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Conventional electron microscopy (EM) involves dehydration, fixation, and staining of biological samples, which distorts the native state of biological molecules and results in several artifacts. Also, the high-energy electron beam damages the sample and makes it difficult to obtain high-resolution images. These issues can be addressed using cryo-EM, which uses frozen samples and gentler electron beams. The technique was developed by Jacques Dubochet, Joachim Frank, and Richard Henderson, for...
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Efficient Flexible Fitting Refinement with Automatic Error Fixing for De Novo Structure Modeling from Cryo-EM Density

Takaharu Mori1, Genki Terashi2, Daisuke Matsuoka1

  • 1RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan.

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|June 18, 2021
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Summary
This summary is machine-generated.

We developed SAUA-FFR, an improved flexible fitting refinement method for protein structure modeling from cryo-electron microscopy data. This approach fixes local errors and uses united-atom models for more accurate and efficient de novo predictions.

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Area of Science:

  • Structural biology
  • Computational biology
  • Biophysics

Background:

  • Protein structure modeling from cryo-electron microscopy (cryo-EM) density maps is challenging.
  • De novo modeling with flexible fitting refinement (FFR) is common but struggles with local structural errors and limited atomic mobility.

Purpose of the Study:

  • To develop an efficient and accurate scheme for flexible fitting refinement (FFR) of protein structures from cryo-EM data.
  • To address limitations of existing de novo modeling methods, specifically local structural errors and refinement suppression.

Main Methods:

  • Proposed SAUA-FFR: fixing local structural errors first, followed by FFR using iterative simulated annealing (SA) molecular dynamics with a united atom (UA) model in an implicit solvent.
  • Selected the best model from multiple FFR runs with varying biasing force constants to minimize overfitting.
  • Applied the scheme to decoys from MAINMAST for eight selected proteins.

Main Results:

  • SAUA-FFR demonstrated improved protein model quality compared to the original MAINMAST scheme, evidenced by better root-mean-square deviation, MolProbity, and RWplus scores.
  • Fixing local errors enhanced secondary structure formation.
  • The UA model facilitated progressive refinement due to increased atomic mobility in implicit solvent.

Conclusions:

  • The SAUA-FFR scheme provides an efficient and accurate method for protein structure modeling from medium-resolution cryo-EM maps.
  • This approach effectively reduces overfitting and improves model quality by addressing common de novo prediction errors.