A Graphic Encoding Method for Quantitative Classification of Protein Structure and Representation of Conformational Changes.

08:00 EDT 4th October 2019 | BioPortfolio

Summary of "A Graphic Encoding Method for Quantitative Classification of Protein Structure and Representation of Conformational Changes."

In order to successfully predict a protein's function throughout its trajectory, in addition to uncovering changes in its conformational state, it is necessary to employ techniques that maintain its 3D information while performing at scale. We propose a protein representation that encodes secondary and tertiary structure into fix-sized, color images, and a neural network architecture (called GEM-net) that leverages our encoded representation. We show the applicability of our method in two ways: (1) performing protein function prediction, hitting accuracies between 78 and 83%, and (2) visualizing and detecting conformational changes in protein trajectories during molecular dynamics simulations.


Journal Details

This article was published in the following journal.

Name: IEEE/ACM transactions on computational biology and bioinformatics
ISSN: 1557-9964


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