Khater and Nassar ﻿
BMC Molecular and Cell Biology(2023) 24:25
https://doi.org/10.1186/s12860-023-00484-3

BMC Molecular andCell Biology

Open Access

RESEARCH

A computational peptide model induces
cancer cells’ apoptosis by docking Kringle 5
to GRP78
Ibrahim Khater1   and Aaya Nassar1,2*   

Abstract
Background Cells can die through a process called apoptosis in both pathological and healthy conditions. Cancer
development and progression may result from abnormal apoptosis. The 78-kDa glucose-regulated protein (GRP78)
is increased on the surface of cancer cells. Kringle 5, a cell apoptosis agent, is bound to GRP78 to induce cancer
cell apoptosis. Kringle 5 was docked to GRP78 using ClusPro 2.0. The interaction between Kringle 5 and GRP78
was investigated.
Results The interacting amino acids were found to be localized in three areas of Kringle 5. The proposed peptide ismade up of secondary structure amino acids that contain Kringle 5 interaction residues. The 3D structure of thepeptide model amino acids was created using the PEP-FOLD3 web tool.
Conclusions The proposed peptide completely binds to the GRP78 binding site on the Kringle 5, signaling that it
might be effective in the apoptosis of cancer cells.
Keywords Cancer, GRP78, Kringle 5, Apoptosis, Molecular docking
Introduction
Cancer is a major global public health concern with its
rising incidence over the past few decades, and it has
significant economic and social impacts [1, 2]. This risein cancer incidence poses a significant challenge to thehealthcare industry, as effective therapeutic treatment
options are often limited, and the development of newtreatments is complex and time-consuming. One of the
Sustainable Development Goals of the United Nationsfor 2030 [3] is to reduce the burden of cancer throughprevention and new treatment methods. During the*Correspondence:
Aaya Nassar
aaya_nassar@cu.edu.eg
1Biophysics Department, Faculty of Science, Cairo University, Giza, Egypt
2
Department of Clinical Research and Leadership, School of Medicine
and Health Sciences, George Washington University, Washington DC, USA

COVID-19 outbreak, which has caused delays and interruptions in cancer screenings, diagnoses, and treatments
around the world, it has been especially important tocoordinate efforts to reduce cancer burden.
Utilizing computational analysis methods, specifically molecular dynamic simulation, is one method for
addressing the problem of therapeutic cancer treatment[4–7]. The computational approach enables researchers
to investigate the atomic-level behavior of molecules andgain insight into the molecular mechanisms that drive
cancer progression and treatment response. By using
molecular dynamic simulation to study the interactions
between cancer cells and potential therapeutic agents,
researchers can identify new targets for drug development and optimize existing treatment strategies [8–10].
Anticancer therapies have been focused on their ability to destroy cancer cells while preserving healthy cells.
Targets for anticancer treatments are molecules thatare selective for cancer cells. Peptides that target cancer© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Khater and Nassar ﻿BMC Molecular and Cell Biology(2023) 24:25

cells could be used in the development of anticancer
drug delivery agents for localizing cancer sites [7, 11].
On the surface of cancer cells, the glucose-regulated
protein 78 (GRP78) exhibits enhanced expression [12,13]. GRP78, located on the endoplasmic reticulum, hasbeen shown to be up-regulated in a number of cancerous
tumors [9, 14, 15]. GRP78 is commonly known as binding immunoglobulin protein (BiP) or the heat shock protein 70 kDa family A member 5 (HSPA5) and is encodedby the HSPA5 gene in humans [16]. HSPA5 belongs to
the HSP70 chaperon family and is found mostly in theendoplasmic reticulum (ER) lumen [17]. GRP78 usuallybinds to the ER stress sensors ATF-6, PERK, and IRE1
and deactivates them. In addition, GRP78 is shifted awayfrom the stress sensors and toward the plasma membrane as a result of ER stress, which generates an accumulation of unfolded proteins. GRP78 is extensively
expressed on the cell surfaces of a number of cancer
types due to their fundamentally elevated ER stress levels, whereas it is relatively weakly expressed on normal
cells [16, 18, 19].
When natural ligands bind to GRP78 on the cell surface, they can cause a number of signaling responses.
GRP78 can then bind to 2-macroglobulin and Cripto on
tumor cells, causing cell survival and proliferation, orbind to Kringle 5 and Par-4, causing cell death [14, 19, 20].
Many studies have focused their efforts on finding small
compounds that can bind to cell surface GRP78 and activate apoptotic pathways as an effective anticancer treatment because cell surface GRP78 agonists can trigger anapoptotic response [8, 10]. Arap et al. (2004) investigated
the use of the stress response chaperone GRP78 as a target for tumor-specific ligands [19]. The authors demonstrated that GRP78 is selectively expressed on the surfaceof tumor cells and that this expression is increased underconditions of cell stress. They then show that phage display technology can be used to identify ligands that bind
specifically to cell surface GRP78 and that these ligands
can be used to selectively target tumor cells in vitro andin vivo. Two potential peptides with anticipated GRP78
binding motifs were designed, evaluated, and tested onmice model [19]. The two animal models were used totest the peptides: nude mice with DU145 prostate subcutaneous tumor xenografts and immunocompetent Balb/c
mice with EF43-fgf4 tumors. In both animal models, thepeptides were able to specifically bind to GRP78, internalize cells, and decrease tumor growth. A similar study
disclosed that peptides targeting GRP78 reduce cancer cell growth without harming other organ cells [21].
While anti-cancer delivery agents targeting GRP78 on
the cell surface is consistent with previous findings, morestudies are needed.

Page 2 of 10

The current study employs computational docking
screening to investigate potential Kringle 5 peptides thatbind to GRP78 and cause cancer cell apoptosis. Dockingof Kringle 5 to GRP78 was performed in silico, and theinteracting peptides were studied.

Materials and methods
Structural analysis

The structure of Kringle 5 was obtained from the databank of protein (PDB ID: 5HPG). The protein structures
were downloaded from (https://​www.​rcsb.​org/​struc​ture/​
5HPG).
Docking Kringle 5 to GRP78

The webserver ClusPro 2.0 for protein docking interactions was used to dock Kringle 5 (5HPG: A) to GRP78.
ClusPro 2.0 is a powerful tool for predicting the threedimensional structure of protein–protein complexes
through molecular docking simulations. It uses a rigidbody docking approach, in which the two protein structures are kept rigid while their orientations are adjusted
to find the optimal binding configuration. The tool,
available at (https://​clusp​ro.​org), is commonly used for
docking analysis of proteins [22–24]. The end-point freeenergy was used to calculate the binding energies of
the complexes. The complexes formed from the docking were sorted in terms of their energy of binding, andcalculated using the MM/GBSA free energy analysis of
the HawkDock program [25]. The MM/GBSA method
combines molecular mechanics (MM) and continuum
electrostatics (GBSA) calculations to estimate the freeenergy of binding based on the molecular interactions
between the two proteins. The MM/GBSA free energy
analysis involves calculating the binding free energy ofthe predicted protein–protein complexes by considering the van der Waals, electrostatic, and solvation interactions between the two proteins. This method allows
researchers to identify the most stable and physiologically relevant complex structures, and rank the predictedcomplexes based on their binding affinity. The complex
formed with the highest binding energy was then chosen,
and the interacting residues of Kringle 5 and GRP78 wereidentified.
Peptide model design

We used ESPript (https://​espri​pt.​ibcp.​fr/​ESPri​pt/​ESPri​pt), a web-based program for extracting and producing
an automated analysis of protein structures, with highquality representations of multiple sequence alignments
of proteins. The program reveals sequence similarities,
generates sequence logos, secondary structure, and residue conservation plots, and offers ways to enhance andKhater and Nassar ﻿BMC Molecular and Cell Biology(2023) 24:25

improve the way they are represented [26]. ESPript version 3 was used to align the amino acid sequences ofKringle 5 with the secondary structure of Kringle 5. The
sequence of the secondary structure that contains themost relevant and nearable Kringle 5 interacting residues
with GRP78 was obtained.
Physicochemical properties and solubility prediction

Page 3 of 10

models [30, 31]. The peptides were sorted based on
their free energy, which was estimated with Swiss-PdbViewer (https://​spdbv.​unil.​ch). The peptide with thelowest free energy was chosen. Both PROCHECK webserver (https://​w ww.​ebi.​ac.​uk/​thorn​ton-​srv/​softw​are/​
PROCH​ECK) [32] and ProSA-web (https://​prosa.​servi​
ces.​came.​sbg.​ac.​at/​prosa.​php) [33] were used to verify
the best model.

The online program ProtParam (http://​web.​expasy.​org/​
protp​aram) was employed to calculate the candidate
peptide characterizations and hydropathicity index,
including aliphatic index (pI), instability index, extinction coefficient, and molecular weight. ProtParam calculates several important protein properties, including
the molecular weight, theoretical pI, amino acid composition, extinction coefficient, and instability index. It
also provides information on the number of positively
charged, negatively charged, and polar residues in theprotein sequence, as well as the percentage of hydrophobic and hydrophilic residues [27]. To analyze andcompare the solubility of peptides in water, we used theonline tool Pepcalc (http://​pepca​lc.​com) [28]. Pepcalc
calculates the molecular weight, net charge, isoelectric
point, amino acid composition, extinction coefficient,and hydrophobicity index. It also predicts the stability of
peptides under different pH and temperature conditions,as well as the solubility and aggregation propensity of thepeptide.

The peptide model was docked to GRP78 once moreusing ClusPro 2.0. The MM/GBSA was used to rank
the docking complexes based on their binding energy.
The compound with the highest binding energy was
selected and searched for interaction residues. The Protein Data Bank in Europe: Protein Interfaces, Surfaces,
and Assemblies (PDBePISA), which is a webserver tool
available at (https://​w ww.​ebi.​ac.​uk/​msd-​srv/​prot_​int/​
cgi-​bin/​piser ​ver) was used to investigate the interactions between the proposed peptide and GRP78. PDBePISA works by analyzing the 3D structures of proteins
and other molecules in the PDB, and identifying theinterfaces and surfaces that are involved in protein–protein, protein–ligand, and protein-DNA interactions.
The program then calculates the binding energies andother physical and chemical properties of these interfaces and generates detailed reports and graphical representations of the results.

Secondary structure prediction

Molecular dynamics simulations

The PSIPRED prediction method (http://​bioinf.​cs.​ucl.​
ac.​uk/​psipr​ed) was used to determine the peptide’s secondary structure [29]. PSIPRED works by analyzing
the sequence profile of a protein, which is generated bysearching a sequence database for proteins that are similar to the target protein. The program then uses a neural network algorithm to predict the probability of eachresidue in the protein sequence being in an alpha-helix,
beta-strand, or coil, with reported accuracies of up to
82% for secondary structure prediction. The stages ofthe prediction method are generating a sequence profile, predicting the initial secondary structure, and filtering the predicted structure. PSIPRED attempts tonormalize the PSIBLAST sequence profile. The initial
secondary structure was then predicted using neuralnetworking.

The protein topologies and parameter files were createdusing the web-based graphical user interface CHARMMGUI [34–36]. The program provides pre-configured
simulation protocols for a variety of simulation types,
including energy minimization, equilibration, and production runs. The software package GROMACS-2019[37] was used with the force field CHARMM36 [38],which is a set of mathematical equations and parameters that describe the interactions between atoms ina molecular system, to simulate molecular dynamics.
The complexes were then neutralized with the molecular simulation tool TIP3P, which approximates watermolecules as three-point charges, with two negative
charges at the oxygen atom and one positive charge at
the hydrogen atoms, using the solvation box [39], byadding appropriate amounts of K + and Cl ions using
the Monte-Carlo ion-placing approach. The system wasequilibrated for 125 ps at a stable number of molecules,
volume, and temperature (NVT) after the system’senergy was reduced for 5000 steps using the steepest
method [40]. Finally, simulations of molecular dynamics
were performed.

Tertiary structure prediction and validation

The online computational framework tool PEP-FOLD3(https:// ​ b iose ​ r v. ​ r pbs. ​ u niv- ​ p aris- ​ d ider​ o t. ​ f r/ ​ s ervi ​ c es/​
PEP-​FOLD3) was utilized to predict the peptide’s threedimensional (3D) structure, generating five possible

Docking peptide model to GRP78

Khater and Nassar ﻿BMC Molecular and Cell Biology(2023) 24:25

Results and discussion
ClusPro 2.0 was used to dock Kringle 5, a cell apoptotic
agent, to GRP78, as demonstrated in Fig. 1A. The interface residues between Kringle 5 and GRP78 were calculated using the MM/GBSA method and aligned with
Kringle 5’s secondary structure using ESPript version 3,
as shown in Fig. 1B. The blue triangles indicate interface
residues. Interacting amino acids (surrounded by black
rectangles) were localized in three regions of Kringle
5. The 35 amino acids contained within the black rectangles were then used to build the peptide model. The
peptide has a molecular weight of 3829.24 kDa and atheoretical isoelectric point (pI) of 4.78, which is lessthan seven, indicating that the protein has a large percentage of negatively charged vs. positively charged residues. It was found that the stability index (Ii) was 21.31,
indicating that the solution was stable. The hydrophilic
nature of the protein and its ability to interact in aqueous solutions were revealed by the significant average ofthe hydropathicity index (GRAVY). The aliphatic index(Ai) was 28, suggesting that the peptide could toleratea broad range of temperatures. Considering that yeast
has a half-life of three minutes, mammals’ reticulocytes

Page 4 of 10

have a half-life of 1.1 h, and E. coli has a half-life of ten
hours. The extinction coefficient (EC) was computed
to be 4720 ­M−1 ­cm−1, displaying a great water solubility, and enabling a quantitative assessment of the ligand
and protein interactions in solution. Table 1 lists thephysicochemical properties of the candidate peptide.
Figure 2A shows the proposed peptide’s PSIPRED-predicted secondary structure. Using the PEP-FOLD3 web
tool, five models of the peptide’s tertiary structure werecreated and rated based on free energies. As indicatedin Fig. 2B, the best model was chosen and given thename PEP 35.
The Ramachandran plot calculates the energy of eachamino acid’s stable conformation psi (ψ) and phi (Ф)
twisting or dihedral angles. Ramachandran plot analysis’ tertiary structure validation revealed that the overall
percentage of favorable and allowed region residues was
100 percent, as shown in Fig. 3A. After ProSA-web wasused to examine the quality and probable flaws in the
crude 3D model, the peptide model earned a Z-score of-3.25, as shown in Fig. 3B. The quality of PEP 35 model
was confirmed using the Ramachandran plot and the
ProSA-web score.

Fig. 1 (A) Cell apoptosis agent Kringle 5 bound to GRP78. (B) The amino acids sequences of GRP78 aligned with its secondary structure using
ESPript3 showing interacting amino acids between GRP78 (referred to by blue triangles) and were concentrated in three regions of Kringle 5 (black
rectangles surround 35 amino acids)

Khater and Nassar ﻿BMC Molecular and Cell Biology(2023) 24:25

Page 5 of 10

Table 1 The physicochemical properties of the peptide
Sequence

LengthDCMFGNGKGYPRAGLEKNYCRNPDGDYCDVPQCAA​ 35

PI

Gravy

MW (Da)Solubility

Half-life (h)IiAiEC ­(M-1 ­cm−1)

4.78-0.8833829.24

Good water solubility

1.1

21.31

28

4720

ClusPro 2.0 was utilized to dock PEP 35 to GRP78. At
the same location as Kringle 5, PEP 35 interacts with
GRP78. GRP78 residues that interact with both PEP 35
and Kringle 5 are highlighted in grey as demonstrated
in Fig. 4A and Fig. 4B, correspondingly. The interactions
between the GRP78 and PEP 35 models were studied
using PDBePISA, which includes hydrogen bonds andsalt bridges, as shown in Table 2. The PEP 35 model
developed 19 hydrogen bonds and 6 salt bridges using
GRP78. This implies that PEP 35 and GRP78 have astrong interaction.

The docking mechanism formed binding between
the legends and the protein, which could be unstable [41]. The simulations revealed a lot about themolecular interactions that keep the complexes stable. The root mean square difference (RMSD) for thecomplex backbone GRP78-PEP 35 model was utilized
to determine the stability of the complex in comparison to the starting structures. Plotting the gyration
radius (RG) as a function of time allowed for thecomplex’s stability to be ascertained [42]. The RMSD
(blue) and RG (red) values of the GRP78-PEP 35

Fig. 2 (A) PSIPRED predicted secondary structure of final peptide (PEP 35) and (B) Predicted 3D structure of final peptide using PEP-FOLD3

Khater and Nassar ﻿BMC Molecular and Cell Biology(2023) 24:25

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Fig. 3 Tertiary structure validation of PEP 35. (A) Ramachandran plot of the final peptide modeled, more than 100% of the amino acids arein the allowed regions and (B) ProSA-web plot of the peptide, giving a Z-score of -3.25

Khater and Nassar ﻿BMC Molecular and Cell Biology(2023) 24:25

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Fig. 4 (A) PEP 35 (red) bound to GRP78 (green) at the same position of Kringle 5 (blue) (B) GRP78 residues interact with PEP35 (in red), Kringle 5 (inblue), and both (in grey)

complex are plotted and shown in Fig. 5. The parameters remain steady over the simulation time, indicatingthat the GRP78-PEP 35 complex is stable. As a result,
PEP 35 could be used as an apoptosis agent in cancer
cells. The results of the current analysis are in linewith the means that GRP78 localizes to the cell surface as previously described by Chen and colleagues[43] and shedding light on the prospect that cancer
cell growth would be hindered by new treatments thattarget GRP78.

Conclusion
The rising incidence of cancer is a major global public health concern, and it has significant economic andsocial effects. We demonstrated using molecular docking
and molecular dynamics simulations that GRP78 binds
to Kringle 5 and triggers the death of tumor cells, indicating that it may be beneficial as a therapeutic agent tosite-direct and localize cancer cells. The efficacy of the
proposed peptide apoptotic pathway requires additional
experimental investigation.

Khater and Nassar ﻿BMC Molecular and Cell Biology(2023) 24:25

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Table 2 The interactions between the GRP78 and PEP 35 analyzed using PDBePISA
PEP 35-GRP78 complex
Hydrogen bonds

Salt bridges

GRP78 residue

Peptide residue

Distance ­(Ao)

GRP78 residue

Peptide residue

Distance ­(Ao)

ASP 105

ASN 6

2.11

ASP 333

ARG 12

3.82

GLN 109

TYR 192.24

LYS 340

ASP 1

2.76

LYS 113

ASN 18

1.67

LYS 340

ASP 1

2.52

LYS 113

TYR 191.65

LYS 344

ASP 1

2.91

ARG 261

ARG 12

1.81

LYS 344

ASP 1

3.36

ASP 333

ARG 12

2.12

LYS 470

ASP 293.82

ASP 333

ARG 12

1.76

SER 337

ARG 12

2.16

LYS 340

ASP 1

1.88

LYS 340

ASP 1

1.76

LYS 344

ASP 1

2.33

LYS 344

ASP 1

2.38

LYS 344

ASP 1

1.91

LYS 344

MET 3

1.66

GLU 465

ALA 35

2.02

GLU 466

CYS 33

2.14

LYS 470

LYS 8

1.74

ASP 471

CYS 33

3.80

ASN 472

LYS 8

1.76

Fig. 5 Plot of the Root Mean Square Deviation (RMSD) for the backbone atoms, radius of gyration (RG) of GRP78-PEP 35 complex as a functionof time over a 100 ns simulation

Khater and Nassar ﻿BMC Molecular and Cell Biology(2023) 24:25

Acknowledgements
The authors acknowledge Bibliotheca Alexandrina (https://​hpc.​bibal​ex.​org) for
providing access to their high-performance supercomputer workstation.
Authors’ contributions
Conceptualization, I.K.; methodology and visualization, I.K.; interpretation, I.K.
and A.N.; writing—original draft preparation and manuscript revision, I.K. and
A.N. All authors read and approved the final manuscript.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Availability of data and materials
The Kringle 5 datasets analyzed during the current study are available in theprotein data bank (https://​www.​rcsb.​org/​struc​ture/​5HPG).

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 8 March 2023 Accepted: 23 June 2023

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