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Scaffolding protein functional sites using deep learning. | LitMetric

AI Article Synopsis

  • The text discusses how proteins perform their functions through specific residues supported by their overall structure and presents deep learning methods to design these functional sites without defining the protein's shape first.
  • Two main approaches are introduced: "constrained hallucination," which optimizes protein sequences to include desired functional elements, and "inpainting," which complements existing functional sites with additional sequences and structures in one step.
  • These techniques are applied to create various protein candidates for applications like immunogens and enzymes, with their effectiveness tested through computational simulations and experiments.

Article Abstract

The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, "constrained hallucination," optimizes sequences such that their predicted structures contain the desired functional site. The second approach, "inpainting," starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network. We use these two methods to design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins and validate the designs using a combination of in silico and experimental tests.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9621694PMC
http://dx.doi.org/10.1126/science.abn2100DOI Listing

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