sheet_fraction and helix_fraction

I have a question I’d like to ask,thank you.
When designing binders against a target, among 600 protein backbones, 591 of them have a sheet_fraction of 0, and the remaining 9 protein backbones that do have a non-zero sheet_fraction also show relatively low sheet content. The structures are dominated by helices. Among 300 protein backbones, every single one contains both helices and loops.
I used the parameters inference_sampler.step_scale=3, inference_sampler.gamma_0=0.2, and is_non_loopy: true. Is the fact that 591 out of 600 protein backbones have a sheet_fraction of 0 related to the parameters I set above, or is it related to the specific target and hotspots?

Before setting these parameters, I tested the generation of 10 protein backbones. In all three groups of parameters I tried, is_non_loopy: true was enabled.

• Group 1: inference_sampler.step_scale=3 and inference_sampler.gamma_0=0.2
• Group 2: inference_sampler.step_scale=1.5 and inference_sampler.gamma_0=6 (the model’s default values)
• Group 3: inference_sampler.step_scale=2.25 and inference_sampler.gamma_0=0.4

In all cases the results still showed a high helix content and extremely low sheet content. I would like to know whether this is caused by the way I set the parameters. Thank you.

The issue names inference_sampler.step_scale, inference_sampler.gamma_0, and is_non_loopy, and compares three parameter groups. Start by tracing where these settings are consumed in the generation pipeline, then compare the listed configurations while accounting for the target and hotspots. Done means determining whether the helix-heavy results are parameter-driven or target-dependent.