<p>Just to add to Maximilian Press's answer. I happen to know about human immunology and I think Maximilian Press has a good idea.</p>
<p>Genetic studies and immunological studies <em>per se</em> in cellular immunology  tend to be restricted within humans. There are limited exceptions to this namely chimpanzees and humanised mammals, most notably humanised mice. In cellular immunology these mammals retain immunologically distinct behaviour but are sufficiently similar to be useful models.</p>
<p>The protein in question is a B-cell repressor, thats more complicated ...</p>
<p>However, using trans-species homology of the protein of interest for</p>
<ul>
<li>chimpanzees</li>
<li>humanised mice</li>
<li>regular mice</li>
</ul>
<p>.. represents a good diversity of proteins to use what appears to be a deep-learning function prediction if this was a traditional cellular immunological study. Any immunologist looking at the biological rationale would recognise the model systems deployed.</p>
<p><strong>Method: Blast</strong> Generating diversity for given protein is easy, except for humans. Just use online Blast and restrict the search to the species of interest and 20000 hits (?I forget the upper limit) is the maximum and select for more diverse genes. This would work for chimps and (possibly) mice. Thus blast a human gene and restrict the output to the species of interest. Human gene diversity - this much bigger than an online blast could be targeted in accordance with human genetic diversity. The cellular immune system for humans does not conform to background diversity <em>in general</em>. Some geographic human populations do have a HLA predominance but there are no absolute association.</p>
<p>The final option is local blasting the human database for that gene, that is doable and objectively used to derive representative diversity - for this purpose is complicated and would advise against it.</p>

CC BY-SA 4.0
2023-06-08T06:08:21.010Z
thanks @M__ . Even if you define your answer as an "addition" to the first one, it was what I was looking for (a general explanation about the database).
HelpNeederStudent
CC BY-SA 4.0
2023-06-08T11:58:16.323Z
Thanks for your feedback @HelpNeederStudent
M__
CC BY-SA 4.0
2023-06-07T17:33:15.610Z
2023-06-07T18:20:10.973Z
2023-06-07T18:20:10.973Z
M__
<p>I'm going to start by assuming that you don't actually want 10 protein sequences from the same species, even though you write this in your comment on M__'s (deleted) answer. This will not be a very informative sample of variation in a given protein, except for extreme examples like antibodies.</p>
<p>To get a good sample of variation you almost certainly want to look across species. The paper that you link to does this by using the UniRef database, they then cluster proteins at the 90% similarity threshold. That is well beyond the variation that you'd expect within a species. See their section &quot;Training datasets&quot;:</p>
<blockquote>
<p>We train our masked language models on a large corpus of protein sequences, UniRef90 (Suzek et al., 2007), retrieved in July 2018. This dataset contains 76,215,872 protein sequences filtered to 90% sequence identity.</p>
</blockquote>
<p>You could use an approach like that in the paper to get similar-ish clusters of proteins whose variation you can then analyze. You can use this, but I am somewhat suspicious of that, without reading the paper in too much depth, because we know rather a lot about orthology/paralogy and how it affects function in these cases. Especially in these very elaborate not very introspectable models I worry about what they will do with inadequately curated data.</p>
<p>So instead I'd probably want to first identify the <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1832-y" rel="nofollow noreferrer">orthology groups</a> (gene families) of interest. These are generally manually curated groupings of proteins.</p>
<p>Once you have the orthology groups that you care about, you can use a database such as <a href="https://www.google.com/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=&amp;cad=rja&amp;uact=8&amp;ved=2ahUKEwjAz-_Tr7D_AhVMAzQIHRULDHYQFnoECA0QAQ&amp;url=https%3A%2F%2Fwww.genome.jp%2Fkegg%2Fko.html&amp;usg=AOvVaw2_1ohJtdx9xARgBfI0LjJv" rel="nofollow noreferrer">KEGG</a> or <a href="https://omabrowser.org/oma/home/" rel="nofollow noreferrer">OMA</a> to identify the specific orthologs (genes) that belong to the group. Then you will have your dataset.</p>

CC BY-SA 4.0
2023-06-08T06:13:20.837Z
Thank you Maximilian for your explanation about the paper. However, if one wants to search for motifs, the dataset should be 10 protein sequences from the same species, right ? Even though, as @M___ has explained, "the cellular immune system for humans does not conform to background diversity in general. Some geographic human populations do have a HLA predominance but there are no absolute association".
HelpNeederStudent
CC BY-SA 4.0
2023-06-08T15:19:20.533Z
@HelpNeederStudent I am not sure why you would need them to be from the same species. You will have to clarify that. The whole idea of a gene family or orthology group is that it's a bunch of curated genes/proteins that have the same essential biological function. The people in your paper certainly aren't concerned with species of origin! You could still theoretically do it within a species, I'm just skeptical that you'll find a level of variation that you can do any work with (my professional though non-expert opinion).
Maximilian Press
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2023-06-08T15:48:24.870Z
Thank you very much !
HelpNeederStudent
CC BY-SA 4.0
2023-06-07T05:08:35.613Z
2023-06-07T05:08:35.613Z
Maximilian Press
<p>My question is exactly the one in the title. I add a particular example for more clarity. If I want to study the sequence of protein <a href="https://en.wikipedia.org/wiki/I%CE%BAB%CE%B1" rel="nofollow noreferrer">IkBα</a>, do I find only one sequence or is there a database that contains many sequences from many human host species ?</p>
<p>EDIT: The analysis that I am going to do is to use NLP models to apply it to protein sequences to extrapolate, for instance, protein function knowledge.</p>
<p>Here is a reference of a research paper <a href="https://www.sciencedirect.com/science/article/pii/S2405471221002039" rel="nofollow noreferrer">Learning the protein language: Evolution, structure, and function</a> that considers this application by taking into account the problem of low number of sequences data.</p>
<p>So I think I should have at least 10 protein sequences.</p>
<p>Thank you in advance.</p>

CC BY-SA 4.0
2023-06-07T13:24:28.490Z
@M__ of course, I am going to read Maximillian's response and to answer !
HelpNeederStudent
<proteins><sequence-analysis><public-databases><public-dataset><deep-learning>
How many protein sequences do we usually have for each species?
# How many protein sequences do we usually have for each species?

(Asked by: username_0 on 2023-06-04)

My question is exactly the one in the title. I add a particular example for more clarity. If I want to study the sequence of protein [IkBα](https://en.wikipedia.org/wiki/I%CE%BAB%CE%B1), do I find only one sequence or is there a database that contains many sequences from many human host species ?

EDIT: The analysis that I am going to do is to use NLP models to apply it to protein sequences to extrapolate, for instance, protein function knowledge.

Here is a reference of a research paper [Learning the protein language: Evolution, structure, and function](https://www.sciencedirect.com/science/article/pii/S2405471221002039) that considers this application by taking into account the problem of low number of sequences data.

So I think I should have at least 10 protein sequences.

Thank you in advance.

**Tags:** proteins, sequence-analysis, public-databases, public-dataset, deep-learning
> **Comments:**
> **username_0**: @M__ of course, I am going to read Maximillian's response and to answer !  

---

## 2 answers

### Answer by username_1 on 2023-06-07. Score: 1 | Accepted answer

Just to add to Maximilian Press's answer. I happen to know about human immunology and I think Maximilian Press has a good idea.

Genetic studies and immunological studies *per se* in cellular immunology tend to be restricted within humans. There are limited exceptions to this namely chimpanzees and humanised mammals, most notably humanised mice. In cellular immunology these mammals retain immunologically distinct behaviour but are sufficiently similar to be useful models.

The protein in question is a B-cell repressor, thats more complicated ...

However, using trans-species homology of the protein of interest for

* chimpanzees
* humanised mice
* regular mice

.. represents a good diversity of proteins to use what appears to be a deep-learning function prediction if this was a traditional cellular immunological study. Any immunologist looking at the biological rationale would recognise the model systems deployed.

**Method: Blast** Generating diversity for given protein is easy, except for humans. Just use online Blast and restrict the search to the species of interest and 20000 hits (?I forget the upper limit) is the maximum and select for more diverse genes. This would work for chimps and (possibly) mice. Thus blast a human gene and restrict the output to the species of interest. Human gene diversity - this much bigger than an online blast could be targeted in accordance with human genetic diversity. The cellular immune system for humans does not conform to background diversity *in general*. Some geographic human populations do have a HLA predominance but there are no absolute association.

The final option is local blasting the human database for that gene, that is doable and objectively used to derive representative diversity - for this purpose is complicated and would advise against it.

> **Comments:**
> **username_0**: thanks @M__ . Even if you define your answer as an "addition" to the first one, it was what I was looking for (a general explanation about the database).  
> **username_1**: Thanks for your feedback @HelpNeederStudent  

---

### Answer by username_2 on 2023-06-07. Score: 2

I'm going to start by assuming that you don't actually want 10 protein sequences from the same species, even though you write this in your comment on M\_\_'s (deleted) answer. This will not be a very informative sample of variation in a given protein, except for extreme examples like antibodies.

To get a good sample of variation you almost certainly want to look across species. The paper that you link to does this by using the UniRef database, they then cluster proteins at the 90% similarity threshold. That is well beyond the variation that you'd expect within a species. See their section "Training datasets":

> We train our masked language models on a large corpus of protein sequences, UniRef90 (Suzek et al., 2007), retrieved in July 2018. This dataset contains 76,215,872 protein sequences filtered to 90% sequence identity.

You could use an approach like that in the paper to get similar-ish clusters of proteins whose variation you can then analyze. You can use this, but I am somewhat suspicious of that, without reading the paper in too much depth, because we know rather a lot about orthology/paralogy and how it affects function in these cases. Especially in these very elaborate not very introspectable models I worry about what they will do with inadequately curated data.

So instead I'd probably want to first identify the [orthology groups](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1832-y) (gene families) of interest. These are generally manually curated groupings of proteins.

Once you have the orthology groups that you care about, you can use a database such as [KEGG](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjAz-_Tr7D_AhVMAzQIHRULDHYQFnoECA0QAQ&url=https%3A%2F%2Fwww.genome.jp%2Fkegg%2Fko.html&usg=AOvVaw2_1ohJtdx9xARgBfI0LjJv) or [OMA](https://omabrowser.org/oma/home/) to identify the specific orthologs (genes) that belong to the group. Then you will have your dataset.

> **Comments:**
> **username_0**: Thank you Maximilian for your explanation about the paper. However, if one wants to search for motifs, the dataset should be 10 protein sequences from the same species, right ? Even though, as @M___ has explained, "the cellular immune system for humans does not conform to background diversity in general. Some geographic human populations do have a HLA predominance but there are no absolute association".  
> **username_2**: @HelpNeederStudent I am not sure why you would need them to be from the same species. You will have to clarify that. The whole idea of a gene family or orthology group is that it's a bunch of curated genes/proteins that have the same essential biological function. The people in your paper certainly aren't concerned with species of origin! You could still theoretically do it within a species, I'm just skeptical that you'll find a level of variation that you can do any work with (my professional though non-expert opinion).  
> **username_0**: Thank you very much !  

---

