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Structure of HIV-1 Capsid Assemblies by Cryo-electron Microscopy and Iterative Helical Real-space Reconstruction

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Helicon: Helical parameter determination and 3D reconstruction from one image.

Daoyi Li¹, Xiaoqi Zhang², Wen Jiang³

¹Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA.

Journal of Structural Biology

|October 23, 2025

Summary

A new computational method, Helicon, simplifies 3D reconstruction of biological macromolecules with helical symmetry. This method accurately determines helical parameters and reconstructs structures from cryo-electron microscopy images.

Keywords:

2D classification Amyloids Cryo-EM De novo 3D reconstruction Image stitching L1/L2 regularization Linear regression

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Last Updated: Jan 14, 2026

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Area of Science:

Structural biology
Computational biology
Biophysics

Background:

Helical symmetry is prevalent in biological macromolecules.
Determining helical parameters and 3D structure de novo is challenging.

Purpose of the Study:

To develop a computational method for efficient helical reconstruction.
To enable de novo 3D structure determination from limited data.

Main Methods:

Helicon method: helical reconstruction as a linear regression problem.
Parameterization by helical twist, rise, and axial symmetry.
Sparse search for parameters and direct reconstruction from 2D class averages or cryo-EM images.

Main Results:

Validated Helicon with simulations and experimental cryo-EM data (helical tubes, filaments, amyloid fibrils).
Imaging stitching and L1 regularization enhance robustness for low-twist and noisy data.
Successfully determined helical parameters and reconstructed a novel tau amyloid structure.

Conclusions:

Helicon provides a robust and efficient approach for de novo helical reconstruction.
The method advances structural analysis of biological macromolecules, including challenging amyloid structures.