Peptide that inhibits cancer cell invasion and metastasis

Abstract

The present invention is aimed at producing a therapeutic peptide that inhibits breast cancer invasion and metastasis. In fact, invasion and metastasis of malignant cells to other organs play a key role in cancer recurrence and patient death. Metastasis of cancer cells from the original site to a secondary tissue, which causes dysfunction, is the cause of 90% of cancer-related deaths. Given the importance of the issue, many efforts have been made to design and develop drugs that inhibit the process of invasion and metastasis, but unfortunately, significant success has not been achieved in this area. One class of drugs available to inhibit the process of invasion are peptides, which have attracted much attention due to their small size and ease of production compared to proteins. On the other hand, advances in the field of computational design of peptides with medicinal properties have made the work much easier for those active in this field. In silico and in vitro studies have shown that the designed peptide has the ability to inhibit the uPA-uPAR system, which is an important factor in the invasion of cancer cells. This peptide has 8 amino acids and has amino and acetyl groups attached to its carboxyl and amino ends, respectively. On the other hand, due to its homology with a part of the CD44 protein, the results showed that the peptide has a tendency to bind to this receptor and the ability to inhibit its role in cancer progression. The aforementioned peptide is also capable of inhibiting the synthesis and activity of matrix metalloproteinases such as MMP-2 and MMP-9.

Description

In the name of God Description of the invention 1. Title of the invention: Peptide inhibiting invasion and metastasis of cancer cells 2. Technical field of the invention: Biotechnology, production of biological drugs 3. Technical problem and statement of the objectives of the invention: Given that cancer is one of the most deadly diseases that threatens human health and imposes large annual costs on the health systems of countries, therefore, timely diagnosis and development of effective treatment strategies to combat it are of great importance. Conventional cancer treatments, mainly because they affect normal tissues in addition to cancerous tissue, cause many side effects such as hair loss, inflammation, digestive problems, and secondary cancers. Therefore, the development of molecular therapies that bind to the target receptor with high specificity can significantly help improve the condition of cancer patients. In this regard, identifying key biological pathways in cancer as a drug target is of great importance. The aim of the present invention was to design and produce a peptide to inhibit and suppress the invasion and metastasis of cancer cells. The designed peptide named IK1 and the sequence "AC-RPSFPPEE-NH2" is able to target the urokinase pathway consisting of uPA and its receptor, uPAR, as well as the biological pathways related to CD44, which are of great importance in the invasion of cancer cells. These biological molecules play an important role in angiogenesis, growth, and invasion of cancer cells through interaction with other molecules. Considering this point, the aim of this study was to design an anti-invasive peptide based on the structure of peptide A6 (AC-KPSSPPEE-NH2). This peptide is an allosteric inhibitor of the uPA-uPAR system that also has the affinity to bind to CD44. The target molecules of IK1, which include the uPA-uPAR system and CD44, are overexpressed in many cancers, so targeting them is a suitable therapeutic strategy.In the present invention, we created a peptide library using computational biology methods that have advantages such as high speed and low cost compared to laboratory protein design methods, and by using two methods of stability maturation and affinity maturation, and based on the structural properties and sequence of the A6 peptide, whose anticancer effects have been confirmed in many studies, but due to its low potency in preventing cancer recurrence, its studies were stopped in the second phase of clinical trials. Among the various peptides that were designed and studied, IK1 showed high potency in inhibiting invasion and the expression of factors interfering in the occurrence of metastasis. 4. Description of the state of prior knowledge: One of the important pathways in the process of cancer progression and metastasis is the urokinase pathway. This pathway consists of two components, including uPA or urokinase and its receptor, uPAR. Urokinase is a proteolytic enzyme whose role was first identified in the process of blood clot breakdown, and at that time, most researchers believed that this system was only effective in dissolving blood clots, but in subsequent studies, it was found that this pathway also plays a role in cases such as inflammation, secretion of matrix proteinases, wound healing, and cancer. Given that the uPA-uPAR system plays an important role in angiogenesis, growth, and invasion of cancer cells, experts have tried hard to facilitate the treatment of cancer by inhibiting this system, but so far not much success has been achieved in this field. Currently, the structure of the uPA-uPAR complex is available in crystallized form, so researchers have made great efforts to inhibit this pathway by designing and developing numerous drug candidates. These drug candidates include a variety of substances such as antibodies, peptides, proteins, and small molecules. Another important molecule in cancer development that was also investigated in this study is CD44. This protein consists of 742 amino acids and is known as a cell surface receptor that has various ligands such as collagen and hyaluronic acid. Following the interaction of CD44 with substances such as hyaluronic acid, pathways such as RAS and PI3K are activated, which in turn facilitate the process of cancer progression. This protein has multiple glycosylation sites and due to the presence of a large IDR, its other regions have not been crystallized so far, except for the second binding site to hyaluronic acid, which has posed challenges in drug design against it. This molecule is also associated with the uPA-uPAR system. The complexity of the pathways involved in cancer development and metastasis has led to the introduction of numerous molecules to inhibit cancer. One of the most popular ones is peptides, which have attracted attention due to their small size and ease of production. So far, many peptides have been designed for the treatment and diagnosis of cancer. Anginex is a synthetic peptide for the treatment of cancer that has a positive charge and a structure consisting of several ß-sheets. This peptide was designed based on the sequence and structure of some proteins including platelet factor-4 and interleukin-8 and has a high potential for inhibiting respiratory system cancers. Another peptide whose anti-cancer properties have been investigated in cases such as glioma is called Chlorotoxin (consisting of 36 amino acids). The exact mechanism of this peptide is not known, but the interaction with MMP-2 and alpha-v integrin is likely to cause therapeutic responses of this peptide, which contains an alpha helix with three antiparallel ß-sheets. BT1718 is another peptide that has entered phase II clinical studies and binds to MMP-14, which is overexpressed in some cancers. This peptide carries an anti-tubulin toxin. p28 is also an anticancer peptide that binds and inhibits CDK2 and cyclin-A, causing cell arrest in the G2–M phase.This peptide, derived from Azurin, enhances the therapeutic effect of other drugs and has shown good response in the treatment of central nervous system tumors. Because this drug contains an amphipathic alpha helix, it has a high penetration ability to enter the cancer cell. Compared to small molecules (good penetration, low interaction strength) and antibodies (good interaction, large size), peptides seem to be more desirable for targeting cancer cells. Tumor homing peptides, which are being developed with the above goal and using techniques such as phage display, have opened a window of hope for targeting cancer treatment and reducing drug side effects. An example of this category is the coop peptide, which has been used to target glioblastoma cells. Also, the use of peptide vaccines for cancer treatment and prevention is an exciting discussion that has witnessed tremendous progress in the last few years and is today considered one of the main candidates for cancer treatment and prevention. Given the wide role that peptides can play in cancer treatment, various techniques are used to design and produce them. Recombinant protein technology, phage display, ribosome display and yeast display are some of these techniques. The use of mutation-based techniques such as direct evolution, direct mutagenesis and random mutagenesis are also other methods that are widely used in the design and production of peptides with therapeutic potential. Although the power and efficiency of the above techniques are not hidden from anyone, the high cost and time-consuming nature have forced researchers to try to find a new solution for designing therapeutic peptides in cancer. Due to the advances made in the field of computational biology and bioinformatics, as well as the increase in the accumulation of biological data, the possibility of designing therapeutic peptides using in silico studies and computational methods has been provided. One of the peptide computational methods that has made great progress in recent years is the rational peptide design method. In this method, peptide modeling, investigation of peptide interactions with target molecules, sequence information, structural features, residue scanning, free energy calculation, and molecular dynamics are used to design peptides with desired properties. In the rational peptide design method, first the desired information is extracted from the target peptide structure, then based on this data, the desired changes are made to increase affinity, stability, or create a new property. Mainly, the tools and algorithms that are currently available implement the rational design process by evaluating energy levels and calculating them following structural changes. As mentioned, different computational methods are used in rational design. One of these methods is molecular simulation, which enables the researcher to examine molecular changes after protein manipulation at the atomic level. Tools such as Gromax, Felt, Schrodinger, Lamps, and Amber are examples of such tools.Another widely used method in protein engineering and rational protein design is the use of alanine scanning, which examines the role of amino acids in a protein in its stability. In fact, in this method, first the amino acids in the protein sequence are replaced with alanine, and the role of the change at each position is analyzed by calculating the free energy. Calculating the free energy of proteins is also a very powerful tool in designing efficient therapeutic molecules. In fact, after any change in the sequence of a protein, by examining its energy level and stability, useful information can be obtained about the therapeutic potential of a molecule. Molecular docking is also another computational method in examining the type of intermolecular interactions, which provides the researcher with useful information by examining the type of intermolecular interactions and predicting the possible location of ligand binding to the receptor. 5. Provide a solution to the existing technical problem along with a detailed, sufficient and integrated description of the invention: Given that both uPA-uPAR and CD44 pathways play a significant role in the development of cancer, therefore, suppression of these two biological pathways can significantly contribute to the improvement of cancer patients. In this study, an attempt was made to design a therapeutic peptide using the A6 peptide sequence, which is a peptide derived from the human urkinase protein, which, in addition to having a high affinity for its targets, has an appropriate lifespan and desirable tissue solubility. Among the designed peptides, the IK1 peptide, which has a sequence similar to a biological sequence (peptide A6), lacks toxic properties and immune system stimulation. Unlike small molecules, which are often unable to interact with the target receptor with high affinity, this peptide was able to interact with its targets with high affinity. In the present invention, an attempt was made to evaluate the efficacy of the IK1 peptide for inhibiting the invasion and metastasis of MDA-MB-231 cells using in silico and in vitro methods. During our study, it was determined that IK1 has high anti-invasive and anti-metastatic properties. 5.1. In silico experiments and their results: 5.1.1 Conducting evolutionary studies The PSI-BLAST algorithm is an advanced method for estimating the similarity between protein sequences. We performed this step to examine the variation of the A6 sequence in other protein structures. For this purpose, we first entered the A6 sequence in the query section. Then, the non-redundant option was selected from the database section. The values ​​​​for max target, expect threshold and word size were selected as 500, 0.05 and 3, respectively. We also used the BLOSUM62 matrix in this step and the PSI-BLAST Threshold value was also set to 0.005. During the search, the iteration value was set to 3 and the results obtained were used using the PSI-BLAST viewer. In fact, to do this, the ASN file generated as the input file for the PSI-BLAST viewer was used. The way this tool analyzes the data is that the positions that have a higher score have been more conserved during evolution. Proline-containing sites are highly conserved, so during mutagenesis, these sites were restricted (shown in Figure 1). 5.1.2 Implementing molecular docking This step was performed to identify the A6 binding pocket on CD44. First, the CD44 structure was downloaded from the RCSB database with the code 1UUH. Then, using the Modeller version 10.2 tool, structural anomalies such as missing residues were removed. Considering that CD44 has structural sugars, the desired sugars were added to the structure using the CHARMM-GUI server. Then, the desired structure was optimized using the prepack and relax modules available in the Rosetta package. Among the predicted structures, the conformation with the most optimal score was selected and docking was performed for it. Also, the A6 structure was first drawn with the ChemDraw version 12 tool and optimized with Rosetta. To predict the binding pocket, first a global docking was performed, then a focused docking was performed with FlexPepDock. The output structures of Rosetta have REU scores, and the structure that has obtained the most negative REU is the best structure. All of the above processes were applied on the UBUNTU operating system version 22.04 using version 3.12 of the Rosetta tool.During docking, using the prepared codes, the structures of the desired sugars were also identified by the tool during the docking process, and the docking was also performed in a way that the structure was flexible. In Figure 2, the A6 complex with CD44, which had acquired the energy level of -449.4REU, is shown. 5.1.3 Peptide library creation In order to create a peptide library based on the A6 structure, we used two algorithms. The first algorithm is a module of the Rosetta tool called ddg-monomer. To use this tool, a resfile was first prepared and based on it, the desired mutations were applied to the structure obtained from the previous steps. Another tool used to create the peptide library was the Osprey tool version 3.2. We used two modules of the Osprey tool, findGMEC and Kstar. The Kstar module, like ddg-monomer, was used to optimize A6 in complex with CD44. While the findGMEC tool was used to optimize the A6 peptide sequence in isolation. All processes were run on the Ubuntu system version 22.04 and Python 3.8 was used to implement the Osprey codes. The mutagenesis process was used to create the library in a simultaneous or multiple mutation manner. 5.1.4 Structural prediction methodology After constructing a peptide library, which consisted of approximately 100,000 peptides, the IK1 peptide, which had the highest stability and structural similarity to A6, was docked to the A6 binding pocket using the FlexPepDock module of Rosetta. This step was performed as described in Section 5.1.2. The binding pocket of the IK1 peptide corresponds to the binding site of the A6 peptide (Figure 3). 5.1.5 Performing Physicochemical Property Prediction In order to predict the physicochemical properties of the target peptide, we used the ToxinPred tool to investigate toxicity. Also, items such as pKa, isoelectric point, charge, and hydrophobicity were evaluated using the ProtParam tool. These properties are listed in Table 2. 5.1.6 How to run molecular dynamics In this stage of the study, after designing the peptide library, we entered the IK1 peptide and A6 peptide complexes with CD44 into the molecular dynamics simulation stage for further investigation. The purpose of this stage was to investigate the thermodynamic factors and investigate the binding affinity of the designed peptides for their receptor. In order to perform the molecular dynamics simulation, the structures were first parameterized. The purpose of parameterization was to define such things as the properties related to the bonds and angles of the target structures. All simulation stages were performed on Ubuntu 22.04 using Gromax version 22.03 compiled with Coda. After parameterizing the systems, the dissolution step in water was performed. In this step, the cubic water box model was used. The TIP3P water model was also used to dissolve the systems. About 25,000 water molecules were added to the systems in this step. Then, in order to neutralize the charge of the systems, ionization was performed using sodium and chlorine ions. In this step, a concentration of 0.15 molar ions was used to neutralize them. The ions replaced the water molecules. In the next step, the energy of the systems was optimized using the steepest descent algorithm. After the optimization process was completed, the potential energy of the systems was plotted to ensure the accuracy of the process. Then, the systems were equilibrated in this step. Achieving equilibrium of the systems is crucial in performing a correct simulation. In the first step, which was carried out with the aim of adjusting the system temperature to 310 degrees Kelvin, the temperature of the systems was equilibrated using the V-rescale algorithm. This step was performed for 1 nanosecond. After that, in the second stage of balancing the system pressure was set to 1 bar.To achieve this goal, the Parrinello-Rahman algorithm was used. The pressure adjustment step was performed for 5 nanoseconds. After the equilibration steps were completed, the temperature and pressure functions of the systems were extracted and examined to ensure that these parameters were adjusted. Finally, the designed systems were examined for 200 nanoseconds. The analysis of the mentioned parameters was performed using the GRACE tool. During the simulation, the distances were set to 1.2 nm and the rshift value was set to 0.9 nm. The step value was also set to 2 femtoseconds. In the first stage of the simulation, factors such as RMSD and Rg were first implemented to check the system's equilibrium. The RMSD factor shows the amount of structural deviation compared to the first simulation time. The Rg factor also shows the amount of compaction of the structure. Then, the amount of structural fluctuations was evaluated by examining the RMSF values ​​of the structure. This factor indicates the mobility of each amino acid during the simulation and can be a feedback of the stability of the system. Finally, the amount of interaction surface of the systems with water was analyzed using the SASA parameter. In fact, this parameter indicates the amount of surface available for a molecule to interact with water. In the second stage of the analysis, items such as molecular distance, number of hydrogen bonds, and number of bonds available at a distance of less than 6 angstroms were examined. In fact, these factors can indicate the affinity of binding. The extracted hydrogen bond was performed to examine the number of bonds between two molecules. The greater number of hydrogen bonds indicates a greater affinity of binding. Also, a smaller distance indicates a greater affinity. Other items such as secondary structure analysis, FEL energy, PCA, and Ramachandran plot were also examined during the simulation. To perform the simulation, we used mdp files.The results of this phase of the study showed that the IK1 peptide has no effect on the structure of CD44 and has a high affinity for this receptor. The results of this phase are shown in Figures 4 to 9. 5.1.7 Performing free energy estimation calculations In this study, the molecular mechanics-Poisson Boltzmann surface area (MM / PBSA) method was used to calculate the binding energy and the effect of structural changes on its value. Here, the binding free energy was calculated using g_mmpbsa during 200 ns of molecular dynamics simulation. During this step, the accessible surface area (SASA) model was used to calculate the nonpolar solvation energy. The binding free energy calculations of peptides and CD44 are measured through the following equation (1): = - ( + (1) where Gcomplex refers to the total free energy of CD44-peptides, and GCD44 and Gpeptides represent the total free energy of CD44 and peptides, respectively. In addition, the free energy for each component was calculated using formula (2): = ( - TS + ((2) Gsolvation is the free energy of solvation, and T and S are the temperature and entropy, respectively. Also, EMM is the average molecular mechanical potential energy in vacuum, which was calculated using equation (3): = ( +( = ( +( + (3) Where Eelec is the electrostatic interaction and Evdw is the van der Waals interaction between the peptides and CD44, and Ebonded is the bonding interaction consisting of angle, bond, dihedral and misfit interactions. Enonbonded is the van der Waals and electrostatic interactions. Also, Ebonded was taken as zero and Gsolvation was calculated by equation (4): = + (4) Where Gpolar and Gnonpolar were determined by the polar solvation energy and SASA energy, respectively. During the free energy calculations, the energy value was calculated for intervals of 250 picoseconds. Also, at this stage, mdp files were used for calculations. As can be seen in Table 3, peptide IK1 has obtained a more favorable energy compared to A6. 5.2. In vitro experiments and their results: 5.2.1 Cell culture The cancer cell line MDA-MB-231, which expresses high levels of CD44, uPA, uPAR, MMP-2, MMP-9, is a cell line derived from breast epithelial tissue (Cailleau, Young et al. 1974), which was provided by the Pasteur Institute of Iran. 5.2.2 Synthesis of peptides All peptide synthesis processes were performed at the Peptide Chemistry Research Institute located at Khajeh Nasir al-Din Tusi University. In order to confirm the accuracy of the synthesis, two techniques, HPLC and MS, were used. The results of this step are depicted in Figure 11. 5.2.3 Cell proliferation assay using MTT assay The MTT assay is based on the production of a purple formazan precipitate, which is produced by the activity of mitochondrial enzymes in living and active cells, from tetrazolium dye. To assess cell viability, 7.5 × 103 cells were plated in each well of a 96-well plate, allowed to adhere to the bottom of the plate, and allowed to pass through a PDB. After 24 hours, the cells were treated with various concentrations of peptides (25, 50, 75, 100, 150, 200, 250, 500, 750 µM) and assessed after 48 hours. After 48 hours, the cell medium was discarded and 20 µL of MTT solution (5 mg / mL PBS) was added. The cells were incubated at 37°C with 5% CO2 for 3–4 hours. Then the cell supernatant was gently removed. To dissolve the formazan, 60 μl of DMSO solution was added to each well and the plate was placed in a shaker incubator for 10-30 minutes. In the next step, the OD was read at 570 nm by an ELISA reader. Also, to remove background light, a reading was taken at 650 nm. The following formula was used to calculate the mortality rate. Cell viability (%) = Of the three peptides studied, peptide IK1 had the lowest IC50. It is important to note that since the IC50 was expressed at very high concentrations, it can be stated that the peptide under study is non-toxic (Figure 12). 5.2.4 Expression analysis of genes involved in invasion and proliferation using RT-qPCR In this study, Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to investigate the changes in the expression of CD44, MMP-2, MMP-9, uPA, and uPAR genes. For this purpose, cells were first treated with peptides for 48 hours. In the next step, RNA from the cells was extracted. Then, the extracted RNA was converted to cDNA, and finally, the expression of the target genes was evaluated using the designed primers. The real-time results showed that among the designed peptides, only IK1 was able to reduce the expression of the studied genes, similar to the control peptide (A6) (Figure 13). Although A6 was more potent than IK1 in reducing the expression of uPA, the IK1 peptide exhibited a higher ability to suppress the expression of other genes such as uPAR, MMP-2, MMP-9, and CD44 compared to A6. 5.2.5 Zymography to examine the activity of MMP-2 and MMP-9 enzymes The expression and activity of MMP-2 and MMP-9 enzymes as an important factor in the invasion of MDA-MB-231 cells were investigated by gelatin zymography assay. 2×105 cells per well were seeded in a 12-well plate. After 24 hours, the cells were placed in culture medium containing 1% serum for one night. Then, the cells were treated with IK1 and A6 peptides at IC50 concentrations. The cell supernatant was transferred to a zymography gel. About 10 μl of sample was injected into each gel well, and a SMOBIO ladder was used. The samples were electrophoresed at a constant voltage of 130-150. During the experiment, bromophenol blue dye, which indicates the rate of progression of the samples along the gel and also serves as a weighting agent for the samples, was used. Before the dye came out of the end of the gel, it was removed from the electrophoresis tank and gently separated between two glass plates. The gel was washed once with deionized water. Then, it was washed for 1 hour in a container containing 2.5% Triton X100 solution to remove SDS from the gel and refold the denatured proteins.After this period, the gel was washed with distilled water and incubated for 24 hours at 37°C in zymography buffer. This buffer contains cations necessary for the activity of metalloproteinases and causes the decomposition of gelatin at the site of gelatinase. After the gel incubation time and washing with distilled water, it was placed in a staining solution on a shaker for 1 hour. Then, it was placed on a shaker for 2 hours in a solution prepared for decolorization so that the colorless bands, which are the sites of gelatinase enzyme action and as a result of gelatin decomposition and lack of colorability of the gel, appeared on the blue background of the gel. For analysis, the gels prepared were analyzed and examined with a GS-800 device from Burford, and densitometry of the gel bands was performed with the ImageJ program. The results of this stage revealed that the IK1 peptide has greater potency in inhibiting MMP-2 activity compared to the A6 peptide. The potency of the designed peptide IK1 and the control peptide A6 in inhibiting MMP-9 activity is equal (Figure 14). 5.2.6 Cell invasion assay to examine cancer cell invasion after peptide treatment Cell invasion assay was performed in transwell chambers coated with Matrigel. The insert used was made of polycarbonate and had a pore size of 8 μm. According to the manufacturer's instructions, trypsinized cells were placed in the upper chamber at a number of 104 × 5 in serum-free medium (180-200 μl). Also, medium containing FBS (10%) was used as a chemical adsorbent in the lower chambers. At this stage, cells were treated with IC50 values ​​of peptides for 48 hours. To perform the cell invasion assay, Matrigel was mixed with serum-free medium at a dilution of 1:4 and injected into the bottom of the insert at a volume of 35 μl and incubated for about 6 hours to stabilize. Then, cells were placed on the surface of Matrigel inside the chamber and incubated and treated for 48 hours. After the treatment time (with IC50 concentration of peptides), the inserts were fixed with 100% methanol at -20°C for 20 minutes. After that, the cells on the upper surface were removed with a cotton swab. Then, the inserts were stained with hematoxylin (3 minutes) for nuclear staining and eosin (30 seconds) for cytoplasm staining.After staining, the inserts were placed in 70 and 100% ethanol alcohol for about 30 seconds, respectively, to dehydrate. The number of cells that passed through the insert was counted using ImageJ software. The results were reported as the mean ± standard deviation of at least three independent experiments. To check the number of cells, about 10 pictures were taken from each insert. For this purpose, the insert was first cut and fixed on the slide surface using a drop of PBS. The insert should be placed on the slide surface in such a way that its downward side (the surface through which the cells passed) was facing you. Then, a conventional microscope was used to observe the cells and it was found that the IK1 peptide has the ability to suppress the invasion of cancer cells (Figure 15). 6. Description of figures, diagrams and tables: Figure 1. This figure shows the output of PSI-BLAST. As can be seen, the proline-containing positions scored very high, which means that these regions are conserved during evolution. Considering this, we tried to keep the proline-containing regions constant in order to build the peptide library, and mutations were performed for the residues that scored less. Figure 2. This image, showing the binding site of the A6 control peptide, highlights the importance of the sugar attached to residue ASN25. Also involved in this interaction is residue ARG41, which is essential for the interaction of CD44 with hyaluronic acid. The carbohydrates and Arg41 are highlighted in green, yellow, and orange, respectively. Figure 3. The designed peptide IK1 (like A6) binds to the site containing ASN25 and ARG41 for interaction with CD44. Figure 4. In this figure, the four parameters RMSD, which indicates the structural shift, RMSF, which indicates the amount of oscillation and movement of each residue, RG, which indicates the amount of compaction, and SASA, which calculates the amount of interaction surface with water, are displayed. Figure 5. In this image, from left to right, the four parameters PDF, PCA, Ramachandran plot, and FEL are depicted. With the exception of FEL, which shows the amount of energy change for different conformations, the other three parameters describe structural changes. Figure 6. This image shows the changes in the secondary structure elements, each color representing a secondary structure element. As can be seen, the interaction of CD44 with the designed peptide does not have a significant effect on the secondary structure of this receptor. Figure 7. The items related to this image are the same as the descriptions included for Figure 4. Figure 8. The items related to this image are the same as the descriptions included for Figure 6. Figure 9. In addition to hydrogen bonds, this image shows the distance between two molecules and the number of bonds less than 6 angstroms for the complexes. A higher number of bonds and a lower distance means a higher binding strength. . The results of this step show that IK1 has a greater ability to form hydrogen bonds with CD44 and had a shorter distance from this receptor during the molecular dynamics simulation. Figure 10. This image shows the four VdW energies, electrostatic energy, polar energy, and non-polar energy that are involved in the formation of the free energy of the studied systems. Figure 11. Mass spectrometry plot of the peptide, confirming its correct synthesis. Figure 12. This figure shows the cytotoxicity of the peptide. For the MTT assay, MDA-MB-231 cells were treated with the studied peptide at different concentrations for 48 hours. As can be seen, at a concentration of 25 μM IK1 is toxic to the studied cells, which can be explained by the low toxicity of this peptide, and the occurrence of toxicity and IC50 at high concentrations. This was also expected because the structure and sequence of this designed peptide are similar to A6, a natural peptide derived from urokinase. The results are the mean ± standard deviation of three different experiments *P<0.05), **P<0.01, ***P<0.001, .(****P<0.0001 Figure 13. . To perform RT-qPCR, total RNA from MDA-MB-231 cells treated with the peptide at IC50 values ​​was extracted and converted to cDNA. It can be seen from this figure that the IK1 peptide has a high potency in repressing the target genes. The results represent the mean ± standard deviation of three different experiments *P<0.05), **P<0.01, ***P<0.001 Figure 14. After 48 hours of peptide treatment, the cultured cell medium was collected for zymography. The lower amount of lysed gelatin in the A6 and IK1-treated groups indicates that these peptides are able to inhibit the synthesis and secretion of matrix metalloproteinases. The white halo indicates the areas containing lysed gelatin, which are adjacent to the bands corresponding to the two enzymes MMP-2 and MMP-9, indicating the amount of activity of these two enzymes in the studied samples. The results represent the mean ± standard deviation of three different experiments *P<0.05), **P<0.01, .(***P<0.001 Figure 15. Shows the rate of cell invasion in samples treated with peptides at IC50 concentration. As can be seen, the rate of cell invasion was significantly reduced in the groups treated with IK1 and A6. The results represent the mean ± standard deviation of three different experiments. *P<0.05), **P<0.01, ***P<0.001 Table 1. In order to design primers, the ID of each gene (NM) was first obtained and primers were designed based on desired parameters using the NCBI primer design module, and finally the designed primers were examined using the Oligo7 tool. Table 2. This table lists the physicochemical properties of the peptides. As can be seen, the peptides studied are non-toxic. Table 3. The g_mmpbsa tool was used to perform free energy calculations. For this task, MDP files with the desired parameters were first prepared and free energy functions were extracted for every 250 picoseconds of the simulation trajectory. 7. Benefits of the invention IK1 is an anti-cell invasion peptide for inhibiting cancer progression. This molecule is able to effectively inhibit important pathways involved in invasion and metastasis, including the urokinase system, CD44, and matrix metalloproteinases. On the other hand, considering its small size that facilitates its production, and the lack of immunogenicity and toxicity, are some of the factors that can make IK1 a suitable drug candidate for cancer treatment. 8. Description of at least one method of implementing the invention In previous studies on the A6 peptide, it has been used in two ways: directly and indirectly. In the direct method, this peptide was injected into the target patients and then the therapeutic properties of this peptide were evaluated. In another method, the benefits of this peptide were used indirectly. In this way, considering that the CD44 receptor is highly expressed on the surface of cancer cells, and on the other hand, the A6 peptide also has a high binding affinity for binding to this receptor, this peptide has been used to functionalize the surface of drug-containing nanocarriers. Considering that the IK1 peptide also has a high binding affinity for the CD44 receptor, one of its methods of application can be its use in studies of the design and development of nanocarriers in order to increase the efficiency of targeted delivery to cancer cells. 9. Industrial application of the invention Given the anti-invasive and anti-metastatic effects of IK1 and its lack of toxicity, this peptide could be a drug candidate for cancer patients in combination with other drugs. .

Claims

The claim claims that 1. The peptide with the sequence AC-RPSFPPEE-NH2 is an allosteric antagonist for the uPA-uPAR system that has the property of inhibiting cancer cell invasion.

2. The peptide according to claim 1 has the ability to bind to CD44, and the glycosylation of this receptor plays an important role in this interaction.

3. The peptide according to claim 1 has an inhibitory effect on the activity of MMP-2 and MMP-9.

4. The peptide according to claim 2 reduces the expression of the CD44 gene.

5. The peptide according to claim 1 reduces the activity of urokinase and its receptor.

6. The peptide according to claims 1 and 2 has appropriate and efficient tissue solubility and distribution.

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