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NEUROBLASTOMA HOMING PEPTIDE SCREENING USING UNREFINED HOMOLOGY STRUCTURE OF

NOREPINEPHRINE TRANSPORTER

YAZAN HADDAD1,2, ZBYNEK HEGER1,2, VOJTECH ADAM1,2

1Department of Chemistry and Biochemistry Mendel University in Brno Zemedelska 1, 613 00 Brno

2Central European Institute of Technology Brno University of Technology

Purkynova 123, 612 00 Brno CZECH REPUBLIC

yazanhaddad@hotmail.com

Abstract: The norepinephrine transporter (hNET) is a potential target for many antidepressants and for neuroblastoma therapeutics. The entrance channel of hNET and also dopamine transporter (DAT) serve as candidate site for targeting by large peptides e.g. α-helix-based. Targeting peptides, also known as homing peptides, are used to direct the delivery of cargo to specific cell types. Peptides of known secondary structures such as α-helix and β-sheet have predictable and stable folding. In this study, approx. 27 peptides, with predictable secondary structures, were evaluated by 20 dockings predictions on unrefined hNET homology model and DAT crystal structure using molecular mechanics (total ~1080 models). As anticipated, peptide size was detrimental for docking in channel space, whereas peptide isoelectrics point did not affect docking. Peptide’s initial non-bonded energy affected docking while overall peptide free energy and initial electrostatic energy did not. Two α-helices showed favorable docking in channel of hNET; namely, GASNGINAYL and SLWERLAYGI with binding energy of -106.2 kJ/mol and -128.6 kJ/mol, respectively. Prior to in vitro and in vivo applications, future work will focus on development of refined accurate model of hNET and application of solvated docking (in presence of water). This study provides new insight to the development of helix-based therapeutic peptides.

Key Words: targeted therapy; norepinephrine transporter; homing peptide; molecular mechanics; docking

INTRODUCTION Targeting peptides, also known as homing peptides, are used to direct the delivery of cargo (drugs)

to specific cell types. Many homing peptides have been investigated for applications in diagnosis and treatment of different types of cancer (van den Berg and Dowdy 2011; Svensen et al. 2012). Current methods of new targeting peptide discovery rely on screening of phage display peptide libraries and one-bead one-compound libraries (Gautam et al. 2014). Peptide-receptor interactions can provide valuable insight for the development of novel and more potent therapeutics. Molecular docking is a primary method for computational evaluation of protein-protein interactions. Docking is comprised of three steps: First, representation of the system (structures of receptor vs. ligand) followed by conformational-space search. Finally, there is an evaluation step which involves ranking or scoring of potential solutions (Halperin et al. 2002). Methods for evaluation of docking can be classified to either empirical (e.g. based on wet lab experimental data) or knowledge-based methods (e.g. based on statistical analysis of distances between atom types in ligand receptor interface). Molecular mechanics (MM) based on evaluation of free binding energy can be an alternative approach for scoring of docking processes; limited only for high throughput of ligand-receptor investigations (dozens or hundreds) because they are computationally expensive (Huang et al. 2006).

Norepinephrine transporter (hNET) is one of the most promising targets for treatment of neuroblastoma (Matthay et al. 2012). It belongs to SLC6 family of sodium dependent neurotransmitter

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transporters involved in translocation of small amino acid or amino acid-like substrates, including serotonin, dopamine, norepinephrine, GABA, taurine, and creatine (Pramod et al. 2013). The first choice to design hNET targeting peptides would be based on mimicking cell-surface interacting proteins with hNET. Unfortunately, all known interacting proteins such as 14-3-3 protein, syntaxin1A, proteinphosphatase 2A (PP2A), PICK1, and Hic-5 bind the intracellular domains of hNET (Sung et al. 2005). Early studies on dopamine and serotonin transporters suggested that they form oligomers (Torres et al. 2003). Due to this, the reasonable approach was to use peptides mimicking hNET itself, presuming that such peptides will have self-affinity to the same molecule. Since linear peptides are highly unpredictable, the secondary structure; particularly alpha helix, was the primary choice for design of stable predictable 3D molecule. Alpha helix is the classical basic element of protein structure. One helix can have more influence on the stability and organization of a protein than any other individual structure element. It is composed of 3.6 residues per turn, with a hydrogen bond between the CO of residue n and the NH of residue n+4 (Richardson 1981).

The aim of this study was to design peptides of known secondary structures that are able to bind the norepinephrine transporter (hNET) for targeted therapy.

MATERIAL AND METHODS

Building of Structure Models The structure of hNET (Uniprot ID: P23975) was constructed using SWISS-MODEL (Bordoli et

al. 2009). The X-ray crystal structure of dopamine Transporter (PDB ID: 4M48), (Penmatsa et al. 2013) with 2.96Å resolution, 59.9% sequence identity and 0.94 coverage was used as model template. MolProbity, (Davis et al. 2007) was used to check the quality of hNET model. Model quality check was only used to assess the weaknesses in the constructed models. No energy minimization, refinement or deliberate change in structure were made. The focus of this work was to develop screening method as a proof of concept. We have recently analyzed and conveyed guidelines for developing accurate model of hNET (Haddad et al. 2016). Future work will focus on employing such model in homing peptide design. Peptide sequences were selected from hNET sequences with known α-helix or β-sheet secondary structures. PepFold, (Maupetit et al. 2009) was used to construct peptide models. Peptide model was acceptable for further analysis when de novo structure resulted in predicted secondary structure conformation. Rejected structures were mostly due to disruptions caused by PRO residue in the middle of the peptide.

Molecular Docking Preparation for docking was done using “dock prep” tool in UCSF Chimera version 1.10.2

(Pettersen et al. 2004). Addition of hydrogens was performed with consideration to possible hydrogen bonds. Protonation states were as follows: GLU, ASP and LYS were charged. HIS was unspecified and determined by method. The assignment of charges was performed according to AMBER ff14SB force field (Maier et al. 2015). All docking experiments were performed using GRAMM-X Protein-Protein Docking Web Server v.1.2.0 (Tovchigrechko and Vakser 2006). Approximately 20 alternative predictions per docking were considered feasible load for screening by molecular mechanics computations.

Molecular Mechanics Molecular Mechanics (MM) force field energy calculation was performed using GROMOS96

forece field, (Scott et al. 1999) in vacuo, in DeepView/Swiss-PDB Viewer v4.1.0 (Guex and Peitsch 1997). Results in text format were transferred to Microsoft Excel for analysis. Accordingly free energy of each amino acid residue was calculated by the equation:

E = ΔGbond + ΔGangles + ΔG torsion + ΔGimproper + ΔGnonBonded + ΔGelectro

Where ΔGbond is the energy of covalent bonds, ΔGangles is the energy of bond angles, ΔGtorsion is the energy resulting from torsion forces, ΔGimproper is the energy resulting from improper clashes, ΔGnonBonded is non-covalent bond energy of van der Waals, and ΔGelectro is energy from electrostatic interaction (ionic bonds and Hydrogen bonds).

ΔΔG Energy of binding (Ebind) in vacuo was calculated according to the equation:

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Ebind = ER+P – ER – EP Where ER+Pis ΔG of docking complex, ER is ΔG of hNET receptor, and EP is ΔG of peptide ligand. Based on distribution of binding energy results (N=1080 docking models, range from -201.0 to

+4.4 × 1019), models were classified in three categories: models with Ebind< 0 kJ/molwere considered favorable, models with low binding energy (0 <Ebind< 1000 kJ/mol) were considered unfavorable, whereas models with Ebind> 1000 kJ/mol were considered highly unfavorable.

RESULTS AND DISCUSSION

hNET Structure Model The human hNET structure constructed by SWISS-MODEL,(Bordoli et al. 2009)was evaluated

taking in consideration the x-ray-based drosophila DAT (PDB ID: 4M48),(Penmatsa et al. 2013)as control reference. Molprobity scores were 2.96 and 1.96 for hNET SwissModel and the x-ray based DAT model, respectively. All 8 Ramachandran outliers in hNET were in the poorly modeled extracellular domain (EL2) whereas no outliers were reported in DAT. Approximately eight rotamer outliers were reported in hNET, whereas in DAT ~12 rotamer outliers were reported. In hNET, the rotamer outliers were either hidden, intracellular or in the EL2 except for TRP80 and THR381 which were in channel. Another worth mentioning remark in the model is the bad clashes which were seven folds more frequent in the hNET model compared to DAT model. We have recently analyzed and conveyed guidelines for developing accurate model of hNET (Haddad et al. 2016). Future work will focus on employing such model in homing peptide design.

Molecular Docking and Molecular Mechanics Peptides with predictable secondary structures (particularly alpha helix based) can be candidates

for homing/targeting carriers of cargo due to their more rigid nature and seclusion as independent protein domains when compared to unpredictable linear peptides. Here, Twenty six α-helix and one β-sheet peptide structures were tested for their affinity to the human hNET transporter and drosophila DAT transporter. Peptide sequences were selected from hNET (Uniprot ID: P23975) based on their secondary structure, and then de novo models were constructed using PepFold. The affinity of each peptide to hNET and DAT transporters was investigated by performing 20 docking predictions via GRAMM-X, and evaluated using molecular mechanics GROMOS96 force field. The GRAMM-X server allows for a maximum of 300 dockings per trial and 10 trials per day, therefore this study was only limited by the computational challenge of molecular mechanics. Models were classified in three categories: models with negative binding energy (Ebind) were considered favorable, models with low binding energy (0 <Ebind< 1000 kJ/mol) were considered unfavorable, whereas models with Ebind> 1000 kJ/mol were considered highly unfavorable. In total, 1080 resulted models were evaluated. The sequences and docking results of 27 peptides in hNET and DAT are shown in (Table 1). Upon docking, no observable change in binding energy was attributed to covalent binding free energy(ΔΔGbond, ΔΔGangles, ΔΔGtorsion, or ΔΔGimproper). This can be explained by the rigid nature of GRAMMX docking that requires no direct change in covalent bonds. Binding energy attributed to van der Waals (ΔΔGnonBonded) as well aselectrostatic (ΔΔGelectro) interactions contributed variably.

As anticipated, peptide size was detrimental for docking in channel space of hNET and DAT (Figure 1a). Peptides composed of less than 13 amino acids showed very high frequency of docking in channels of hNET and DAT, while overall charge of peptide (pI) did not show any correlation (Figure 1b).Therefore,molecular weight is one of the major considerations for the cargo that can be linked to such homing peptides. Peptide’s initial non-bonded energy affected docking while overall peptide free energy and initial electrostatic energy did not (Figure 1c-e). Two helices showed favorable docking in channel of hNET; namely, GASNGINAYL and SLWERLAYGI with binding energy of -106.2 kJ/mol and -128.6 kJ/mol, respectively (Figure 1g-f). For GASNGINAYL, the effect of van der Waals interaction (ΔΔGnonBonded = -179.9 kJ/mol) compensated for electrostatic repulsion (ΔΔGelectro = +67.3 kJ/mol). The ΔΔG contributed by the peptide were -50.5 kJ/mol suggesting that the electrostatic repulsion was higher from amino acid residues of hNET than residues of peptide.In the case of SLWERLAYGI both nonbonded and electrostatic interaction contributed to binding (ΔΔGnonBonded = -90.2 kJ/mol and ΔΔGelectro = -43.3 kJ/mol). The ΔΔG contributed by the peptide residues was-62.1

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kJ/mol. GASNGINAYL peptide was also docked in channel of DAT in two models (Ebind = -139.8 and -88.0 kJ/mol) whereas three models of SLWERLAYGI showed docking in channel of DAT (Ebind = -192.3, -58.5 and -12.8 kJ/mol). Five other peptides showed energy favorable dockings in channel of DAT: IDAATQIFFSL (Ebind = -201.0 and -152.8 kJ/mol), TFWAVVFFVMLLALG (Ebind = -52.8 kJ/mol), TFSTFLLALFC (Ebind = -96.4 kJ/mol), and IYVLTLLDT (Ebind = -72.9 kJ/mol). These findings are confronted with two main limitations: The accuracy of structural models and the docking in vacuum space. Water solvent and possibly other salt ions are reported to play major role in receptor-ligand interactions. Exploration of docking in the presence of water can now be done using HADDOCK approach (van Zundert et al. 2016). In this study, the two main determinants in binding, namely van der Waals and electrostatic interactions are sensitive to both temperature and ionic strength (particularly influenced by pH), respectively. Temperature and ionic strength are factors that should be considered in differences between hypothetical and experimental binding parameters.

Table 1 Peptide models and summary of docking in channel entrance of hNET and DAT. No. Peptide 2° Amino

Acids MWt pI Dockings in

channel N=20

Favorable dockings in

channel hNET DAT hNET DAT

1 IDFLLSVVGFA α 11 1180 3.80 16 15 2 MPLFYMELALGQYN α 14 1690 4.00 8 11 3 GVGYAVILIALYVG α 14 1408 5.52 15 14 4 YAVILIALYVG α 11 1194 5.52 19 15 5 NVIIAWSLYYLFS α 13 1589 5.52 6 9 6 IAWSLYYLFS α 10 1262 5.52 16 16 7 WQLLLCLMVVVIVLY α 15 1805 5.52 7 10 8 YFVLFVLLVHGVT α 13 1507 6.74 12 12 9 YFVLFVLLVHG α 11 1307 6.74 13 12 10 GASNGINAYL α 10 979 5.52 20 18 1 2 11 IDAATQIFFSL α 11 1225 3.80 19 15 2 12 YRDALLTSSINCITSFV α 17 1903 5.83 9 2 13 VSGFAIFSILGYMAHE α 16 1742 5.24 10 3 14 GFAIFSILGYMAHE α 14 1556 5.24 13 6 15 INCITSFVSGFAIFSILG α 18 1889 5.52 8 4 16 ITSFVSGFAIFSILG α 15 1559 5.52 13 8 17 TFWAVVFFVMLLALG α 15 1714 5.19 4 10 1 18 LDSSMGGMEAVITGLAD α 17 1667 3.49 12 7 19 TFSTFLLALFC α 11 1262 5.18 14 13 1 20 IYVLTLLDT α 9 1050 3.80 19 17 1 21 GTSILFAVLMEAI α 13 1365 4.00 16 16 22 VDRFSNDIQQMM α 12 1484 4.21 8 10 23 YWRLCWKFVS α 10 1388 9.31 14 10 24 AFLLFVVVVSII α 12 1320 5.57 19 13 25 PLTYDDYIFP β 10 1243 3.56 17 16 26 WANWVGWGIALSSMVLV α 17 1889 5.52 11 6 27 SLWERLAYGI α 10 1207 5.72 19 18 1 3

CONCLUSION It is possible to design homing peptides using approach of molecular docking and evaluation using

molecular mechanics. Here, two α-helices showed favorable docking in channel of hNET; namely, GASNGINAYL and SLWERLAYGI. It is important to develop accurate homology model of hNET, to confirm these findings prior to in vitro and in vivo analysis.

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Figure 1 Molecular docking inside channel of neurotransmitter transporters

(a) Role of peptide size. (b) Role of peptide charge/isoelectric point. (c) Role of peptide total initial free energy. (d) Role of peptide initial electrostatic free energy. (e) Role of peptide initial non bonded free energy. (f) Docking of GASNGINAYL at channel of hNET. (g) Docking of SLWERLAYGI peptide at channel of hNET. Peptides are shown in green. hNET is shown in orange ribbon and transparent grey surface.

ACKNOWLEDGEMENTS The research was financially supported by the Czech Agency for Healthcare Research, AZV (15-28334A).

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