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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.

DOI:10.1007/s10822-017-0064-z
PMID:28900792
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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本文引用的文献

1
Prospective Evaluation of Free Energy Calculations for the Prioritization of Cathepsin L Inhibitors.组织蛋白酶L抑制剂优先级排序的自由能计算的前瞻性评估
J Med Chem. 2017 Mar 23;60(6):2485-2497. doi: 10.1021/acs.jmedchem.6b01881. Epub 2017 Mar 13.
2
Acylguanidine Beta Secretase 1 Inhibitors: A Combined Experimental and Free Energy Perturbation Study.酰基胍β 分泌酶 1 抑制剂:一项实验与自由能微扰联合研究。
J Chem Theory Comput. 2017 Mar 14;13(3):1439-1453. doi: 10.1021/acs.jctc.6b01141. Epub 2017 Feb 3.
3
Modeling protein assemblies: Critical Assessment of Predicted Interactions (CAPRI) 15 years hence.: 6TH CAPRI evaluation meeting April 17-19 Tel-Aviv, Israel.
SAMPL6抽样挑战:评估结合自由能计算的可靠性和效率。
J Comput Aided Mol Des. 2020 May;34(5):601-633. doi: 10.1007/s10822-020-00290-5. Epub 2020 Jan 27.
4
D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.D3R 大分子对接挑战赛 4:蛋白质-配体构象、亲和力排序和相对结合自由能的盲态预测。
J Comput Aided Mol Des. 2020 Feb;34(2):99-119. doi: 10.1007/s10822-020-00289-y. Epub 2020 Jan 23.
5
Improving small molecule virtual screening strategies for the next generation of therapeutics.改进小分子虚拟筛选策略,以用于下一代疗法。
Curr Opin Chem Biol. 2018 Jun;44:87-92. doi: 10.1016/j.cbpa.2018.06.006. Epub 2018 Jun 17.
蛋白质组装体建模:十五年后的蛋白质相互作用预测关键评估(CAPRI)。:第六届CAPRI评估会议,4月17 - 19日,以色列特拉维夫
Proteins. 2017 Mar;85(3):357-358. doi: 10.1002/prot.25233.
4
D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions.2015年D3R重大挑战:蛋白质-配体构象与亲和力预测评估
J Comput Aided Mol Des. 2016 Sep;30(9):651-668. doi: 10.1007/s10822-016-9946-8. Epub 2016 Sep 30.
5
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J Chem Inf Model. 2016 Sep 26;56(9):1856-71. doi: 10.1021/acs.jcim.6b00220. Epub 2016 Aug 24.
6
Critical assessment of methods of protein structure prediction: Progress and new directions in round XI.蛋白质结构预测方法的批判性评估:第十一轮的进展与新方向
Proteins. 2016 Sep;84 Suppl 1(Suppl 1):4-14. doi: 10.1002/prot.25064. Epub 2016 Jun 1.
7
Alchemical Free Energy Calculations and Isothermal Titration Calorimetry Measurements of Aminoadamantanes Bound to the Closed State of Influenza A/M2TM.金刚烷胺与甲型流感病毒M2离子通道蛋白封闭态结合的炼金术自由能计算及等温滴定量热法测量
J Chem Inf Model. 2016 May 23;56(5):862-76. doi: 10.1021/acs.jcim.6b00079. Epub 2016 May 9.
8
Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald.使用AMBER在GPU上进行常规微秒级分子动力学模拟。2. 显式溶剂粒子网格埃瓦尔德方法
J Chem Theory Comput. 2013 Sep 10;9(9):3878-88. doi: 10.1021/ct400314y. Epub 2013 Aug 20.
9
OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins.OPLS3:一种提供广泛覆盖药物样小分子和蛋白质的力场。
J Chem Theory Comput. 2016 Jan 12;12(1):281-96. doi: 10.1021/acs.jctc.5b00864. Epub 2015 Dec 1.
10
FESetup: Automating Setup for Alchemical Free Energy Simulations.FESetup:用于无配分函数自由能模拟的自动化设置。
J Chem Inf Model. 2015 Dec 28;55(12):2485-90. doi: 10.1021/acs.jcim.5b00368. Epub 2015 Nov 13.