGF-␤, TNF receptor I, TNF receptor II, interleukin-6 receptor, lselectin, and Notch (Scheller et al., 2011; Gooz, 2010; Stephenson and Avis, 2012). But how TACE recognizes the cleavage sequence of these proteins remains an unsolved question (Scheller et al., 2011; Hartmann et al., 2013). Previous studies proved that the substrate recognition of TACE is not random, but with clear sequence preference. For example, Caescu et al. (2009) and Lambert et al. (2005) noticed obvious residue preference at the P4–P4 sites by using peptide libraries. However, no structural information was provided in their studies to explain the sequence preference. Recently, computational modeling has provided an excellent tool to understand protein–peptide specificity (Smith and Kortemme, 2010; Kaufmann et al., 2011; Walshe et al., 2009; Grigoryan et al., 2009; Goldschmidt et al., 2010; Fu et al., 2007; Mandell and Kortemme, 2009; Kota et al., 2009; Humphris and Kortemme, 2008; Smith and Kortemme, 2011; Babor et al., 2011) and elucidate enzyme–peptide specificity/activity mechanisms (Chaudhury and Gray, 2009; London et al., 2011). Therefore, we wanted to see if computational modeling could help to understand the substrate recognition mechanism of TACE. In this study, we made a complex model of a substrate peptide (TNFtide) and the catalytic domain of TACE (TACEcat) (Fig. 1). Overall, the substrate peptide shows a beta-strand conformation, which nicely forms an anti-parallel beta-sheet with a neighbor beta-strand of TACEcat. This conformation is similar to the peptide–enzyme complex models of TACE-homologous proteins MMP3, ADAM9 and ADAM10 (Manzetti et al., 2003). By forming the beta-sheet conformation with TACEcat, the substrate peptide can therefore have high binding affinity. More importantly, in this way, the peptide can place the sidechain of the P1 residue deeply in the S1 pocket, and bring the cleavage bond to the catalytic zinc atom.

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Fig. 4. The docking energy landscapes of different peptides with TACEcat were obtained in FlexPepDocking-AbInitio protocol in Rosetta (Raveh et al., 2011). The peptide–protein interface scores (I sc) are plotted against the peptide RMSD values. The RMSD value shows the conformation variation of the peptide in the complex model, ˚ (A) TNFtide; (B) TENtide; (C) TNFtide4A; (D) TNFtide7F; (E) compared to the initial extended peptide. The correct peptide conformation has RMSD values around 3.0–5.0 A. TNFtide4A7F; (F) TNFtide4M7M; (G) TACEtide.

From the TNFtide-TACEcat complex model, we were interested to know if the P2 residue and the P2 residue are important for the substrate recognition of TACEcat, since the sidechains of them are highly exposed and do not show stable interactions with TACEcat in the model. By introducing mutations into these two sites, we found that these two positions can outstandingly affect the substrate cleavage efficiency (Fig. 3). To understand the possible reason that these two positions affect the substrate cleavage efficiency, we computationally investigated the binding energies of different peptides with TACEcat (Fig. 4). The interface scores of different peptide-TACEcat complex models are similar, which means that the mutations at P2/P2 sites do not severely affect the binding energy. This is actually consistent with the sidechain conformation of these two residues mentioned above. Since the P2/P2 sites are relatively far away from the cleavage bond between the P1/P1 sites, we supposed that once the complex structure forms, the cleavage efficiency should be independent of the sidechains of the P2/P2 residues. Therefore, subjected to further investigation, we reasoned that a possible explanation is the P2/P2 residues affect the substrate cleavage in the substrate binding phase. Our computational analysis supported

this speculation, showing that the mutations at the P2/P2 sites can shift the conformation search behavior (Fig. 4). So the rate of the conformation search of the substrate peptide to find the correct binding orientation could play an important role in the substrate processing of TACE. Structurally, the substrate-binding site of TACEcat is highly electro-negative as computed with APBS (Baker et al., 2001) (Fig. 5), and it has been reported that this special electrostatic potential has a great influence on the substrate specificity of TACE (Sagi and Milla, 2008; Solomon et al., 2007; Solomon, 2004; Stone et al., 1999; Milla, 1999). The introduced mutations in our peptide designs eliminated the polar sidechains of the P2/P2 residues, which could make a big difference to the electrostatic interaction between the peptide and TACEcat. Therefore, we reasoned that the activity difference of the P2/P2 mutant peptides is largely affected by electrostatic steered inter-molecular interactions (Wade et al., 1998), since various steps can affect a enzyme catalytic cycle, including the formation of a coupled interactions that bring the substrate closer, orienting it properly, and providing a favorable electrostatic environment (Sagi and Milla, 2008; Benkovic and Hammes-Schiffer, 2003). We should mention that, although the P2 residue in TACEtide is Ala, it seems

S. Liu et al. / Molecular Immunology 62 (2014) 122–128

Fig. 5. The surface electrostatic potential of TACEcat was computed with APBS (Baker et al., 2001). The surface is shown as the van der Waals surface and colored by potential on solvent accessible surface. The catalytic zinc atom is shown in sphere.

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