FFF: Fragments-Guided Flexible Fitting for Building Complete Protein Structures
Weijie Chen, Xinyan Wang, Yuhang Wang

TL;DR
This paper introduces FFF, a novel method combining fragment recognition and structure prediction to build complete protein structures from cryo-EM maps, overcoming low SNR issues and outperforming baseline methods.
Contribution
The paper presents FFF, a new approach that integrates recognition-based fragment generation with flexible fitting, enhancing complete protein structure modeling from cryo-EM data.
Findings
FFF outperforms baseline methods in benchmark tests.
The method effectively captures structural features from cryo-EM maps.
It successfully constructs complete protein models using predicted fragments.
Abstract
Cryo-electron microscopy (cryo-EM) is a technique for reconstructing the 3-dimensional (3D) structure of biomolecules (especially large protein complexes and molecular assemblies). As the resolution increases to the near-atomic scale, building protein structures de novo from cryo-EM maps becomes possible. Recently, recognition-based de novo building methods have shown the potential to streamline this process. However, it cannot build a complete structure due to the low signal-to-noise ratio (SNR) problem. At the same time, AlphaFold has led to a great breakthrough in predicting protein structures. This has inspired us to combine fragment recognition and structure prediction methods to build a complete structure. In this paper, we propose a new method named FFF that bridges protein structure prediction and protein structure recognition with flexible fitting. First, a multi-level…
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Taxonomy
TopicsAdvanced Electron Microscopy Techniques and Applications · Enzyme Structure and Function · Genomics and Phylogenetic Studies
MethodsAlphaFold
