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De novo protein fold design through sequence-independent fragment assembly simulations

蛋白质设计 蛋白质数据库 折叠(高阶函数) 序列空间 蛋白质结构 蛋白质结构预测 复制品 计算生物学 蛋白质工程 蛋白质二级结构 蛋白质折叠 序列(生物学) 计算机科学 生物 遗传学 数学 生物化学 艺术 纯数学 巴拿赫空间 视觉艺术 程序设计语言
作者
Robin Pearce,Xiaoqiang Huang,Gilbert S. Omenn,Yang Zhang
出处
期刊:Proceedings of the National Academy of Sciences of the United States of America [National Academy of Sciences]
卷期号:120 (4) 被引量:10
标识
DOI:10.1073/pnas.2208275120
摘要

De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.
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