Predicting ligand binding affinity with alchemical free energy methods in a polar model binding site.
Predicting ligand binding affinity with alchemical free energy methods in a polar model binding site.
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使用炼金术自由能方法预测极性模型结合位点中的配体结合亲和力。
DOI:
10.1016/j.jmb.2009.09.049
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发表时间:
2009-12-11
影响因子:
5.6
通讯作者:
Shoichet, Brian K.
中科院分区:
文献类型:
--
作者:
Boyce, Sarah E.;Mobley, David L.;Rocklin, Gabriel J.;Graves, Alan P.;Dill, Ken A.;Shoichet, Brian K.
We present a combined experimental and modeling study of organic ligand molecules binding to a slightly polar engineered cavity site in T4 lysozyme (L99A/M102Q). For modeling, we computed alchemical absolute binding free energies. These were blind tests performed prospectively on 13 diverse, previously untested candidate ligand molecules. We predicted that eight compounds would bind to the cavity and five would not; 11 of 13 predictions were correct at this level. The RMS error to the measurable absolute binding energies was 1.8 kcal/mol. In addition, we computed relative binding free energies for six phenol derivatives starting from two known ligands: phenol and catechol. The average RMS error in the relative free energy prediction was 2.5 (phenol) and 1.1 (catechol) kcal/mol. To understand these results at atomic resolution, we obtained x-ray co-complex structures for nine of the diverse ligands and for all six phenol analogs. The average RMSD of the predicted pose to the experiment was 2.0Å (diverse set), 1.8Å (phenol derived predictions) and 1.2Å (catechol derived predictions). We found that to predict accurate affinities and rank-orderings required near-native starting orientations of the ligand in the binding site. Unanticipated binding modes, multiple ligand binding, and protein conformational change all proved challenging for the free energy methods. We believe these results can help guide future improvements in physics-based absolute binding free energy methods.
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影响因子:
4.1
作者:
BENNETT, CH
通讯作者:
BENNETT, CH
DOI:
10.1107/s0907444904019158
发表时间:
2004-12-01
影响因子:
2.2
作者:
Emsley, P;Cowtan, K
通讯作者:
Cowtan, K
影响因子:
56.9
作者:
BASH, PA;SINGH, UC;KOLLMAN, PA
通讯作者:
KOLLMAN, PA
影响因子:
7.3
作者:
Graves, AP;Brenk, R;Shoichet, BK
通讯作者:
Shoichet, BK
影响因子:
15
作者:
Chang, CE;Gilson, MK
通讯作者:
Gilson, MK