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AmIGoMR

Am I good for MR?


Overview

AmIGoMR (Am I good for MR?) predicts the probability of Molecular Replacement (MR) success when a comparative model created on the base of a given template is used. AmIGoMR operates only on the target and template sequences and does not require information about the native tertiary or secondary structure.

Usage

To execute AmIgoMR, a user must visit the following page: http://iimcb.genesilico.pl/pawlo/amigomr/submission.py, then he or she must define the sequence of the target protein and either sequences of template proteins or pdbcodes of these proteins. In majority of cases, the results will be generated in less than 10 minutes.

The AmIGoMR is also integrated in GeneSilico Fold Prediction Metaserver, a gateway to various methods for protein structure prediction, To execute AmIGoMR for a given job of the GeneSilico Fold Prediction Metaserver, a user  must visit the job’s page and then press the “Execute AmIGoMR” button.

Results

The user will get the table that presents the probability of solving MR search with a model generated for a given template. The results are presented for each of following types of search models:

  1. ALL_20 – full atom models, no information about local model accuracy was used - the B-factor of each atom was set to 20.
  2. ALL_IDEAL – B-factor values were modified according to the real local accuracy of a model, calculated by superposition of the model onto the true structure.
  3. BACKBONE_20 – models as in ALL_20, but containing backbone atoms only (all side-chain atoms were removed).
  4. BACKBONE_IDEAL - models as in ALL_IDEAL, but backbone atoms only (all side-chain atoms were removed).
  5. ALL_CA_IDEAL – B-factor values of each residue in the model were modified according to the real local accuracy of the position of this residue’s C-α atom.
  6. BACKBONE_CA_IDEAL - models as in ALL_CA_IDEAL, but backbone atoms only (all side-chain atoms were removed).
  7. MetaMQAP-evaluated - B-factors values were modified according to the local model accuracy predicted by the MetaMQAP method [14] (see equation 2) (see below). Since MetaMQAP predicts deviation only for C- alpha atoms, the B-factor value predicted for each C-α atom was transferred to all main-chain atoms of the residue under consideration. In addition, all side-chain atoms were removed.
  8. MetaMQAPclust-evaluated – similar to MetaMQAP-evaluated, but the local model accuracy was predicted by the new MetaMQAPclust method instead of MetaMQAP.
  9. POLYALA – plyalanine templates - template structures converted to polyalanine models.
  10. POLYALA_20 – similar to POLYALA, but the B-factor value of each atom was set to 20.

Important

In our MR-workflow, AmIgoMR predicts the MR success almost as accurately as GDT_TS does. Nevertheless, for different home-made MR workflows, AmIgoMR may less accurately estimate the absolute probability of solving MR with a given model. However, in such cases our method can still help to rank models according to their usefulness for MR.

References

The reference to the method will be updated, once the work has been published.



Contact: Marcin Pawlowski, pawlowski.science@yahoo.com

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