ArticleIEEE/ACM transactions on computational biology and bioinformatics
A Graphic Encoding Method for Quantitative Classification of Protein Structure and Representation of Conformational Changes.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Increasing the Efficiency of Ensemble Molecular Dynamics Simulations with Termination of Unproductive Trajectories Identified at Runtime.The journal of physical chemistry. A · 2025Article
- High frequency accuracy and loss data of random neural networks trained on image datasets.Data in brief · 2022Article
- A survey of algorithms for transforming molecular dynamics data into metadata forPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
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Authors and funding
8 authors.
Funding
Abstract
In order to successfully predict a proteins 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 extend 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 accuracy between 78 and 83 percent, and (2) visualizing and detecting conformational changes in protein trajectories during molecular dynamics simulations.
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Registered trials
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