Grant: Operating Grant

We have seen great advances in the understanding of cancer. Likewise, our knowledge about how to manipulate proteins, the body's machinery, has greatly increased. However, the discovery of new drugs remains at a slow and, surprisingly, decreasing pace. This is partly due to the fact that the number of target proteins for drugs has stagnated. This in turn is caused by the pharmaceutical industry focusing on a few select protein types due to the high risk and cost of drug development. In this project, we will create a new list of potential target proteins that will serve as new candidate drug targets and develop inhibitors based on peptides to them. We will accomplish by combining a unique combination of techniques from computational biology, combinatorial biochemistry and cancer biology. Briefly, we can make use of knowledge from genomic and network sciences to prioritize proteins that are more likely to have greater effects in cancer. Such a method will be fed by the rich amounts of data on cancer cell lines that have been generated in the past few years as well as other sources of genomic data. Secondly, we will utilize sophisticated models from structural biology to select proteins that are optimally suited for peptide binders and predict their sequence. In a second step, we will make use of state-of-the-art combinatorial chemistry that will yield potent inhibitors against these proteins. Lastly, with our collaborators from cancer biology, we can screen selected inhibitors for biological efficacy.