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Deciphering the Structural Code of Proteins With Deep Graph Learning
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DOI：10.1109/TCBBIO.2025.3604017.png)
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Deciphering the three-dimensional structure of proteins remains a grand challenge in biology and medicine, as it holds the key to understanding their biological functions and facilitating drug discovery. In this paper, we introduce DECIPHER (Deep Encoding of Cellular Interactions and Protein HiErarchical Representation), a novel deep graph learning framework for protein structure prediction. By representing proteins as graphs, where residues and atoms serve as nodes and their interactions form edges, we capture the intricate spatial relationships within these complex biomolecules. Our framework consists of two complementary modules: 1) a general protein structure prediction module that employs residue and atomic graphs to predict backbone and side-chain conformations, respectively, and utilizes SE(3) transformation for structure optimization; and 2) an antibody-specific structure prediction module that incorporates a dual-track network architecture to model sequence co-evolution and structural template information, coupled with a physics-based energy optimization process. Through extensive experiments on multiple benchmark datasets, we demonstrate that our approach significantly outperforms state-of-the-art methods, setting new standards for accuracy and efficiency in protein structure prediction. By deciphering the structural code of proteins, our work paves the way for accelerated research on protein function and opens up new avenues for rational drug design and discovery.
Keyword:
Protein structure prediction
graph neural networks
antibody prediction
Protein structure prediction
graph neural networks
antibody prediction
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