FINDMOL: automated identification of macromolecules in electron-density maps
E W McKee1, L D Kanbi, K L Childs
1Department of Computer Science, Texas A&M University, 301 H. R. Bright Building, 3112 Texas A&M University, College Station, TX 77843, USA.
Acta Crystallographica. Section D, Biological Crystallography
|October 22, 2005
Summary
The FINDMOL algorithm simplifies macromolecular structure determination by reassembling fragmented electron-density maps. This method aids crystallographers in building accurate models, even with low-resolution data.
Area of Science:
- Structural Biology
- Biophysics
- Computational Biology
Background:
- Automating macromolecular structure determination using X-ray crystallography requires building models into electron-density maps.
- Conventional crystallographic asymmetric units often fragment biological molecules, complicating model interpretation.
Purpose of the Study:
- To introduce the FINDMOL algorithm for efficiently parsing electron-density maps.
- To improve the process of model building in X-ray crystallography, especially with challenging data.
Main Methods:
- The FINDMOL algorithm analyzes trace points from skeletonized electron-density maps.
- It does not require prior information like sequence or molecule count.
- Tested on density-modified maps from medium- to low-resolution data.
Main Results:
- FINDMOL typically reconstructs the biological unit from fragmented maps.
- Secondary structural elements like alpha-helices and beta-sheets are readily identifiable.
- In remaining cases, only a few molecular fragments are generated.
Conclusions:
- FINDMOL assists crystallographers in manual and automatic model building.
- The algorithm is effective even with low-resolution data and poor phases.
- It enhances the efficiency and accuracy of macromolecular structure determination.
