A team of researchers from the Flatiron Institute is improving the production of peptide drugs - to speed up the search for promising new compounds.
Vikram Mulligan, a biochemistry researcher at the Flatiron Institute, has been working with a team of researchers harnessing Argonne Leadership Computing Facility (ALCF) resources with the aim of improving the production of peptide drugs. “Existing peptide drugs rank among our best medicines, but almost all of them have been discovered in nature,” Mulligan comments. “They’re not something we could design rationally—that is, until very recently,”
The original motivation for the research lies in Mulligan’s postdoctoral work at the University of Washington’s Baker Lab, in which he sought to apply what was determined to be accurate methods for designing proteins that could fold in specific ways. “Proteins are the functional molecules in our cells, the molecules responsible for all the interesting cellular activities that take place,” Mulligan explained. “It is their geometry that dictates those activities.”
Naturally occurring proteins produced by living cells are built from just 20 amino acids. In the laboratory, however, chemists can synthesise molecules from thousands of building blocks, allowing for innumerable structure combinations. This effectively means that a scientist might be able to manufacture, for example, enzymes capable of catalyses that no natural enzyme could perform.
Mulligan is particularly interested in making small peptides that can act as drugs that bind to some target, either in the human body or in a pathogen, and that treat a given disease by altering the function of that target.
To this end, via a project supported through DOE’s INCITE program, Mulligan is using ALCF computational resources, including the Theta supercomputer, to advance the design of new peptide compounds with techniques including physics-based simulations and machine learning. His team’s work at the ALCF is driven by the Rosetta software suite, the applications and libraries of which enable protein and peptide structure prediction and design, RNA fold prediction, and more.
Mulligan’s research aims to treat a broad spectrum of diseases, as evidence suggests that peptide-based compounds have the potential to operate as an especially versatile class of drugs.
While they are easily and effectively administered, a primary therapeutic limitation of small-molecule drugs, by contrast, is that they often display an equal affinity for other sites in a patient as they do for the intended target. This translates into being a source of side effects for the patient taking the drugs.
“Small-molecule drugs are like simple luggage keys in that they can unlock more than what they’re made for,” Mulligan said.
Adapting to exascale
Ongoing and future work requires substantial revision of the Rosetta software suite for next-generation optimisation, including accelerator-based computing systems such as the ALCF’s Polaris and Aurora supercomputers.
“Rosetta started its life in the late 1990s as a protein modelling package written in FORTRAN, and it's been subsequently rewritten several times,” Mulligan said. “While it’s written in modern C++, it is beginning to show its age; even the latest version was written more than 10 years ago. We’ve continually refactored the code to try to make it more general, try to make it work for non-natural amino acids, but taking advantage of modern hardware has posed challenges.” While the software parallelises on central processing units (CPUs) and scales well, graphics processing units (GPUs) are not supported to their full capability.
“Because Polaris is a hybrid CPU-GPU system, as the exascale Aurora system will be, I and others are working on rewriting Rosetta's core functionality from scratch. By creating a successor to the current software, it's my hope that we can continue to use these software methods efficiently on new hardware for years to come and that we can build atop them to permit more challenging molecular design tasks to be tackled."
The full story, written by Nils Heinonen, appears on the ALCF website.
