<p>Other have addressed why AlphaFold2 cannot really get the necessary signal from MSA (Multiple Sequence Alignment) and memory to properly model a variant, but I thought I'd add my observations/rants.</p>
<p>I have modelled <em>in silico</em> both engineering and clinical variants in the past, using PyRosetta, MD and other tools. For me, AlphaFold2 has really stood out but in a different way than the hype.</p>
<h2>Variants can have complicated effects</h2>
<p>I developed a tool Venus, <a href="https://venus.sgc.ox.ac.uk/" rel="nofollow noreferrer">https://venus.sgc.ox.ac.uk/</a>, which collates different pieces of information to help figure out the effect a variant was having beyond a simple ∆∆G calculation. This is because often a variant is having a variety of effects, for example a stable but catalytically dead variant can still form a complex and sequester a critical component (e.g. in <a href="https://pubmed.ncbi.nlm.nih.gov/31353023/" rel="nofollow noreferrer">ref</a> POLR2E-sequestering POR2A is more pathogenic than a simple truncation). This is not only true with human clinical variants, but also with engineering variants: for one protein I have worked on, DogCatcher, lowering the isoelectric point away from neutral pH was critical, not the stabilisation of the transition state (<a href="https://pubmed.ncbi.nlm.nih.gov/34324879/" rel="nofollow noreferrer">ref</a>). Another example is that the entrance loops in cytochromes drive selectivity more so than the active site configuration.</p>
<h2>The unknowns: alt conformers and complexes</h2>
<p>I will be the first to admit that most folk want a deleterious ∆∆G value and will p-value shop for it and not catering for this is a bad idea for a publication. But in terms of mechanism, the biggest issue I face are dynamic processes involving a residue where the other active/inactive conformer is unknown or when the residue is a surface residue possible at the interface in a complex, which is known simply to exist from a coIP western. No nearby gnomAD variants or presence of post-translational sites or sequence conservation do show that something may be important but not why.</p>
<h2>Enter AlphaFold2</h2>
<p>AlphaFold2, via the more user-friendly ColabFold, allow the creation of wild type monomers, homo-oligomers, but also hetero-oligomers (complexes) and alternative conformations.
These days I seen a cartoon of an interaction and I model it in quintuplicate. Sometimes nothing binds, but sometimes a complex is found 5 out of 5. The weirdest part is that it reveals some novel mechanism that makes sense that has probably never been seen before, but is beyond the scope of the experiment so one has the power to just put them aside —interfaces which are stronger phosphorylated, inhibitory domains binding in the same site but in a different way, unexpected complex arrangements etc. So the true revolutionary power of AlphaFold2 is predicting complexes and alternative conformations, which can be used to predict the mechanism of a variant.</p>
<h2>WIP</h2>
<p>Complexes do not always resolve and there is a lot of discussion in terms of massaging the MSA —e.g. boney-fish+tetrapods only etc. And there is very little cross-linking mass-spec data available for validation and/or constraining. But hopefully things will get better.</p>
<h2>Footnote</h2>
<p>The last paragraph sounds like an exaggeration, so I best give an example. Here is an example for the drawer from today. A target protein (catalytic domain = pale) has a regulatory terminal which inhibits in isolation (terminus = turquoise), however, when modelled with another protein (mustard) this terminal (terminus = coral) is flipped away. Thus revealing the mechanism by which the mustard protein sequesters the regulatory helix (coral), which would otherwise (turquoise) inhibit the catalytic domain (pale cyan).  In the literature, this is a western blot. Where a variant of interest involved in this mechanism one state may be more energetically favourable by scoring the ∆G of the different &quot;snapshots&quot; (=static conformers).
<a href="https://i.sstatic.net/xzf2h.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/xzf2h.png" alt="complex" /></a></p>

CC BY-SA 4.0
2022-08-25T15:57:25.660Z
What is MSA?? I dont get the footnote, I know I am dumb but whould it  be easy to reformulate for dumb people ?
pippo1980
CC BY-SA 4.0
2022-08-29T12:17:41.600Z
This is a detailed answer, but I feel like it doesn't address the question at hand.
jambajuice
CC BY-SA 4.0
2022-08-25T15:12:32.343Z
2022-08-25T16:22:26.553Z
2022-08-25T16:22:26.553Z
Matteo Ferla
<p>This answer is from common knowledge rather than specific infield expertise. The question could be more expertly answered by other members.</p>
<p>If you really want to pursue this type of calculation, it's possible that something like <code>INPS-MD</code> <em>de novo</em> -&gt; protein structure stability from variants will provide an answer. So maybe compare the outputs with <code>AlphaFold</code>?</p>
<p>Intuitively, I don't think it's cool, just my opinion. In my opinion, an experimentally derived structure is required as the basis for your calculation, where you want to consider the impact of an amino acid mutation.</p>
<p>It's important to separate <em>de novo</em> prediction from variant prediction. Alphafold is <em>de novo</em> prediction. What you are looking at is structure stability from a variant (see Summary). That's my core answer, these different calculations. To explain this more clearly ...</p>
<p>Good variant stability calculators are DUET (2014) and <a href="https://academic.oup.com/bioinformatics/article/32/16/2542/1743481?login=false" rel="nofollow noreferrer">INPS-MD</a> (2016), amongst others. Just to reiterate, a good starting point is an experimentally derived protein structure.</p>
<p>The webservers are here:</p>
<ul>
<li><a href="http://structure.bioc.cam.ac.uk/duet" rel="nofollow noreferrer">http://structure.bioc.cam.ac.uk/duet</a></li>
<li><a href="https://inpsmd.biocomp.unibo.it/inpsSuite" rel="nofollow noreferrer">https://inpsmd.biocomp.unibo.it/inpsSuite</a></li>
</ul>
<p>I personally don't know <code>Alphafold</code> but understand its predecessors which accommodate convolutions by leveraging deep-learning. The basis of the predecessor's calculations firstly are <em>de novo</em> prediction and secondly can miss the impact of variants if they are not directly involved in the core-algorithm. It's about identifying which amino acids are interacting considering all available homologous amino acids within a database. The calculation can predict local interactions with extreme accuracy, but it's a prediction. It will also depend on whether there are single amino acid changes or two- or more amino acid changes (they could compensate each other, particularly for a natural variant).</p>
<p>In contrast, DUET and INPS-MD, amongst a lot of other similar programs, consider the property shift of an individual amino acid in relation to its immediate local stereo-chemical environment measured by a shift of free-energy.</p>
<p>Essentially, these are two sets of very different calculations.</p>
<p><strong>Summary</strong> The problem I intuitively see is if <em>de novo</em> prediction in any form is used, okay they are very good, but they're just predictions. Variant structural predictions are also just predictions  which are very widely respected. Stacking <em>de novo</em> prediction onto predicting protein structure stability resulting from variants has scope for error, especially if there was some hidden non-linear effect.</p>
<p>There could be additional issues because the <em>de novo</em> calculations immediately prior to <code>Alphafold</code> don't predict the precise local distances.</p>
<hr />
<p><code>AlphaFold</code> would simply respond</p>
<blockquote>
<p>'it's not been trained'</p>
</blockquote>
<p>They'd want a wet-lab experimental dataset with wild-type vs. variants, if these exist with sufficient abundance to prevent over-training. It would be a very skewed dataset so of limited application in my personal opinion.</p>

CC BY-SA 4.0
2022-08-23T14:11:16.437Z
2022-08-24T15:43:35.923Z
2022-08-24T15:43:35.923Z
M__
<p>As marcin points out in the comments: by &quot;AphaFold (AF) has not been trained to predict structural consequences of point mutations&quot; it is meant &quot;AF is not able to tell you whether your point mutation is destabilizing or not, and if it is destabilizing then the structure it predicts will most probably be wrong&quot;.</p>
<p>It is totally possible that AF predicts the structure of a protein well when only non destabilizing mutations are introduced. As pointed out in the paragraph marcin quotes, however: &quot;AlphaFold has not been validated for predicting the effect of mutations.&quot; And as long as we don't know, we should be warry of any of AlphaFold predictions with point mutations. Maybe it works, maybe it doesn't, or maybe it's case dependent.</p>

CC BY-SA 4.0
2022-08-24T00:29:37.477Z
2022-08-24T00:29:37.477Z
O.Laprevote
<p>In the literature, it specifically states that AlphaFold has &quot;Has not been trained to predict structural consequences of <strong>point mutations</strong>&quot;. See : <a href="https://alphafold.com/faq" rel="noreferrer">https://alphafold.com/faq</a></p>
<blockquote>
<p>AlphaFold has not been validated for predicting the effect of mutations. In particular, AlphaFold is not expected to produce an unfolded protein structure given a sequence containing a destabilising point mutation</p>
</blockquote>
<p>What I want to understand, is why?</p>
<p>If I change <strong>one amino acid</strong>, and it's <strong>not destabilising</strong>, why can't AlphaFold use its training and form a moderately confident structure? I mean, to a computer, what's the difference between a wild-type and a mutated protein? I know what you may be thinking, why don't you just <strong>try it</strong> yourself, and see if AlphaFold works. But if I don't have the <strong>experimental structure</strong> of the mutant, how on earth am I supposed to believe that this is even close to the theoretical real thing?</p>
<p>Maybe I'm not understanding the <strong>main premise</strong>, but it's something that's been bugging me for a while now. Any insight into this would be much appreciated.</p>

CC BY-SA 4.0
2022-08-25T15:45:45.567Z
What happens if you mutate a Gly to Pro ??
pippo1980
CC BY-SA 4.0
2022-08-29T12:12:19.307Z
This is a good point. I have tried this in fact, and obtained the same structure. Bizarre.
jambajuice
CC BY-SA 4.0
2022-08-29T14:21:58.447Z
Cmon Gly to Pro where Pro can't assume Gly φ ? @jambajuice
pippo1980
<protein-structure><mutations><modelling><deep-learning><point-mutation>
Why can't AlphaFold predict the consequences of point-mutations?
# Why can't AlphaFold predict the consequences of point-mutations?

(Asked by: username_0 on 2022-08-23)

In the literature, it specifically states that AlphaFold has "Has not been trained to predict structural consequences of **point mutations**". See : <https://alphafold.com/faq>

> AlphaFold has not been validated for predicting the effect of mutations. In particular, AlphaFold is not expected to produce an unfolded protein structure given a sequence containing a destabilising point mutation

What I want to understand, is why?

If I change **one amino acid**, and it's **not destabilising**, why can't AlphaFold use its training and form a moderately confident structure? I mean, to a computer, what's the difference between a wild-type and a mutated protein? I know what you may be thinking, why don't you just **try it** yourself, and see if AlphaFold works. But if I don't have the **experimental structure** of the mutant, how on earth am I supposed to believe that this is even close to the theoretical real thing?

Maybe I'm not understanding the **main premise**, but it's something that's been bugging me for a while now. Any insight into this would be much appreciated.

**Tags:** protein-structure, mutations, modelling, deep-learning, point-mutation
> **Comments:**
> **username_1**: What happens if you mutate a Gly to Pro ??  
> **username_0**: This is a good point. I have tried this in fact, and obtained the same structure. Bizarre.  
> **username_1**: Cmon Gly to Pro where Pro can't assume Gly φ ? @jambajuice  

---

## 3 answers

### Answer by username_2 on 2022-08-25. Score: 4

Other have addressed why AlphaFold2 cannot really get the necessary signal from MSA (Multiple Sequence Alignment) and memory to properly model a variant, but I thought I'd add my observations/rants.

I have modelled *in silico* both engineering and clinical variants in the past, using PyRosetta, MD and other tools. For me, AlphaFold2 has really stood out but in a different way than the hype.

## Variants can have complicated effects

I developed a tool Venus, <https://venus.sgc.ox.ac.uk/>, which collates different pieces of information to help figure out the effect a variant was having beyond a simple ∆∆G calculation. This is because often a variant is having a variety of effects, for example a stable but catalytically dead variant can still form a complex and sequester a critical component (e.g. in [ref](https://pubmed.ncbi.nlm.nih.gov/31353023/) POLR2E-sequestering POR2A is more pathogenic than a simple truncation). This is not only true with human clinical variants, but also with engineering variants: for one protein I have worked on, DogCatcher, lowering the isoelectric point away from neutral pH was critical, not the stabilisation of the transition state ([ref](https://pubmed.ncbi.nlm.nih.gov/34324879/)). Another example is that the entrance loops in cytochromes drive selectivity more so than the active site configuration.

## The unknowns: alt conformers and complexes

I will be the first to admit that most folk want a deleterious ∆∆G value and will p-value shop for it and not catering for this is a bad idea for a publication. But in terms of mechanism, the biggest issue I face are dynamic processes involving a residue where the other active/inactive conformer is unknown or when the residue is a surface residue possible at the interface in a complex, which is known simply to exist from a coIP western. No nearby gnomAD variants or presence of post-translational sites or sequence conservation do show that something may be important but not why.

## Enter AlphaFold2

AlphaFold2, via the more user-friendly ColabFold, allow the creation of wild type monomers, homo-oligomers, but also hetero-oligomers (complexes) and alternative conformations.
These days I seen a cartoon of an interaction and I model it in quintuplicate. Sometimes nothing binds, but sometimes a complex is found 5 out of 5. The weirdest part is that it reveals some novel mechanism that makes sense that has probably never been seen before, but is beyond the scope of the experiment so one has the power to just put them aside —interfaces which are stronger phosphorylated, inhibitory domains binding in the same site but in a different way, unexpected complex arrangements etc. So the true revolutionary power of AlphaFold2 is predicting complexes and alternative conformations, which can be used to predict the mechanism of a variant.

## WIP

Complexes do not always resolve and there is a lot of discussion in terms of massaging the MSA —e.g. boney-fish+tetrapods only etc. And there is very little cross-linking mass-spec data available for validation and/or constraining. But hopefully things will get better.

## Footnote

The last paragraph sounds like an exaggeration, so I best give an example. Here is an example for the drawer from today. A target protein (catalytic domain = pale) has a regulatory terminal which inhibits in isolation (terminus = turquoise), however, when modelled with another protein (mustard) this terminal (terminus = coral) is flipped away. Thus revealing the mechanism by which the mustard protein sequesters the regulatory helix (coral), which would otherwise (turquoise) inhibit the catalytic domain (pale cyan). In the literature, this is a western blot. Where a variant of interest involved in this mechanism one state may be more energetically favourable by scoring the ∆G of the different "snapshots" (=static conformers).
[![complex](https://i.sstatic.net/xzf2h.png)](https://i.sstatic.net/xzf2h.png)

> **Comments:**
> **username_1**: What is MSA?? I dont get the footnote, I know I am dumb but whould it  be easy to reformulate for dumb people ?  
> **username_0**: This is a detailed answer, but I feel like it doesn't address the question at hand.  

---

### Answer by username_3 on 2022-08-23. Score: 1

This answer is from common knowledge rather than specific infield expertise. The question could be more expertly answered by other members.

If you really want to pursue this type of calculation, it's possible that something like `INPS-MD` *de novo* -> protein structure stability from variants will provide an answer. So maybe compare the outputs with `AlphaFold`?

Intuitively, I don't think it's cool, just my opinion. In my opinion, an experimentally derived structure is required as the basis for your calculation, where you want to consider the impact of an amino acid mutation.

It's important to separate *de novo* prediction from variant prediction. Alphafold is *de novo* prediction. What you are looking at is structure stability from a variant (see Summary). That's my core answer, these different calculations. To explain this more clearly ...

Good variant stability calculators are DUET (2014) and [INPS-MD](https://academic.oup.com/bioinformatics/article/32/16/2542/1743481?login=false) (2016), amongst others. Just to reiterate, a good starting point is an experimentally derived protein structure.

The webservers are here:

* <http://structure.bioc.cam.ac.uk/duet>
* <https://inpsmd.biocomp.unibo.it/inpsSuite>

I personally don't know `Alphafold` but understand its predecessors which accommodate convolutions by leveraging deep-learning. The basis of the predecessor's calculations firstly are *de novo* prediction and secondly can miss the impact of variants if they are not directly involved in the core-algorithm. It's about identifying which amino acids are interacting considering all available homologous amino acids within a database. The calculation can predict local interactions with extreme accuracy, but it's a prediction. It will also depend on whether there are single amino acid changes or two- or more amino acid changes (they could compensate each other, particularly for a natural variant).

In contrast, DUET and INPS-MD, amongst a lot of other similar programs, consider the property shift of an individual amino acid in relation to its immediate local stereo-chemical environment measured by a shift of free-energy.

Essentially, these are two sets of very different calculations.

**Summary** The problem I intuitively see is if *de novo* prediction in any form is used, okay they are very good, but they're just predictions. Variant structural predictions are also just predictions which are very widely respected. Stacking *de novo* prediction onto predicting protein structure stability resulting from variants has scope for error, especially if there was some hidden non-linear effect.

There could be additional issues because the *de novo* calculations immediately prior to `Alphafold` don't predict the precise local distances.

---

`AlphaFold` would simply respond

> 'it's not been trained'

They'd want a wet-lab experimental dataset with wild-type vs. variants, if these exist with sufficient abundance to prevent over-training. It would be a very skewed dataset so of limited application in my personal opinion.


---

### Answer by username_4 on 2022-08-24. Score: 1

As marcin points out in the comments: by "AphaFold (AF) has not been trained to predict structural consequences of point mutations" it is meant "AF is not able to tell you whether your point mutation is destabilizing or not, and if it is destabilizing then the structure it predicts will most probably be wrong".

It is totally possible that AF predicts the structure of a protein well when only non destabilizing mutations are introduced. As pointed out in the paragraph marcin quotes, however: "AlphaFold has not been validated for predicting the effect of mutations." And as long as we don't know, we should be warry of any of AlphaFold predictions with point mutations. Maybe it works, maybe it doesn't, or maybe it's case dependent.


---

