Answers.Body: <p>Co-variance with convolution is an extremely hot method, so DMPFold via psicov. Checking the reality of the folding is obviously important, convolutions can be missed, inaccuracies can happen.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-09-25T10:51:31.607Z
Answers.Text: DMPfold uses PSICOV in its pipeline. Does this mean that DMPfold does quality assessment in its pipeline?
Answers.UserDisplayName: DanielSebas
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Answers.CreationDate: 2019-09-25T11:07:12.443Z
Answers.Text: It would be good for you to read the publications on psicov, to understand it. I believe @jgreener is part of the group that developed it
Answers.UserDisplayName: M__
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Answers.CreationDate: 2019-09-25T14:50:47.870Z
Answers.Text: DMPfold has a model quality predictor that is specific to DMPfold. It can only score DMPfold models because it uses some internal measures, such as how well the final model matches the initial distance prediction. DMPfold shouldn't therefore be thought of as a model quality assessment method. PSICOV is part of DMPfold but isn't related to model quality assessment at all - it predicts residue-residue contacts by inverting covariance matrices.
Answers.UserDisplayName: jgreener
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-09-25T15:17:09.273Z
Answers.Text: Straight covariance results in convolution in the non- deep learning sense, ie pure statistical sense (I haven't explained that very well,but hope it makes some sense)
Answers.UserDisplayName: M__
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Answers.CreationDate: 2019-09-24T21:44:03.810Z
Answers.LastActivityDate: 2019-09-24T21:44:03.810Z
Answers.UserDisplayName: M__
Answers.Body: <p>Have a read of the <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/prot.25767" rel="nofollow noreferrer">Estimation of model accuracy in CASP13</a> paper to get the latest rankings.</p>
  
  <p>Generally you would run the model quality software on the models you have generated and choose the best. Also have a look at the structures in PyMol, you learn a lot that way. See, for instance, how much variety there is, and where the models vary. Is there a conserved core in many of the models? Or is there just a blob of "spaghetti" that doesn't look like an ordered protein?</p>
  
  <p>If you have many models (e.g. from a Rosetta run) you might want to look into clustering the models first to reduce the number you have to work with.</p>
  
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Answers.CreationDate: 2019-09-24T13:21:42.880Z
Answers.LastActivityDate: 2019-09-24T13:21:42.880Z
Answers.UserDisplayName: jgreener
Body: <p>How do I perform protein model quality assessment?</p>
  
  <p>I will obtain predicted structures from DMPfold, I-Tasser, and Rosetta, what should be the next approach to select a structure from among this set of models?</p>
  
  <p>Note, I have been looking at some, appropriate methods such as MetaMQAP, QA-RecobineIt, but their servers are not working. </p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-24T14:54:56.023Z
Comments.Text: Please don't ask "what is the best". The best will always be a matter of opinion and the best method for your data will depend on exactly what kind of data you have. Please [edit] your question and rephrase it so it isn't asking for opinion (best is an opinion).
Comments.UserDisplayName: terdon
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-24T21:57:31.927Z
Comments.Text: Edited to be compliant
Comments.UserDisplayName: M__
Tags: <phylogenetics><phylogeny><protein-structure>
Title: Protein model quality assessment
ThreadText: # Protein model quality assessment
  
  (Asked by: username_0 on 2019-09-24)
  
  How do I perform protein model quality assessment?
  
  I will obtain predicted structures from DMPfold, I-Tasser, and Rosetta, what should be the next approach to select a structure from among this set of models?
  
  Note, I have been looking at some, appropriate methods such as MetaMQAP, QA-RecobineIt, but their servers are not working.
  
  **Tags:** phylogenetics, phylogeny, protein-structure
  
  > **Comments:**
  > **username_1**: Please don't ask "what is the best". The best will always be a matter of opinion and the best method for your data will depend on exactly what kind of data you have. Please [edit] your question and rephrase it so it isn't asking for opinion (best is an opinion).  
  > **username_2**: Edited to be compliant  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_3 on 2019-09-24. Score: 2 | Accepted answer
  
  Have a read of the [Estimation of model accuracy in CASP13](https://onlinelibrary.wiley.com/doi/full/10.1002/prot.25767) paper to get the latest rankings.
  
  Generally you would run the model quality software on the models you have generated and choose the best. Also have a look at the structures in PyMol, you learn a lot that way. See, for instance, how much variety there is, and where the models vary. Is there a conserved core in many of the models? Or is there just a blob of "spaghetti" that doesn't look like an ordered protein?
  
  If you have many models (e.g. from a Rosetta run) you might want to look into clustering the models first to reduce the number you have to work with.
  
  
  ---
  
  ### Answer by username_2 on 2019-09-24. Score: 0
  
  Co-variance with convolution is an extremely hot method, so DMPFold via psicov. Checking the reality of the folding is obviously important, convolutions can be missed, inaccuracies can happen.
  
  > **Comments:**
  > **username_0**: DMPfold uses PSICOV in its pipeline. Does this mean that DMPfold does quality assessment in its pipeline?  
  > **username_2**: It would be good for you to read the publications on psicov, to understand it. I believe @username_3 is part of the group that developed it  
  > **username_3**: DMPfold has a model quality predictor that is specific to DMPfold. It can only score DMPfold models because it uses some internal measures, such as how well the final model matches the initial distance prediction. DMPfold shouldn't therefore be thought of as a model quality assessment method. PSICOV is part of DMPfold but isn't related to model quality assessment at all - it predicts residue-residue contacts by inverting covariance matrices.  
  > **username_2**: Straight covariance results in convolution in the non- deep learning sense, ie pure statistical sense (I haven't explained that very well,but hope it makes some sense)  
  
  ---
  
  