Using artificial neural networks designed to emulate the inner workings of the human brain, deep-learning algorithms deftly peruse and analyze large quantities of data. Applying this technique to science problems can help unearth historically elusive solutions. One such challenge involves a biophysical phenomenon known as protein folding. Although researchers know that proteins must morph into specific 3D shapes via this process to function properly, the intricacies of intermediate stages between the initial unfolded state and the final folded state are both critically important to their eventual purpose and notoriously difficult to characterize. Researchers at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory (ORNL) employed a suite of deep-learning techniques to identify and observe these temporary yet notable structures. They published their findings in BMC Bioinformatics. The team adapted an existing deep-learning algorithm known as a convolutional variational autoencoder (CVAE), which automatically extracted relevant information about protein folding configurations from molecular dynamics (MD) simulations. The researchers ran these simulations on Summitdev, a small-scale precursor to Summit, currently the world’s most powerful supercomputer, which is located at the Oak Ridge Leadership Computing Facility (OLCF), a DOE Office of Science User Facility at ORNL. By studying the folding pathways of three different proteins—namely Fs-peptide, villin… Read full this story
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