通过深度突变扫描推断蛋白质的三维结构，这一成果由哈佛大学医学院Debora S. Marks团队取得。2019年7月出版的《Nature Genetics》杂志发表了这一最新研究成果。

研究人员利用高通量基因突变筛选测定蛋白的三维(3D)结构。受自然进化序列共变成功计算蛋白质和RNA折叠的启发，课题组探讨是否“实验室”合成序列变异也可能产生3D结构。该团队分析了五个大规模突变筛选，发现在实验中具有最大阳性上位性的残基对足以确定三维折叠。课题组人员表明，最强的上位配对的遗传来源于三种蛋白，核糖酶和蛋白质交互揭示3D、大分子之间的接触。使用这些实验性上位对，该课题组计算GB1域（在晶体结构的1.8Å内）和WW域（2.1Å）的从头开始折叠。

该课题组研究人员提出了减少接触预测所需突变体数量的策略，这表明基于基因组学的技术可以有效地预测3D结构。

附：英文原文

Title: Inferring protein 3D structure from deep mutation scans

Author: Nathan J. Rollins, Kelly P. Brock, Frank J. Poelwijk, Michael A. Stiffler, Nicholas P. Gauthier, Chris Sander, Debora S. Marks

Issue&Volume: Volume 51 Issue 7, July 2019

Abstract: 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 1.8A of the crystal structure) and a WW domain (2.1A). We propose strategies that reduce the number of mutants needed for contact prediction, suggesting that genomics-based techniques can efficiently predict 3D structure.

DOI: 10.1038/s41588-019-0432-9

Source: https://www.nature.com/articles/s41588-019-0432-9