Answers.Body: <p>If you have information on which part of the protein are interacting, and its sequence, you can search for homologous or similar sequences in other proteins. This would give you a list of potential targets and probably a similarity score which you can also use. Then you would have something to run simulations on. </p>
  
  <p>And I think that with 3D structure you should be able to run simulations <em>in silico</em> in this case (eventhough it is probably more common to look for the ligands to the protein, not proteins to the ligand). </p>
  
  <p><em>In silico</em> simulations are computationally intensive, but I´ve also seen some being run on a laptop, so you don´t necessarily need access to HPC-clusters, or supercomputer. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-25T07:43:10.677Z
Answers.Text: This sounds like something you could possibly do, but is it safe to assume that the evolution of interactions is way faster than the sequence evolution? Is there any described method or paper using the approach you suggested?
Answers.UserDisplayName: Kamil S Jaron
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Answers.CreationDate: 2019-03-25T10:16:45.940Z
Answers.Text: @Kamil S Jaron And why we want to assume that?
Answers.UserDisplayName: Oka
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-25T10:43:47.803Z
Answers.Text: You can find information in uniprot.org database (on structure, interactions, domains and motifs and similarity) and it also have supporting data on the data sources and literature citations.
Answers.UserDisplayName: Oka
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Answers.CreationDate: 2019-03-24T21:22:58.737Z
Answers.LastActivityDate: 2019-03-25T11:29:04.333Z
Answers.LastEditDate: 2019-03-25T11:29:04.333Z
Answers.UserDisplayName: Oka
Body: <p>Given a known interaction of a molecule with some proteases (having X-rays images too) is it possible to predict interactions with other proteins either using their 3D structure or not? </p>
  
  <p>If anyone has already tried such techniques I would like to hear a little brief on that subject. Which are the possible techniques, computational power needed and so on.</p>
  
Tags: <protein-structure><interactions><drugs>
Title: Finding new drug-protein targets/interactions using a known one as base
ThreadText: # Finding new drug-protein targets/interactions using a known one as base
  
  (Asked by: username_0 on 2018-04-30)
  
  Given a known interaction of a molecule with some proteases (having X-rays images too) is it possible to predict interactions with other proteins either using their 3D structure or not?
  
  If anyone has already tried such techniques I would like to hear a little brief on that subject. Which are the possible techniques, computational power needed and so on.
  
  **Tags:** protein-structure, interactions, drugs
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-03-24. Score: 1
  
  If you have information on which part of the protein are interacting, and its sequence, you can search for homologous or similar sequences in other proteins. This would give you a list of potential targets and probably a similarity score which you can also use. Then you would have something to run simulations on.
  
  And I think that with 3D structure you should be able to run simulations *in silico* in this case (eventhough it is probably more common to look for the ligands to the protein, not proteins to the ligand).
  
  *In silico* simulations are computationally intensive, but I´ve also seen some being run on a laptop, so you don´t necessarily need access to HPC-clusters, or supercomputer.
  
  > **Comments:**
  > **username_2**: This sounds like something you could possibly do, but is it safe to assume that the evolution of interactions is way faster than the sequence evolution? Is there any described method or paper using the approach you suggested?  
  > **username_1**: @username_2 And why we want to assume that?  
  > **username_1**: You can find information in uniprot.org database (on structure, interactions, domains and motifs and similarity) and it also have supporting data on the data sources and literature citations.  
  
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