Predicting protein fold using known stracture
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16 months ago
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Hi,

I have a beta lactamase gene that I would like to construct into a 3D stracture.

There are prevouos 3D stractures available for similar proteins.

I was looking for a way to use the 3D to predict the differences caused bySNPs, and there is so much information out there that I am not sure what the right tool is to implement.

Will https://salilab.org/modeller/ be a good tool for this?

Recommnandations will be highly appreciated

protein modeller prediction 3D • 1.0k views
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Check out alphafold. https://alphafold.ebi.ac.uk/

If you cannot find it there you can try modelling with Rosetta. https://www.rosettacommons.org/software/servers

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Modelling software alone is unlikely to show you specifically the effect of a given mutation. You'd need to perform some sort of molecular dynamics simulation with the mutated structures to see what effect this has on stability, bond formation etc.

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Likely, the interaction with beta-lactamase antibiotics or inhibitors is of interest, e.g. in this structure https://www.rcsb.org/structure/1llb If the variants affect the protein stoichiometry they might also affect binding. Docking simulation with AutoDock might be in order.

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It depends on what is the quantification of "similar" here. In principle, you could use Modeller to generate mutant proteins from a single wild-type, however, to generate the wild-type itself you could use tools like AlphaFold2 where you need not specify a set of "similar" references. You could either install AlphaFold2 on your local machine if you have enough resources, if you are someone working remotely with limited access to resources then you could use an already created Google colab page - here you paste your protein sequence and follow the steps to get structure predictions

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They only vary by a few SNPs (less than 10)

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First, filter the variants by their effect, using e.g. snpEff, or just translate the CDS if it is bacterial. Remove synonymous variants and frameshift, early stop codon, and other non-sense variants, and keep only non-synonymous coding variants. Check if you have a good experimental template in PDB, optimally from the same species (are you working with E. coli?), e.g.: https://www.rcsb.org/structure/1BTL . If that is the case, I would use modeller. Try different tools if unsure.

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