DNA has been in high-tech news lately due to its useability in various applications ranging from self-assembled biostructures to building blocks for next-generation electronics. One of the challenges with using DNA in such applications is tailoring its complex electron mobility. The Expanse supercomputer at the San Diego Supercomputer Center (SDSC) at UC San Diego has recently provided researchers at UC Riverside with a testbed to calculate just that. Specifically, researchers used predictive quantum chemistry calculations to understand how electrons move in DNA, which is analogous to how electrons move in wires – but this work examines how they move in DNA strands. Conducting such data-intense calculations required access to a supercomputer.
“In recent years, DNA has attracted significant attention in nanoelectronics and information storage since it can adopt complex geometries and is inherently stable in a multitude of chemical environments – yet we just couldn’t quite understand its electron mobility,” said Bryan Wong, professor of materials science and engineering at UC Riverside’s Marlan and Rosemary Bourns College of Engineering. “Thanks to ACCESS allocations on Expanse, our research team was able to use extremely large quantum mechanical calculations to understand these large biological systems, which could form the building blocks of next-generation biologically-based electronics.”
The research has been published in a special “Early-Career and Emerging Researchers” issue in the Journal of Physical Chemistry B.
“ACCESS allocations on Expanse at SDSC made this research project possible because the quantum calculations on these DNA strands were extremely computationally intensive due to the large size of these systems. For example, the largest of these structures constitutes one of the most extensive quantum mechanical studies of these periodic biological structures to date; therefore, access to Expanse saved us immense time since this would take more than several months on our own computing resources at UC Riverside.”Bryan Wong, professor, materials science and engineering, UC Riverside
According to Wong, his team has always been interested in applying their knowledge of quantum simulations to large complex systems in biology. “The part of our study that surprised us the most was that double-stranded DNA has very different electronic properties than its single-stranded DNA components,” he said. “More simply: double-stranded DNA does not behave like the ‘sum of its parts,’ and it was interesting to discover that some DNA strands conduct electrons better than others.”
Researchers on the team included former UC Riverside graduate students Hyuna Kwon (now a postdoctoral associate at Lawrence Livermore National Laboratory), Anshuman Kumar and Mauro Del Ben of LBNL.
Del Ben, a research scientist at LBNL, said that he was eager to continue the work with the team as their next steps will implement the effects of surrounding water on the DNA strands.
“The upcoming simulations will also require the use of a supercomputer, and we are appreciative of the ACCESS allocations on Expanse to continue our work with Wong,” Del Ben said.
This research was funded as part of a Department of Energy SciDAC project. Allocations on Expanse were provided by ACCESS (allocation no. TG-ENG160024).
More information on obtaining an ACCESS allocation can be found here.
Resource Provider Institution: San Diego Supercomputing Center (SDSC)
Affiliations: Wong Research Group, UC Riverside, LBNL
Funding Agency: This work was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program under Award Number DE-SC0022209. This work used the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) Expanse computing cluster at the University of California, San Diego, through allocation TG-ENG160024.
Grant or Allocation Number(s): TG-ENG160024 and DE-SC0022209
The science story featured here was enabled by the ACCESS program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.