Body: <p><em>Disclaimer: I am a machine learning researcher working on network science and want to use proteins to test an algorithm of mine using real-world data. My bioinformatics knowledge is minimal.</em></p>
  <p>I need to create a dataset of <a href="https://en.wikipedia.org/wiki/Protein_contact_map" rel="nofollow noreferrer">contact maps</a>. Namely, for a given set of proteins, I want to find the set of binary matrices that represent each of their contact maps. I understand that this binary matrix is obtained by thresholding the distances between atoms with some criteria. Thus, it would also be helpful to find the set of distance matrices for these proteins (I can just threshold them afterward).</p>
  <p>For computational reasons, I would prefer proteins that are as small as possible. Namely, proteins that have the fewest amount of amino acids. Also, I need the proteins to be homologous since I want to be able to do a Multiple Sequence Alignment (MSA) to them and analyze the matrix returned by the MSA with my machine learning algorithm.</p>
  <p><strong>So my questions is: given a reference protein (let's say <a href="https://www.uniprot.org/uniprotkb/P07947/entry" rel="nofollow noreferrer">YES_HUMAN</a> for example), where can I find a list of homologous proteins and the contact maps for all of them?</strong></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-22T16:34:48.683Z
Comments.Text: Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.
Comments.UserDisplayName: Community
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-23T14:57:46.300Z
Comments.Text: Finding homologs is easy enough, there are many databases of homologous sequences, you can get this from Ensembl or similar places. I would instead focus this question on getting the contact maps since that is something I personally have no idea where to find. Once you've figured out how to get a set of contact maps, then selecting the homologous sequences won't be hard.
Comments.UserDisplayName: terdon
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-23T22:19:11.557Z
Comments.Text: @terdon As far as I understand, contact maps can be computed (or estimated) by thresholding the distances between the amino acids that form the sequence. Even if you don't know where to obtain the contact maps directly, would you know where I can find a dataset of proteins with the positions of the amino acids (i.e., of their $C_\alpha$ atoms)?
Comments.UserDisplayName: Tendero
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-25T09:31:14.213Z
Comments.Text: @Tendero The Protein Data Bank has both experimentally determined, and computationally predicted 3D models of proteins https://www.rcsb.org/
Comments.UserDisplayName: Pallie
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-25T21:34:44.310Z
Comments.Text: according to wikipedia The average size of a protein increases from Archaea to Bacteria to Eukaryote (283, 311, 438 residues and 31, 34, 49 kDa respectively so your yes_human with 543 is not that small , or is it ?
Comments.UserDisplayName: pippo1980
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-25T21:57:05.820Z
Comments.Text: try https://ngdc.cncb.ac.cn/hgd/gene and browse for yes_human
Comments.UserDisplayName: pippo1980
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-25T21:59:11.590Z
Comments.Text: https://ngdc.cncb.ac.cn/hgd/gene?type=&keyword=P07947%20
Comments.UserDisplayName: pippo1980
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-25T22:22:43.310Z
Comments.Text: @pippo1980 In Table 1 from [this paper](https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0028766&type=printable) it says that YES_HUMAN has a length of 48. What does that mean then?
Comments.UserDisplayName: Tendero
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-25T22:45:30.973Z
Comments.Text: https://www.uniprot.org/uniprotkb/P07947/entry#sequences
Comments.UserDisplayName: pippo1980
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-25T23:00:30.050Z
Comments.Text: @Tendero Crystallographic structure of the SH3 domain of the human c-Yes tyrosine kinase: Loop flexibility and amyloid aggregation. https://www.rcsb.org/structure/2HDA
Comments.UserDisplayName: pippo1980
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-25T23:01:09.597Z
Comments.Text: https://en.m.wikipedia.org/wiki/SH3_domain
Comments.UserDisplayName: pippo1980
Tags: <phylogenetics><proteins><protein-structure><machine-learning><multiple-sequence-alignment>
Title: How to create a dataset of contact maps of homologous proteins?
ThreadText: # How to create a dataset of contact maps of homologous proteins?
  
  (Asked by: username_0 on 2024-03-22)
  
  *Disclaimer: I am a machine learning researcher working on network science and want to use proteins to test an algorithm of mine using real-world data. My bioinformatics knowledge is minimal.*
  
  I need to create a dataset of [contact maps](https://en.wikipedia.org/wiki/Protein_contact_map). Namely, for a given set of proteins, I want to find the set of binary matrices that represent each of their contact maps. I understand that this binary matrix is obtained by thresholding the distances between atoms with some criteria. Thus, it would also be helpful to find the set of distance matrices for these proteins (I can just threshold them afterward).
  
  For computational reasons, I would prefer proteins that are as small as possible. Namely, proteins that have the fewest amount of amino acids. Also, I need the proteins to be homologous since I want to be able to do a Multiple Sequence Alignment (MSA) to them and analyze the matrix returned by the MSA with my machine learning algorithm.
  
  **So my questions is: given a reference protein (let's say [YES\_HUMAN](https://www.uniprot.org/uniprotkb/P07947/entry) for example), where can I find a list of homologous proteins and the contact maps for all of them?**
  
  **Tags:** phylogenetics, proteins, protein-structure, machine-learning, multiple-sequence-alignment
  
  > **Comments:**
  > **username_1**: Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.  
  > **username_2**: Finding homologs is easy enough, there are many databases of homologous sequences, you can get this from Ensembl or similar places. I would instead focus this question on getting the contact maps since that is something I personally have no idea where to find. Once you've figured out how to get a set of contact maps, then selecting the homologous sequences won't be hard.  
  > **username_0**: @username_2 As far as I understand, contact maps can be computed (or estimated) by thresholding the distances between the amino acids that form the sequence. Even if you don't know where to obtain the contact maps directly, would you know where I can find a dataset of proteins with the positions of the amino acids (i.e., of their $C_\alpha$ atoms)?  
  > **username_3**: @username_0 The Protein Data Bank has both experimentally determined, and computationally predicted 3D models of proteins https://www.rcsb.org/  
  > **username_4**: according to wikipedia The average size of a protein increases from Archaea to Bacteria to Eukaryote (283, 311, 438 residues and 31, 34, 49 kDa respectively so your yes_human with 543 is not that small , or is it ?  
  > **username_4**: try https://ngdc.cncb.ac.cn/hgd/gene and browse for yes_human  
  > **username_4**: https://ngdc.cncb.ac.cn/hgd/gene?type=&keyword=P07947%20  
  > **username_0**: @username_4 In Table 1 from [this paper](https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0028766&type=printable) it says that YES_HUMAN has a length of 48. What does that mean then?  
  > **username_4**: https://www.uniprot.org/uniprotkb/P07947/entry#sequences  
  > **username_4**: @username_0 Crystallographic structure of the SH3 domain of the human c-Yes tyrosine kinase: Loop flexibility and amyloid aggregation. https://www.rcsb.org/structure/2HDA  
  > **username_4**: https://en.m.wikipedia.org/wiki/SH3_domain  
  
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  ## 0 answers
  
  