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A Protocol for Computer-Based Protein Structure and Function Prediction
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Structure solution with ARCIMBOLDO using fragments derived from distant homology models.

Massimo Sammito1, Kathrin Meindl, Iñaki M de Ilarduya

  • 1Instituto de Biología Molecular de Barcelona, Barcelona Science Park, Barcelona, Spain.

The FEBS Journal
|July 1, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces ARCIMBOLDO-SHREDDER, a new method for solving the crystallographic phase problem using molecular replacement. It optimizes search models by systematically shredding and scoring fragments against experimental data, improving structural determination.

Keywords:
ARCIMBOLDOMltEdensity modificationfragment searchmacromolecular phasing

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Area of Science:

  • Structural biology
  • X-ray crystallography
  • Computational biology

Background:

  • Molecular replacement is crucial for solving the crystallographic phase problem, requiring accurate structural models.
  • Existing methods for selecting suitable models from homologs can be computationally intensive.
  • The structure of MltE, an endolytic peptidoglycan lytic transglycosylase from Escherichia coli, was previously unknown.

Purpose of the Study:

  • To describe the solution of the MltE structure.
  • To introduce an optimized strategy for molecular replacement model building.
  • To present the ARCIMBOLDO-SHREDDER method for fragment-based model optimization.

Main Methods:

  • Utilized ARCIMBOLDO for ab initio phasing with small, accurate fragments.
  • Employed fragment trimming against the correlation coefficient before density modification and autotracing.
  • Developed and implemented ARCIMBOLDO-SHREDDER for systematic template shredding and scoring against rotation function solutions.

Main Results:

  • Successfully determined the structure of MltE, an outer membrane-anchored endolytic peptidoglycan lytic transglycosylase.
  • The asymmetric unit contains a dimer of the 194 amino acid protein.
  • The ARCIMBOLDO-SHREDDER strategy significantly optimized the search model against experimental data.

Conclusions:

  • ARCIMBOLDO-SHREDDER provides an effective strategy for optimizing search models in molecular replacement.
  • This method enhances the process of solving complex crystallographic structures.
  • The successful structure determination of MltE validates the utility of the new approach.