Montalvillo Ortega Fernando, Hossain Fariha, Volobouev Vladimir V, Meloni Gabriele, Torabifard Hedieh, Morcos Faruck
Department of Chemistry and Biochemistry, University of Texas at Dallas, Richardson, 75080 Texas, United States.
Department of Biological Sciences, University of Texas at Dallas, Richardson, 75080 Texas, United States.
ACS Cent Sci. 2025 Jul 10;11(8):1452-1466. doi: 10.1021/acscentsci.5c00708. eCollection 2025 Aug 27.
Design and synthesis of functionally active artificial proteins is challenging, as it requires simultaneous consideration of interconnected factors, such as fold, dynamics, and function. These evolutionary constraints are encoded in protein sequences and can be learned through the latent generative landscape (LGL) framework to predict functional sequences by leveraging evolutionary patterns, enabling exploration of uncharted sequence space. By simulating designed proteins through molecular dynamics (MD), we gain deeper insights into the interdependencies governing structure and dynamics. We present a synergized workflow combining LGL with MD and biochemical characterization, allowing us to explore the sequence space effectively. This approach has been applied to design and characterize two artificial multidomain ATP-driven transmembrane copper transporters, with native-like functionality. This integrative approach proved effective in revealing the intricate relationships between sequence, structure, and function.
设计和合成具有功能活性的人工蛋白质具有挑战性，因为这需要同时考虑相互关联的因素，如折叠、动力学和功能。这些进化限制编码在蛋白质序列中，可以通过潜在生成景观（LGL）框架来学习，以利用进化模式预测功能序列，从而探索未知的序列空间。通过分子动力学（MD）模拟设计的蛋白质，我们对控制结构和动力学的相互依赖性有了更深入的了解。我们提出了一种将LGL与MD和生化表征相结合的协同工作流程，使我们能够有效地探索序列空间。这种方法已应用于设计和表征两种具有天然样功能的人工多结构域ATP驱动的跨膜铜转运蛋白。这种综合方法被证明在揭示序列、结构和功能之间的复杂关系方面是有效的。
