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解决蛋白质功能设计中的上位效应。

Addressing epistasis in the design of protein function.

机构信息

Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel.

出版信息

Proc Natl Acad Sci U S A. 2024 Aug 20;121(34):e2314999121. doi: 10.1073/pnas.2314999121. Epub 2024 Aug 12.

Abstract

Mutations in protein active sites can dramatically improve function. The active site, however, is densely packed and extremely sensitive to mutations. Therefore, some mutations may only be tolerated in combination with others in a phenomenon known as epistasis. Epistasis reduces the likelihood of obtaining improved functional variants and dramatically slows natural and lab evolutionary processes. Research has shed light on the molecular origins of epistasis and its role in shaping evolutionary trajectories and outcomes. In addition, sequence- and AI-based strategies that infer epistatic relationships from mutational patterns in natural or experimental evolution data have been used to design functional protein variants. In recent years, combinations of such approaches and atomistic design calculations have successfully predicted highly functional combinatorial mutations in active sites. These were used to design thousands of functional active-site variants, demonstrating that, while our understanding of epistasis remains incomplete, some of the determinants that are critical for accurate design are now sufficiently understood. We conclude that the space of active-site variants that has been explored by evolution may be expanded dramatically to enhance natural activities or discover new ones. Furthermore, design opens the way to systematically exploring sequence and structure space and mutational impacts on function, deepening our understanding and control over protein activity.

摘要

蛋白质活性部位的突变可以显著改善其功能。然而,活性部位非常密集,对突变极其敏感。因此,一些突变可能只有在与其他突变结合时才能被容忍,这种现象被称为上位性。上位性降低了获得改良功能变体的可能性,并极大地减缓了自然和实验室进化过程。研究揭示了上位性的分子起源及其在塑造进化轨迹和结果中的作用。此外,基于序列和人工智能的策略可以从自然或实验进化数据中的突变模式推断上位性关系,从而用于设计功能蛋白变体。近年来,这些方法的组合以及原子设计计算已成功预测了活性部位中高度功能组合的突变。这些突变被用于设计数千种功能活性位点变体,证明尽管我们对上位性的理解仍然不完整,但对于准确设计至关重要的一些决定因素现在已经有了足够的了解。我们得出结论,通过进化探索的活性位点变体空间可能会得到极大扩展,以增强自然活性或发现新的活性。此外,设计为系统地探索序列和结构空间以及突变对功能的影响开辟了道路,从而加深了我们对蛋白质活性的理解和控制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a49c/11348311/117ce7ff2169/pnas.2314999121fig01.jpg

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