[Market strategy] G's or no G's in tetraloops? (Refining the lab results)

Strategy for refining lab results after first round. This is not mine idea, this is what we players sometimes do with the best designs, when we get the results back from round 1 or later. We sometimes just remove G’s in the tetraloops of a highscoring design or add G’s to them, to see if we can improve the score. This have shown to have a potential for a rather large change in score, in positive direction.

Designs without G’s in the tetraloop, seem to have a score advantage among highscoring designs. They also have a “dark side”. For the absence of them to work, the design needs to be strong, stabile and in balance with itselves. Else removing the G’s in the tetraloops , will only reveal any weakness hidden in the design. Which is why Mat mostly gets away with his non-G tetraloops as he is a brilliant designer. Only in a deadsure good design, removing G’s in tetraloops is good idea.

So this strategy will not end up being a highscoring market strategy, but it has the potential of greatly improving the score of a single design and making winners of the highscoring designs.

This could be made into two different strategies for what to do with highscoring designs on 90% correct folding and over:

Strategy 1) Remove G’s in tetraloops. Any designs with G’s in the tetraloops (or anything else besides A’s) should be removed. Then re-synthesise these designs, to find the highest scoring design among them.

Extra use for this strategy:
This strategy could also be used to pick out which of the highscoring designs is already stabile and resting in them selves. And thereby worth to work with.

Strategy 2) It will be usefull if the opposite strategy is tested as well, to see if there is an positive effect of adding G’s in tetraloops. Sometimes this was also the case in earlier labs.

But those two strategies should be kept seperate, so they don’t even out each other, then the market strategy will have no value.

This is not a regular market strategy, as there is no penalizing. It is only interested in creating sure winners out of already good designs. Which can be usefull in cases, where there is a need for a lab round two.

Dear Eli,

Your strategy has been added to our implementation queue with task id 60. You can check the schedule of the implementation here.

As you may already know, our team is focusing on compiling previously submitted strategies right now so it’s hard for us to tell the ETA for this strategy. We’ll definitely update this post once we have more information - and we’re sorry about the continued delays in implementations.

EteRNA team