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Related Experiment Video

Updated: May 6, 2026

X-Ray Crystallography to Study the Oligomeric State Transition of the Thermotoga maritima M42 Aminopeptidase TmPep1050

X-Ray Crystallography to Study the Oligomeric State Transition of the Thermotoga maritima M42 Aminopeptidase TmPep1050

Published on: May 13, 2020

Model morphing and sequence assignment after molecular replacement.

Thomas C Terwilliger¹, Randy J Read, Paul D Adams

¹Bioscience Division, Los Alamos National Laboratory, Mail Stop M888, Los Alamos, NM 87545, USA.

Acta Crystallographica. Section D, Biological Crystallography

|November 6, 2013

Summary

This summary is machine-generated.

This study introduces a new computational procedure to improve protein structure models by accurately assigning amino acid sequences. The method enhances residue identification in crystallographic models, significantly improving structural accuracy.

Keywords:

loop-building model building model–map correlation morphing sequence assignment

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Area of Science:

Structural biology
Computational chemistry
Biophysics

Background:

Molecular replacement is a key technique in determining protein structures.
Current methods like morphing refine atomic coordinates but do not correct residue identity errors.
Accurate residue assignment is crucial for understanding protein function.

Purpose of the Study:

To develop a procedure to improve protein model completeness and residue sequence accuracy after initial model building.
To address the limitation of morphing, which cannot correct incorrect residue identities.

Main Methods:

A novel procedure removes residues from morphed models that do not fit the electron density map.
Sequence assignment is performed using chain connectivity information from the working model and electron density map.
The method was tested on a 3.2 Å resolution structure and various templates with low sequence identity.

Main Results:

The procedure increased correctly identified residues from 88 to 247 out of 359 in a test case.
For templates with 7-36% sequence identity, the mean fraction of correctly identified residues rose from 33% to 47%.
The method is simple to apply and integrated into the Phenix software package.

Conclusions:

The developed procedure significantly enhances the accuracy of residue assignment in crystallographic protein models.
This method improves structural model quality, especially for low-resolution data and homologous structures.
The approach offers a valuable tool for structural biologists using molecular replacement.