Quadir Farhan, Roy Raj S, Soltanikazemi Elham, Cheng Jianlin
Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States.
Front Mol Biosci. 2021 Aug 23;8:716973. doi: 10.3389/fmolb.2021.716973. eCollection 2021.
Proteins interact to form complexes. Predicting the quaternary structure of protein complexes is useful for protein function analysis, protein engineering, and drug design. However, few user-friendly tools leveraging the latest deep learning technology for inter-chain contact prediction and the distance-based modelling to predict protein quaternary structures are available. To address this gap, we develop DeepComplex, a web server for predicting structures of dimeric protein complexes. It uses deep learning to predict inter-chain contacts in a homodimer or heterodimer. The predicted contacts are then used to construct a quaternary structure of the dimer by the distance-based modelling, which can be interactively viewed and analysed. The web server is freely accessible and requires no registration. It can be easily used by providing a job name and an email address along with the tertiary structure for one chain of a homodimer or two chains of a heterodimer. The output webpage provides the multiple sequence alignment, predicted inter-chain residue-residue contact map, and predicted quaternary structure of the dimer. DeepComplex web server is freely available at http://tulip.rnet.missouri.edu/deepcomplex/web_index.html.
蛋白质相互作用形成复合物。预测蛋白质复合物的四级结构对于蛋白质功能分析、蛋白质工程和药物设计很有用。然而，利用最新深度学习技术进行链间接触预测和基于距离的建模来预测蛋白质四级结构的用户友好型工具很少。为了填补这一空白，我们开发了DeepComplex，一个用于预测二聚体蛋白质复合物结构的网络服务器。它使用深度学习来预测同二聚体或异二聚体中的链间接触。然后，通过基于距离的建模，将预测的接触用于构建二聚体的四级结构，该结构可以交互式地查看和分析。该网络服务器可免费访问，无需注册。通过提供作业名称、电子邮件地址以及同二聚体一条链或异二聚体两条链的三级结构，即可轻松使用。输出网页提供多序列比对、预测的链间残基-残基接触图以及二聚体的预测四级结构。DeepComplex网络服务器可在http://tulip.rnet.missouri.edu/deepcomplex/web_index.html免费获取。
