ArticlePLoS computational biology2022
Fast protein structure comparison through effective representation learning with contrastive graph neural networks.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 32 citations in OpenAlex.
- Benchmarking protein sequence and structure search methods for remote homology detection.Genome biology · 2026Article
- Chirobiophore: A Novel Framework for Quantifying Biochirality in Macromolecular Systems.Biomolecules · 2026Article
- Identification of prospective PETases across prokaryotes using an in silico approach.BMC biology · 2026Article
- Faithful Interpretation of Protein Structures through Weighted Persistent Homology Improves Evolutionary Distance Estimation.Molecular biology and evolution · 2025Article
- Fast protein structure searching using structure graph embeddings.Bioinformatics advances · 2025Article
- PS-GO parametric protein search engine.Computational and structural biotechnology journal · 2024Article
- Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering.Cell research · 2024Article
- Article
- Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction.Bioinformatics (Oxford, England) · 2024Article
- Structure-based prediction of nucleic acid binding residues by merging deep learning- and template-based approaches.PLoS computational biology · 2023Article
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5 authors at 1 institution in 1 country.
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Abstract
Protein structure alignment algorithms are often time-consuming, resulting in challenges for large-scale protein structure similarity-based retrieval. There is an urgent need for more efficient structure comparison approaches as the number of protein structures increases rapidly. In this paper, we propose an effective graph-based protein structure representation learning method, GraSR, for fast and accurate structure comparison. In GraSR, a graph is constructed based on the intra-residue distance derived from the tertiary structure. Then, deep graph neural networks (GNNs) with a short-cut connection learn graph representations of the tertiary structures under a contrastive learning framework. To further improve GraSR, a novel dynamic training data partition strategy and length-scaling cosine distance are introduced. We objectively evaluate our method GraSR on SCOPe v2.07 and a new released independent test set from PDB database with a designed comprehensive performance metric. Compared with other state-of-the-art methods, GraSR achieves about 7%-10% improvement on two benchmark datasets. GraSR is also much faster than alignment-based methods. We dig into the model and observe that the superiority of GraSR is mainly brought by the learned discriminative residue-level and global descriptors. The web-server and source code of GraSR are freely available at www.csbio.sjtu.edu.cn/bioinf/GraSR/ for academic use.
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