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Homology modeling, molecular docking, and molecular dynamics simulations elucidated α-fetoprotein binding modes
© Shen et al; licensee BioMed Central Ltd. 2013
Published: 9 October 2013
An important mechanism of endocrine activity is chemicals entering target cells via transport proteins and then interacting with hormone receptors such as the estrogen receptor (ER). α-Fetoprotein (AFP) is a major transport protein in rodent serum that can bind and sequester estrogens, thus preventing entry to the target cell and where they could otherwise induce ER-mediated endocrine activity. Recently, we reported rat AFP binding affinities for a large set of structurally diverse chemicals, including 53 binders and 72 non-binders. However, the lack of three-dimensional (3D) structures of rat AFP hinders further understanding of the structural dependence for binding. Therefore, a 3D structure of rat AFP was built using homology modeling in order to elucidate rat AFP-ligand binding modes through docking analyses and molecular dynamics (MD) simulations.
Homology modeling was first applied to build a 3D structure of rat AFP. Molecular docking and Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) scoring were then used to examine potential rat AFP ligand binding modes. MD simulations and free energy calculations were performed to refine models of binding modes.
A rat AFP tertiary structure was first obtained using homology modeling and MD simulations. The rat AFP-ligand binding modes of 13 structurally diverse, representative binders were calculated using molecular docking, (MM-GBSA) ranking and MD simulations. The key residues for rat AFP-ligand binding were postulated through analyzing the binding modes.
The optimized 3D rat AFP structure and associated ligand binding modes shed light on rat AFP-ligand binding interactions that, in turn, provide a means to estimate binding affinity of unknown chemicals. Our results will assist in the evaluation of the endocrine disruption potential of chemicals.
The potential for environmental and exogenous chemicals to interfere with hormone (endocrine) systems in both humans and wildlife has been an international scientific debate that has persisted for many decades . Concerns associated with so-called endocrine disruptors (EDs) led to requirements in the Food Quality Protection Act of 1996 (FQPA1996) and the Safe Drinking Water Act Amendments of 1996 (SDWA Amendments 1996) for the Environmental Protection Agency (EPA) to screen and identify substances with hormonal effects. In accordance to these acts, the EPA developed the Endocrine Disruptor Screening Program (EDSP) to identify chemicals with potential for endocrine disruption . The endocrine system comprises glands that produce hormones and the receptors that respond to those hormones , as well as other proteins that can bind the hormones in serum . EDs can mimic endogenous hormone ligands acting as agonists, partial agonists, or antagonists, altering gene expression and homeostasis, resulting in adverse developmental, reproductive, neurological and immune system effects . The ability of chemicals to bind hormone receptors is a major mechanism for altering endocrine activity. Exogenous chemical binding to ER is particularly concerning due to potential for altering normal estrogen signaling through genomic and non-genomic pathways [6–8].
AFP is a serum protein in mammals that is produced in the yolk sac and liver of a developing fetus . It is a member of the albuminoid gene superfamily. In human, AFP has long been used as a serum marker for fetal defects and tumor progression . In rodents, AFP sequesters endogenous estrogen , blocking entry where it would otherwise induce ER-mediated responses.
To better estimate ER binding potential of a chemical in rodents, it is important to know its rat AFP binding properties. In 1972, Uriel et al. first reported the binding properties of rat AFP to steroidal chemicals using an immuno-autoradiographic assay . Thereafter, rat AFP has been used to study in vitro binding and in vivo transport of steroids [13–15]. Recently, we developed a competitive binding assay using rat amniotic fluid, and used it to measure AFP binding affinities to 125 chemicals in 15 diverse, structural categories . Some 53 chemicals from 13 categories bound AFP, while 72 chemicals did not. Importantly, we previously also measured ER binding affinity for 114 of the 125 chemicals, of which 47 bound both ER and AFP, 42 bound only ER, and 19 bound neither AFP nor ER. ER binding was not measured for the remaining 11 chemicals. These data provide a large dataset to study ligand to rat AFP binding preferences. Two possible estrogen-binding sites on rodent AFP have been proposed based on experimental binding data [16, 17]; however, no 3D structures for rat AFP (with or without bound ligands) were available to confirm the binding sitesand structural dependencies on site activity.
Computational methods to predict protein structure and ligand-protein interaction have been successfully applied in biochemical research for decades. Terentiev et al. have reported the 3D homology model of human AFP in 2012. In their work, the 3D model of human AFP was built to study the interaction of human AFP and diethylstilbestrol (DES), which is a strong ER binder and a validated endocrine disruptor. Herein, we built a 3D structure for rat AFP using homology modeling with subsequent optimization with MD simulations. Using the optimized 3D rat AFP structure, the rat AFP-ligand binding modes for 13structurally diverse rat AFP binders were calculated with molecular docking, followed by refinement using MD simulations.
This study reports the first 3D structure of rat AFP that was built through homology modeling and optimized using MD simulations. The 3D structure was demonstrated to be stable and trustworthy. Based on the 3D structure, the ligand binding modes of 13 structural diverse rat AFP binders were elucidated using molecular docking. Moreover, rat AFP conformation changes induced by ligands during the MD simulations were observed. Ligand binding free energies of the rat AFP binders were calculated using the MM-GBSA method and revealed that rat AFP can accommodate structurally diverse ligands having different electrostatic and hydrophobic properties. Glu206 was found to be the most important residue for rat AFP binding to flavones and mycoestrogens, while Tyr168 was most important for binding benzophenones and coumarin.
Materials and methods
The sequence of rat AFP was downloaded from the universal protein resource (Uniprot)  (entry: P02773). The template for sequence alignment was identified through searching rat AFP on PDB using the BLASTp  program provided by Uniprot with default parameters. The 3D structure of rabbit serum albumin (Uniprot ID: G1U9S2) was downloaded from PDB (PDB ID: 4V5F)  as the template structure. The homology model of rat AFP was built with Prime 3.1 in Schrödinger Suite (Schrödinger, LLC, New York, NY). The secondary structure of rat AFP was predicted using the SSpro program bundled with Prime. The target (rat AFP) and template (rabbit serum albumin) sequences were aligned using the ClustralW method employed in Prime, followed by manual adjustment to avoid big gaps in the secondary structure domain. The original ligand in the template structure was removed before homology modeling.
The initial 3D structure of rat AFP obtained from homology modeling was optimized using MD simulation. The Amber ff03.R1  force field was applied to the protein. Topology and parameter files were generated using the LEaP program. MD simulation was conducted using Amber11 . The 3D rat AFP structure was surrounded by a truncated octahedron periodic box of TIP3Pwater molecules with a margin of 10.0 Å along each dimension. Sodium ions were added to the system to maintain its charge neutrality. All covalent bonds to hydrogen atoms were constrained using the SHAKE algorithm. Electrostatic interactions were calculated using the particle-mesh Ewald (PME) algorithm with a cutoff of 10 Å for Lennard-Jones interactions. Periodic boundary conditions were applied to avoid edge effects. Prior to MD production, 500 steps of steepest-descent minimization and 1500 steps of conjugated gradient minimization were applied to the solvent and the entire model system, respectively. The entire system was heated from 0 to 300 K gradually over 30 ps (picoseconds) using the NVT (constant volume and normal temperature) ensemble with the solutes restrained by a weak harmonic potential. During the heating, time constant for heat bath coupling for the solute was set as 1.0.Afterward, 140 ps of equilibrations were carried out in the NPT (constant normal pressure and normal temperature) ensemble via three steps: first the solutes were restrained while the waters and counter-ions were equilibrated in the first 20 ps; then the side chains of rat AFP were relaxed in the next 20 ps; last, all the restraints were removed in the last 100 ps. Finally, 10 ns (nanoseconds) MD simulations were conducted at 1 atm and 300 K under the NPT ensemble with a time step of 2 fs (femtoseconds). The temperature was controlled using Langevin dynamics. The coordinates of all atoms in the system were saved every 1 ps during the entire MD simulations.
Docking grid generation
Prior to molecular docking, the optimized 3D rat AFP structure was prepared using the "Protein Preparation Wizard" workflow in Schrödinger Suite. Bond orders were assigned and hydrogen atoms were added to the protein. The structure was then minimized to reach the converged root mean square deviation (RMSD) of 0.30 Å with the OPLS_2005 force field. Probable ligand binding sites (on or near the protein surface) were searched using SiteMap2.6 in Schrödinger Suite. Then, contour maps of hydrophobic and hydrophilic fields were generated. The hydrophilic maps were further divided into donor, acceptor, and metal-binding regions. Finally, all the sites were assessed by calculating various properties. Thereafter, a docking grid was defined using "Receptor Grid Generation" in Schrödinger Suite. The grid enclosing box was centered in rat AFP with an internal size of 14 × 14 × 14 (x × y × z, Å). The grid was made large enough to cover all the potential ligand binding sites in the protein. Since the active site of rat AFP is not tight and encapsulated, the scaling factor of Van der Waals radius was set as 1.0 with a partial atomic charge less than 0.15 e, which means no scaling is done in this case.
The optimized 3D structures of the 13 rat AFP binders were docked into the docking grid in the 3D structure of rat AFP using Glide5.8 in Schrödinger Suite with standard precision (SP). The final binding poses with the top glide score were selected for further optimization through MD simulations.
MD simulations were performed for the 13 rat AFP-ligand complexes using the similar protocol described in the MD optimization section. The ligands were optimized at the Hartree-Fock level with the 6-31G(d,p) basis set using Gaussian 09 (Gaussian, Inc., Wallingford, CT). Restrained electrostatic potential (RESP) charges were then calculated using the B3LYP/cc-pVTZ quantum mechanical method. The general Amber force field (GAFF) was applied to the complexes . Topology and parameter files were generated using the LEaP program .The force field parameters of ligands wereobtained from the Antechamber modulein the AmberTools1.5 program . The system minimization, heating, and equilibration were carried out in the same manner used for the optimization of 3D rat AFP structure described above. The MD simulations were performed for up to 60 ns for each complex system. The coordinates of atoms in the complex were saved every 10ps during the simulations.
Binding free energy calculation
The polar contribution (ΔGGB) was calculated with the modified GB model described by Onufriev et al.  using εw= 80 and εp=1.0. SASA is the solvent-accessible area that is determined using the linear combination of pairwise overlaps method . The surface tension proportionality constant γ and the free energy of nonpolar solvation for a point solute b were set to 0.0072 kcal/mol/Å2 and 0.00 kcal/mol, respectively. The radius of the probe sphere used to calculate SASA was set to 1.4 Å. The entropy calculation is only a rough estimation with normal mode analysis. The calculated free energies were used for comparisons among the 13 rat AFP binders. Therefore, the entropy term was not included in our analyses. The final binding free energy for a rat AFP binder was the average value from the 1000 snapshots in the last 10 ns of MD simulations.
Results and discussions
Optimized 3D structure of rat AFP
The initial alignment of rat AFP sequence with the template sequence was obtained using ClustalW. The alignment is consistent with experimental results  reporting that 15 disulphide bridges in the template structure are perfectly aligned to the rat AFP sequence (see Figure 3, where cystines are highlighted in yellow). The region of residues 70-110 in rat AFP could not be aligned with the template because rat AFP is larger. This region was manually adjusted according to the predicted secondary structures of rat AFP to avoid large gaps located inside the secondary structures of the template. The final alignment (32% identity, 51% positivity, and 4% gaps) used in the homology modeling and in the secondary structure prediction is shown in Figure 3. Besides the disulphide bridges, most of the secondary structures align well between the template and rat AFP. The initial homology models of rat AFP were built using Prime, with the model with the lowest energy used for further optimization.
Binding modes generated by molecular docking
The two putative rat AFP binding sites proposed without the benefit of crystal structure data were speculative [16, 17]. Herein, we used SiteMap  to search and analyze the potential ligand binding sites in rat AFP. To elucidate possible binding modes of different ligands, a large box was defined to cover all the potential binding sites generated by SiteMap. Therefore, the 13 ligands adopted the most favorable binding poses.
The binding poses of the 13 rat AFP binders were assessed using the GScore scoring function comprised van der Waals energy, Coulomb energy, hydrophobic interactions, hydrogen bonding, polar interactions, and rotatable bonds penalty. The docking scores of the 13 rat AFP binders as well as their experimentally- determined binding affinities (IC50) are given in supplementary Figure S2. Quercetin had the lowest docking score. The docking results revealed that a hydrogen bonding network formed between the hydroxyl groups in the dihydroxybenzene part of quercetin and the residues Glu206, Tyr306 (supplementary Figure S2 in additional file 1). Such a hydrogen bonding network is likewise observed in the docking poses of coumestrol, α-zearalanol, diethylstilbestrol (DES), dioxybenzone, and heptyl p-hydroxybenzoate (supplementary Figure S2 in additional file 1). Estrone and 2,3,4,5-tetrachloro-4'-biphenylol were the two most potent rat AFP binders, and were also highest ranked in the docking analyses. Further examination of the bind poses indicated major contributions to their affinity are hydrophobic interactions.
Binding modes refined by MD simulations
While molecular docking has been successfully used in predicting binding poses of ligands for many proteins, it has also failed in estimating of ligand binding affinity [35, 36]. One of the major reasons for failure is treatment of proteins as "rigid" molecules in order to save computational time, and thus not allowing their conformations to adjust during docking. The rigidity assumption works well for proteins that lack flexibility. However, AFP is a very flexible protein, and AFP conformation change induced by some ligands has been reported [37, 38]. MD simulation has been extensively applied to study conformation changes in protein-ligand interactions [39, 40], protein dynamics [41, 42], and protein folding [43, 44]. Given AFP flexibility, MD simulations were deemed necessary to compare rat AFP conformation changes anticipated to differ across the 13 structurally diverse binders.
Calculated Binding Free Energies in Comparison with Available Experimental Data (All in kcal/mola)
To make direct comparisons between experimental binding affinities, (ΔGexp) was estimated from binding affinity data using ΔGexp ≈ -RT ln IC50 with results listed in Table 1. The reason we can make such approximation is that the dissociation constant Kd is proportional to Zreactants/Zproducts, where Z is ensemble partition function. Consequently, the MM-GBSA binding free energies were much lower than the binding free energies estimated from experimental data. Analyzing the components in the MM-GBSA biding free energies indicated electrostatic energy to be the major contributor for binders with high polarity. For example, ΔGelec was -157 kcal/mol for quercetin that contains 5 hydroxyl groups, while ΔGelec was 128 kcal/mol for α-zearalanol that contains 3 hydroxyl groups. These two ligands have less unfavorable/positive nonpolar penalties ΔGnp of 36 and 10 kcal/mol, respectively.
AFP can change the availability of estrogenic chemicals to enter target cells. Knowledge of the binding affinity of an estrogenic chemical is important to estimate its potential endocrine activity. Such ER-mediated activity can be altered in rat through binding to AFP that sequesters estrogen in rat in circulating serum. The tertiary structures of AFP are crucial to understanding AFP-ligand interactions for evaluating chemical endocrine activity. Our results on binding interactions between chemicals and rat AFP would be helpful for further studies including evaluation of endocrine disruption potential of chemicals in the human environment, and designing more efficient drug products that target ER or compete with AFP sequestration of drugs.
The findings and conclusions in this article have not been formally disseminated by the US Food and Drug Administration (FDA) and should not be construed to represent the FDA determination or policy.
This research was supported in part by an appointment to the Research Participation Program at the National Center for Toxicological Research (Jie Shen and Wenqian Zhang) administered by the Oak Ridge Institute for Science and Education though an interagency agreement between the U.S. Department of Energy and the U.S. Food and Drug Administration. All high-performance computations were performed using the Blue Meadow in FDA Scientific Computing Lab.
Publication costs of this article were funded by the US government.
This article has been published as part of BMC Bioinformatics Volume 14 Supplement 14, 2013: Proceedings of the Tenth Annual MCBIOS Conference. Discovery in a sea of data. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/14/S14.
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