Protein-Protein Interactions as Cancer Therapy Targets - Essay Example

NAME : XXXXXX TUTORS : XXXXX COURSE : XXXXX TITLE : PROTEIN-PROTEIN INTERACTIONS AS CANCER THERAPY TARGETS INSTITUTION: XXXXX DATE: 2008 Protein-Protein Interactions as Cancer Therapy Targets Introduction Cancer can be defined as a disease which results in continued, abnormal growth of human cells and these cells have a capacity to move from that particular part of the body to another part of the body hence its spread is easy and fast. In case the patient fails to receive treatment early enough, he/she may die. Cancer is hence a disease that requires a lot of attention. Reports from areas where cancer cases are prevalent reveal that the disease prevails where the several proteins combining genes. Where protein-protein interactions are in large numbers, a strong base forms which leads to anti-cancer agents emerging. The formed agents are used in reducing and totally curbing the growth as well as multiplication of cancer cells. It is worth noting that the binding of protein to protein molecules tend to have a large surface area and thus it is very risky at these particular points where binding takes place. Researches show that there are smaller molecule inhibitors but the protein-protein structures are numerous. An example of this is a combination of p53-MDM2. Such interactions bring about what I s commonly referred to as ‘oncology targets’. These are simply the various protein combinations that bring about more defenses against spread of the cancer cells. The evaluation of these ‘oncology targets’ may assist in discovery of other important drugs. This analysis/evaluation is based on the structural features of the molecules being bound together. The other important thing considered is mainly the measurements or sizes of the participating molecules or ‘sites’. The other major factor considered is how closely they resemble each other, what’s commonly called their ‘drug-likeness’. By employing the use of protein-protein linkage networks, the ‘topology’ method clearly shows how it is possible, by engaging combinations of drugs, to detect the synergetic effects of using multiple proteins. This can apply in the case of varied cancer types. This method is made up of two distinct stages: In the first stage, data regarding the gene and that of how the proteins interact is integrated together and then a linked to come up with different types of tissues and the types of cancer. The other one involves coming up with systems that enable us to find specific combinations of targets that greatly destroys the cancer network within the body. The targets are expected to have very small damages to normal body functioning .This is to say the target should not have a negative effect on the normal cells and other functionings in the body system. Cancer exists in many strands and there has to be specific combinations of drugs for these targets individually. The drug’s validity is a very important issue. Checking the validity is done by examining performance of the drugs practically ,that is, administering the drugs on cancer patients a specific example being that the drugs are administered on a prostate cancer patient This method has been proved to be functional and it can be used in dealing with several other diseases of cells such as sickle cell disease. Protein-protein interactions refer to the link between amino acid molecules and the study of these links from the perspective of biochemistry, signal transduction and networks (Agrawal 2004). Protein-protein combinations or interactions are the various amino acid molecules related or with a common features. The basis of understanding networks is based on these protein-protein interactions. These interactions are very important in a human being’s biological functioning. Another important thing relating to these interactions is that through a process known as signal transduction, signals emanating from outer parts of the cell can be transmitted to the interior parts of the cell and this forms the basis on which spread and transmission of cancer is based. Through a long time interaction of the proteins, proteins could be carrying each other .Another type of protein interaction involves a short-term interaction where by the end result is that one protein will modify the other one .An example of this is where a kinase protein deposits a phosphate molecule to another targeted protein. Due to the effect of these altering proteins, protein interactions will themselves change with time. Proteins of a certain group called bromo domain will only interact with acetylated lysine. This modification of amino acids can alter protein-protein interactions. Protein-protein interaction therefore is essential to every living cell as knowledge regarding these interactions assist in dealing with diseases such as cancer. Implication Mutations, especially of BRAD1, are closely linked to high risks of both breast as well as ovarian cancer. These mutations of the genes are responsible for loss of many proteins. However, with improved screeners available currently, it is possible to identify the BRAD variants that are carriers, whether classified or unclassified. It is hard to ascertain whether a variant is classified or not (Soffer 2005). Gene abnormalities such as mutations result in cancer. Specific genes known as oncogenes, also referred to as tumor deactivating genes, usually change the detection and transmission paths of protein-protein combinations. By examining the way proteins interact in body places with cancer cells and in those places considered normal, we get to understand the concept termed ‘carcinogenesis.’ Carcinogenesis is another of the ways that can be applied in combating cancer by way of protein-protein combinations or interactions. It involves about three stages: First, the protein-protein combinations or interactions to be used in checking the normal cells and cancerous ones are constructed. After the construction is done, a model that is not linear, together with the AIC is used so as to detect and get rid of protein-protein combinations which are not genuine. Microarray data assists in detecting these false protein-protein interactions. A microarray data can be defined as a DNA microarray if it has a DNA component forming part of it. DNA results from proteins building up. DNA resembles the other micro arrays but the only difference is that in their detection duty, they can measure and utilize DNA. DNA microarrays as Delgado 2006, puts it are essential in measuring changes in levels of reaction. It is also important in detecting or sensing nucleotide polymorphisms especially single ones. Micro arrays differ depending on their functioning, their accuracy level and efficiency rate (maximum achievement) as well as their cost differ according to the design employed as well as the methods used in evaluating the data. We can engage such targets as BCL2 caspase-3 and also TP53.One advantage of using this method is that it makes it very simple to detect and deal with cancerous protein-protein combination sites which usually bring about ‘apoptosis’,. It is also easy to identify likely molecular targets that can contribute to research and invention of drugs that fight cancer. Cancer can be contained by interactome method. This is the study of how molecules in a particular cell interact. There can occur; mutation of genes, the genes can be deleted, they can be translocated. The genes can also be translated or they can also undergo insertion. All these occur inside the cell while the genes are interacting. In order for a human being to undergo normal growth as well as development, his/her cells need to be replaced every now and then. However if there occurs the alterations named above (tumor genesis), then the protein-protein combinations will be affected seriously. Proteins are important in the apoptosis process, which ensures that dead cells get replaced every time by new cells. Since the tumor genesis usually interferes with proteins-proteins combination, then the whole process of apoptosis breaks down hence cells start to accumulate at some points on the body, a characteristic of cancer-cells. Most protein-protein combinations or interactions do occur during apoptosis since proteins are released into the cytoplasm of which these proteins undergo the apoptosis process. Among the best methods of getting maximized protein-protein combinations is by computation method as there are lesser chances of getting false-positives in the experiments. Non-linear models of calculating prove to be much better than linear models as its characterization is more complete and accurate. It is important to know that scholastic models are more preferable as they are more open in translation. The Systems-based method puts together related protein-protein comparison networks and also reveals the likely drug targets. This can be by comparing both normal with abnormal cells’ setting inside the body. The traditional approach however, readjusts processes downwards as it aims at dealing with single molecules, one at a time. The main disadvantage facing this traditional approach method is that only one molecule cannot elaborately give a vivid description of the reality inmost of the biological systems. Taking the protein-protein interaction systems for normal and abnormal cells (those affected by cancer) gotten from microarray data, it is possible to establish the likely drug targets. This is through using systems-based approach. In the systems-based approach, protein-protein interaction systems are constructed from large experiments along with sophisticated databases. Next, the micro array data is used in altering the networks or systems. Networks or systems are altered based on models that are non-linear stochastic together with AIC (Akaike Information Criterion). After this, the coordination or the system of the abnormal cells and normal cells are compared. Lastly, the amount of protein hubs present is established. Database use is essential so as to be able to exactly find out parameters which are to be used in establishing protein-protein combination networks. Rough networks can bring about erroneous and indecisive conclusions. It has been established that when several databases are used, it is easier to notice the protein-protein combinations and hence it will lead to more accurate predictions about the functions of the unknown proteins (Soffer 2005). The other method is by identifying the domains of proteins. This method however, is not accurate in identifying drug targets that could be essential in future. The modeling method is seen to be accurate in determining the anti-cancer targets hence it is more readily dependable upon. Also, this method is very specific since it points out target proteins clearly using Gene Ontology way. It hence comes out as expository and it assists researchers in getting any other drug targets. They can easily do so by checking cellular mechanisms that likewise form part of cancer. The major problem facing this method is that it’s very hard to use this method for comparison purposes between the many strands of cancer. Also this method only looks at or targets a particular protein at a time and hence culminating in inability to compare the various strands .Most strands of cancer discourage the pro-apoptotic proteins. One of the roles of proteins is that it regulates the rate at which cells grow hence since these cancer strands deactivate proteins, cells will grow in excess resulting in cell-stress. However, there are some variants that are not classified since in most times, a large number of relevant controls are required Current analyses however, reveal that there is no relationship between the profit functional loss and the continued poor health of the cancer ailing patient. It has been revealed that defective apoptosis often come up in human cancers due the effects of a type of protein called BCL2 When there are lots of BLC2 protein or too small quantity of BCL2 protein, chances of cancer occurring is very high. BCL2 proteins act as regulators of mitochondria’s ability to be permeable. It will excite the release of the cytochrome. Alongside BCL2, there are other types of proteins including BCL2L1, which work with the mitochondrial outer layer with the nuclear membrane of the endoplasmic reticulum such that they withhold these membranes upright (Bader 2005). Medical reports reveal that tumor cells can avoid chemotherapy .This is by resisting apoptosis. In this case, the epithelial cells lose any sensation towards apoptosis. (Espinoza 2001) Some of the proteins that actively participate in apoptosis are BCL-2, BIRC-5, NRAS, MKI67, E1F-1, PPP3CA, TP53, PCNA, MAPK, CCND-1, EGFR, among others. The figure below (on the left) shows total protein-protein combination for apoptosis in both normal and abnormal cells. The second diagram on the right shows cancer-perturbed protein-protein interactions in apoptosis system or network and the protein-protein combinations of apoptosis. . One of the functions of protein is that as part of many large molecules, they take part in particular biological functions or processes. An example is urination whereby urine is passed out as a waste product and it contains a certain percentage of amino acids, a product of proteins. The proteins are categorized into classes or families. For example the family of ‘death receptors’ usually bring about death of cells in that they stimulate the formation of caspases-8 or caspases-10 and these stimulate caspase-3 which directly bring about cell death. TNF death- receptor also helps in maintaining normal tissue homeostasis. Members of this TNF family are main targets as anti-cancer drugs. Activating TNF family members will lead to death of cancer cells only. This will not affect normal cells within the body. By targeting these TNF-related receptors (TRAIL), their antibodies can become resistant hence when such a drug is administered, tumors can become resistance hence this could kill normal body cells. Activation of the apoptosis pathway is brought about by both interior stress and exterior. On the other hand, members forming BCL-2 family are known to be favored targets in cancer therapy.BCL-2 proteins are also known to regulate and activate cytochrome to be released which is usually in the mitochondria. This family plays an important role in keeping growth rate of tumor cells low. Most of the normal body cells rely on proteins in the BCL-2 family for their maintenance of functioning of their mitochondria. Therefore, targeting BCL-2 proteins is likely to negatively affect normal body cells as they mainly rely on this family for their normal functioning. Gene therapy has to be carried out so as to activate Bax. Bax will stimulate or induce apoptosis. The BCL family of proteins also tends to inhibit the growth of tumors in the body. This family is made up of three subfamilies: anti-apoptotic proteins, pro-apoptotic proteins and that are pro-apoptotic multi-domain. Anti-apoptotic ones include BCL 2 and BCL2L1; pro-apoptotic ones include BID and BIM while the pro-apoptotic multi-domain sub family consists of BAX and BAK. BCL 2 is believed to be a favorable drug target as it differentiates between protein-protein combination networks for normal and abnormal cells (Hanahan 2000). The family of MAPK proteins usually aid in most signaling pathways and inhibitors such as CDKNIA (an apoptotic regulator) is essential in cell-cycle arrest as well as in inducing apoptosis. Below is a table of some protein targets, with their respective extends of disturbance. Major targets Extend of perturbation or disturbance Principle agents Literature applied BCL2 35 ABT/GREL583/ G3139-738 [33,35,38] CASPAS-3 24 Capses synthetic stimulators of caspases /IAP targets [33,34,36] BAX 17 Bax group vectors [34] TP53 17 inhibitors of MDM2 [4,34,38,39] BCL2L1 13 BCL-Xl detect [38] CDKN1AS 13 [43] TNFS 11 [4,36] EGFR 10 (Herceptin group) [34] MAPK1 9 CI- ARRY -142886 family [41,42] MAPK3 9 ARRY-142886 family [41,42] MYC 9 (Proteosome deactivator) [40] BID 8 [34] CASP9 8 Caspase family [33,34,36] CCND 8 [43] CFLARD 8 [22] CYC 8 [36,38] TNFGRSF6 8 [32] Protein-protein combinations or interactions are usually non-linear and interactive protein partners form the input while the output is the target profile. The cancer-perturbed protein-protein combination systems assist in locating the potential target drugs for promoting apoptosis. Caspases form a big part of apoptotic response system is caspases. When caspase inhibitors are targeted, cancer cells will become apoptotic. Currently, organic molecules that inhibit protein-protein combinations in cancer have been discovered. Other protein-protein combinations usually regulate apoptosis. These proteins are prone to inhibition by the small particles.The scientific technique is applied in visualizing of protein-protein systems/ networks. Kurt Kohn came up with a map in 1999 showing control of cell cycle. Fields and Uetz later did publish a paper, to follow up the same on protein-protein combination or yeast interaction. This paper brought about many combining proteins. They showed that this interaction or combination was majorly determined by two-hybrid testing. By using a graph directed by force, they were able to automatically come up with an image of their system (Greenhalgh 2001). Investigation and detection of protein-protein interactions involves a variety of methods with each of them having its strengths as well as weaknesses. The method to be employed will depend on two factors: sensitivity as well as the specificity required. Some of the methods used are as explained below: A screen, known as the two-hybrid screen is used in finding out interaction between various fusion proteins within the cells. This method can be used in identification of binding partners within a protein easily. Most recent method is In-vivo crosslinking in which the protein complexes by use of protein analogs that are photo-reactive. This method was brought up 2005 by researchers conducted by Max Planck Institute Photo reactive diazirine analogs are used to grow the cells to become leucine and methionine.Leucine and methionine are added into proteins. When exposed to ultraviolet light, the diazirines become activate and get attached to interacting proteins that are within the amino acid analog that are photo-reactive Chemical crosslinking method is generally used bind protein interactions together. Cross linkers under this method are DTBP crosslinker, NHS-ester crosslinker and also BS3.Dual Polarization Interferometry (DPI) is used in measuring rate of protein-protein interactions. It also provides exact values of the molecular size, density and the mass. In this method, one species of the proteins is fixed as a waveguide. In Protein-protein docking, it’s not fully satisfactory just to predict protein-protein interaction using the 3 dimensional structures only. To measure protein-protein interaction, surface-plasmon oscillation is used (Senderowicz 1999). Fluorescence correlation method labels one of the proteins with a visible dye. The other amino acid remains unlabeled and these two proteins are later mixed together with data outputs. The total part of the protein that is labeled will aid in detecting total affinity of the protein to bind. SPINE (Strep-protein association experiment) method uses both reversible crosslinking and formaldehyde together with incorporated affinity tag in detecting protein partners that are interacting in vivo. Chemical crosslinking process is usually accompanied by MALDI (an analytical technique called mass spectrometry) of high mass and they can be used to evaluate protein interactions that are intact .Is done before trying to isolate the interacting proteins. This method is used in evaluating combinations among proteins that are not tagged. References Agrawal, Peres 2004, New molecular targets for anticancer therapy. Biology Today 4:514–516. PMID: 10602548. Bader, De Baman 2005, The Aim of apoptosis pathways in cancer therapy. CA Cancer 55:142-169. Delgado, Cliff 2006, Micro array data evaluation and drug discovery. Soc Clin Oncol 2:564-603. Espinoza Antony 2001, Vascular dystrophy growth factor and apoptosis in cancer therapy. The Oncologist 101:1008-1021. PMID: 10565548. Greenhalgh, Benjamin 2001, Protein Combination. Oncogene 56: 497-511. Epub 2001 Dec 28. Hanahan, D 2000, Weinberg RA: Strongholds of cancer. Mol Carcinog 89:40-65. PMID: 13685932 Senderowicz, T 1999, Small molecule modulators. Biology 67: 17- 21. Epub 1999 Dec 28. Soffer, S 2005, Vascular cell endothelial growth factor .Nature 359: 518-628. PMID: 12685932 Read More