Didier Devaurs, a former postdoctoral researcher in computer science at Rice who worked in Lydia Kavraki’s group, reports promising computational work in cancer research aimed at improving immunotherapy outcomes by identifying more effective personalized treatments.
In an article published in BMC Molecular and Cell Biology, Devaurs recounts how he and fellow graduate and postdoctoral students, in research conducted at Rice, tested interactions between thousands of pairs of molecules to advance immunotherapy research aimed at combating cancer.
Devaurs used the Comet supercomputer at the San Diego Supercomputer Center to evaluate their new molecular docking tool, Docking INCrementally (DINC). The findings suggest the approach can make predictions of molecular interactions that other docking tools miss. Such predictions are difficult to make, especially in regard to immunotherapy, which uses the immune system to treat cancer by docking long peptides to receptors.
“Immunotherapy is an innovative cancer treatment that has shown promising results,” Devaurs said. “It consists of ‘training’ a patient’s cells by recognizing specific tumor-derived peptides, which are fragments of proteins within a cell.”
Each cancer patient has a unique set of tumor-derived proteins and must be treated in an individualized fashion. The goal of the DINC tool is to assist with identifying the pertinent peptides for cancer immunotherapy.
Devaurs is now a postdoctoral research associate at Université Grenoble Alpes in France. From 2014 to 2018 he was a Keck Fellow and postdoctoral research associate in the lab of Kavraki, the Noah Harding Professor of Computer Science, professor of bioengineering, of electrical and computer engineering, and of mechanical engineering.
“The computational challenge here is that thousands of tumor-derived peptides were tested, and each test required computing on Comet,” Devaurs said. “Our study showed that Comet has the computational power to make predictions that could be useful to immunotherapy.”
The next step is to assess how to rank the predictions to identify only the most realistic ones to clinicians working on cancer treatment.
Access to Comet was done via the National Science Foundation’s Extreme Science and Engineering Discovery Environment program. Funding came from the National Institutes of Health, the Informatics Technology for Cancer Research Initiative of the National Cancer Institute and the Cancer Prevention and Research Institute of Texas. The work was also supported by a training fellowship from the Gulf Coast Consortia through the Computational Cancer Biology Training Program.