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Macromolecular ab initio phasing enforcing secondary and tertiary structure.

Claudia Millán1, Massimo Sammito1, Isabel Usón2

  • 1Structural Biology, Molecular Biology Institute of Barcelona , Baldiri Reixac 15, Barcelona, 08028, Spain.

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Summary

Ab initio phasing of macromolecular structures is advanced by the ARCIMBOLDO method, combining fragment searches with density modification. This approach enables solving complex protein structures without prior phase information.

Keywords:
ARCIMBOLDOab initio phasingmacromolecular structureα-helices

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Area of Science:

  • Structural Biology
  • Biophysics
  • Computational Biology

Background:

  • Ab initio phasing of macromolecular structures is challenging due to limitations in structure size and data resolution.
  • Existing methods include Patterson function, density modification, and data extrapolation.

Purpose of the Study:

  • To develop and present an improved ab initio phasing method for macromolecular structures.
  • To overcome the barriers of structure size and data resolution in crystallographic phasing.

Main Methods:

  • The ARCIMBOLDO method combines fragment identification (e.g., polyalanine alpha-helices) using PHASER with density modification by SHELXE.
  • It involves parallel testing of numerous fragment groups, often requiring grid or supercomputing resources.
  • The method can utilize various fragment libraries, including helices, beta-strands, and local folds.

Main Results:

  • The ARCIMBOLDO method successfully solved several unknown macromolecules with thousands of atoms at resolutions around 2 Å.
  • The approach allows for density modification and main-chain tracing to reveal protein structures.
  • Simplified software in the 2014 release automates grid access for difficult cases and can run on single workstations for simpler ones.

Conclusions:

  • ARCIMBOLDO provides a robust strategy for ab initio phasing, expanding the scope of structure determination.
  • The method's flexibility in fragment utilization and computational approach makes it adaptable for various macromolecular targets.
  • The ongoing development and simplification of the software enhance its accessibility and applicability in structural biology.