Inferring protein 3D structure from deep mutation scans

By Nathan J. Rollins, Kelly P. Brock, F. J. Poelwijk, M. Stiffler, N. Gauthier, Christopher Sander, Debora Marks. 2019

143 citations recorded by the source corpus.

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. The authors present a method for determining 3D protein structures using high-throughput mutation experiments. Pairs of residues with the largest positive epistasis are sufficient to determine the 3D fold.

Subfields: Molecular Biology

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In Molecular Biology

1. Jörn M. Schmiedel
2. Jian Peng
3. Huimin Zhao
4. Ali Madani
5. Wesley Wei Qian
6. L. Varshney
7. Ben Lehner
8. Lam Vo

Citations in the corpus

• BERTology Meets Biology: Interpreting Attention in Protein Language Models (2020, background): “For example, sequence models leveraging principles of co-evolution, whether modeling pairwise or higher-order interactions, have enabled prediction of structure or function [66].”
• Evolutionary context-integrated deep sequence modeling for protein engineering (2020, background): “It was found that epistasis interactions, quantified by deep mutagenesis scanning (DMS) of proteins, can be used to infer protein contacts and structures [33,34] ​.”
• Determining protein structures using deep mutagenesis (2019, background): “We note that our approach is likely to be only one of several that could work 47 .”