School of Computer and Information Technology, Xinyang Normal University, Xinyang, China.
Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.
Methods Mol Biol. 2025;2867:247-260. doi: 10.1007/978-1-0716-4196-5_15.
Deciphering molecular-level mechanisms that govern protein-protein interactions (PPIs) relies in part on the accurate prediction of protein-binding partners and protein-binding residues. These predictions can be used to support a wide spectrum of applications that include development of PPI networks and protein docking programs, drug design studies, and investigations of molecular details that underlie certain diseases. Computational methods that predict protein-binding residues offer convenient, inexpensive, and relatively accurate data that can aid these efforts. We introduce and describe a user-friendly webserver for the SCRIBER method that conveniently provides state-of-the-art predictions of protein-binding residues and that minimizes cross-predictions, i.e., incorrect prediction of residues that bind other/non-protein ligands as protein binding. SCRIBER relies on a two-layer architecture that is specifically designed to reduce the cross-predictions. We motivate and explain this predictive architecture. We describe how to use the webserver, interact with its web interface, and collect, read, and understand results generated by SCRIBER. The SCRIBER webserver is available at http://biomine.cs.vcu.edu/servers/SCRIBER/ .
解析控制蛋白质-蛋白质相互作用 (PPIs) 的分子水平机制部分依赖于准确预测蛋白质结合伙伴和蛋白质结合残基。这些预测可用于支持广泛的应用，包括开发 PPI 网络和蛋白质对接程序、药物设计研究以及研究某些疾病背后的分子细节。预测蛋白质结合残基的计算方法提供了方便、廉价且相对准确的数据，可以辅助这些工作。我们引入并描述了一个用于 SCRIBER 方法的用户友好型网络服务器，该服务器可方便地提供最新的蛋白质结合残基预测，并最大限度地减少交叉预测，即错误预测与其他/非蛋白质配体结合的残基作为蛋白质结合。SCRIBER 依赖于一种两层架构，该架构专门设计用于减少交叉预测。我们将激发并解释这种预测架构。我们描述了如何使用网络服务器，与它的网络界面交互，并收集、读取和理解 SCRIBER 生成的结果。SCRIBER 网络服务器可在 http://biomine.cs.vcu.edu/servers/SCRIBER/ 获得。
