Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH 43210, USA.
Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Brief Bioinform. 2023 Sep 20;24(5). doi: 10.1093/bib/bbad308.
Ion mobility coupled to mass spectrometry informs on the shape and size of protein structures in the form of a collision cross section (CCSIM). Although there are several computational methods for predicting CCSIM based on protein structures, including our previously developed projection approximation using rough circular shapes (PARCS), the process usually requires prior experience with the command-line interface. To overcome this challenge, here we present a web application on the Rosetta Online Server that Includes Everyone (ROSIE) webserver to predict CCSIM from protein structure using projection approximation with PARCS. In this web interface, the user is only required to provide one or more PDB files as input. Results from our case studies suggest that CCSIM predictions (with ROSIE-PARCS) are highly accurate with an average error of 6.12%. Furthermore, the absolute difference between CCSIM and CCSPARCS can help in distinguishing accurate from inaccurate AlphaFold2 protein structure predictions. ROSIE-PARCS is designed with a user-friendly interface, is available publicly and is free to use. The ROSIE-PARCS web interface is supported by all major web browsers and can be accessed via this link (https://rosie.graylab.jhu.edu).
离子淌度与质谱联用，以碰撞截面（CCS）的形式提供蛋白质结构的形状和大小信息。尽管有几种基于蛋白质结构预测 CCS 的计算方法，包括我们之前开发的使用粗糙圆形形状的投影逼近（PARCS），但该过程通常需要具备命令行界面的先验经验。为了克服这一挑战，我们在此介绍一个基于 Rosetta Online Server 的网络应用程序（ROSIE）网络服务器，用于使用 PARCS 的投影逼近从蛋白质结构预测 CCS。在这个网络界面中，用户只需提供一个或多个 PDB 文件作为输入。我们的案例研究结果表明，CCSIM 预测（使用 ROSIE-PARCS）具有很高的准确性，平均误差为 6.12%。此外，CCS 和 CCSPARCS 之间的绝对差异有助于区分准确和不准确的 AlphaFold2 蛋白质结构预测。ROSIE-PARCS 设计具有用户友好的界面，公开可用且免费使用。ROSIE-PARCS 网络界面支持所有主要的网络浏览器，并可通过此链接（https://rosie.graylab.jhu.edu）访问。
