Point transformer for protein structural heterogeneity analysis using CryoEM.

Muyuan Chen, Muchen Li, Renjie Liao

Abstract readPreprint

In one paragraph

Article in ArXiv, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Muyuan ChenDivision of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Stanford University.

Muchen LiDepartment of Electrical and Computer Engineering, The University of British Columbia.

Renjie LiaoDepartment of Electrical and Computer Engineering, The University of British Columbia.

Funding

Comprehensive analysis of macromolecule structural variability in CryoEM/CryoETR01GM150905 · NIGMS · STANFORD UNIVERSITY · PI Muyuan Chen · 2023 to 2026

$1.5M

NIGMS NIH HHS R01 GM150905

6 · The paper itself

Abstract

Structural dynamics of macromolecules is critical to their structural-function relationship. Cryogenic electron microscopy (CryoEM) provides snapshots of vitrified protein at different compositional and conformational states, and the structural heterogeneity of proteins can be characterized through computational analysis of the images. For protein systems with multiple degrees of freedom, it is still challenging to disentangle and interpret the different modes of dynamics. Here, by implementing Point Transformer, a self-attention network designed for point cloud analysis, we are able to improve the performance of heterogeneity analysis on CryoEM data, and characterize the dynamics of highly complex protein systems in a more human-interpretable way.

Identifiers

PMID41647221

PMCPMC12869410

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.