The utility of comparative models and the local model quality for protein crystal structure determination by Molecular Replacement

By Marcin Pawlowski, Janusz M. Bujnicki. 2012

8 citations recorded by the source corpus.

Computational models of protein structures were proved to be useful as search models in Molecular Replacement (MR), a common method to solve the phase problem faced by macromolecular crystallography. The success of MR depends on the accuracy of a search model. Unfortunately, this parameter remains unknown until the final structure of the target protein is determined. During the last few years, several Model Quality Assessment Programs (MQAPs) that predict the local accuracy of theoretical models have been developed. In this article, we analyze whether the application of MQAPs improves the utility of theoretical models in MR. For our dataset of 615 search models, the real local accuracy of a model increases the MR success ratio by 101% compared to corresponding polyalanine templates. On the contrary, when local model quality is not utilized in MR, the computational models solved only 4.5% more MR searches than polyalanine templates. For the same dataset of the 615 models, a workflow combining MR with predicted local accuracy of a model found 45% more correct solution than polyalanine templates. To predict such accuracy MetaMQAPclust, a “clustering MQAP” was used. Using comparative models only marginally increases the MR success ratio in comparison to polyalanine structures of templates. However, the situation changes dramatically once comparative models are used together with their predicted local accuracy. A new functionality was added to the GeneSilico Fold Prediction Metaserver in order to build models that are more useful for MR searches. Additionally, we have developed a simple method, AmIgoMR (Am I good for MR?), to predict if an MR search with a template-based model for a given template is likely to find the correct solution.

Subfields: Materials Chemistry, Molecular Biology

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

  1. Björn Wallner
  2. Yan Wang
  3. Zhidong Xue
  4. Jouko Virtanen
  5. Yang Zhang
  6. G. Bunkoczi
  7. J. Tesmer
  8. Randy J. Read

In Molecular Biology

  1. Yan Wang
  2. Yang Zhang
  3. Zhidong Xue
  4. Jouko Virtanen
  5. Björn Wallner
  6. Randy J. Read
  7. G. Bunkoczi
  8. J. Tesmer

Citations in the corpus