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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 its 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 its 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.

Its 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). Thats my core answer, these different calculations. To look to explain this more clearly ...

Good variant stability calculations are DUET (2014) and INPS-MD (2016), amongst others. Just to reiterate a good starting point is an experimentally derived protein structure.

The webserves are here,

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

I personally don't know Alphafold but understand its predecessor's 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. Its about identifying which amino acids are interacting considering all available homologous amino acid within a database. The calculation can be predict local interactions with extreme accuracy, but its a prediction. It will also depend on whether there are a single amino acid change or a 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 and 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 then used, okay they are very good, but they're just predictions. Variant structural prediction 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 doesn't predict the precise local distances.

AlphaFold would is simply respond

'its not been trained'

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

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 (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.

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 its 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 its 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.

Its 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). Thats my core answer, these different calculations. To look to explain this more clearly ...

Good variant stability calculations are DUET (2014) and INPS-MD (2016), amongst others. Just to reiterate a good starting point is an experimentally derived protein structure.

The webserves are here,

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

I personally don't know Alphafold but understand its predecessor's 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. Its about identifying which amino acids are interacting considering all available homologous amino acid within a database. The calculation can be predict local interactions with extreme accuracy, but its a prediction. It will also depend on whether there are a single amino acid change or a 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 and 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 then used, okay they are very good, but they're just predictions. Variant structural prediction 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 doesn't predict the precise local distances.

AlphaFold would is simply respond

'its not been trained'

They'd want a wet-lab experimental dataset with wild-type vs. variants, if these exist with sufficient abundance to prevent overtraining. It would be a very skewed dataset.

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 its 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 its 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.

Its 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). Thats my core answer, these different calculations. To look to explain this more clearly ...

Good variant stability calculations are DUET (2014) and INPS-MD (2016), amongst others. Just to reiterate a good starting point is an experimentally derived protein structure.

The webserves are here,

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

I personally don't know Alphafold but understand its predecessor's 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. Its about identifying which amino acids are interacting considering all available homologous amino acid within a database. The calculation can be predict local interactions with extreme accuracy, but its a prediction. It will also depend on whether there are a single amino acid change or a 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 and 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 then used, okay they are very good, but they're just predictions. Variant structural prediction 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 doesn't predict the precise local distances.

AlphaFold would is simply respond

'its not been trained'

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

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 its 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 its 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.

Its 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). Thats my core answer, these different calculations. To look to explain this more clearly ...

The applications you want for variant stability calculations are DUET (2014) and INPS-MD (2016), amongst others. Just to reiterate a good starting point is an experimentally derived protein structure.

The webserves are here,

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

I personally don't know Alphafold but understand its predecessor's 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. Its about identifying which amino acids are interacting considering all available homologous amino acid within a database. The calculation can be predict local interactions with extreme accuracy, but its a prediction. It will also depend on whether there are a single amino acid change or a 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 and 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 then used, okay they are very good, but they're just predictions. Variant structural prediction 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 doesn't predict the precise local distances.

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 its 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 its 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.

Its 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). Thats my core answer, these different calculations. To look to explain this more clearly ...

Good variant stability calculations are DUET (2014) and INPS-MD (2016), amongst others. Just to reiterate a good starting point is an experimentally derived protein structure.

The webserves are here,

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

I personally don't know Alphafold but understand its predecessor's 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. Its about identifying which amino acids are interacting considering all available homologous amino acid within a database. The calculation can be predict local interactions with extreme accuracy, but its a prediction. It will also depend on whether there are a single amino acid change or a 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 and 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 then used, okay they are very good, but they're just predictions. Variant structural prediction 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 doesn't predict the precise local distances.

AlphaFold would is simply respond

'its not been trained'

They'd want a wet-lab experimental dataset with wild-type vs. variants, if these exist with sufficient abundance to prevent overtraining. It would be a very skewed dataset.