文章基本信息

标题：Accurate positioning of functional residues with robotics-inspired computational protein design
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作者：Cody Krivacic ; Kale Kundert ; Xingjie Pan 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2022
卷号：119
期号：11
DOI：10.1073/pnas.2115480119
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance Computational 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. Proteins achieve their complex functions, such as molecular recognition with high affinity and specificity, through intricate three-dimensional geometries in functional sites. To engineer new protein functions, accurate positioning of amino acid functional groups is therefore critical but has remained difficult to achieve by computational methods because of current limitations in the design of new conformations with arbitrary user-defined geometries. Here, we introduce two computational methods capable of generating and predicting new local protein geometries: fragment kinematic closure (FKIC) and loophash kinematic closure (LHKIC). FKIC and LHKIC integrate two approaches: robotics-inspired kinematics of protein conformations and insertion of peptide fragments. We show that FKIC and LHKIC predict native-like conformations at atomic accuracy and with up to 140-fold improvements in sampling efficiency over previous approaches. We then use these methods to create a design protocol, pull into place (PIP), to position functionally important side chains via design of backbone conformations. We validate PIP by remodeling a sizeable active site region in an enzyme and confirming the engineered new conformations of two designs with crystal structures. The described methods can be applied broadly to the design of user-defined geometries for new protein functions.
关键词：encomputational protein designstructure predictiondesign of functionRosetta