PDF(Pubmed)
Abstract：
Francis Crick\'s global parameterization of coiled coil geometry has been widely useful for guiding design of new protein structures and functions. However, design guided by similar global parameterization of beta barrel structures has been less successful, likely due to the deviations required from ideal beta barrel geometry to maintain extensive inter-strand hydrogen bonding without introducing considerable backbone strain. Instead, beta barrels and other protein folds have been designed guided by 2D structural blueprints; while this approach has successfully generated new fluorescent proteins, transmembrane nanopores, and other structures, it requires considerable expert knowledge and provides only indirect control over the global barrel shape. Here we show that the simplicity and control over shape and structure provided by global parametric representations can be generalized beyond coiled coils by taking advantage of the rich sequence-structure relationships implicit in RoseTTAFold based inpainting and diffusion design methods. Starting from parametrically generated idealized barrel backbones, both RFjoint inpainting and RFdiffusion readily incorporate the backbone irregularities necessary for proper folding with minimal deviation from the idealized barrel geometries. We show that for beta barrels across a broad range of global beta sheet parameterizations, these methods achieve high in silico and experimental success rates, with atomic accuracy confirmed by an X-ray crystal structure of a novel beta barrel topology, and de novo designed 12, 14, and 16 stranded transmembrane nanopores with conductances ranging from 200 to 500 pS. By combining the simplicity and control of parametric generation with the high success rates of deep learning based protein design methods, our approach makes the design of proteins where global shape confers function, such as beta barrel nanopores, more precisely specifiable and accessible.
摘要：
FrancisCrick的卷曲螺旋几何结构的全局参数化对指导新蛋白质结构和功能的设计具有广泛的有用性。然而,由类似的β桶结构全局参数化指导的设计不太成功，可能是由于与理想的β桶几何形状要求的偏差，以保持广泛的股间氢键键合而不引入相当大的主链应变。相反，β桶和其他蛋白质折叠已经被设计的二维结构蓝图的指导;虽然这种方法已经成功地产生了新的荧光蛋白，跨膜纳米孔，和其他结构，它需要相当的专业知识，只提供对全球桶形的间接控制。在这里，我们表明，通过利用基于RoseTTAFold的修补和扩散设计方法中隐含的丰富的序列-结构关系，可以超越卷曲线圈，对全局参数表示提供的形状和结构的简单性和控制进行推广。从参数化生成的理想化桶骨架开始，RFjoint油漆和RFdiffusion都很容易结合正确折叠所需的骨架不规则性，而与理想的桶形几何形状的偏差最小。我们表明，对于广泛的全球β表参数化的β桶，这些方法获得了很高的计算机模拟和实验成功率，新的β桶拓扑结构的X射线晶体结构证实了原子精度，并且从头设计了12、14和16个链的跨膜纳米孔，其电导率范围为200至500pS。通过将参数生成的简单性和控制性与基于深度学习的蛋白质设计方法的高成功率相结合，我们的方法设计了整体形状赋予功能的蛋白质，如β桶纳米孔，更精确地可指定和可访问。
