VoroMQAAffiliation

Click on the links below to view different info pages.

If you have more questions, do not hesitate to ask kliment@ibt.lt.

Input

As an input, the VoroMQA server accepts one or more structures (models) in PDB format. Input structures can contain multiple chains, biological assemblies are also accepted. User can provide a sequence to filter and renumber the residues in the submitted PDB files. User can also enable the evaluation of inter-chain interface in addition to the whole structure assessment.

Output

As an output, the server provides global scores, local (per-residue) scores, and additional local context information on secondary structure and solvent accessibility. Moreover, if the evaluation of inter-chain interactions was requested, the server provides interface quality scores, interface energy estimates, and local scores for residues involved in inter-chain interfaces.

All the local scores are presented in forms of interactive (clickable) plots and interactive color-coded scoring profiles and 3D structures. Various visualizations can be turned off and on, allowing a user to focus on some of the features without being distracted by the others, which is particularly useful when viewing results for multiple models on a single page. The VoroMQA server also provides an interactive plot for viewing and interpreting global scores of multiple models.

Context help

Almost every page contains a "Show help" button. It can be clicked to view the appropriate instructions and hints for the page.

Interpreting global VoroMQA scores

  • A vast majority of high quality experimentally determined structures have VoroMQA scores greater than 0.4.
  • A relatively very small fraction of the native structures have VoroMQA scores less than 0.3.
  • The plot below shows the distribution of the VoroMQA global scores of high-quality PDB structures:
  • For quickly interpreting a global VoroMQA score of a structural model, the following simple rule can be used:
    • If the score is greater than 0.4, then the model is likely good.
    • If the score is less than 0.3, then the model is likely bad.
    • If the score is between 0.3 and 0.4, then the model cannot be reliably classified as either good or bad based on just VoroMQA.