The number of repeats and the unique combination of Actions you should run when making mutations to your protein will depend on two factors. First, how large will your following experimental tests be; how many sequences are you looking to generate? Second, what level of protein optimization are you working at; how many mutations do you intend to make? Consider which parameters and scenarios below are the best fit for you:
SIZE OF EXPERIMENTAL TEST:
- Small: a few sequences
- Medium: hundreds of sequences
- Large: thousands or millions of sequences
LEVEL OF OPTIMIZATION:
- Point mutations: You are looking for small changes, just single point mutations in each tested sequence. For example when the protein you are working with is fairly well behaved or already performs well in your assay.
- Multiple mutations: You are looking for many changes, multiple mutations at a time per tested gene sequence. For example when the protein you are working with has little or no activity.
Below are several recommended courses of action based on the parameters described above:
1.) Small test for point mutations:
For a small sized test of point mutations use the DDG tool. Instead of using the DDG tool directly you may also run Design first with a very tight filter on total score to identify mutations and then test these mutations in DDG. For information on how to use DDG click here.
You may need to download the Design results into a sequence viewing tool to identify the common mutations. For additional workflows on analyzing sequences from Design results click here.
2.) Large test for many mutations (library design):
For a large sized test of many mutations alternate between runs of Design and Minimize to generate many sequences in batches. These sequences can then be downloaded collection by collection for sequence analysis to identify common mutations at each position. These can then be generated by degenerate codons to create large library sizes.
3.) Small test for many mutations:
For a small sized test of many mutations alternate between runs of Design and Minimize to generate greater sequence diversity. Then you may consider two different approaches. One option is to filter structures stringently by total score and only select a few of the best scored sequences to order.
A second option is to download many of the sequences and generate a phylogenetic tree in a sequence analysis package. This will allow you to pick the most “representative” sequence from each branch of the tree. This approach is a way to sample lots of diversity but only test a relatively small number of sequences.