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. 2022 Oct 12;13:6028. doi: 10.1038/s41467-022-33729-4

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — Starting from a node (subcomplex) a new node is selected based on the previously backpropagated scores. From this node, a random node is added (expansion). A complete assembly process is then simulated by adding nodes randomly until an entire complex is assembled or a stop caused by too much overlap is reached. The complex is scored, and the score is backpropagated to all previous nodes, which yields support for the previous selections. The final result is that the nodes most likely to result in high-scoring complexes are joined in a path containing all chains. The principle for the complex 6ESQ is shown.