Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, USA.
Institute of Computing Science, Poznan University of Technology, Poznan, 60-965, Poland.
Nucleic Acids Res. 2021 Jul 2;49(W1):W86-W92. doi: 10.1093/nar/gkab296.
Structure-guided drug design depends on the correct identification of ligands in crystal structures of protein complexes. However, the interpretation of the electron density maps is challenging and often burdened with confirmation bias. Ligand identification can be aided by automatic methods such as CheckMyBlob, a machine learning algorithm that learns to generalize ligand descriptions from sets of moieties deposited in the Protein Data Bank. Here, we present the CheckMyBlob web server, a platform that can identify ligands in unmodeled fragments of electron density maps or validate ligands in existing models. The server processes PDB/mmCIF and MTZ files and returns a ranking of 10 most likely ligands for each detected electron density blob along with interactive 3D visualizations. Additionally, for each prediction/validation, a plugin script is generated that enables users to conduct a detailed analysis of the server results in Coot. The CheckMyBlob web server is available at https://checkmyblob.bioreproducibility.org.
基于结构的药物设计依赖于正确识别蛋白质复合物晶体结构中的配体。然而，电子密度图的解释具有挑战性，并且常常受到确认偏误的影响。配体的识别可以通过自动方法来辅助，例如 CheckMyBlob，这是一种机器学习算法，它可以从存储在蛋白质数据库中的一系列基团中学习概括配体描述。在这里，我们介绍了 CheckMyBlob 网络服务器，这是一个可以在未经建模的电子密度图片段中识别配体或验证现有模型中配体的平台。该服务器处理 PDB/mmCIF 和 MTZ 文件，并为每个检测到的电子密度团块返回排名前 10 的最可能配体，以及交互式 3D 可视化。此外，对于每个预测/验证，都会生成一个插件脚本，使用户能够在 Coot 中对服务器结果进行详细分析。CheckMyBlob 网络服务器可在 https://checkmyblob.bioreproducibility.org 获得。
