PPubMed20 Jan 2009Cited 21×

Model-building strategies for low-resolution X-ray crystallographic data Model-building strategies for low-resolution data

Anjum M. Karmali, Tom L. Blundell, Nicholas Furnham

Abstract

Interpretation of low-resolution X-ray crystallographic data can prove to be a difficult task. The challenges faced in electron-density interpretation, the strategies that have been employed to overcome them and developments to automate the process are reviewed.

A figure from Model-building strategies for low-resolution X-ray crystallographic data Model-building strategies for low-resolution data — fig. from the paper

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Review of strategies and emerging automation for building atomic models from low‑resolution X‑ray crystallographic data.

What’s new

i.Summarizes classical and modern model‑building techniques for low‑resolution electron‑density maps
ii.Proposes a general action plan that combines homology fragments, biochemical data, and automated tools
iii.Compares low‑resolution X‑ray crystallography with electron‑microscopy map interpretation
iv.Highlights case studies (ribosome, photosystem I, 5‑lipoxygenase‑activating protein, etc.) illustrating successful strategies

How they did it

The authors performed a literature review of experimental and computational approaches used to interpret low‑resolution X‑ray data, describing both manual and automated techniques. They illustrated the methods with examples from previously solved structures and outlined a step‑by‑step workflow for model construction.

What they found

Plant photosystem I structure solved at 4.4 Å using backbone fragments and idealized helices
Human 5‑lipoxygenase‑activating protein model built at 4.0 Å with ARP/wARP and MIFit
Ribosome structures solved at resolutions from 5.5 Å to 7.8 Å using combined molecular‑replacement and biochemical data
Demonstrated that automated programs (Buccaneer, SOLVE/RESOLVE, ARP/wARP, RAPPER) can generate partial models at resolutions as low as ~4 Å

Caveats

✕Side‑chain placement remains difficult when density is ambiguous or fragmented
✕Automated methods can misinterpret density at low resolution, requiring manual correction
✕Reliance on available high‑resolution fragments or homologues limits applicability to novel folds
✕Integration of diverse data (EM maps, proteomics, biophysical restraints) is still in early stages

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