<p>I'd consult references about AlphaFold. For example, wikipedia has a &quot;<a href="https://en.wikipedia.org/wiki/AlphaFold" rel="nofollow noreferrer">Limitations</a>&quot; page on the tool, though the page doesn't mention anything about synthetic proteins.</p>
<p>The hesitation that I would have is that the tool relies to some extent on coevolutionary models of amino acids to infer interactions at both training and prediction steps, so throwing a synthetic protein at it may confuse it. See e.g. slide 10 <a href="https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf" rel="nofollow noreferrer">here</a>.</p>
<p>In an <a href="https://github.com/deepmind/alphafold/issues/569" rel="nofollow noreferrer">interview</a>, they acknowledge this rather directly:</p>
<blockquote>
<p>Now, if you have a protein which is completely new, which does not have an evolutionary history—a synthetic protein, which has just been designed— then our system would not be as accurate as it would be for existing proteins.</p>
</blockquote>
<p>I am not totally clear on whether this is due to learning a latent representation of constraints on similar proteins, or whether the tool attempts to directly find homologs. The <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371605/" rel="nofollow noreferrer">paper</a> methods suggest to me that it is the former, but I'm not sure. They describe training on only chunks of a big MSA. If that's the case, I'd guess that they still can use information about the natural chunks of your synthetic protein, and aren't expecting to be able to do a full-length protein alignment.</p>
<p>So I think that you can expect the prediction to be <em>relatively</em> low quality. It is still probably much better than any alternative way of predicting structure!</p>
<p>As always, not a bad idea to <a href="https://github.com/deepmind/alphafold" rel="nofollow noreferrer">post an issue on github</a> asking this question.</p>

CC BY-SA 4.0
2023-01-24T20:45:24.293Z
This is a very good point indeed, it will rely on convolution as part of its algorithm and chimeras will not follow this assumption.
M__
CC BY-SA 4.0
2023-01-24T19:34:57.140Z
2023-01-24T19:34:57.140Z
Maximilian Press
<p>I want to predict the folding of a chimeric antigen receptor sequence. These sequences are completely made out of small parts of different proteins. Do you know if AlphaFold can be used for this?</p>

CC BY-SA 4.0
2023-01-18T19:01:39.567Z
Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
Community
CC BY-SA 4.0
2023-01-24T20:47:39.033Z
From @MaximillianPress's response, the answer is no because a chimera has no history the domains evolving together and that is a central part of the algorithms assumptions. Please do remember to mark an answer as "accepted" when the question has been successfully answered.
M__
<protein-structure>
How can I predict the folding of a synthetic protein?
# How can I predict the folding of a synthetic protein?

(Asked by: username_0 on 2023-01-18)

I want to predict the folding of a chimeric antigen receptor sequence. These sequences are completely made out of small parts of different proteins. Do you know if AlphaFold can be used for this?

**Tags:** protein-structure
> **Comments:**
> **username_1**: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.  
> **username_2**: From @MaximillianPress's response, the answer is no because a chimera has no history the domains evolving together and that is a central part of the algorithms assumptions. Please do remember to mark an answer as "accepted" when the question has been successfully answered.  

---

## 1 answers

### Answer by username_3 on 2023-01-24. Score: 1

I'd consult references about AlphaFold. For example, wikipedia has a "[Limitations](https://en.wikipedia.org/wiki/AlphaFold)" page on the tool, though the page doesn't mention anything about synthetic proteins.

The hesitation that I would have is that the tool relies to some extent on coevolutionary models of amino acids to infer interactions at both training and prediction steps, so throwing a synthetic protein at it may confuse it. See e.g. slide 10 [here](https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf).

In an [interview](https://github.com/deepmind/alphafold/issues/569), they acknowledge this rather directly:

> Now, if you have a protein which is completely new, which does not have an evolutionary history—a synthetic protein, which has just been designed— then our system would not be as accurate as it would be for existing proteins.

I am not totally clear on whether this is due to learning a latent representation of constraints on similar proteins, or whether the tool attempts to directly find homologs. The [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371605/) methods suggest to me that it is the former, but I'm not sure. They describe training on only chunks of a big MSA. If that's the case, I'd guess that they still can use information about the natural chunks of your synthetic protein, and aren't expecting to be able to do a full-length protein alignment.

So I think that you can expect the prediction to be *relatively* low quality. It is still probably much better than any alternative way of predicting structure!

As always, not a bad idea to [post an issue on github](https://github.com/deepmind/alphafold) asking this question.

> **Comments:**
> **username_2**: This is a very good point indeed, it will rely on convolution as part of its algorithm and chimeras will not follow this assumption.  

---

