Building de novo cryo-electron microscopy structures collaboratively with citizen scientists

Area of Science:

• Structural biology
• Computational biology
• Biophysics

Background:

• Cryo-electron microscopy (cryo-EM) resolution is rapidly improving.
• Existing computational tools for atomic model building and refinement need enhancement.
• Accurate 3D structural models are crucial for understanding protein function.

Purpose of the Study:

• To explore the use of the game Foldit for de novo atomic model building in cryo-EM.
• To assess the potential of human-computer collaboration for accelerating structure generation.
• To provide a novel computational approach for improving cryo-EM structural analysis.

Main Methods:

• Utilized the crowd-sourced protein folding game Foldit.
• Integrated Foldit gameplay with cryo-EM data for de novo model generation.
• Compared models generated through Foldit collaboration with existing methods.

Main Results:

• Demonstrated successful generation of high-quality de novo structural models using Foldit.
• Showcased the potential for rapid model building through collaborative gameplay.
• Indicated that Foldit-assisted modeling complements current cryo-EM workflows.

Conclusions:

• Human-computer collaboration via Foldit offers a promising avenue for advancing cryo-EM structural biology.
• This approach can significantly expedite the generation of accurate atomic models.
• The integration of gaming platforms can enhance the efficiency and accessibility of structural determination.