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使用自由能预测(FEP)预测法在 D3R 大挑战 2 中预测法尼醇 X 受体的相对结合亲和力。

Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP.

机构信息

Computational Chemistry and Biology, Merck KGaA, Darmstadt, Germany.

出版信息

J Comput Aided Mol Des. 2018 Jan;32(1):265-272. doi: 10.1007/s10822-017-0064-z. Epub 2017 Sep 12.

Abstract

Physics-based free energy simulations have increasingly become an important tool for predicting binding affinity and the recent introduction of automated protocols has also paved the way towards a more widespread use in the pharmaceutical industry. The D3R 2016 Grand Challenge 2 provided an opportunity to blindly test the commercial free energy calculation protocol FEP+ and assess its performance relative to other affinity prediction methods. The present D3R free energy prediction challenge was built around two experimental data sets involving inhibitors of farnesoid X receptor (FXR) which is a promising anticancer drug target. The FXR binding site is predominantly hydrophobic with few conserved interaction motifs and strong induced fit effects making it a challenging target for molecular modeling and drug design. For both data sets, we achieved reasonable prediction accuracy (RMSD ≈ 1.4 kcal/mol, rank 3-4 according to RMSD out of 20 submissions) comparable to that of state-of-the-art methods in the field. Our D3R results boosted our confidence in the method and strengthen our desire to expand its applications in future in-house drug design projects.

摘要

基于物理的自由能模拟已逐渐成为预测结合亲和力的重要工具,最近自动协议的引入也为其在制药行业的更广泛应用铺平了道路。D3R 2016 年大挑战 2 为盲目测试商业自由能计算协议 FEP+提供了机会,并评估了其相对于其他亲和力预测方法的性能。目前的 D3R 自由能预测挑战围绕着两个实验数据集展开,涉及法尼醇 X 受体 (FXR) 的抑制剂,FXR 是一种很有前途的抗癌药物靶点。FXR 结合位点主要是疏水性的,很少有保守的相互作用基序和强烈的诱导契合效应,这使得它成为分子建模和药物设计的一个具有挑战性的目标。对于这两个数据集,我们实现了合理的预测精度(RMSD≈1.4 kcal/mol,在 20 个提交项中,根据 RMSD 排名 3-4),与该领域的最先进方法相当。我们的 D3R 结果增强了我们对该方法的信心,并加强了我们在未来内部药物设计项目中扩展其应用的愿望。

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