An NMR-based scoring function improves the accuracy of binding pose predictions by docking by two orders of magnitude
An NMR-based scoring function improves the accuracy of binding pose predictions by docking by two orders of magnitude
复制标题
基于 NMR 的评分函数通过对接将结合姿势预测的准确性提高了两个数量级
DOI:
10.1007/s10858-011-9590-5
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发表时间:
2012
影响因子:
2.7
通讯作者:
T. Carlomagno
中科院分区:
文献类型:
--
作者:
J. Orts;S. Bartoschek;C. Griesinger;P. Monecke;T. Carlomagno
Low-affinity ligands can be efficiently optimized into high-affinity drug leads by structure based drug design when atomic-resolution structural information on the protein/ligand complexes is available. In this work we show that the use of a few, easily obtainable, experimental restraints improves the accuracy of the docking experiments by two orders of magnitude. The experimental data are measured in nuclear magnetic resonance spectra and consist of protein-mediated NOEs between two competitively binding ligands. The methodology can be widely applied as the data are readily obtained for low-affinity ligands in the presence of non-labelled receptor at low concentration. The experimental inter-ligand NOEs are efficiently used to filter and rank complex model structures that have been pre-selected by docking protocols. This approach dramatically reduces the degeneracy and inaccuracy of the chosen model in docking experiments, is robust with respect to inaccuracy of the structural model used to represent the free receptor and is suitable for high-throughput docking campaigns.
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影响因子:
7.4
作者:
M. V. van Dongen;J. Weigelt;Jonas Uppenberg;J. Schultz;M. Wikström
通讯作者:
M. Wikström
影响因子:
16.6
作者:
Orts, Julien;Tuma, Jennifer;Carlomagno, Teresa
通讯作者:
Carlomagno, Teresa
影响因子:
2.2
作者:
Orts, Julien;Griesinger, Christian;Carlomagno, Teresa
通讯作者:
Carlomagno, Teresa
影响因子:
--
作者:
M. Reese;Víctor M. Sánchez;K. Kubíček;J. Meiler;M. Blommers;C. Griesinger;T. Carlomagno
通讯作者:
T. Carlomagno
DOI:
--
发表时间:
2010
期刊:
Proteins: Structure, Function, and Bioinformatics
影响因子:
--
作者:
B. Rai;G. Tawa;A. Katz;C. Humblet
通讯作者:
C. Humblet