<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>

CC BY-SA 4.0
2019-09-25T10:51:31.607Z
DMPfold uses PSICOV in its pipeline. Does this mean that DMPfold does quality assessment in its pipeline?
DanielSebas
CC BY-SA 4.0
2019-09-25T11:07:12.443Z
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
M__
CC BY-SA 4.0
2019-09-25T14:50:47.870Z
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.
jgreener
CC BY-SA 4.0
2019-09-25T15:17:09.273Z
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)
M__
CC BY-SA 4.0
2019-09-24T21:44:03.810Z
2019-09-24T21:44:03.810Z
M__
<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>

CC BY-SA 4.0
2019-09-24T13:21:42.880Z
2019-09-24T13:21:42.880Z
jgreener
<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>

CC BY-SA 4.0
2019-09-24T14:54:56.023Z
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).
terdon
CC BY-SA 4.0
2019-09-24T21:57:31.927Z
Edited to be compliant
M__
<phylogenetics><phylogeny><protein-structure>
Protein model quality assessment
# 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.


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### 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 @jgreener 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)  

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