UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158.
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158.
Proc Natl Acad Sci U S A. 2022 Mar 15;119(11):e2115480119. doi: 10.1073/pnas.2115480119. Epub 2022 Mar 7.
SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods. Then, as a proof of concept, we leverage these methods to design new protein conformations in the enzyme ketosteroid isomerase that change the protein's preference for a key functional residue. Our computational methods are openly accessible and can be applied to the design of other intricate geometries customized for new user-defined protein functions.
意义计算蛋白质设计有望通过创建具有许多新的和有用的功能的蛋白质来推进医学和生物技术的应用。然而，新的功能需要设计特定的、通常是非规则的原子级几何形状，这仍然是一个主要的挑战。在这里，我们开发了计算方法，可以比现有方法更准确地设计和预测局部蛋白质几何形状。然后，作为概念验证，我们利用这些方法来设计酶酮甾体异构酶中的新蛋白质构象，从而改变蛋白质对关键功能残基的偏好。我们的计算方法是公开可用的，可应用于为新的用户定义的蛋白质功能定制的其他复杂几何形状的设计。
