Ferruz Noelia, Noske Jakob, Höcker Birte
Department of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany.
Bioinformatics. 2021 Oct 11;37(19):3182-3189. doi: 10.1093/bioinformatics/btab253.
Duplication and recombination of protein fragments have led to the highly diverse protein space that we observe today. By mimicking this natural process, the design of protein chimeras via fragment recombination has proven experimentally successful and has opened a new era for the design of customizable proteins. The in silico building of structural models for these chimeric proteins, however, remains a manual task that requires a considerable degree of expertise and is not amenable for high-throughput studies. Energetic and structural analysis of the designed proteins often require the use of several tools, each with their unique technical difficulties and available in different programming languages or web servers.
We implemented a Python package that enables automated, high-throughput design of chimeras and their structural analysis. First, it fetches evolutionarily conserved fragments from a built-in database (also available at fuzzle.uni-bayreuth.de). These relationships can then be represented via networks or further selected for chimera construction via recombination. Designed chimeras or natural proteins are then scored and minimized with the Charmm and Amber forcefields and their diverse structural features can be analyzed at ease. Here, we showcase Protlego's pipeline by exploring the relationships between the P-loop and Rossmann superfolds, building and characterizing their offspring chimeras. We believe that Protlego provides a powerful new tool for the protein design community.
Protlego runs on the Linux platform and is freely available at (https://hoecker-lab.github.io/protlego/) with tutorials and documentation.
Supplementary data are available at Bioinformatics online.
蛋白质片段的复制和重组导致了我们如今所观察到的高度多样化的蛋白质空间。通过模拟这一自然过程,经由片段重组设计蛋白质嵌合体已在实验中取得成功,并开启了可定制蛋白质设计的新时代。然而,这些嵌合蛋白质结构模型的计算机构建仍然是一项需要相当专业知识的手工任务,并不适用于高通量研究。对设计出的蛋白质进行能量和结构分析通常需要使用多种工具,每种工具都有其独特的技术难题,且以不同的编程语言或网络服务器提供。
我们实现了一个Python软件包,它能够对嵌合体进行自动化的高通量设计及其结构分析。首先,它从一个内置数据库(也可在fuzzle.uni - bayreuth.de获取)中提取进化上保守的片段。然后这些关系可以通过网络来表示,或者进一步选择用于通过重组构建嵌合体。接着,使用Charmm和Amber力场对设计出的嵌合体或天然蛋白质进行评分和能量最小化,并能轻松分析它们多样的结构特征。在这里,我们通过探索P环和罗斯曼超折叠之间的关系、构建并表征它们的子代嵌合体来展示Protlego的流程。我们相信Protlego为蛋白质设计领域提供了一个强大的新工具。
Protlego在Linux平台上运行,可在(https://hoecker - lab.github.io/protlego/)免费获取,同时还提供教程和文档。
补充数据可在《生物信息学》在线获取。