PDF(Pubmed)
Abstract：
Understanding how proteins evolve under selective pressure is a longstanding challenge. The immensity of the search space has limited efforts to systematically evaluate the impact of multiple simultaneous mutations, so mutations have typically been assessed individually. However, epistasis, or the way in which mutations interact, prevents accurate prediction of combinatorial mutations based on measurements of individual mutations. Here, we use artificial intelligence to define the entire functional sequence landscape of a protein binding site in silico, and we call this approach Complete Combinatorial Mutational Enumeration (CCME). By leveraging CCME, we are able to construct a comprehensive map of the evolutionary connectivity within this functional sequence landscape. As a proof of concept, we applied CCME to the ACE2 binding site of the SARS-CoV-2 spike protein receptor binding domain. We selected representative variants from across the functional sequence landscape for testing in the laboratory. We identified variants that retained functionality to bind ACE2 despite changing over 40% of evaluated residue positions, and the variants now escape binding and neutralization by monoclonal antibodies. This work represents a crucial initial stride toward achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation.
摘要：
了解蛋白质在选择压力下如何进化是一个长期的挑战。搜索空间的巨大限制了系统地评估多个同时突变的影响，所以突变通常是单独评估的。然而,上位性,或者突变相互作用的方式，基于对单个突变的测量，阻止了对组合突变的准确预测。这里,我们使用人工智能来定义蛋白质结合位点的整个功能序列景观，我们称这种方法为完全组合突变计数(CCME)。通过利用CCME，我们能够在这个功能序列景观中构建一个完整的进化连接图。作为概念的证明，我们将CCME应用于SARS-CoV-2刺突蛋白受体结合域的ACE2结合位点。我们从整个功能序列景观中选择了代表性的变体用于实验室测试。我们确定了尽管改变了超过40％的评估残基位置，但仍保留了结合ACE2的功能的变体，和变体现在逃避结合和单克隆抗体的中和。这项工作代表了朝着实现病原体进化的精确预测迈出的关键第一步，开辟主动缓解的途径。
