Distance-based ab initio protein structure prediction

项目摘要 预测蛋白质的三维结构，而不使用已知的结构， 蛋白质数据库（PDB）作为模板（从头算）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，但从头建模是一个 相对新生的，特别是对于具有复杂拓扑结构和多个 域.在从头算建模方面取得进展的必要性是显而易见的。很多蛋白质序列 没有（可识别的）模板在PDB，和实验结构的步伐 决心与问题的规模不相称。在此，我们提出一个新的 方法从头建模，包括新的深度学习架构，以预测内部 残差距离和域边界以及鲁棒的迭代优化方法， 根据预测的距离构建三级结构。该项目建立在成功的基础上， 我们目前的R 01，特别是2018年Cheng集团的出色表现， 全球蛋白质结构预测实验-CASP 13-我们的MULTICOM套件 与谷歌DeepMind的AlphaFold一起，被列为三级结构预测的前三名。 这些方法将作为新兴的远程监测领域的开放源代码工具加以实施。 从头计算蛋白质结构建模我们将应用这些方法来研究蛋白质同源寡聚体 和自组装，基于我们的新发现， 同源寡聚体可以通过深度学习方法从共同进化信号中预测 嵌入在蛋白质单体的多重序列比对中。此外，我们将应用 预测蛋白质折叠、功能位点、超家族和蛋白质间相互作用的方法 含有“未知功能的必需结构域”（eDUF）的蛋白质，这是一组进化上 一种保守的必需蛋白质，代表蛋白质的重要未知区域 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.