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Best Use of HPC in
Life Sciences
Readers’ Choice Awards
Researchers at The University of Texas Austin have developed an enzyme that can break down environment-throttling plastics that typically take centuries to degrade in just a matter of hours to days. The project focuses on polyethylene terephthalate (PET), a significant polymer found in most consumer packaging. It makes up 12% of all global waste. The researchers used a machine learning model to generate novel mutations to a natural enzyme called PETase that allows bacteria to degrade PET plastics. The model predicts which mutations in these enzymes would accomplish the goal of quickly depolymerizing post-consumer waste plastic at low temperatures. The Texas Advanced Computing Center’s (TACC) Maverick2 supercomputer powered deep learning models that helped engineer the plastic-eating enzyme. A patent has been filed for the technology, which could help in future landfill cleanup and greening of high waste-producing industries.
Editors’ Choice Awards
Determining protein structures is critical to many branches of life sciences research. AlphaFold, an AI system developed by Google’s DeepMind, predicts a protein’s 3D structure from its amino acid sequence. It regularly achieves accuracy competitive with experiments. Since being introduced one year ago, AlphaFold has computationally determined more than 200 million protein structures, covering nearly every protein known. AlphaFold is able to solve a protein’s complex structure based only on its amino acid sequence. DeepMind and EMBL’s European Bioinformatics Institute (EMBL-EBI) have partnered to create AlphaFold DB to make these predictions freely available to the scientific community. About 500,000 researchers have used the tool so far since its introduction.
