Peptide probes derived from pertuzumab by molecular dynamics modeling for HER2 positive tumor imaging

Xiaoliang Yang; Zihua Wang; Zhichu Xiang; Dan Li; Zhiyuan Hu; Wei Cui; Lingling Geng; Qiaojun Fang

doi:10.1371/journal.pcbi.1005441

Abstract

A high level of HER2 expression in breast cancer correlates with a higher tumor growth rate, high metastatic potential, and a poor long-term patient survival rate. Pertuzumab, a human monoclonal antibody, can reduce the effect of HER2 overexpression by preventing HER2 dimerization. In this study, a combination protocol of molecular dynamics modeling and MM/GBSA binding free energy calculations was applied to design peptides that interact with HER2 based on the HER2/pertuzumab crystal structure. Based on a β hairpin in pertuzumab from Glu46 to Lys65—which plays a key role in interacting with HER2—mutations were carried out in silico to improve the binding free energy of the hairpin that interacts with the Phe256-Lys314 of the HER2 protein. Combined the use of one-bead-one-compound library screening, among all the mutations, a peptide (58F63Y) with the lowest binding free energy was confirmed experimentally to have the highest affinity, and it may be used as a new probe in diagnosing and treating HER2-positive breast cancer.

Author summary

Many therapeutic approaches, including the human monoclonal antibodies trastuzumab and pertuzumab, target the human epidermal growth factor receptor 2 (HER2) of any breast cancer that features HER2 overexpression. Compared to these antibodies, peptides have many advantages, including lower cost, easier synthesis, high affinity, and lower toxicity. Here, we first designed peptides that interact with HER2 protein based on the HER2/pertuzumab crystal structure (PDB entry: 1S78), using a combination protocol of molecular dynamics modeling, molecular mechanics/generalized Born solvent-accessible surface area (MM/GBSA) binding free energy calculations. Then, combined with the peptide library screening, six peptides were selected for further analysis and experimental validations. The results of ex vivo and in vivo experiments confirmed that one peptide (58F63Y) in particular has a strong affinity and high specificity to HER2-overexpressing tumors. This may due to more paired residues and lower binding free energy in peptide 58F63Y and HER2 complex based on free energy decomposition analysis and distances calculation. While both in silico and in vitro screenings point to the same high-affinity peptide, the findings suggest that in silico screening based on calculated binding free energies is rather reliable. Additionally, based on the calculation of binding free energies among mutants, we can reduce the library capacity of one-bead-one-compound screening. In summary, we present a rather simple and rapid means of deriving a peptide with a clear binding site to its target protein.

Citation: Yang X, Wang Z, Xiang Z, Li D, Hu Z, Cui W, et al. (2017) Peptide probes derived from pertuzumab by molecular dynamics modeling for HER2 positive tumor imaging. PLoS Comput Biol 13(4): e1005441. https://doi.org/10.1371/journal.pcbi.1005441

Editor: Bert L. de Groot, Max Planck Institute for Biophysical Chemistry, GERMANY

Received: November 23, 2016; Accepted: March 4, 2017; Published: April 13, 2017

Copyright: © 2017 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work is supported by the “Strategic Priority Research Program” of the Chinese Academy of Sciences (XDA09040300), the Chinese Academy of Sciences (“100 Talents Program of the Chinese Academy of Sciences”: Y4362911ZX), and the Beijing Natural Science Foundation Beijing Natural Science Foundation (5142021). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Human epidermal growth factor receptor 2 (HER2) is a prominent target in breast cancer diagnosis and treatment, as approximately 20–30% of patients with breast cancer overexpressing the HER2 receptor [1,2], a 185-kD transmembrane glycoprotein with 1,255 amino acids [3]. The HER2 gene is a proto-oncogene that maps to chromosome 17q21. HER2 contains four domains (I, II, III, and IV) that comprise a ligand-binding extracellular portion, a single transmembrane helix, a tyrosine kinase domain closely related to the Janus kinases, and a C-terminal tail with a number of tyrosine phosphorylation sites that serve as a scaffold for adaptor molecules and enzymes in facilitating downstream signaling [4]. The heterodimerization of HER2 with any of the other three HER family receptors results in autophosphorylation of the terminal carboxyl segment and initiates a variety of signaling pathways that regulate cell growth, proliferation, and metastasis [5–7].

Currently, a number of therapeutic approaches have been developed to antagonize the effects of HER2 overexpression; these approaches include the humanized monoclonal antibodies trastuzumab and pertuzumab [8]. Trastuzumab demonstrates clinical benefits in the treatment of HER2-positive breast cancer, in both early and metastatic stages. One year of trastuzumab therapy is recommended for all patients with HER2-positive breast cancer who are also receiving chemotherapy [9]. However, as trastuzumab becomes a routine therapy, resistance can develop following an initial robust response; a lack of response to initiation has also been observed among patients [10,11]. The other antibody drug, pertuzumab, has received US Food and Drug Administration approval for the treatment of HER2-positive metastatic breast cancer. Trastuzumab and pertuzumab bind to different epitopes in the extracellular domain of HER2, and their mechanisms of action differ. Pertuzumab binds the pocket of domain II, inhibits HER2 dimerization with other receptors, and leads to slowed tumor growth. Trastuzumab, on the other hand, binds to subdomain IV [12], and works by inhibiting the PI3K/Akt, Mirk, and hKIS pathways and promoting proteolytic cleavage of the extracellular domain [13]. However, both drugs have been shown to stimulate the antibody-dependent cellular cytotoxicity mechanism [14].

It has been commonly recognized that, compared to antibody drugs, small peptides are cost-effective, have good tissue and membrane permeability, high target specificity, and low toxicity. Moreover, specific modifications to targeting peptides can be employed to provide diverse biosensing functions; this strategy has been leveraged to develop a method by which to detect metastatic tumor cells in primary tumors [15].

The science of molecular dynamics (MD) has been widely applied to chemical physics, materials science, and the modeling of biomolecules—such as interactions between ligands and receptors [16,17]—by simulating the physical movement of atoms and molecules based on a family of molecular mechanics force fields. The MM/GBSA method is often used to estimate the free energy of solute–solvent interactions. In this method, a generalized Born (GB) model is used to approximate the Poisson–Boltzmann equation, based on modeling a molecule as a set of spheres. The accessible surface area (SA) approximates the experimental value of the averaged behavior of many highly dynamic solvent molecules between the transfer free energy and the surface area of a solute molecule [18].

The one-bead-one-compound (OBOC) [19–21] library method can be used to systematically synthesize and screen the peptide library of a target protein. It is a simple means of rapidly identifying small molecules that bind with high affinity to receptor molecules. The strategy has been modified by many researchers to overcome the several limitations inherent in the original approach [22]. Our research group has previously advanced it to a lab-on-chip system that embraces the whole peptide screening process—from single bead trapping to the final sequencing of peptides—by using MALDI–TOF–MS [23,24].

In this study, we use a combination protocol comprising MD, MM/GBSA binding free energy calculation [25–27] to derive peptides that interact with HER2 protein based on the HER2/pertuzumab crystal structure (PDB entry: 1S78). In silico mutations were performed to screen for peptides with the lowest binding free energy, and OBOC peptide library screening was then carried out. Both the binding free energy calculation and the OBOC library screening found the peptide 58F63Y to have the highest affinity to HER2. 58F63Y, together with five other peptides, were selected for further analysis and experimental validation. All results show that the peptide 58F63Y binds most favorably to HER2, with a dissociation constant (K_D) of 536 nmol/L. The results of ex vivo and in vivo experiments using mouse xenografted tumors confirm that this peptide has strong affinity and high specificity to HER2. Binding free energy decomposition analysis [28–30] and distances calculation using Pymol found that there are more paired residues with low binding free energy and distances of less than 5 Å, which may explain the high affinity. Compared to other peptides that target HER2 [31], peptide 58F63Y is unique in that it is acquired based on simulation using a different primary model with binding sites on domain II of the HER2 protein. Given its low toxicity, this peptide may be used as an alternative probe in the diagnosis and treatment of HER2-positive breast cancer and contributes to the HER2-targeting peptide library.

Results/Discussion

Structure analysis of the HER2/pertuzumab complex

Pertuzumab is a monoclonal antibody marketed by Genentech for the treatment of HER2-positive breast cancer. Pertuzumab binds to HER2 at the center of domain II, sterically blocking the pocket essential to receptor dimerization and signaling. The HER2/pertuzumab crystal structure was obtained from the Protein Data Bank. In this structure, the soluble extracellular domain of HER2 [32] was crystallized in complex with the Fab fragment of the disulfide anti-HER2 monoclonal antibody pertuzumab [33]. Based on the calculation of distances for all residues between HER2 and pertuzumab and the selection of those within 5 Å, we found a peptide fragment of 20 residues in length (sequence: EWVADVNPNSGGSIYNQRFK) with a beta folding layer structure, named 4665, that plays an important role in the interactions (Fig 1A). The HER2/fragment 4665 complex was chosen for further simulation analysis. MM/GBSA free energy was calculated based on 500 snapshots from 7 to 10 ns of MD simulation trajectories (Fig 1B) for each complex, as described in the Materials and methods section. The results show that the predicted binding free energy between 4665 and HER2 is –48.53 kcal/mol with the van der Waals (ΔE_vdw) contribution being a main component.

Download:

Fig 1. Analysis of HER2/pertuzumab complex.

(A) Structure of the HER2 (green)/pertuzumab (yellow) complex. The fragment from Glu46 to Lys65 of pertuzumab is shown in red. The side chains that participate in key interactions are shown as sticks. Calculated distances (< 5 Å) are indicated with blue dash lines. (B) Backbone RMSDs as a function of time for the initial and successive structure of HER2/4665 complex in MD trajectories. (C) Binding free energies and individual energy terms for HER2/4665 and binding free energy decomposition for each residue of 4665. (* means that ΔG_tot does not contain -TΔS energy)

https://doi.org/10.1371/journal.pcbi.1005441.g001

Mutations based on 4665

To improve the affinity of 4665 against HER2, we undertook single and double mutations and performed MD simulations to estimate the binding affinity. The properties of the interacting amino acids in both HER2 and peptides, as well as the space among the interactions, were considered. As shown in Fig 1C, Glu46, Trp47 and Lys65 have low energy contributions to the HER2/4665 complex. In addition, Asn52-Asn54 has almost no contributions to the binding. Therefore, mutations were made mostly in two beta strands in 4665: one is from Val48 to Val51 with high hydrophobicity, and the other is from Ser55 to Phe64. The basic rules are that mutations should favor electrostatic and van der Waals interactions, and do not cause steric overlap. Briefly, the van der Waals (ΔE_vdw) contribution is a main component in HER2/4665 interactions as suggested from the free energy calculation. As a consequence, residues in the strand of Ser55-Phe64 were preferably mutated to nonpolar amino acids (Ser55, Gly56, Gly57 and Gln62) to increase the van der Waals (ΔE_vdw) contribution. Moreover, residues with large side chains are mutated into amino acids with similar side chain groups, such as Val48, Ala49, Gly56 and Gly57. We also took into account of the inter spaces between the peptide and HER2. For example, Ser58 is located on a beta strand that is close to the HER2 fragment Phe256-Lys314 but with a large spatial distance, so Ser58 was mutated to the residues with larger side chain groups or nonpolar amino acids. Arg63 is also located on a beta strand that is closer to the HER2 fragment Phe256-Lys314. Considering its distance (3.9 Å) to Phe257, Arg63 is mutated into residues with side chain groups no larger than benzol methyl or nonpolar residues. Finally, we first carried out 59 single mutations based on the 4665 sequence by performing MD simulations, and binding free energies were calculated for each mutant. Seventeen single mutations with binding free energies < –48.53 kcal/mol were selected to create combinations of double mutations, thus resulting in another 56 mutants. All mutations and their binding free energies are shown in Tables 1 and 2. Among these mutations, 34 sequences have lower binding free energies than the original 4665 peptide; peptide 58F63Y (sequence: EWVADVNPNSGGFIYNQYFK) is the lowest, and so it is expected to bind most tightly to HER2.

Download:

Table 1. Binding free energies and individual energy terms for HER2 and 4665 (bold) or its single mutant peptide complexes calculated by MM/GBSA (kcal/mol).

https://doi.org/10.1371/journal.pcbi.1005441.t001

Download:

Table 2. Binding free energies and individual energy terms for HER2 and 4665 (bold) or its double mutant peptide complexes calculated by MM/GBSA (kcal/mol).

https://doi.org/10.1371/journal.pcbi.1005441.t002

OBOC peptide library screening

The results of our previous work show that when receptor–ligand interactions are similar—save for only a few residue differences—computational binding free energy calculations can closely reflect the relative affinity of peptide binding [31,34]. In the current study, to determine whether the above in silico screening correctly identified a peptide with affinity among the highest ones, the OBOC peptide library approach (Fig 2A) was later performed. We designed the peptide library based on the calculated binding free energies of peptides/HER2 with single and double mutations, using MD simulations (Tables 1 and 2). Mutations with binding free energies lower than the wild type 4665 (ΔG_tot < –50 kcal/mol) were selected, and the intersection of single and double mutations from 55 to 64 was used in library construction (Fig 2B), resulting in a library of 5184 sequences. Biological screening of the OBOC peptide library is routinely carried out as described in the Materials and methods section. Three positive beads were identified, and following MALDI–TOF–MS/MS analysis, one of them was found to be the same as the peptide 58F63Y, which has the lowest binding free energy with MD simulation (Fig 2C).

Download:

Fig 2. OBOC peptide library screening.

A. A sketch of the method. B. Selected amino acids for mutation in each position based on MD simulations. C. MS/MS peaks of peptide 58F63Y.

https://doi.org/10.1371/journal.pcbi.1005441.g002

Dissociation constants of peptides measured by surface plasmon resonance imaging

After combining the results, peptide 58F63Y was selected for further experimental validation. Another double mutant 55V63Y and its single mutations 58F, 63Y, and 55V—as well as the original wild type 4665—were also selected to facilitate better comparison. The sequences of these six peptides were aligned using Clustal Omega; the results are shown in S1 Fig. Surface plasmon resonance imaging (SPRi)—which has been previously used to estimate interactions between molecules for the purposes of disease diagnosis, drug discovery, and peptide screening [23,24,35–37]—was used in this study to estimate the dissociation constants of peptides binding to HER2, as described in the Materials and methods section. The dissociation constant was calculated from kinetic constants obtained by fitting association and dissociation curves to real-time binding and washing data. While none of the four peptides shows any affinity to the HSA protein (S2 Fig), Fig 3 indicates that the K_D values of the peptides 4665, 58F, 63Y, 55V, 58F63Y, and 55V63Y with HER2 protein are 9.86 μmol/L, 1.32 μmol/L, 1.54 μmol/L, 64.6 μmol/L, 0.536 μmol/L, and 8.16 μmol/L, respectively. We can see that, to some extent, the K_D values agree with the binding free energies from the simulation, which range from –48 kcal/mol to –66 kcal/mol. This finding is consistent with that of our previous work, in which it was found that computational binding free energy from MM/GBSA can be used to estimate the relative affinity of peptide binding. Based on the SPRi results, three peptides (58F, 63Y, 58F63Y) with the highest affinity, as well as the starting peptide 4665, were chosen for later confocal fluorescence imaging analyses.

Download:

Fig 3. SPRi detection of the binding affinity of peptides.

4665 (A), 58F (B), 63Y (C), 55V (D), 58F63Y (E), and 55V63Y (F) towards HER2. The dissociation constant was calculated from kinetic constants obtained by fitting association and dissociation curves to real-time binding and washing data. 58F63Y has the highest affinity of all six peptides.

https://doi.org/10.1371/journal.pcbi.1005441.g003

Validation of affinity and specificity of peptides to HER2 high expression cells

Four tumor cell lines (SKBR3, MCF7, MDA-MB-468, and 293A), each with a different HER2 expression level, were used in confocal fluorescence imaging analysis to confirm the binding specificity of peptides to the HER2 protein. Among these cell lines, the expression of HER2 was found to be high in SKBR3, medium in MCF7, and low in MDA-MB-468 and in 293A. As shown in Fig 4 and S3 Fig, when treated with Cy5.5-peptides, fluorescent intensities are strong in SKBR3 cells, but weaker in MCF7 cells and absent in MDA-MB-468 and 293A cells. Especially, SKBR3 cells treated with Cy5.5-58F63Y show the strongest fluorescence, thus indicating that 58F63Y binds with the highest affinity to HER2 (Fig 4B); this finding is consistent with the aforementioned MD calculation and SPRi analytical results. All these results confirm that peptides can specifically bind at the cellular level to the extracellular domain of the HER2 protein. Peptide 58F63Y (which had the highest affinity) and the wild type 4665 were chosen for subsequent in vivo studies. Toxicity of peptides 58F63Y and 4665 to HUVEC and SKBR3 cells was also measured. 4665 and 58F63Y shows no toxicity to both cell lines (S4 Fig).

Download:

Fig 4. Confocal fluorescence imaging analysis of cells incubated with Cy5.5-labeled peptides.

(A) SKBR3 cell line with high HER2 expression, (B) Quantified intensities of Cy5.5-peptides in SKBR3 cell line, (C) MCF cell line with medium HER2 expression, and (D) 293A cell line with no HER2 expression. Cy5.5-labeled peptide is shown in red, and Hoechst in blue.

https://doi.org/10.1371/journal.pcbi.1005441.g004

Confirmation of affinity and specificity of peptides to HER2-positive tumors

To investigate the affinity and specificity of peptides to HER2-positive tumors in vivo, nude mice bearing subcutaneous SKBR3 tumor xenografts were intravenously injected with Cy5.5-labeled peptides and Cy5.5 as the control; they were then subjected to whole-body optical imaging, using a small animal in vivo imaging system (CRI Maestro 2). Fig 5A shows clear differences between the tumor images of mice with Cy5.5–58F63Y or Cy5.5–4665 and those of the control mice. The intensities are plotted in bar charts (Fig 5C) that indicate that binding affinity increases 5.09-fold for 58F63Y and 3.52-fold for 4665, relative to the control. Moreover, fluorescence images of the dissected organs of the experimental mice, taken 30 min postinjection with Cy5.5-labeled peptides or control Cy5.5, were acquired for further examination. Both images and quantification in the bar charts (Fig 5B and 5D) show that tumors treated with 58F63Y and 4665 have significantly high fluorescence signals, compared to those in the controls. Among all the organs, the kidney was found to have the highest background signal for both peptides and control, probably due to the toxic effect of Cy5.5 [38,39]. Taken together, all the results demonstrate that 58F63Y has high specificity and affinity for HER2-positive tumors.

Download:

Fig 5. In vivo and ex vivo imaging of tumor targeting by 4665 and 58F63Y.

(A) in vivo fluorescence imaging of 4665 and 58F63Y to tumor, (B) ex vivo fluorescence imaging of tumor accumulation and biodistribution, and (C, D) quantification of the fluorescence signals in vivo and ex vivo, respectively. Fluorescence intensity was measured in terms of counts/energy/area and presented as an average (n = 3).

https://doi.org/10.1371/journal.pcbi.1005441.g005

In silico analysis of interactions between peptides and HER2

To better understand the reason of the high affinity of 58F63Y to HER2, a more detailed in silico analysis was performed. Another four mutants—as well as the original wild type 4665—were also selected for a more comprehensive comparison. To verify peptide structure stability after binding to HER2, the root mean square deviation (RMSD) values of the backbone atoms of the initial structure and of successive simulated structures were calculated for all six HER2/peptides complexes. S5 Fig shows that for all six complexes, the RMSD values become stable after about 5000 ps in the MD trajectories, thus indicating the convergence of each peptide and the complex structures towards an equilibrium state. A glance at the results of the free energy decomposition analysis of the peptides found that more residues have low energies in the mutants than in 4665. Specifically, for 4665, except Tyr60, no other residue contributes energy < –2.5 kcal/mol (Fig 6A). However, in the mutants (Fig 6B–6F)—besides Asn54 and Ser55 in all mutants—the following were found to contribute energy < –2.5 kcal/mol: Tyr60 and Gln62 in 58F; Ser58, Ile59, and Tyr63 in 63Y; Gln62 in 55V; Ser58, Phe59, Gln62, and Arg63 in 58F63Y; and Asn52 and Ile59 in 55V63Y. That is to say, each mutant has more than three residues that are favorable to HER2 binding. From S6 Fig, we can see that all six peptides overlay in the same pocket of the HER2 protein, thus indicating that the binding sites of these peptides remain the same as those in the wild type. MM/GBSA binding free energy calculation and decomposition analysis of the HER2 protein reveals that fragment 236–314 has major interactions with these peptides, and that for each mutant, more than three residues have binding free energies < –3 kcal/mol (S7 Fig). Together, the beneficial changes of each mutant contribute to a lower binding free energy than that in wild type 4665. Models from PyMOL were used to better visualize the key interacting residues in peptides/HER2 complexes (S8 Fig). In Fig 7, 18 residues pairs have distances of less than 5 Å in 58F63Y/HER2 (236-314), compared with 10 pairs in 4665/HER2 (236-314). As a summary, more residues pairs with lower binding free energy and close distances in the 58F63Y/HER2 complex may contribute to the high affinity of 58F63Y.

Download:

Fig 6. Binding free energy decomposition for each residue of the ligands in the six complexes.

HER2/4665 (A), HER2/58F (B), HER2/63Y (C), HER2/55V (D), HER2/58F63Y (E), and HER2/55V63Y (F). The green bars in the figure represent a binding free energy < –2.5 kcal/mol. Each mutant from B to F has more than three residues that are favorable to HER2 binding.

https://doi.org/10.1371/journal.pcbi.1005441.g006

Download:

Fig 7. Paired residues in HER2/peptide complexes.

(A) 4665/HER2 fragment 236-314; (B) 58F63Y/HER2 fragment 236-314; (C) Paired residues within 5 Å and their distances in the 4665/HER2 and (D) 58F63Y/HER2 complexes. Blue cartoons stand for the mutant peptides; yellow cartoons for the fragment 236 to 314 in the HER2 protein; and red dashed lines show paired residues within 5 Å.

https://doi.org/10.1371/journal.pcbi.1005441.g007

In summary, based on the crystal structure of HER2/pertuzumab, we acquired a peptide that was 20 residues in length (i.e., 58F63Y) that targets the HER2 protein; these findings were derived through the use of mutations and the computational calculation of the affinity with a combination protocol of molecular dynamics modeling, MM/GBSA binding free energy calculations, as well as the screening of an OBOC peptide library based on the mutations from the in silico modeling. This work proves that MM/GBSA binding free energy can be used to reflect the relative affinity of peptide binding closely. The peptide 58F63Y has a K_D value of 536 nmol/L and binds to HER2 at the same site as the parent fragment of pertuzumab. Through confocal fluorescence imaging and in vivo and ex vivo studies, the peptide was found to have high affinity and specificity for the extracellular domain of HER2. We expect this peptide to serve as an alternative probe that can be used in combination with others to improve the early detection, diagnosis, and targeted therapy of HER2-positive breast cancer.

Methods

Prepare the initial structure

The primary sequences of the pertuzumab fragment from 46 to 65 (named 4665) and its mutants 58F, 63Y, 55V, 58F63Y, and 55V63Y were aligned by using the Clustal Omega program, which is available on EMBnet website (http://www.ebi.ac.uk/Tools/msa/clustalo/). The model for the HER2/4665 complex derives from the crystal structure of the HER2 extracellular region and pertuzumab in the RCSB PDB. The model for HER2/4665 was constructed based on the crystal structure of HER2/pertuzumab, by keeping related amino acids in pertuzumab. Other models were constructed based on HER2/4665.

MD simulations

The AMBER03 force field was used to investigate the potentials of the complexes in the following molecular mechanics minimizations and MD simulations [40]. Missing atoms were added by using the tleap program. The whole system was solvated with TIP3P [41] water molecules in a truncated octahedron box with a minimum solute box-edge distance of 12 Å [42]. Then, the largest negative coulombic potential around the protein was randomly neutralized with counter-ions Na⁺ placed on the grids. The numbers of water molecules and Na⁺ ions in each system are listed in the supporting information (S1 Table). To remove poor-quality contacts between the complex and the solvent molecules, three-step energy minimization was performed by using the sander module of AMBER12 prior to undertaking the MD simulations. First, the whole protein was fixed and the water molecules and counter-ions were minimized; second, the backbone atoms of the protein were fixed and the side chains were minimized using the same settings as above; third, the whole system was minimized without any constraints. The first two stages consisted of a 5,000-cycle steepest descent and a 2,500-cycle conjugate gradient minimization; the final step consisted of 10,000 cycles of steepest descent and 5,000 cycles of conjugate gradient minimization. The SHAKE [43] method was applied to constrain covalent bonds related to hydrogen atoms, with a tolerance of 10⁻⁵ Å. Particle Mesh Ewald [44] was employed to adequately deal with long-range electrostatic interactions, and in the MD simulations, the cutoff distances for nonbond energy interactions were set to 12 Å [45]. Then, the entire system was gradually heated from 0 to 310 K in seven steps [46,47] over 60 ps [46,48] in the NVT (canonical ensemble). Finally, 10 ns MD simulations were implemented with a 2 fs [49] time step under the constant temperature of 310 K. During the sampling process, the trajectories were saved every 0.2 ps, and the conformations generated from the simulations were used in further analysis.

Binding free energy calculations

MM/GBSA [50] serves as an effective computational tool in analyzing biomolecular interactions. When used with knowledge-based energy terms, MM/GBSA can help determine the binding free energies of all systems, based on the calculation of the average free energies of solvation (ΔG_bind) between targeted protein and ligands over trajectories of MD simulation. The MM/GBSA method can be summarized as the following equation.

(1)

In which, ΔG_bind represents the binding free energy in solution consisting of the molecular mechanics free energy (ΔE_MM), the conformational entropic effect to binding (−TΔS) in the gas phase, and the solvation free energy containing polar contribution (ΔG_GB) and nonpolar contribution (ΔG_SA). The ΔE_MM term includes ΔE_ele (electrostatic) and ΔE_vdw (van der Waals) energies and was calculated by the sander module of AMBER12. The polar contribution was calculated by using the GB [51] mode, with solvent and the solute dielectric constants set to 80 and 4, respectively. Additionally, the nonpolar energy was estimated, with a solvent-probe radius of 1.4 Å: ΔG_SA = 0.0072 × ΔSASA [52], by the LCPO method [50] based on the SASA model [53]. For each ligand–protein, 500 snapshots were taken from 7 to 10 ns on the MD trajectories. Due to the low prediction accuracy and the high computational cost [54,55] upon the nmode module in AMBER12 as well as their similar values in analogical system [31,34], the entropic contribution was ignored in the calculation of the predicted total binding free energy (ΔG_tot^* means that ΔG_tot does not contain -TΔS energy).

Free energy decomposition analysis

The specific inhibitor-residue interaction spectra were generated by using MM/GBSA decomposition analysis [28,56] undertaken through the mm_pbsa program of AMBER12. Four kinds of energy were found—namely, ΔE_vdw, ΔE_ele, ΔG_GB, and ΔG_SA—and each contributed to the binding interaction of each ligand–residue pair. The ΔE_vdw and ΔE_ele energy terms were calculated by the sander module of AMBER12. The polar contribution (ΔG_GB) to solvation energy was calculated by using the GB module and the parameters for the GB calculation were developed by Onufriev et al. [57]. The nonpolar solvation contribution (ΔG_SA) part was computed based on the SASA determined through the ICOSA method [52]. All energy components were calculated by using 500 snapshots extracted from the last 3 ns of the MD trajectories. After undertaking the decomposition process, the free energy contribution could be allocated to each residue from the association between the receptor and the ligand. Graphic visualizations and presentations of protein structures were generated by using PyMOL [58–60].

Magnetic beads screening from peptide library

The OBOC peptide library was designed based on binding free energies derived from the MD simulations of single and double mutants that are lower than the HER2/4665 complex. These mutations are in the 4665 fragment of pertuzumab from 55 to 64: EWVADVNPNX₅₅X₅₆X₅₇X₅₈X₅₉X₆₀N₆₁X₆₂X₆₃X₆₄K. According to computational calculations, the V, S, and M mutants have lower energies for X₅₅. This is also the case for G, M, and Y for X₅₆; G, A, and V for X₅₇; S, F, and H for X₅₈; I and R for X₅₉; Y and W for X₆₀; Q and F for X₆₂; R, W, Q, and V for X₆₃; and F and R for X₆₄. The result is a 3*3*3*3*2*2*2*4*2 = 5184 library capacity.

The OBOC library synthesis and screening was performed as per the previously used method [61–64]. Briefly, HBTU (4 mmol) and Fmoc-amino acid (4 mmol) reagent was dissolved in 0.4 mol/L N-Methyl morpholine in N,N-dimethylformamide and coupled with the solid phase supporting materials for 40 min during the coupling step. A 20% piperidine was used to remove the Fmoc group for 10 min in the deprotection step. During the OBOC library synthesis, the amino acid coupling process was carried out in the “split” step, while the deprotection process was carried out in the “pool” step. After elongation, a trifluoroacetic acid cleavage reagent was introduced to cleave the side chain protection group of each residue. Afterwards, the solid phase supporting materials were incubated with 5% milk, then with HER2/biotin complex, and then with monodispersed magnetic streptavidin microspheres. Each step was performed in an incubator at 37°C for 2 h, and followed by three washes with PBS. The HER2 protein was biotinylated using a biotinylation kit (Solulink Inc., USA). Positive beads with dark colors were picked out for the in situ chemical cleavage before MALDI–TOF–MS/MS analysis, and a 30 mg/mL cyanogen bromide solution was used overnight.

Peptide synthesis

Peptides were synthesized using Fmoc strategy solid phase peptide synthesis [65–67]. Unsophisticated peptides were purified using a Hitachi HPLC system (L-7100, Japan) on a TSK gel ODS-100V reversed-phase column. Peptides were eluted with a linear gradient of 5–80% acetonitrile containing 0.1% trifluoroacetic acid at a flow rate of 2 mL/min within 25 min. Peptides were then subjected to MALDI–TOF–MS (Bruker Daltonics) analysis. Purified peptides were dried in vacuum desiccators and then stored at –20°C until further use.

2-(1H-benzotriazole-1-yl)-1, 1, 3, 3-tetramethyluronium hexafluorophosphate was purchased from GL Biochem (China). Trifluoroacetic acid and fluorescein 5-isothiocyanate were acquired from Sigma-Aldrich Co. LLC (USA). N-Methyl morpholine and N, N-dimethylformamide were acquired from a Beijing chemical plant (China).

SPRi assay

For SPRi analysis, a cysteine residue linked to the amino terminal of all peptides was used for interacting with a bare gold chip bearing a 47.5-nm thickness. First, 1 μL peptides at 1 mg/mL was added to the gold surface of the chip and incubated overnight at 4°C. The chip was then washed with PBS and deionized water three times, and 5% nonfat milk was applied to block overnight at 4°C. After the chip was washed again with PBS and water, it was dried with nitrogen for later use. Human serum albumin (HSA) protein (Sigma-Aldrich Co. LLC) was used as the control. HER2 (Sino Biological Inc., China) and HSA proteins (Sigma-Aldrich Co. LLC) were dissolved in PBST and diluted to 10, 5, 2.5, 1.25, or 0.625 μg/mL. The SPRi analytical procedure was carried out on the prepared SPRi chip by running PBST buffer for baseline stabilization, followed by the protein sample, a PBST running buffer for washing, and finally 0.5% H₃PO₄ in deionized water for regeneration. This cycle was repeated for each concentration of HER2 and HSA protein at 20, 10, 5, 2.5, 1.25, and 0.625 μg/mL. Real-time binding signals were recorded and analyzed through the use of a PlexArray HT system (Plexera LLC, Bothell, WA, USA). The dissociation constant was calculated by fitting the association–dissociation curves.

Immunocytochemistry

Four cell lines (SKBR3, MCF7, MDA-MB-468, and 293A) were seeded at a density of 3000 cells/mL into culture dishes and allowed to culture overnight with 5% CO₂ at 37°C. Cell nuclei was stained with 1 mM Hoechst 33342 in 200 μL cell culture medium and incubated at 37°C for 15 min. Then, cells were incubated in culture medium with 50 μM Cy5.5-labeled peptide at 4°C for 20 min. Finally, cells were washed three times with cold PBS for observation. An Olympus FV1000-IX81 confocal-laser scanning microscope was used for confocal fluorescence imaging. An FV5-LAMAR 633 nm laser was used as the excitation source, and the emission wave length was collected at 690 nm. Hoechst 33342 was excited by a FV5-LD405-2 405 nm laser and collected within the range of 422–472 nm. All microscope parameters were identical for all observations of the binding ability of the various peptides.

Ethics statement

All animal experiments were conducted in compliance with the Beijing University Animal Study Committee’s requirements vis-à-vis the care and use of laboratory animals. The Beijing University Animal Study Committee approved the experimental protocols.

In vivo and ex vivo fluorescence imaging

Five to six-week-old Balb/c female nude mice were subcutaneously administered approximately 1 × 10⁷ SKBR3 cells into the right hind leg, to establish xenografted tumors. Thereafter, tumor size was periodically measured with a caliper, and mice with tumors of 6−8 mm in diameter were selected for the following small animal experiments. Cy5.5–NHS (Lumiprobe) was used to label peptides. Either Cy5.5-peptides or the control Cy5.5 (1 μM, 200 μL) was intravenously injected into tumor-bearing nude mice via the tail vein. The mice were anesthetized and fluorescence signals measured using the small animal in vivo imaging system 30 min postinjection. Three mice were used for each peptide and for control. Near-infrared fluorescence imaging of tumor-bearing nude mice were taken with an exposure time of 50 ms, using the Cy5.5 filter sets (excitation: 673 nm; emission: 707 nm), and the intensities were quantified using the same software. Then, fluorescence images of the main organs and of tumors dissected from nude mice were individually taken as above.

Supporting information

S1 Fig. Alignment of the sequences of the wild type and the five mutant peptides by Clustal Omega.

https://doi.org/10.1371/journal.pcbi.1005441.s001

(PDF)

S2 Fig. SPRi analysis of the binding affinity of peptides.

4665 (A), 58F (B), 63Y (C), 55V (D), 58F63Y (E), and 55V63Y (F) toward HSA protein. The dissociation constant was calculated from the kinetic constants obtained by fitting the association and dissociation curves to the real-time binding and washing data.

https://doi.org/10.1371/journal.pcbi.1005441.s002

(PDF)

S3 Fig. Confocal fluorescence imaging analysis of the MDA-MB-468 cell line with low HER2 expression after incubating with peptides labeled with Cy5.5.

https://doi.org/10.1371/journal.pcbi.1005441.s003

(PDF)

S4 Fig.

Toxicity of the peptides to HUVEC (A) and SKBR3 (B). 4665 and 58F63Y shows no toxicity to both cell lines.

https://doi.org/10.1371/journal.pcbi.1005441.s004

(PDF)

S5 Fig. Backbone RMSDs as a function of time for the initial and successive structures of HER2/peptides complexes in MD trajectories.

(A) HER2/4665, (B) HER2/58F, (C) HER2/63Y, (D) HER2/55V, (E) HER2/58F63Y, and (F) HER2/55V63Y. The RMSD values become stable after about 5000ps in the MD trajectories, indicating the convergence of each peptide structure towards an equilibrium state.

https://doi.org/10.1371/journal.pcbi.1005441.s005

(PDF)

S6 Fig. Comparison of the interactions of HER2/peptides.

HER2/4665 (A), HER2/58F (B), HER2/63Y (C), HER2/55V (D), HER2/58F63Y (E), and HER2/55V63Y (F). Gray cartoons are the HER2 protein, and blue cartoons are the mutant peptides. Orange spots show the main contributing residues of each peptide interacting with HER2 protein.

https://doi.org/10.1371/journal.pcbi.1005441.s006

(PDF)

S7 Fig. Binding free energy decomposition for key residues of the HER2 protein in the six complexes.

HER2/4665 (A), HER2/58F (B), HER2/63Y (C), HER2/55V (D), HER2/58F63Y (E), and HER2/55V63Y (F). Green bars in the figure represent binding free energy <–3 kcal/mol. Therefore, each mutant shown in B to F has more than three residues that contribute to the HER2/peptide complexes.

https://doi.org/10.1371/journal.pcbi.1005441.s007

(PDF)

S8 Fig. Binding models of HER2/peptides complexes.

4665/HER2 (A), 58F/HER2 (B), 63Y4665/HER2 (C), 55V/HER2 (D), 58F63Y/HER2 (E), and 55V63Y/HER2 (F). Blue cartoons stand for mutant peptides. Key residues (binding free energy <–1 kcal/mol) in HER2 protein are shown as yellow sticks.

https://doi.org/10.1371/journal.pcbi.1005441.s008

(PDF)

S1 Table. Numbers of water molecules and Na+ ions added in all simulation systems.

https://doi.org/10.1371/journal.pcbi.1005441.s009

(PDF)

Author Contributions

Conceptualization: QF WC LG.
Data curation: XY LG ZX DL.
Formal analysis: LG WC.
Funding acquisition: QF.
Investigation: XY ZW ZX.
Methodology: LG XY QF.
Project administration: QF LG ZH.
Resources: ZH QF.
Software: LG WC XY.
Supervision: QF LG.
Validation: QF LG XY.
Visualization: QF LG XY.
Writing – original draft: XY QF.
Writing – review & editing: XY QF.

References

1. Owens MA, Horten BC, Da Silva MM (2004) HER2 amplification ratios by fluorescence in situ hybridization and correlation with immunohistochemistry in a cohort of 6556 breast cancer tissues. Clin Breast Cancer 5: 63–69. pmid:15140287
- View Article
- PubMed/NCBI
- Google Scholar
2. Reese DM, Slamon DJ (1997) HER-2/neu signal transduction in human breast and ovarian cancer. Stem Cells 15: 1–8.
- View Article
- Google Scholar
3. Coussens L, Yang-Feng TL, Liao YC, Chen E, Gray A, et al. (1985) Tyrosine kinase receptor with extensive homology to EGF receptor shares chromosomal location with neu oncogene. Science 230: 1132–1139. pmid:2999974
- View Article
- PubMed/NCBI
- Google Scholar
4. Franklin MC, Carey KD, Vajdos FF, Leahy DJ, de Vos AM, et al. (2004) Insights into ErbB signaling from the structure of the ErbB2-pertuzumab complex. Cancer Cell 5: 317–328. pmid:15093539
- View Article
- PubMed/NCBI
- Google Scholar
5. Olayioye MA (2001) Update on HER-2 as a target for cancer therapy: intracellular signaling pathways of ErbB2/HER-2 and family members. Breast Cancer Res 3: 385–389. pmid:11737890
- View Article
- PubMed/NCBI
- Google Scholar
6. Roy V, Perez EA (2009) Beyond trastuzumab: small molecule tyrosine kinase inhibitors in HER-2-positive breast cancer. Oncologist 14: 1061–1069. pmid:19887469
- View Article
- PubMed/NCBI
- Google Scholar
7. Landau M, Ben-Tal N (2008) Dynamic equilibrium between multiple active and inactive conformations explains regulation and oncogenic mutations in ErbB receptors. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 1785: 12–31.
- View Article
- Google Scholar
8. Fry DW (2003) Mechanism of action of erbB tyrosine kinase inhibitors. Exp Cell Res 284: 131–139. pmid:12648472
- View Article
- PubMed/NCBI
- Google Scholar
9. Mates M, Fletcher GG, Freedman OC, Eisen A, Gandhi S, et al. (2015) Systemic targeted therapy for her2-positive early female breast cancer: a systematic review of the evidence for the 2014 Cancer Care Ontario systemic therapy guideline. Curr Oncol 22: S114–122. pmid:25848335
- View Article
- PubMed/NCBI
- Google Scholar
10. Vogel CL, Cobleigh MA, Tripathy D, Gutheil JC, Harris LN, et al. (2002) Efficacy and safety of trastuzumab as a single agent in first-line treatment of HER2-overexpressing metastatic breast cancer. J Clin Oncol 20: 719–726. pmid:11821453
- View Article
- PubMed/NCBI
- Google Scholar
11. Seidman AD, Berry D, Cirrincione C, Harris L, Muss H, et al. (2008) Randomized phase III trial of weekly compared with every-3-weeks paclitaxel for metastatic breast cancer, with trastuzumab for all HER-2 overexpressors and random assignment to trastuzumab or not in HER-2 nonoverexpressors: final results of Cancer and Leukemia Group B protocol 9840. J Clin Oncol 26: 1642–1649. pmid:18375893
- View Article
- PubMed/NCBI
- Google Scholar
12. Sliwkowski MX, Mellman I (2013) Antibody therapeutics in cancer. Science 341: 1192–1198. pmid:24031011
- View Article
- PubMed/NCBI
- Google Scholar
13. Molina MA, Codony-Servat J, Albanell J, Rojo F, Arribas J, et al. (2001) Trastuzumab (herceptin), a humanized anti-Her2 receptor monoclonal antibody, inhibits basal and activated Her2 ectodomain cleavage in breast cancer cells. Cancer Res 61: 4744–4749. pmid:11406546
- View Article
- PubMed/NCBI
- Google Scholar
14. Scheuer W, Friess T, Burtscher H, Bossenmaier B, Endl J, et al. (2009) Strongly enhanced antitumor activity of trastuzumab and pertuzumab combination treatment on HER2-positive human xenograft tumor models. Cancer Res 69: 9330–9336. pmid:19934333
- View Article
- PubMed/NCBI
- Google Scholar
15. Li H, Huang Y, Yu Y, Li W, Yin Y, et al. (2015) Peptide-Based Method for Detection of Metastatic Transformation in Primary Tumors of Breast Cancer. Anal Chem 87: 9251–9256. pmid:26301995
- View Article
- PubMed/NCBI
- Google Scholar
16. Rahman A (1964) Correlations in the Motion of Atoms in Liquid Argon. Physical Review 136: A405–A411.
- View Article
- Google Scholar
17. Alder BJ, Wainwright TE (1959) Studies in Molecular Dynamics. I. General Method. The Journal of Chemical Physics 31: 459–466.
- View Article
- Google Scholar
18. Richards FM (1977) Areas, Volumes, Packing, and Protein Structure. Annual Review of Biophysics and Bioengineering 6: 151–176. pmid:326146
- View Article
- PubMed/NCBI
- Google Scholar
19. Lam KS, Salmon Se Fau - Hersh EM, Hersh Em Fau - Hruby VJ, Hruby Vj Fau - Kazmierski WM, Kazmierski Wm Fau - Knapp RJ, et al. A new type of synthetic peptide library for identifying ligand-binding activity.
20. Brown JM, Hoffmann WD, Alvey CM, Wood AR, Verbeck GF, et al. (2010) One-bead, one-compound peptide library sequencing via high-pressure ammonia cleavage coupled to nanomanipulation/nanoelectrospray ionization mass spectrometry. Analytical Biochemistry 398: 7–14. pmid:19891951
- View Article
- PubMed/NCBI
- Google Scholar
21. Qian Y, Wang W, Wang Z, Han Q, Jia X, et al. (2016) Switchable probes: pH-triggered and VEGFR2 targeted peptides screening through imprinting microarray. Chemical Communications 52: 5690–5693. pmid:27035754
- View Article
- PubMed/NCBI
- Google Scholar
22. Astle JM, Simpson LS, Huang Y, Reddy MM, Wilson R, et al. (2010) Seamless bead to microarray screening: rapid identification of the highest affinity protein ligands from large combinatorial libraries. Chem Biol 17: 38–45. pmid:20142039
- View Article
- PubMed/NCBI
- Google Scholar
23. Wang W, Wei Z, Zhang D, Ma H, Wang Z, et al. (2014) Rapid screening of peptide probes through in situ single-bead sequencing microarray. Anal Chem 86: 11854–11859. pmid:25370073
- View Article
- PubMed/NCBI
- Google Scholar
24. Wang W, Zhang D, Wei Z, Wang Z, Bu X, et al. (2015) Label-free detection microarray for novel peptide ligands screening base on MS-SPRi combination. Talanta 134: 705–711. pmid:25618725
- View Article
- PubMed/NCBI
- Google Scholar
25. Wang J, Hou T, Xu X (2006) Recent Advances in Free Energy Calculations with a Combination of Molecular Mechanics and Continuum Models. Current Computer - Aided Drug Design volume 2: 287–306(220).
- View Article
- Google Scholar
26. Kollman PA, Massova I,., Reyes C,., Kuhn B,., Huo S,., et al. (2000) Calculating Structures and Free Energies of Complex Molecules: Combining Molecular Mechanics and Continuum Models. Accchemres 33: 889–897.
- View Article
- Google Scholar
27. Homeyer N, Gohlke H (2012) Free Energy Calculations by the Molecular Mechanics Poisson−Boltzmann Surface Area Method. Molecular Informatics 31: 114–122. pmid:27476956
- View Article
- PubMed/NCBI
- Google Scholar
28. Gohlke H, Kiel C, Case DA (2003) Insights into protein-protein binding by binding free energy calculation and free energy decomposition for the Ras-Raf and Ras-RalGDS complexes. J Mol Biol 330: 891–913. pmid:12850155
- View Article
- PubMed/NCBI
- Google Scholar
29. Hou T, Xu Z, Zhang W, McLaughlin WA, Case DA, et al. (2009) Characterization of domain-peptide interaction interface: a generic structure-based model to decipher the binding specificity of SH3 domains. Mol Cell Proteomics 8: 639–649. pmid:19023120
- View Article
- PubMed/NCBI
- Google Scholar
30. Hou T, Zhang W, Case DA, Wang W (2008) Characterization of domain-peptide interaction interface: a case study on the amphiphysin-1 SH3 domain. J Mol Biol 376: 1201–1214. pmid:18206907
- View Article
- PubMed/NCBI
- Google Scholar
31. Geng L, Wang Z, Yang X, Li D, Lian W, et al. (2015) Structure-based Design of Peptides with High Affinity and Specificity to HER2 Positive Tumors. Theranostics 5: 1154–1165. pmid:26284145
- View Article
- PubMed/NCBI
- Google Scholar
32. Hudziak RM, Ullrich A (1991) Cell transformation potential of a HER2 transmembrane domain deletion mutant retained in the endoplasmic reticulum. J Biol Chem 266: 24109–24115. pmid:1684181
- View Article
- PubMed/NCBI
- Google Scholar
33. Vajdos FF, Adams CW, Breece TN, Presta LG, de Vos AM, et al. (2002) Comprehensive functional maps of the antigen-binding site of an anti-ErbB2 antibody obtained with shotgun scanning mutagenesis. J Mol Biol 320: 415–428. pmid:12079396
- View Article
- PubMed/NCBI
- Google Scholar
34. Geng L, Wang Z, Jia X, Han Q, Xiang Z, et al. HER2 Targeting Peptides Screening and Applications in Tumor Imaging and Drug Delivery.
35. Zhu L, Wang K, Cui J, Liu H, Bu X, et al. (2014) Label-free quantitative detection of tumor-derived exosomes through surface plasmon resonance imaging. Anal Chem 86: 8857–8864. pmid:25090139
- View Article
- PubMed/NCBI
- Google Scholar
36. Gorodkiewicz E, Guszcz T, Roszkowska-Jakimiec W, Kozlowski R (2014) Cathepsin D serum and urine concentration in superficial and invasive transitional bladder cancer as determined by surface plasmon resonance imaging. Oncol Lett 8: 1323–1327. pmid:25120717
- View Article
- PubMed/NCBI
- Google Scholar
37. Villiers MB, Cortay JC, Cortes S, Bloquel B, Brichler S, et al. (2015) Protein-peptide arrays for detection of specific anti-hepatitis D virus (HDV) genotype 1, 6, and 8 antibodies among HDV-infected patients by surface plasmon resonance imaging. J Clin Microbiol 53: 1164–1171. pmid:25631795
- View Article
- PubMed/NCBI
- Google Scholar
38. Holtke C, Waldeck J, Kopka K, Heindel W, Schober O, et al. (2009) Biodistribution of a nonpeptidic fluorescent endothelin A receptor imaging probe. Mol Imaging 8: 27–34. pmid:19344573
- View Article
- PubMed/NCBI
- Google Scholar
39. Abulrob A, Brunette E, Slinn J, Baumann E, Stanimirovic D (2007) In vivo time domain optical imaging of renal ischemia-reperfusion injury: discrimination based on fluorescence lifetime. Mol Imaging 6: 304–314. pmid:18092515
- View Article
- PubMed/NCBI
- Google Scholar
40. Duan Y, Wu C, Chowdhury S, Lee MC, Xiong G, et al. (2003) A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. J Comput Chem 24: 1999–2012. pmid:14531054
- View Article
- PubMed/NCBI
- Google Scholar
41. Guillot B (2002) A reappraisal of what we have learnt during three decades of computer simulations on water. Journal of Molecular Liquids 101: 219–260.
- View Article
- Google Scholar
42. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics 79: 926–935.
- View Article
- Google Scholar
43. Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak JR (1984) Molecular dynamics with coupling to an external bath. The Journal of Chemical Physics 81: 3684–3690.
- View Article
- Google Scholar
44. Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, et al. (1995) A smooth particle mesh Ewald method. The Journal of Chemical Physics 103: 8577–8593.
- View Article
- Google Scholar
45. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems. The Journal of Chemical Physics 98: 10089–10092.
- View Article
- Google Scholar
46. Cui W, Wei Z Fau - Chen Q, Chen Q Fau - Cheng Y, Cheng Y Fau - Geng L, Geng L Fau - Zhang J, et al. Structure-based design of peptides against G3BP with cytotoxicity on tumor cells.
47. Cheng Y, Zhang F Fau - Chen Q, Chen Q Fau - Gao J, Gao J Fau - Cui W, Cui W Fau - Ji M, et al. Structural basis of specific binding between Aurora A and TPX2 by molecular dynamics simulations.
48. Cheng Y, Zhang F, Chen Q, Gao J, Cui W, et al. (2011) Structural basis of specific binding between Aurora A and TPX2 by molecular dynamics simulations. J Chem Inf Model 51: 2626–2635. pmid:21919471
- View Article
- PubMed/NCBI
- Google Scholar
49. Ryckaert J-P, Ciccotti G, Berendsen HJC (1977) Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes. Journal of Computational Physics 23: 327–341.
- View Article
- Google Scholar
50. Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, et al. (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33: 889–897. pmid:11123888
- View Article
- PubMed/NCBI
- Google Scholar
51. Tsui V, Case DA Theory and applications of the generalized Born solvation model in macromolecular simulations. 275-291 p.
52. Weiser J, Shenkin PS, Still WC (1999) Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). Journal of Computational Chemistry 20: 217–230.
- View Article
- Google Scholar
53. Connolly M (1983) Analytical molecular surface calculation. Journal of Applied Crystallography 16: 548–558.
- View Article
- Google Scholar
54. Hou T, Wang J, Li Y, Wang W (2011) Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51: 69–82. pmid:21117705
- View Article
- PubMed/NCBI
- Google Scholar
55. Sun H, Li Y, Shen M, Tian S, Xu L, et al. (2014) Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring. Phys Chem Chem Phys 16: 22035–22045. pmid:25205360
- View Article
- PubMed/NCBI
- Google Scholar
56. Zoete V, Meuwly M, Karplus M (2005) Study of the insulin dimerization: binding free energy calculations and per-residue free energy decomposition. Proteins 61: 79–93. pmid:16080143
- View Article
- PubMed/NCBI
- Google Scholar
57. Onufriev A, Bashford D, Case DA (2004) Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins 55: 383–394. pmid:15048829
- View Article
- PubMed/NCBI
- Google Scholar
58. Lua RC, Lichtarge O (2010) PyETV: a PyMOL evolutionary trace viewer to analyze functional site predictions in protein complexes. Bioinformatics 26: 2981–2982. pmid:20929911
- View Article
- PubMed/NCBI
- Google Scholar
59. Seeliger D, de Groot BL (2010) Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J Comput Aided Mol Des 24: 417–422. pmid:20401516
- View Article
- PubMed/NCBI
- Google Scholar
60. Makarewicz T, Kazmierkiewicz R (2013) Molecular dynamics simulation by GROMACS using GUI plugin for PyMOL. J Chem Inf Model 53: 1229–1234. pmid:23611462
- View Article
- PubMed/NCBI
- Google Scholar
61. Wang Z, Wang W, Geng L, Hu Z (2015) Distinguishing of tumor cell-targeting peptide ligands through a color-encoding microarray. Lab on a Chip 15: 4512–4516. pmid:26530232
- View Article
- PubMed/NCBI
- Google Scholar
62. Wang Z, Wang W, Bu X, Wei Z, Geng L, et al. (2015) Microarray Based Screening of Peptide Nano Probes for HER2 Positive Tumor. Analytical Chemistry 87: 8367–8372. pmid:26218790
- View Article
- PubMed/NCBI
- Google Scholar
63. Wang W, Wei Z, Wang Z, Ma H, Bu X, et al. (2014) A continuous flow microfluidic-MS system for efficient OBOC screening. RSC Advances 4: 61767–61770.
- View Article
- Google Scholar
64. Wang W, Wei Z, Zhang D, Ma H, Wang Z, et al. (2014) Rapid Screening of Peptide Probes through In Situ Single-Bead Sequencing Microarray. Analytical Chemistry 86: 11854–11859. pmid:25370073
- View Article
- PubMed/NCBI
- Google Scholar
65. Due Larsen B, Holm A (1998) Sequence-assisted peptide synthesis (SAPS). J Pept Res 52: 470–476. pmid:9924991
- View Article
- PubMed/NCBI
- Google Scholar
66. Ball HL, Mascagni P (1996) Chemical synthesis and purification of proteins: a methodology. Int J Pept Protein Res 48: 31–47. pmid:8844261
- View Article
- PubMed/NCBI
- Google Scholar
67. Made V, Els-Heindl S, Beck-Sickinger AG (2014) Automated solid-phase peptide synthesis to obtain therapeutic peptides. Beilstein J Org Chem 10: 1197–1212. pmid:24991269
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Owens MA, Horten BC, Da Silva MM (2004) HER2 amplification ratios by fluorescence in situ hybridization and correlation with immunohistochemistry in a cohort of 6556 breast cancer tissues. Clin Breast Cancer 5: 63–69. pmid:15140287
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Reese DM, Slamon DJ (1997) HER-2/neu signal transduction in human breast and ovarian cancer. Stem Cells 15: 1–8.
View Article
Google Scholar

[6] View Article

[7] Google Scholar

[ref3] 3. Coussens L, Yang-Feng TL, Liao YC, Chen E, Gray A, et al. (1985) Tyrosine kinase receptor with extensive homology to EGF receptor shares chromosomal location with neu oncogene. Science 230: 1132–1139. pmid:2999974
View Article
PubMed/NCBI
Google Scholar

[9] View Article

[10] PubMed/NCBI

[11] Google Scholar

[ref4] 4. Franklin MC, Carey KD, Vajdos FF, Leahy DJ, de Vos AM, et al. (2004) Insights into ErbB signaling from the structure of the ErbB2-pertuzumab complex. Cancer Cell 5: 317–328. pmid:15093539
View Article
PubMed/NCBI
Google Scholar

[13] View Article

[14] PubMed/NCBI

[15] Google Scholar

[ref5] 5. Olayioye MA (2001) Update on HER-2 as a target for cancer therapy: intracellular signaling pathways of ErbB2/HER-2 and family members. Breast Cancer Res 3: 385–389. pmid:11737890
View Article
PubMed/NCBI
Google Scholar

[17] View Article

[18] PubMed/NCBI

[19] Google Scholar

[ref6] 6. Roy V, Perez EA (2009) Beyond trastuzumab: small molecule tyrosine kinase inhibitors in HER-2-positive breast cancer. Oncologist 14: 1061–1069. pmid:19887469
View Article
PubMed/NCBI
Google Scholar

[21] View Article

[22] PubMed/NCBI

[23] Google Scholar

[ref7] 7. Landau M, Ben-Tal N (2008) Dynamic equilibrium between multiple active and inactive conformations explains regulation and oncogenic mutations in ErbB receptors. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 1785: 12–31.
View Article
Google Scholar

[25] View Article

[26] Google Scholar

[ref8] 8. Fry DW (2003) Mechanism of action of erbB tyrosine kinase inhibitors. Exp Cell Res 284: 131–139. pmid:12648472
View Article
PubMed/NCBI
Google Scholar

[28] View Article

[29] PubMed/NCBI

[30] Google Scholar

[ref9] 9. Mates M, Fletcher GG, Freedman OC, Eisen A, Gandhi S, et al. (2015) Systemic targeted therapy for her2-positive early female breast cancer: a systematic review of the evidence for the 2014 Cancer Care Ontario systemic therapy guideline. Curr Oncol 22: S114–122. pmid:25848335
View Article
PubMed/NCBI
Google Scholar

[32] View Article

[33] PubMed/NCBI

[34] Google Scholar

[ref10] 10. Vogel CL, Cobleigh MA, Tripathy D, Gutheil JC, Harris LN, et al. (2002) Efficacy and safety of trastuzumab as a single agent in first-line treatment of HER2-overexpressing metastatic breast cancer. J Clin Oncol 20: 719–726. pmid:11821453
View Article
PubMed/NCBI
Google Scholar

[36] View Article

[37] PubMed/NCBI

[38] Google Scholar

[ref11] 11. Seidman AD, Berry D, Cirrincione C, Harris L, Muss H, et al. (2008) Randomized phase III trial of weekly compared with every-3-weeks paclitaxel for metastatic breast cancer, with trastuzumab for all HER-2 overexpressors and random assignment to trastuzumab or not in HER-2 nonoverexpressors: final results of Cancer and Leukemia Group B protocol 9840. J Clin Oncol 26: 1642–1649. pmid:18375893
View Article
PubMed/NCBI
Google Scholar

[40] View Article

[41] PubMed/NCBI

[42] Google Scholar

[ref12] 12. Sliwkowski MX, Mellman I (2013) Antibody therapeutics in cancer. Science 341: 1192–1198. pmid:24031011
View Article
PubMed/NCBI
Google Scholar

[44] View Article

[45] PubMed/NCBI

[46] Google Scholar

[ref13] 13. Molina MA, Codony-Servat J, Albanell J, Rojo F, Arribas J, et al. (2001) Trastuzumab (herceptin), a humanized anti-Her2 receptor monoclonal antibody, inhibits basal and activated Her2 ectodomain cleavage in breast cancer cells. Cancer Res 61: 4744–4749. pmid:11406546
View Article
PubMed/NCBI
Google Scholar

[48] View Article

[49] PubMed/NCBI

[50] Google Scholar

[ref14] 14. Scheuer W, Friess T, Burtscher H, Bossenmaier B, Endl J, et al. (2009) Strongly enhanced antitumor activity of trastuzumab and pertuzumab combination treatment on HER2-positive human xenograft tumor models. Cancer Res 69: 9330–9336. pmid:19934333
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref15] 15. Li H, Huang Y, Yu Y, Li W, Yin Y, et al. (2015) Peptide-Based Method for Detection of Metastatic Transformation in Primary Tumors of Breast Cancer. Anal Chem 87: 9251–9256. pmid:26301995
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref16] 16. Rahman A (1964) Correlations in the Motion of Atoms in Liquid Argon. Physical Review 136: A405–A411.
View Article
Google Scholar

[60] View Article

[61] Google Scholar

[ref17] 17. Alder BJ, Wainwright TE (1959) Studies in Molecular Dynamics. I. General Method. The Journal of Chemical Physics 31: 459–466.
View Article
Google Scholar

[63] View Article

[64] Google Scholar

[ref18] 18. Richards FM (1977) Areas, Volumes, Packing, and Protein Structure. Annual Review of Biophysics and Bioengineering 6: 151–176. pmid:326146
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref19] 19. Lam KS, Salmon Se Fau - Hersh EM, Hersh Em Fau - Hruby VJ, Hruby Vj Fau - Kazmierski WM, Kazmierski Wm Fau - Knapp RJ, et al. A new type of synthetic peptide library for identifying ligand-binding activity.

[ref20] 20. Brown JM, Hoffmann WD, Alvey CM, Wood AR, Verbeck GF, et al. (2010) One-bead, one-compound peptide library sequencing via high-pressure ammonia cleavage coupled to nanomanipulation/nanoelectrospray ionization mass spectrometry. Analytical Biochemistry 398: 7–14. pmid:19891951
View Article
PubMed/NCBI
Google Scholar

[71] View Article

[72] PubMed/NCBI

[73] Google Scholar

[ref21] 21. Qian Y, Wang W, Wang Z, Han Q, Jia X, et al. (2016) Switchable probes: pH-triggered and VEGFR2 targeted peptides screening through imprinting microarray. Chemical Communications 52: 5690–5693. pmid:27035754
View Article
PubMed/NCBI
Google Scholar

[75] View Article

[76] PubMed/NCBI

[77] Google Scholar

[ref22] 22. Astle JM, Simpson LS, Huang Y, Reddy MM, Wilson R, et al. (2010) Seamless bead to microarray screening: rapid identification of the highest affinity protein ligands from large combinatorial libraries. Chem Biol 17: 38–45. pmid:20142039
View Article
PubMed/NCBI
Google Scholar

[79] View Article

[80] PubMed/NCBI

[81] Google Scholar

[ref23] 23. Wang W, Wei Z, Zhang D, Ma H, Wang Z, et al. (2014) Rapid screening of peptide probes through in situ single-bead sequencing microarray. Anal Chem 86: 11854–11859. pmid:25370073
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref24] 24. Wang W, Zhang D, Wei Z, Wang Z, Bu X, et al. (2015) Label-free detection microarray for novel peptide ligands screening base on MS-SPRi combination. Talanta 134: 705–711. pmid:25618725
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref25] 25. Wang J, Hou T, Xu X (2006) Recent Advances in Free Energy Calculations with a Combination of Molecular Mechanics and Continuum Models. Current Computer - Aided Drug Design volume 2: 287–306(220).
View Article
Google Scholar

[91] View Article

[92] Google Scholar

[ref26] 26. Kollman PA, Massova I,., Reyes C,., Kuhn B,., Huo S,., et al. (2000) Calculating Structures and Free Energies of Complex Molecules: Combining Molecular Mechanics and Continuum Models. Accchemres 33: 889–897.
View Article
Google Scholar

[94] View Article

[95] Google Scholar

[ref27] 27. Homeyer N, Gohlke H (2012) Free Energy Calculations by the Molecular Mechanics Poisson−Boltzmann Surface Area Method. Molecular Informatics 31: 114–122. pmid:27476956
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref28] 28. Gohlke H, Kiel C, Case DA (2003) Insights into protein-protein binding by binding free energy calculation and free energy decomposition for the Ras-Raf and Ras-RalGDS complexes. J Mol Biol 330: 891–913. pmid:12850155
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref29] 29. Hou T, Xu Z, Zhang W, McLaughlin WA, Case DA, et al. (2009) Characterization of domain-peptide interaction interface: a generic structure-based model to decipher the binding specificity of SH3 domains. Mol Cell Proteomics 8: 639–649. pmid:19023120
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref30] 30. Hou T, Zhang W, Case DA, Wang W (2008) Characterization of domain-peptide interaction interface: a case study on the amphiphysin-1 SH3 domain. J Mol Biol 376: 1201–1214. pmid:18206907
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref31] 31. Geng L, Wang Z, Yang X, Li D, Lian W, et al. (2015) Structure-based Design of Peptides with High Affinity and Specificity to HER2 Positive Tumors. Theranostics 5: 1154–1165. pmid:26284145
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref32] 32. Hudziak RM, Ullrich A (1991) Cell transformation potential of a HER2 transmembrane domain deletion mutant retained in the endoplasmic reticulum. J Biol Chem 266: 24109–24115. pmid:1684181
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref33] 33. Vajdos FF, Adams CW, Breece TN, Presta LG, de Vos AM, et al. (2002) Comprehensive functional maps of the antigen-binding site of an anti-ErbB2 antibody obtained with shotgun scanning mutagenesis. J Mol Biol 320: 415–428. pmid:12079396
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref34] 34. Geng L, Wang Z, Jia X, Han Q, Xiang Z, et al. HER2 Targeting Peptides Screening and Applications in Tumor Imaging and Drug Delivery.

[ref35] 35. Zhu L, Wang K, Cui J, Liu H, Bu X, et al. (2014) Label-free quantitative detection of tumor-derived exosomes through surface plasmon resonance imaging. Anal Chem 86: 8857–8864. pmid:25090139
View Article
PubMed/NCBI
Google Scholar

[126] View Article

[127] PubMed/NCBI

[128] Google Scholar

[ref36] 36. Gorodkiewicz E, Guszcz T, Roszkowska-Jakimiec W, Kozlowski R (2014) Cathepsin D serum and urine concentration in superficial and invasive transitional bladder cancer as determined by surface plasmon resonance imaging. Oncol Lett 8: 1323–1327. pmid:25120717
View Article
PubMed/NCBI
Google Scholar

[130] View Article

[131] PubMed/NCBI

[132] Google Scholar

[ref37] 37. Villiers MB, Cortay JC, Cortes S, Bloquel B, Brichler S, et al. (2015) Protein-peptide arrays for detection of specific anti-hepatitis D virus (HDV) genotype 1, 6, and 8 antibodies among HDV-infected patients by surface plasmon resonance imaging. J Clin Microbiol 53: 1164–1171. pmid:25631795
View Article
PubMed/NCBI
Google Scholar

[134] View Article

[135] PubMed/NCBI

[136] Google Scholar

[ref38] 38. Holtke C, Waldeck J, Kopka K, Heindel W, Schober O, et al. (2009) Biodistribution of a nonpeptidic fluorescent endothelin A receptor imaging probe. Mol Imaging 8: 27–34. pmid:19344573
View Article
PubMed/NCBI
Google Scholar

[138] View Article

[139] PubMed/NCBI

[140] Google Scholar

[ref39] 39. Abulrob A, Brunette E, Slinn J, Baumann E, Stanimirovic D (2007) In vivo time domain optical imaging of renal ischemia-reperfusion injury: discrimination based on fluorescence lifetime. Mol Imaging 6: 304–314. pmid:18092515
View Article
PubMed/NCBI
Google Scholar

[142] View Article

[143] PubMed/NCBI

[144] Google Scholar

[ref40] 40. Duan Y, Wu C, Chowdhury S, Lee MC, Xiong G, et al. (2003) A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. J Comput Chem 24: 1999–2012. pmid:14531054
View Article
PubMed/NCBI
Google Scholar

[146] View Article

[147] PubMed/NCBI

[148] Google Scholar

[ref41] 41. Guillot B (2002) A reappraisal of what we have learnt during three decades of computer simulations on water. Journal of Molecular Liquids 101: 219–260.
View Article
Google Scholar

[150] View Article

[151] Google Scholar

[ref42] 42. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics 79: 926–935.
View Article
Google Scholar

[153] View Article

[154] Google Scholar

[ref43] 43. Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak JR (1984) Molecular dynamics with coupling to an external bath. The Journal of Chemical Physics 81: 3684–3690.
View Article
Google Scholar

[156] View Article

[157] Google Scholar

[ref44] 44. Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, et al. (1995) A smooth particle mesh Ewald method. The Journal of Chemical Physics 103: 8577–8593.
View Article
Google Scholar

[159] View Article

[160] Google Scholar

[ref45] 45. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems. The Journal of Chemical Physics 98: 10089–10092.
View Article
Google Scholar

[162] View Article

[163] Google Scholar

[ref46] 46. Cui W, Wei Z Fau - Chen Q, Chen Q Fau - Cheng Y, Cheng Y Fau - Geng L, Geng L Fau - Zhang J, et al. Structure-based design of peptides against G3BP with cytotoxicity on tumor cells.

[ref47] 47. Cheng Y, Zhang F Fau - Chen Q, Chen Q Fau - Gao J, Gao J Fau - Cui W, Cui W Fau - Ji M, et al. Structural basis of specific binding between Aurora A and TPX2 by molecular dynamics simulations.

[ref48] 48. Cheng Y, Zhang F, Chen Q, Gao J, Cui W, et al. (2011) Structural basis of specific binding between Aurora A and TPX2 by molecular dynamics simulations. J Chem Inf Model 51: 2626–2635. pmid:21919471
View Article
PubMed/NCBI
Google Scholar

[167] View Article

[168] PubMed/NCBI

[169] Google Scholar

[ref49] 49. Ryckaert J-P, Ciccotti G, Berendsen HJC (1977) Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes. Journal of Computational Physics 23: 327–341.
View Article
Google Scholar

[171] View Article

[172] Google Scholar

[ref50] 50. Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, et al. (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33: 889–897. pmid:11123888
View Article
PubMed/NCBI
Google Scholar

[174] View Article

[175] PubMed/NCBI

[176] Google Scholar

[ref51] 51. Tsui V, Case DA Theory and applications of the generalized Born solvation model in macromolecular simulations. 275-291 p.

[ref52] 52. Weiser J, Shenkin PS, Still WC (1999) Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). Journal of Computational Chemistry 20: 217–230.
View Article
Google Scholar

[179] View Article

[180] Google Scholar

[ref53] 53. Connolly M (1983) Analytical molecular surface calculation. Journal of Applied Crystallography 16: 548–558.
View Article
Google Scholar

[182] View Article

[183] Google Scholar

[ref54] 54. Hou T, Wang J, Li Y, Wang W (2011) Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51: 69–82. pmid:21117705
View Article
PubMed/NCBI
Google Scholar

[185] View Article

[186] PubMed/NCBI

[187] Google Scholar

[ref55] 55. Sun H, Li Y, Shen M, Tian S, Xu L, et al. (2014) Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring. Phys Chem Chem Phys 16: 22035–22045. pmid:25205360
View Article
PubMed/NCBI
Google Scholar

[189] View Article

[190] PubMed/NCBI

[191] Google Scholar

[ref56] 56. Zoete V, Meuwly M, Karplus M (2005) Study of the insulin dimerization: binding free energy calculations and per-residue free energy decomposition. Proteins 61: 79–93. pmid:16080143
View Article
PubMed/NCBI
Google Scholar

[193] View Article

[194] PubMed/NCBI

[195] Google Scholar

[ref57] 57. Onufriev A, Bashford D, Case DA (2004) Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins 55: 383–394. pmid:15048829
View Article
PubMed/NCBI
Google Scholar

[197] View Article

[198] PubMed/NCBI

[199] Google Scholar

[ref58] 58. Lua RC, Lichtarge O (2010) PyETV: a PyMOL evolutionary trace viewer to analyze functional site predictions in protein complexes. Bioinformatics 26: 2981–2982. pmid:20929911
View Article
PubMed/NCBI
Google Scholar

[201] View Article

[202] PubMed/NCBI

[203] Google Scholar

[ref59] 59. Seeliger D, de Groot BL (2010) Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J Comput Aided Mol Des 24: 417–422. pmid:20401516
View Article
PubMed/NCBI
Google Scholar

[205] View Article

[206] PubMed/NCBI

[207] Google Scholar

[ref60] 60. Makarewicz T, Kazmierkiewicz R (2013) Molecular dynamics simulation by GROMACS using GUI plugin for PyMOL. J Chem Inf Model 53: 1229–1234. pmid:23611462
View Article
PubMed/NCBI
Google Scholar

[209] View Article

[210] PubMed/NCBI

[211] Google Scholar

[ref61] 61. Wang Z, Wang W, Geng L, Hu Z (2015) Distinguishing of tumor cell-targeting peptide ligands through a color-encoding microarray. Lab on a Chip 15: 4512–4516. pmid:26530232
View Article
PubMed/NCBI
Google Scholar

[213] View Article

[214] PubMed/NCBI

[215] Google Scholar

[ref62] 62. Wang Z, Wang W, Bu X, Wei Z, Geng L, et al. (2015) Microarray Based Screening of Peptide Nano Probes for HER2 Positive Tumor. Analytical Chemistry 87: 8367–8372. pmid:26218790
View Article
PubMed/NCBI
Google Scholar

[217] View Article

[218] PubMed/NCBI

[219] Google Scholar

[ref63] 63. Wang W, Wei Z, Wang Z, Ma H, Bu X, et al. (2014) A continuous flow microfluidic-MS system for efficient OBOC screening. RSC Advances 4: 61767–61770.
View Article
Google Scholar

[221] View Article

[222] Google Scholar

[ref64] 64. Wang W, Wei Z, Zhang D, Ma H, Wang Z, et al. (2014) Rapid Screening of Peptide Probes through In Situ Single-Bead Sequencing Microarray. Analytical Chemistry 86: 11854–11859. pmid:25370073
View Article
PubMed/NCBI
Google Scholar

[224] View Article

[225] PubMed/NCBI

[226] Google Scholar

[ref65] 65. Due Larsen B, Holm A (1998) Sequence-assisted peptide synthesis (SAPS). J Pept Res 52: 470–476. pmid:9924991
View Article
PubMed/NCBI
Google Scholar

[228] View Article

[229] PubMed/NCBI

[230] Google Scholar

[ref66] 66. Ball HL, Mascagni P (1996) Chemical synthesis and purification of proteins: a methodology. Int J Pept Protein Res 48: 31–47. pmid:8844261
View Article
PubMed/NCBI
Google Scholar

[232] View Article

[233] PubMed/NCBI

[234] Google Scholar

[ref67] 67. Made V, Els-Heindl S, Beck-Sickinger AG (2014) Automated solid-phase peptide synthesis to obtain therapeutic peptides. Beilstein J Org Chem 10: 1197–1212. pmid:24991269
View Article
PubMed/NCBI
Google Scholar

[236] View Article

[237] PubMed/NCBI

[238] Google Scholar

Figures

Abstract

Author summary

Introduction

Results/Discussion

Structure analysis of the HER2/pertuzumab complex

Mutations based on 4665

OBOC peptide library screening

Dissociation constants of peptides measured by surface plasmon resonance imaging

Validation of affinity and specificity of peptides to HER2 high expression cells

Confirmation of affinity and specificity of peptides to HER2-positive tumors

In silico analysis of interactions between peptides and HER2

Methods

Prepare the initial structure

MD simulations

Binding free energy calculations

Free energy decomposition analysis

Magnetic beads screening from peptide library

Peptide synthesis

SPRi assay

Immunocytochemistry

Ethics statement

In vivo and ex vivo fluorescence imaging

Supporting information

S1 Fig. Alignment of the sequences of the wild type and the five mutant peptides by Clustal Omega.

S2 Fig. SPRi analysis of the binding affinity of peptides.

S3 Fig. Confocal fluorescence imaging analysis of the MDA-MB-468 cell line with low HER2 expression after incubating with peptides labeled with Cy5.5.

S4 Fig.

S5 Fig. Backbone RMSDs as a function of time for the initial and successive structures of HER2/peptides complexes in MD trajectories.

S6 Fig. Comparison of the interactions of HER2/peptides.

S7 Fig. Binding free energy decomposition for key residues of the HER2 protein in the six complexes.

S8 Fig. Binding models of HER2/peptides complexes.

S1 Table. Numbers of water molecules and Na+ ions added in all simulation systems.

Author Contributions

References