A resource for comparing AF-Cluster and other AlphaFold2 sampling methods

We are excited that Porter et al. have explored [1-3] the AF-Cluster [4] algorithm - this is critical for the field to advance. Increasingly many methods have been reported for perturbing and sampling AlphaFold2 (AF2) [5]. If multiple methods achieve similar results, that does not in itself invalidate any method, nor does it answer why these methods work. To help the field continue to try to answer these questions, we wish to highlight a few discrepancies between the AF-Cluster method as presented originally in our work [4] and the subsequent discussion in refs. [1-3]. We hope that this short work clarifies potential misunderstandings. Ref. [3] contains calculations that question the reproducibility of our reported predictions in [4]. Critically, we could only reproduce the calculations in [3] by using different AF2 settings. Therefore, those results cannot be directly compared with results in our paper. Given the different settings used, we felt the strong need to present further controls in this response to contextualize [3]'s calculations and show that our original conclusions are robust to several parameters. We have created a more user-friendly Colab notebook that now integrates the AF-Cluster sequence clustering step with other AF2 sampling methods, enabling the community to more readily compare predictions from these different methods. ### Competing Interest Statement D.K. is a co-founder of Relay Therapeutics and MOMA Therapeutics. The remaining authors declare no competing interests.