Inferring protein 3D structure from deep mutation scans


We describe an experimental method of three-dimensional (3D) structure determination that exploits the increasing ease of high-throughput mutational scans. Inspired by the success of using natural, evolutionary sequence covariation to compute protein and RNA folds, we explored whether ‘laboratory’, synthetic sequence variation might also yield 3D structures. We analyzed five large-scale mutational scans and discovered that the pairs of residues with the largest positive epistasis in the experiments are sufficient to determine the 3D fold. We show that the strongest epistatic pairings from genetic screens of three proteins, a ribozyme and a protein interaction reveal 3D contacts within and between macromolecules. Using these experimental epistatic pairs, we compute ab initio folds for a GB1 domain (within 1.8 Å of the crystal structure) and a WW domain (2.1 Å). We propose strategies that reduce the number of mutants needed for contact prediction, suggesting that genomics-based techniques can efficiently predict 3D structure.

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Fig. 1: Genetic experiments can be used to discover epistatic interactions and solve the 3D fold.
Fig. 2: Experimental epistasis pairs reveal structural contacts in the GB1 protein.
Fig. 3: Experimental epistasis pairs reveal contacts in the WW domain, RRM domain and twister ribozyme.
Fig. 4: Predicted 3D structures from experimental epistasis scores alone.
Fig. 5: Only a small fraction of all double mutants is needed to determine the 3D fold.

Data availability

The main data analyzed in this study are publicly available from the original publications (refs. 13,18,36,38,42,43). All other data supporting the findings of this study are available within the article and its Supplementary Information files, and from the GitHub repository (

Code availability

The code used in this study (along with folded models) is available at, and utilities for folding and ranking are available from the EVcouplings GitHub repository (


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The authors thank the Marks, Sander and Silver laboratories for discussion and support. The authors also thank S. Ovchinnikov for performing ab initio structure predictions with Rosetta for comparison. Partial financial support for C.S. was provided from the US NIH (RO1 GM106303). K.P.B. thanks the NIH (R01 R01GM120574) for financial support.

Author information




N.K.R., K.P.B. and D.S.M. performed the main analyses. N.J.R., K.P.B. and D.S.M. wrote the manuscript. F.J.P., M.A.S., N.P.G. and C.S. helped edit the manuscript. D.S.M. conceived the project. D.S.M. and C.S. supervised the study.

Corresponding author

Correspondence to Debora S. Marks.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–9

Reporting Summary

Supplementary Table 1

Epistasis and fitness values for all double mutants

Supplementary Table 2

Top epistasis pairs for each pair of positions, with distance in Xtal or NMR

Supplementary Table 3

Precision of top epistatic pairs versus contacts in experimental structures

Supplementary Table 4

Epistasis versus experimental secondary structure scores

Supplementary Table 5

Accuracy of ab initio models versus experimental structures

Supplementary Table 6

RMSD between experimental structures

Supplementary Table 7

Small-library subsampling 3D fold accuracy and epistatic pair precisions

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Rollins, N.J., Brock, K.P., Poelwijk, F.J. et al. Inferring protein 3D structure from deep mutation scans. Nat Genet 51, 1170–1176 (2019).

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