Varför binder dasatinib mycket bra till vissa proteiner men mindre bra till andra?

Sammanfattning: Introduction: Cancer is a group of about 200 different types of diseases. Cancer can appear in different cells and tissues in the body when abnormal cells grow in an uncontrolled manner and invade other tissues. A multistep process that occurs over a long period eventually leads to the onset of cancer. Kinases are enzymes that catalyze phosphorylation, that is, the reaction in which a phosphate group from ATP is transferred to another molecule. Kinase phosphorylation is an important mechanism for modifying proteins and thus regulating various cellular functions. Overactivity of kinases can lead to loss of control of signal transducer cascades controlled by kinases and it produces harmful effects such as cardiovascular disease, diabetes and cancer. Chronic myeloid leukemia (CML) is a rare blood cancer in which an abnormal BCR-ABL fusion protein occurs in neoplastic cells. This causes leukocytes to be produced abnormally and damages the bone marrow and blood. Dasatinib is a tyrosine kinase inhibitor indicated for the treatment of CML. Dasatinib inhibits the activity of the BCR-ABL kinase, but since many kinases are similar, it also binds to other kinases. Some of the kinases that dasatinib inhibits and that we analyzed in this study are ABL1, PTK6, MYT1 and STK10. Purpose: The purpose of this study was to explain with the help of the structures of the proteins ABL1, PTK6, MYT1 and STK10 why dasatinib binds well to some proteins but less well to others. Methods: Using the structures of the proteins, an analysis of interactions between dasatinib and the selected proteins has been performed. We then built a model for each protein with only the amino acids that binds to the drug and finally used these structures to estimate the binding energy ΔGb with the PM7 theoretical method. The PM7 method was used to calculate the energy of molecules. The results correspond to the enthalpy of formation ΔfH. The Gibbs energy is estimated by including the solvent, which also accounts for entropy changes, most of which are mediated by the solvent. The equation used to calculate Gibbs energy for protein-drug binding was: ΔGb = ΔfG(complex) - [ΔfG(drug) + ΔfG(protein)]. Results: The results show that there is a link between the number of dasatinib-protein interactions and Kd. Some proteins had several different structures in the same crystal. In such cases there was an extensive variation in the number of interactions between the different structures and dasatinib. The connection between the number of interactions and Kd was not perfect. Using the number of interactions we were able to distinguish between the proteins that bind best to dasatinib, which were ABL1 and PTK6, and the proteins that bind worst which were MYT1 and STK10. We could however not explain which protein binds best based on the number of interactions. The theoretical calculation of ΔGb follows the same trend (the more interactions there are in the structures, the lower is ΔGb). Discussion and conclusion: A relatively quick and easy calculation using a built model (dasatinib with protein binding interaction) could be used to distinguish between the best and worst binders. When it comes to the difference between ABL1 and PTK6, our analysis failed. A large difference between the calculated ΔGb values ​​for different structures of the same protein makes it difficult to choose the right structure for an analysis. A factor that could explain the discrepancy in the analysis is that the structure of the protein changes when it binds to the drug and such a change requires an investment of Gibbs energy. In general, experimental binding energies were higher than the calculated ones. For example, experimental ΔGb for MYT1 was -9 kcal/mol but the calculated ΔGb was -33.2 kcal/mol. This may be because the calculation with PM7 is too simplified.