Suppr超能文献

BSP-SLIM:一种基于预测蛋白结构的盲配体-蛋白低分辨率对接方法。

BSP-SLIM: a blind low-resolution ligand-protein docking approach using predicted protein structures.

机构信息

Department of Biological Chemistry, Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA.

出版信息

Proteins. 2012 Jan;80(1):93-110. doi: 10.1002/prot.23165. Epub 2011 Oct 4.

Abstract

We developed BSP-SLIM, a new method for ligand-protein blind docking using low-resolution protein structures. For a given sequence, protein structures are first predicted by I-TASSER; putative ligand binding sites are transferred from holo-template structures which are analogous to the I-TASSER models; ligand-protein docking conformations are then constructed by shape and chemical match of ligand with the negative image of binding pockets. BSP-SLIM was tested on 71 ligand-protein complexes from the Astex diverse set where the protein structures were predicted by I-TASSER with an average RMSD 2.92 Å on the binding residues. Using I-TASSER models, the median ligand RMSD of BSP-SLIM docking is 3.99 Å which is 5.94 Å lower than that by AutoDock; the median binding-site error by BSP-SLIM is 1.77 Å which is 6.23 Å lower than that by AutoDock and 3.43 Å lower than that by LIGSITE(CSC) . Compared to the models using crystal protein structures, the median ligand RMSD by BSP-SLIM using I-TASSER models increases by 0.87 Å, while that by AutoDock increases by 8.41 Å; the median binding-site error by BSP-SLIM increase by 0.69Å while that by AutoDock and LIGSITE(CSC) increases by 7.31 Å and 1.41 Å, respectively. As case studies, BSP-SLIM was used in virtual screening for six target proteins, which prioritized actives of 25% and 50% in the top 9.2% and 17% of the library on average, respectively. These results demonstrate the usefulness of the template-based coarse-grained algorithms in the low-resolution ligand-protein docking and drug-screening. An on-line BSP-SLIM server is freely available at http://zhanglab.ccmb.med.umich.edu/BSP-SLIM.

摘要

我们开发了 BSP-SLIM,这是一种使用低分辨率蛋白质结构进行配体-蛋白质盲目对接的新方法。对于给定的序列,首先通过 I-TASSER 预测蛋白质结构;将假定的配体结合位点从与 I-TASSER 模型类似的同源模板结构中转到;然后通过配体与结合口袋的负像的形状和化学匹配来构建配体-蛋白质对接构象。BSP-SLIM 在来自 Astex 多样性集的 71 个配体-蛋白质复合物上进行了测试,其中蛋白质结构通过 I-TASSER 预测,在结合残基上的平均 RMSD 为 2.92 Å。使用 I-TASSER 模型,BSP-SLIM 对接的配体 RMSD 中位数为 3.99 Å,比 AutoDock 低 5.94 Å;BSP-SLIM 的结合位点误差中位数为 1.77 Å,比 AutoDock 低 6.23 Å,比 LIGSITE(CSC)低 3.43 Å。与使用晶体蛋白质结构的模型相比,使用 I-TASSER 模型的 BSP-SLIM 的配体 RMSD 中位数增加了 0.87 Å,而 AutoDock 的配体 RMSD 中位数增加了 8.41 Å;BSP-SLIM 的结合位点误差中位数增加了 0.69 Å,而 AutoDock 和 LIGSITE(CSC)的结合位点误差中位数分别增加了 7.31 Å和 1.41 Å。作为案例研究,BSP-SLIM 用于六种靶蛋白的虚拟筛选,平均分别在库的前 9.2%和 17%中优先考虑活性的 25%和 50%。这些结果表明,基于模板的粗粒度算法在低分辨率配体-蛋白质对接和药物筛选中是有用的。一个在线的 BSP-SLIM 服务器可以在 http://zhanglab.ccmb.med.umich.edu/BSP-SLIM 免费获得。

相似文献

9
Docking studies on DNA intercalators.DNA 嵌入剂的对接研究。
J Chem Inf Model. 2014 Jan 27;54(1):96-107. doi: 10.1021/ci400352t. Epub 2013 Dec 13.

引用本文的文献

2
Protein-Ligand Blind Docking Using CB-Dock2.使用 CB-Dock2 进行蛋白质-配体盲对接。
Methods Mol Biol. 2024;2714:113-125. doi: 10.1007/978-1-0716-3441-7_6.
4
CHARMM-GUI for Template-Based Virtual Ligand Design in a Binding Site.CHARMM-GUI 用于结合部位基于模板的虚拟配体设计。
J Chem Inf Model. 2021 Nov 22;61(11):5336-5342. doi: 10.1021/acs.jcim.1c01156. Epub 2021 Nov 10.
6
FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening.FRAGSITE:基于片段的虚拟配体筛选方法。
J Chem Inf Model. 2021 Apr 26;61(4):2074-2089. doi: 10.1021/acs.jcim.0c01160. Epub 2021 Mar 16.

本文引用的文献

2
How significant is a protein structure similarity with TM-score = 0.5?蛋白质结构相似度 TM 值为 0.5 有多大意义?
Bioinformatics. 2010 Apr 1;26(7):889-95. doi: 10.1093/bioinformatics/btq066. Epub 2010 Feb 17.
9
Docking and chemoinformatic screens for new ligands and targets.对接和计算化学筛选新配体和靶标。
Curr Opin Biotechnol. 2009 Aug;20(4):429-36. doi: 10.1016/j.copbio.2009.08.003. Epub 2009 Sep 3.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验