3D protein structure from genetic epistasis experiments

Abstract

High-throughput experimental techniques have made possible the systematic sampling of the single mutation landscape for many proteins, defined as the change in protein fitness as the result of point mutation sequence changes. In a more limited number of cases, and for small proteins only, we also have nearly full coverage of all possible double mutants. By comparing the phenotypic effect of two simultaneous mutations with that of the individual amino acid changes, we can evaluate epistatic effects that reflect non-additive cooperative processes. The observation that epistatic residue pairs often are in contact in the 3D structure led to the hypothesis that a systematic epistatic screen contains sufficient information to identify the 3D fold of a protein.To test this hypothesis, we examined experimental double mutants for evidence of epistasis and identified residue contacts at 86% accuracy, including secondary structure elements and evidence for an alternative all--helical conformation. Positively epistatic contactscorresponding to compensatory mutations, restoring fitnesswere the most informative. Folded models generated from top-ranked epistatic pairs, when compared with the known structure, were accurate within 2.4 Å over 53 residues, indicating the possibility that 3D protein folds can be determined experimentally with good accuracy from functional assays of mutant libraries, at least for small proteins. These results suggest a new experimental approach for determining protein structure.

Protein model discrimination attempts using mutational sensitivity, predicted secondary structure, and model quality information

“…In the current work, we aimed to explore how single mutant data, which are currently much easier to generate than double mutant data, can be combined with other approaches to improve model discrimination. There is no question that as experimental double mutant data becomes available for more proteins, it will be a powerful tool for model discrimination and protein structure prediction …”

Protein Structural Information and Evolutionary Landscape by In Vitro Evolution

“…This may allow to investigate by DCA-like methods evolutionary younger proteins, like eukaryotic-only or vertebrate-only proteins or human proteins of neurobiological interest, ultimately solving species-specific questions that need species-specific answers. Pilot work on structure prediction from molecular evolution experiments have been published in preprint form during the development of this study 25,26,44 . However, the strategy used to collect their data can only be applied to proteins strictly under 200 amino acids and can thus be used solely on a small fraction of the proteome from all three domains of life 45 .…”

“…Random mutagenesis is able to quickly and cheaply create a heavily mutagenized library. We first thought to adopt the protocol suggested by Rollins 25 and Schmiedel 26 which uses complete combinatorial two-residues deep mutational scanning to create a 50 amino acids receptor domain library 27 . However, this approach would have been prohibitively expensive for common proteins since the number of required double mutants grows with the square of protein length.…”

Elucidating the molecular determinants of Aβ aggregation with deep mutational scanning

“…For example, others have used sitesaturation mutagenesis and deep mutational scanning data to evaluate proposed structural models (Bajaj et al 2008;Khare et al 2019). Additionally, deep mutational scanning data have now been used to generate distance constraints for the prediction of tertiary protein structure (Schmiedel and Lehner 2018;Rollins et al 2018).…”