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Graph identification of proteins in tomograms (GRIP-Tomo) 2.0: Topologically aware classification for proteins
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DOI：10.1002/pro.70721.png)
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Abstract
En 中文
Cryo-electron tomography (cryo-ET) enables structural characterization of biomolecules under near-native conditions. Existing approaches for interpreting the resulting three-dimensional volumes are computationally expensive and have difficulty interpreting density associated with small proteins/complexes. To explore alternate approaches for identifying proteins in cryo-ET data, we pursued a Graph Network and topologically invariant approach. Here, we report on a fast algorithm that distinguishes volumes containing protein density from noise by searching for nuances of evolutionarily conserved motifs and the geometric characteristics of protein structure. Graph Identification of Proteins in Tomograms (GRIP-Tomo) 2.0 is a machine-learning pipeline that extracts interpretable topological features of protein structures within noisy experimental backgrounds. Compared to version 1.0, the new pipeline includes three upgrades that significantly improve performance, including synthetic tomogram generation simulating realistic noise, graph-based persistent feature extraction as protein fingerprints, and High Performance Computing acceleration. GRIP-Tomo 2.0 achieves over 90% accuracy in distinguishing proteins from noise for synthetic datasets and over 80% accuracy for real datasets with Angstroms per pixel close to 1 from the protein mixtures of in-house samples, which represents a foundational step toward advancing cryo-ET workflows and empowering automated detection of both small and large proteins for visual proteomics. https://github.com/EMSL-Computing/grip-tomo.
Keywords:
cross-domain learning
cryo-electron tomography
explainable machine learning
synthetic templates
topological data analysis
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