Petr Šulc, an assistant professor at Arizona State University’s School of Molecular Sciences and the Biodesign Center for Molecular Design and Biomimetics, recently won a National Science Foundation Early Career Development (CAREER) award.
Šulc’s laboratory is highly interdisciplinary and widely applies the methods of statistical physics and computational modeling to problems in chemistry, biology and nanotechnology. The group develops new multi-scale models to study the interactions between biomolecules, in particular within the framework of the design and simulations of nanostructures and DNA and RNA devices.
“This research grant will allow our lab to expand the scope of the systems we study and allow us to design a new class of nanodevices and nanomaterials that incorporate DNA, RNA and proteins, as well as ‘other molecules,’ explained Šulc. “Just as the complex machines we use every day – planes, cars and computer chips – require sophisticated computer-aided design tools to ensure they perform a desired function, there is an urgent need for access to such methods in molecular sciences.”
Professor Tijana Rath, Director of the School of Molecular Sciences, said: “Petr Šulc and his group are doing extremely innovative molecular science, using the methods of computational chemistry and physics to study DNA molecules and of RNA in the context of biology as well as nanotechnology. . Our young faculty members in the School of Molecular Sciences have an extraordinary record of achievement, and Professor Šulc is an example of this.
The prestigious CAREER program supports the early career development activities of teacher-researchers who most effectively integrate research and education into the mission of their organization. It offers five-year research grants to each recipient.
DNA and RNA are the basic molecules of life. They perform many functions, including the storage and transfer of information in living cells. They also have promising applications in the field of nanotechnology where engineered DNA and RNA strands are used to assemble nanoscale structures and devices. As Šulc explains, “It’s a bit like playing with Lego blocks, except that each Lego block is only a few nanometers (one millionth of a millimeter), and instead of placing each block where it should go, you place them in a box and shake it randomly until only the structure you want comes out.
This process is called self-assembly, and Šulc and his colleagues use computer modeling and design software to come up with the building blocks that reliably assemble into the desired shape at nanoscale resolution. .
“Promising applications of this field include diagnostics, therapeutics and construction of new materials,” says Šulc. “My lab developed the software to design these blocks, and we work closely with experimental groups at ASU as well as other universities in the United States and Europe.”
They are also interested in applying machine learning methods to sets of biological sequences and using neural networks to design DNA or RNA sequences that will specifically bind to a target protein of interest, such as than the SARS-CoV-2 spike protein.
Video of SMS Junior Faculty Spotlight – Petr Šulc
Computer-aided design software is often used in our large-scale world to design computer chips, cars, and planes so that device operation can initially be tested and optimized in simulation. Nanoscale construction, however, presents multiple challenges. Unlike our macroworld, nanostructures are usually achieved by self-assembly, where individual components randomly diffuse until they meet and assemble into a target structure.
In order to obtain more complex structures that will self-assemble with high yields, there is a need for a new simulation framework that can efficiently and, at the same time, accurately represent the assembly and function of nanostructures.
The Šulc lab will develop a new modeling framework capable of simulating self-assembled DNA nanostructures.
The research team will use this new framework to optimize high-efficiency nanostructure assembly and computer-design new types of reconfigurable nanostructures. Next, the team will extend the modeling platform to allow for the incorporation of other organic/inorganic molecules and materials.
Overall, this award will facilitate the creation of new nanodevices capable of performing complex tasks that would be difficult to achieve experimentally without a sophisticated modeling platform. It will bring the field closer to large-scale industrial applications.
To realize the educational component of the project, the laboratory in Šulc will develop new learning opportunities for university students and the general public. The main effort will be to develop an online citizen science platform, where users can use the simulation platform to design and optimize structures themselves, enabling the crowdsourcing of nanotechnology designs.