Data from: Strong selection significantly increases epistatic interactions in the long-term evolution of a protein

Epistatic interactions between residues determine a protein’s adaptability and shape its evolutionary trajectory. When a protein experiences a changed environment, it is under strong selection to find a peak in the new fitness landscape. It has been shown that strong selection increases epistatic interactions as well as the ruggedness of the fitness landscape, but little is known about how the epistatic interactions change under selection in the long-term evolution of a protein. Here we analyze the evolution of epistasis in the protease of the human immunodeficiency virus type 1 (HIV-1) using protease sequences collected for almost a decade from both treated and untreated patients, to understand how epistasis changes and how those changes impact the long-term evolvability of a protein. We use an information-theoretic proxy for epistasis that quantifies the co-variation between sites, and show that positive information is a necessary (but not sufficient) condition that detects epistasis in most cases. We analyze the “fossils” of the evolutionary trajectories of the protein contained in the sequence data, and show that epistasis continues to enrich under strong selection, but not for proteins whose environment is unchanged. The increase in epistasis compensates for the information loss due to sequence variability brought about by treatment, and facilitates adaptation in the increasingly rugged fitness landscape of treatment. While epistasis is thought to enhance evolvability via valley-crossing early-on in adaptation, it can hinder adaptation later when the landscape has turned rugged. However, we find no evidence that the HIV-1 protease has reached its potential for evolution after 9 years of adapting to a drug environment that itself is constantly changing. We suggest that the mechanism of encoding new information into pairwise interactions is central to protein evolution not just in HIV-1 protease, but for any protein adapting to a changing environment.

氨基酸残基间的上位性相互作用（Epistatic interactions）决定蛋白质的适应性，并塑造其演化轨迹。当蛋白质遭遇环境变化时，会面临强烈的选择压力，以在新的适应度景观（fitness landscape）中找到适应峰。已有研究表明，强选择会增强上位性相互作用以及适应度景观的崎岖度，但目前对蛋白质长期演化过程中选择压力下上位性相互作用的变化规律仍知之甚少。本研究利用近十年来从接受治疗与未接受治疗的患者体内采集的人类免疫缺陷病毒1型（HIV-1）蛋白酶序列，分析该蛋白酶的上位性演化规律，以探究上位性相互作用的变化模式，及其对蛋白质长期演化能力的影响。我们采用一种基于信息论的上位性替代指标，用于量化位点间的共变异程度，并证实正信息是多数情况下检测到上位性相互作用的必要（但非充分）条件。我们对序列数据中蕴含的蛋白质演化轨迹“化石”展开分析，结果显示：在强选择压力下，上位性相互作用的水平持续提升，但环境未发生改变的蛋白质则无此现象。上位性水平的升高弥补了治疗带来的序列变异所造成的信息损失，并助力蛋白质在愈发崎岖的药物治疗适应度景观中完成适应。尽管此前研究认为，上位性相互作用可通过在适应早期跨越适应谷来提升演化能力，但当适应度景观变得崎岖时，其后期反而会阻碍适应过程。然而，我们未发现任何证据表明，在历经9年适应不断变化的药物环境后，HIV-1蛋白酶已达到其演化潜力上限。我们提出，将新信息编码为成对相互作用的机制，不仅是HIV-1蛋白酶演化的核心机制，同时也是所有适应多变环境的蛋白质演化的核心机制。