For example, “the deep neural network AlphaFold was trained to predict protein structures and does an amazing job,” stated Dr. This work was recently published in Proceedings of the National Academy of Sciences.ĭeep neural networks that incorporate multi-layered data into a trainable model have enhanced the predictive accuracy of numerous modeled systems. Therefore, their findings support the use of structural and sequencing data in modeling networks of protein-peptide interactions. They also noted that the structural input allowed for additional flexibility to include longer peptides as compared to another modeling approach that only used sequence data. From their efforts, they discovered that predicting protein-peptide binders and non-binders was accurate for immune peptide recognition data and again excellent for the protein domain interaction datasets. The researchers trained this model with data from major histocompatibility complex (MHC)-peptide recognition in adaptive immunity datasets and tested it in this same context and in others which included peptide-protein domain interactions. The Bradley Lab in the Public Health Sciences Division at Fred Hutchinson Cancer Center developed a deep neural network that incorporates not only sequence data but also protein structural data to model and predict protein-peptide “binders” and “non-binders”. Specifically, defining “binders” and “non-binders” can inform on signaling within or between neighboring cells that occur under normal conditions, disease states, and in response to pathogens as a part of one’s immune response. Uncovering which proteins interact with which short peptide or protein fragment is an extremely complex puzzle. However, there are some puzzles that force even the best puzzlers to nominate modeling over experimental approaches to help decipher how the pieces fit together. Solving puzzles experimentally is enjoyable for many researchers. ![]() ![]() Viruses, Vaccines and Infectious Diseases. ![]() Institutional Partners & Collaborations.Vaccine and Infectious Disease Division.
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