A wide variety of protein and peptidomimetic design tasks require matching functional 3D motifs to potential oligomeric scaffolds. For example, during enzyme design, one aims to graft active-site patterns—typically consisting of 3–15 residues—onto new protein surfaces. Identifying protein scaffolds suitable for such active-site engraftment requires costly searches for protein folds that provide the correct side chain positioning to host the desired active site. Other examples of biodesign tasks that require similar fast exact geometric searches of potential side chain positioning include mimicking binding hotspots, design of metal binding clusters and the design of modular hydrogen binding networks for specificity. In these applications, the speed and scaling of geometric searches limits the scope of downstream design to small patterns. Here, we present an adaptive algorithm capable of searching for side chain take-off angles, which is compatible with an arbitrarily specified functional pattern and which enjoys substantive performance improvements over previous methods. We demonstrate this method in both genetically encoded (protein) and synthetic (peptidomimetic) design scenarios. Examples of using this method with the Rosetta framework for protein design are provided. Our implementation is compatible with multiple protein design frameworks and is freely available as a set of python scripts (https://github.com/JiangTian/adaptive-geometric-search-for-protein-design).

中文翻译：

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用于大分子支架选择的自适应几何搜索算法

各种各样的蛋白质和拟肽设计任务需要将功能性 3D 基序与潜在的寡聚支架相匹配。例如，在酶设计过程中，人们的目标是将活性位点模式（通常由 3-15 个残基组成）移植到新的蛋白质表面上。识别适合这种活性位点植入的蛋白质支架需要昂贵的搜索蛋白质折叠，以提供正确的侧链定位以容纳所需的活性位点。需要对潜在侧链定位进行类似的快速精确几何搜索的生物设计任务的其他示例包括模拟结合热点、金属结合簇的设计以及用于特异性的模块化氢结合网络的设计。在这些应用中，几何搜索的速度和缩放将下游设计的范围限制为小图案。在这里，我们提出了一种能够搜索侧链起飞角度的自适应算法，该算法与任意指定的功能模式兼容，并且比以前的方法具有实质性的性能改进。我们在基因编码（蛋白质）和合成（拟肽）设计场景中演示了这种方法。提供了将该方法与 Rosetta 框架一起用于蛋白质设计的示例。我们的实现与多种蛋白质设计框架兼容，并且可以作为一组Python脚本免费提供（https://github.com/JiangTian/adaptive-geometric-search-for- Protein-design）。