Despite the significant progress made in cancer treatment options in the last 20 years, many cancer targets have still yet to be drugged, which majorly include transcription factors (TFs), such as c-MYC. Recent work showed strong evidence that the transcription machinery is recruited by liquid-like phase separation (LLPS) of activation domains (ADs) of TFs. LLPS is a newly appreciated intracellular phenomenon that underlies the condensate formation in cells, which is primarily driven by intrinsically disordered proteins (IDPs) or intrinsically disordered regions (IDRs) of large proteins. TFs contain large IDRs that participate in transcriptional condensates that form via LLPS. Transcriptiona...

Read More

Despite the significant progress made in cancer treatment options in the last 20 years, many cancer targets have still yet to be drugged, which majorly include transcription factors (TFs), such as c-MYC. Recent work showed strong evidence that the transcription machinery is recruited by liquid-like phase separation (LLPS) of activation domains (ADs) of TFs. LLPS is a newly appreciated intracellular phenomenon that underlies the condensate formation in cells, which is primarily driven by intrinsically disordered proteins (IDPs) or intrinsically disordered regions (IDRs) of large proteins. TFs contain large IDRs that participate in transcriptional condensates that form via LLPS. Transcriptional condensates are shown to be aberrant in tumor cells but the progress to develop drugs against TFs that participate in LLPS has been limited by the extremely dynamic nature of ADs of TFs. Here, I propose to develop a computational platform that will enable discovering drugs against these aberrant condensates by systematically interrogating the LLPS of IDRs of TFs. Our computational platform will enable rapid screening of small molecules that can disrupt the aberrant function of these TFs by probing experimentally inaccessible length and time scales that are required by the extremely dynamic and disordered nature of these proteins. Through my proposed collaborations within the University and the MD Anderson Cancer Center, the drug candidates will be rapidly tested in cells and animal models. I identify breast cancer as the target to develop my methodology, but it can be applied more broadly for TF targets in other cancers as well. The ensemble-based computational drug discovery approach proposed here is a fundamental innovation for computer-aided drug discovery. The ideas proposed here and the products that will come out of this project have a great potential for commercialization and founding startup companies to contribute to the Texas economy.

Read Less