Lerner Michael G, Bowman Anna L, Carlson Heather A
Biophysics Research Division and Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI 48109-1065, USA.
J Chem Inf Model. 2007 Nov-Dec;47(6):2358-65. doi: 10.1021/ci700167n. Epub 2007 Sep 18.
Escherichia coli dihydrofolate reductase (DHFR) is a long-standing target for enzyme studies. The influence of protein motion on its catalytic cycle is significant, and the conformation of the M20 loop is of particular interest. We present receptor-based pharmacophore models-an equivalent of solvent-mapping of binding hotspots-based on ensembles of protein conformations from molecular dynamics simulations of DHFR.NADPH in both the closed and open conformation of the M20 loop. The optimal models identify DHFR inhibitors over druglike non-inhibitors; furthermore, high-affinity inhibitors of E. coli DHFR are preferentially identified over general DHFR inhibitors. As expected, models resulting from simulations with DHFR in the productive conformation with a closed M20 loop have better performance than those from the open-loop simulations. Model performance improves with increased dynamic sampling, indicating that including a greater degree of protein flexibility can enhance the quest for potent inhibitors. This was compared to the limited conformational sampling seen in crystal structures, which were suboptimal for this application.
大肠杆菌二氢叶酸还原酶（DHFR）长期以来一直是酶研究的靶点。蛋白质运动对其催化循环的影响很大，M20环的构象尤其令人关注。我们基于受体构建了药效团模型——这相当于基于DHFR-NADPH在M20环的闭合和开放构象下的分子动力学模拟得到的蛋白质构象集合对结合热点进行溶剂映射。最优模型能够识别出DHFR抑制剂而非类药物非抑制剂；此外，与一般的DHFR抑制剂相比，能够优先识别出大肠杆菌DHFR的高亲和力抑制剂。正如预期的那样，由处于具有闭合M20环的生产性构象的DHFR模拟得到的模型比开环模拟得到的模型表现更好。随着动态采样的增加，模型性能得到改善，这表明纳入更大程度的蛋白质灵活性可以增强对强效抑制剂的寻找。这与晶体结构中有限的构象采样进行了比较，晶体结构在该应用中并非最优。
