FLEXR: automated multi-conformer model building using electron-density map sampling.

Area of Science:

• Structural Biology
• Computational Biology
• Biochemistry

Background:

• High-resolution electron-density maps contain valuable protein conformational dynamics.
• Alternative conformations are often missed in Protein Data Bank (PDB) models due to manual modeling challenges.
• This underrepresentation limits the understanding of protein function and ligand interactions.

Purpose of the Study:

• To develop an automated tool, FLEXR, for detecting and modeling multiple protein conformers.
• To integrate these alternative conformations into structural models for refinement and analysis.
• To bridge the gap between electron-density data and comprehensive structural representation.

Main Methods:

• FLEXR utilizes Ringer-based electron-density sampling to build explicit multi-conformer models.
• The program automates the detection, building, and inspection of alternative conformers.
• Validation was performed on high-quality crystal structures ranging from 0.8 to 1.85 Å resolution.

Main Results:

• FLEXR successfully uncovered hidden side chain and backbone conformations missed by manual or existing methods.
• Analysis of ligand-binding sites revealed novel insights into protein-ligand binding mechanisms.
• The generated multi-conformer models provide a more complete representation of protein structures.

Conclusions:

• FLEXR facilitates the inclusion of explicit multi-conformer states in crystallographic models.
• These models better represent high-energy features in electron-density maps, aiding downstream applications like ligand discovery.
• The tool enhances the utility of structural data for understanding protein dynamics and interactions.