Distance-based ab initio protein structure prediction

项目摘要 预测蛋白质的三维结构，而不使用来自 蛋白质数据库(PDB)作为模板(从头开始)仍然是计算的一个巨大挑战 生物学。尽管基于模板的建模现在是一个成熟的领域，但从头开始建模是一种 相对较新的一种，尤其是具有复杂拓扑和多种结构的大蛋白质 域名。显然，需要在从头算建模方面取得进展。很多蛋白质序列都是这样的 在PDB中没有(可识别的)模板，以及实验结构的节奏 决心与问题的严重程度不相称。在此，我们提出了一种新的 一种由新型深度学习结构组成的从头建模方法 残基距离和区域边界以及稳健的迭代优化方法 根据预测的距离构建三级结构。这个项目建立在成功的 我们现在的R01，特别是郑氏集团在2018年的出色表现 全球蛋白质结构预测实验-CASP13-我们的Multicom套件 与谷歌DeepMind的AlphaFold并列前三名。 这些方法将作为基于距离的新兴领域的开源工具来实现 从头算蛋白质结构模型。我们将应用这些方法来研究蛋白质的同源低聚物。 和自组装，基于我们的新发现，即四元结构在 可以通过深度学习方法从共同进化信号中预测同源低聚物 嵌入到蛋白质单体的多个序列比对中。此外，我们将应用 预测折叠、功能部位、超家族和蛋白质-蛋白质相互作用的方法 含有“未知功能的基本结构域”(EDUF)的蛋白质，这是一组进化上 保守的必需蛋白质，代表蛋白质中一个重要的未知区域 功能/折叠空间。对eDUF的多样化和代表性子集的预测将是 通过与DR的结构生物学小组的独特合作进行了实验验证。 坦纳。

Project Summary Predicting the three-dimensional structures of proteins without using known structures from the Protein Data Bank (PDB) as templates (ab initio) remains a grand challenge of computational biology. Whereas template-based modeling is now a mature field, ab initio modeling is a comparatively nascent one, especially for large proteins with complex topologies and multiple domains. The need for advances in ab initio modeling is evident. A lot of protein sequences do not have (recognizable) templates in the PDB, and the pace of experimental structure determination is incommensurate with the scale of the problem. Herein, we propose a new approach to ab initio modeling that consists of novel deep learning architectures to predict inter- residue distances and domain boundaries as well as robust, iterative optimization methods to construct tertiary structures from the predicted distances. This project builds on the success of our current R01, particularly the outstanding performance of the Cheng group in the 2018 worldwide protein structure prediction experiment – CASP13 – where our MULTICOM suite ranked among the top three tertiary structure predictors, alongside Google DeepMind’s AlphaFold. The methods will be implemented as open-source tools for the emerging field of distance-based ab initio protein structure modeling. We will apply the methods to study protein homo-oligomers and self-assemblies, based on our novel discovery that the quaternary structure contacts within homo-oligomers can be predicted by deep learning methods from the co-evolutionary signals embedded in multiple sequence alignments of protein monomers. Furthermore, we will apply the methods to predict the folds, functional sites, superfamilies, and protein-protein interactions of proteins that contain “essential Domains of Unknown Function” (eDUFs), a group of evolutionarily conserved, essential proteins that represents an important uncharted region of protein function/fold space. The predictions for a diverse and representative subset of eDUFs will be experimentally validated through a unique collaboration with the structural biology group of Dr. Tanner.