Cancer is considered the silent pandemic of the 21st century and the second leading cause of death worldwide. The significant heterogeneity of this disease, seen across various cancer types,... Show moreCancer is considered the silent pandemic of the 21st century and the second leading cause of death worldwide. The significant heterogeneity of this disease, seen across various cancer types, individuals, and even tumor cells, makes it extremely challenging to treat effectively and safely in all patients. Personalized oncology has emerged as an efficient strategy to leverage the differences present in cancer for the selective targeting of tumor cells. This approach aims to reduce side effects while maintaining or enhancing therapeutic efficacy. However, the availability of personalized therapies is currently limited, leaving many cancer patients longing for more selective treatments. In this context, computational tools play a crucial role in exploring unresolved questions in cancer research and accelerating the discovery of new proteins that can be selectively targeted in anticancer therapies. One main advantage of using computational tools is the ability to investigate promising protein families that have been overlooked in cancer research due to experimental limitations or publication bias, such as membrane proteins. This thesis delves into the potential of computational tools in prioritizing novel targets, mutations, and drugs for use in personalized oncology, with a specific focus on membrane proteins. Show less
Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to... Show moreCancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of "wet-lab" experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets. Show less
Receptors tyrosine kinases or RTKs are cell surface receptors that regulate numerous cellular processes, but also have a critical role in the development and progression of many types of cancer.... Show moreReceptors tyrosine kinases or RTKs are cell surface receptors that regulate numerous cellular processes, but also have a critical role in the development and progression of many types of cancer. The overexpression of EphA4, a member of the RTK family, has been observed in a variety of malignant carcinomas. The aim of the research project associated with this thesis was to develop high affinity inhibitors of the tyrosine kinase EphA4. Ligand discovery was based on two complementary approaches, a computational screen and an NMR based screen using Target Immobilized NMR Screening (TINS). In addition, orthogonal biophysical methods including Surface Plasmon Resonance (SPR) and protein observed NMR were employed to analyse fragment binding. The crystal structure of the EphA4 kinase domain was solved and the structure of the kinase domain in complex with dasatinib, a well-known kinase inhibitor, was also elucidated. The in silico approach discovered a potent inhibitor of EphA4 for which the binding mode was elucidated via X-ray crystallography. Moreover, the TINS approach identified two compounds that may constitute starting points for the generation of more potent EphA4 inhibitors. Show less
