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. 2022 Dec 30;3(2):164–173. doi: 10.1038/s43588-022-00375-1

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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 — a, Left: we withhold an antibody (315-23-1C09, boxed in red) from our dataset and recreate the neutralization landscape. Middle: the location of the withheld antibody is triangulated on the new landscape using a few measurements. Right: the neutralization against the remaining viruses is predicted using antibody–virus distance. Gridlines represent a 10× drop in neutralization. b, The 600 predicted versus measured IC₅₀ values after withholding every antibody in our panel. c, We withhold either antibodies or viruses (1 or 10 of each), triangulate each entity using a subset of measurements, and predict the remaining measurements. In b and c, the shaded band represents ≤4-fold error, where 1-fold error represents an exact prediction.

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