I am interested in finding out the effect of single random mutation in the core of protein on the packing of the core. For example, I have 100 aa protein. Now, if I mutate any single residue, which is in the core, to all possible residues, I want to predict how these each mutations are altering the packing. What is the best way to go on for this? I am an experimental researcher and would appreciate your contribution about the computational tools, which can be used for such studies.
You can try Fold-X, which will give a measure of the stability change upon mutation. The program is relatively fast, although not fast enough to do a 100^20 mutation matrix.
http://foldx.crg.es/manual1.jsp
http://www.ncbi.nlm.nih.gov/pubmed/17482644
This method has the advantage of breaking the effect of the mutation down into easily understandable physical interaction terms such as increases in van der waal clashes or alterations in the hydrophobic effect from changes in buried surface area. Another way, similar to the paper above, is to see how often a given amino acid appears in that position in a series of structurally related proteins. You can see a comparison of the two techniques in this paper in the context of full protein design in this paper (called physics based model (PBM) and evolutionary based model (EBM) in the paper)
https://www.researchgate.net/publication/258031760_An_Evolution-Based_Approach_to_De_Novo_Protein_Design_and_Case_Study_on_Mycobacterium_tuberculosis
A third way, and probably the most accurate for single mutations, is to make a prediction for stability based on known data for single mutations from WT. There are several servers for this
http://folding.biofold.org/i-mutant/i-mutant2.0.html
http://babylone.ulb.ac.be/popmusic/
For single mutations this is probably the most accurate way. I am guessing the Popmusic systematic test may be what you want.
Note that the actual calculation of del del G is not particularly quantitative
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3289912/?report=classic
http://bioinformatics.oxfordjournals.org/content/25/19/2537.long
Article An Evolution-Based Approach to De Novo Protein Design and Ca...
You can try Fold-X, which will give a measure of the stability change upon mutation. The program is relatively fast, although not fast enough to do a 100^20 mutation matrix.
http://foldx.crg.es/manual1.jsp
http://www.ncbi.nlm.nih.gov/pubmed/17482644
This method has the advantage of breaking the effect of the mutation down into easily understandable physical interaction terms such as increases in van der waal clashes or alterations in the hydrophobic effect from changes in buried surface area. Another way, similar to the paper above, is to see how often a given amino acid appears in that position in a series of structurally related proteins. You can see a comparison of the two techniques in this paper in the context of full protein design in this paper (called physics based model (PBM) and evolutionary based model (EBM) in the paper)
https://www.researchgate.net/publication/258031760_An_Evolution-Based_Approach_to_De_Novo_Protein_Design_and_Case_Study_on_Mycobacterium_tuberculosis
A third way, and probably the most accurate for single mutations, is to make a prediction for stability based on known data for single mutations from WT. There are several servers for this
http://folding.biofold.org/i-mutant/i-mutant2.0.html
http://babylone.ulb.ac.be/popmusic/
For single mutations this is probably the most accurate way. I am guessing the Popmusic systematic test may be what you want.
Note that the actual calculation of del del G is not particularly quantitative
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3289912/?report=classic
http://bioinformatics.oxfordjournals.org/content/25/19/2537.long
Article An Evolution-Based Approach to De Novo Protein Design and Ca...
I have same issue for point mutation in coding region. I check the effect of AA changes by MuPRO and SIFT. I used Phyre 2, PSIPRED, SWISS-MODEL, HHCCEE, for prediction of 2d and 3d structure but I don't know how to interpret the result. If anybody help I appreciate.
- Badges
- Science topic
- Similar topics
- Biological Science
- Molecular Biology
- Biomolecules
- Proteins