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Podcast: Supercomputing the Emergence of Material Behavior

Structural features of C98RhuA crystals. a, Surface representation of a C98RhuA tetramer, with position 98 highlighted in black, and a schematic of its oxidative self-assembly into porous 2D crystals.

In this TACC Podcast, Chemists at the University of California, San Diego describe how they used supercomputing to design a sheet of proteins that toggle between different states of porosity and density. This is a first in biomolecular design that combined experimental studies with computation done on supercomputers.

The research, published in April 2018 edition of Nature Chemistry, could help create new materials for renewable energy, medicine, water purification, and more.

“We did an extensive set of molecular dynamics simulations and experiments, which explained the basis of the unusual structural dynamics of these artificial proteins, based on which we were able to make rational decisions and alter the structural dynamics of the assembly,” said study co-author Akif Tezcan, a professor of chemistry and biochemistry at UCSD.

F. Akif Tezcan, Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla.

Our idea was to be able to build complex materials, like evolution has done, using proteins as building blocks.

Tezcan’s team worked with the protein L-rhamnulose-1-phosphate aldolase (RhuA), which was modified with cysteine amino acids in its four corners at position 98 (C98RhuA). He and his group had previously published work on the self-assembly of this artificial, two-dimensional protein architecture, which he said showed an interesting behavior called auxeticity.

These crystalline assemblies can actually open and close in coherence,” Tezcan said. “As they do, they shrink or expand equally in X and Y directions, which is the opposite of what normal materials do. We wanted to investigate what these motions are due to and what governs them.” An example of auxeticity can be seen in the Hoberman Sphere, a toy ball that expands through its scissor-like hinges when you pull the ends apart.

“Our goal was to be able to do the same thing, using proteins as building blocks, to create new types of materials with advanced properties,” Tezcan said. “The example that we’re studying here was essentially the fruit of those efforts, where we used this particular protein that has a square-like shape, which we attached to one another through chemical linkages that were reversible and acted like hinges. This allowed these materials to form very well-ordered crystals that were also dynamic due to the flexibility of these chemical bonds, which ended up giving us these new, emergent properties.”

All of these computing clusters that XSEDE provides are actually quite useful for all molecular dynamic simulations.

The all-atom molecular simulations of the C98RhuA crystal lattices were used to map the free-energy landscape. This energy landscape looks like a natural landscape, with valleys, mountains, and mountain passes, explained study co-author Francesco Paesani, a professor of chemistry and biochemistry at UCSD.

Francesco Paesani, Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla.

“The valleys become the most stable configurations of your protein assemblies,” Paesani said, which the molecular system prefers over having to spend energy to go over a mountain. And the mountain passes show the way from one stable structure to another.

Typically, free energy calculations are very expensive and challenging because essentially what you’re trying to do is sample all possible configurations of a molecular system that contains thousands of atoms. And you want to know how many positions these atoms can acquire during a simulation. It takes a lot of time and a lot of computer resources,” Paesani said.

To meet these and other computational challenges, Paesani has been awarded supercomputer allocations through XSEDE, the Extreme Science and Engineering Discovery Environment, funded by the National Science Foundation.

Fortunately, XSEDE has provided us with an allocation on Maverick, the GPU computing clusters at the Texas Advanced Computing Center,” Paesani said. Maverick is a dedicated visualization and data analysis resource architected with 132 NVIDIA Tesla K40 “Atlas” graphics processing units (GPU) for remote visualization and GPU computing to the national community.

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