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Minimum epistasis interpolation for sequence-function relationships

Juannan Zhou, David M. McCandlish
doi: https://doi.org/10.1101/657841
Juannan Zhou
Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724
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David M. McCandlish
Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724
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  • For correspondence: mccandlish@cshl.edu
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Abstract

Massively parallel phenotyping assays have provided unprecedented insight into how multiple mutations combine to determine biological function. While these assays can measure phenotypes for thousands to millions of genotypes in a single experiment, in practice these measurements are not exhaustive, so that there is a need for techniques to impute values for genotypes whose phenotypes are not directly assayed. Here we present a method based on the idea of inferring the least epistatic possible sequence-function relationship compatible with the data. In particular, we infer the reconstruction in which mutational effects change as little as possible across adjacent genetic backgrounds. Although this method is highly conservative and has no tunable parameters, it also makes no assumptions about the form that genetic interactions take, resulting in predictions that can behave in a very complicated manner where the data require it but which are nearly additive where data is sparse or absent. We apply this method to analyze a fitness landscape for protein G, showing that our technique can provide a substantially less epistatic fit to the landscape than standard methods with little loss in predictive power. Moreover, our analysis reveals that the complex structure of epistasis observed in this dataset can be well-understood in terms of a simple qualitative model consisting of three fitness peaks where the landscape is locally additive in the vicinity of each peak.

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  • ↵1 jzhou{at}cshl.edu

  • A new supplemental .zip file is uploaded

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted June 04, 2019.
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Minimum epistasis interpolation for sequence-function relationships
Juannan Zhou, David M. McCandlish
bioRxiv 657841; doi: https://doi.org/10.1101/657841
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Minimum epistasis interpolation for sequence-function relationships
Juannan Zhou, David M. McCandlish
bioRxiv 657841; doi: https://doi.org/10.1101/657841

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