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D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.D3R 大挑战 2:蛋白质-配体构象、亲和力排序和相对结合自由能的盲预测。
J Comput Aided Mol Des. 2018 Jan;32(1):1-20. doi: 10.1007/s10822-017-0088-4. Epub 2017 Dec 4.
2
HYDROPHOBE Challenge: A Joint Experimental and Computational Study on the Host-Guest Binding of Hydrocarbons to Cucurbiturils, Allowing Explicit Evaluation of Guest Hydration Free-Energy Contributions.水凝胶挑战:烃类与葫芦脲主体相互作用的实验与计算联合研究,可明确评估客体水合自由能贡献。
J Phys Chem B. 2017 Dec 14;121(49):11144-11162. doi: 10.1021/acs.jpcb.7b09175. Epub 2017 Dec 1.
3
Catalytic diversity in self-propagating peptide assemblies.自蔓延肽组装中的催化多样性。
Nat Chem. 2017 Aug;9(8):805-809. doi: 10.1038/nchem.2738. Epub 2017 Feb 27.
4
OpenMM 7: Rapid development of high performance algorithms for molecular dynamics.OpenMM 7:分子动力学高性能算法的快速开发。
PLoS Comput Biol. 2017 Jul 26;13(7):e1005659. doi: 10.1371/journal.pcbi.1005659. eCollection 2017 Jul.
5
Evaluating Force Field Performance in Thermodynamic Calculations of Cyclodextrin Host-Guest Binding: Water Models, Partial Charges, and Host Force Field Parameters.评估环糊精主客体结合热力学计算中的力场性能:水模型、部分电荷和主体力场参数。
J Chem Theory Comput. 2017 Sep 12;13(9):4253-4269. doi: 10.1021/acs.jctc.7b00359. Epub 2017 Aug 4.
6
Attach-Pull-Release Calculations of Ligand Binding and Conformational Changes on the First BRD4 Bromodomain.关于首个BRD4溴结构域上配体结合和构象变化的附着-拉动-释放计算
J Chem Theory Comput. 2017 Jul 11;13(7):3260-3275. doi: 10.1021/acs.jctc.7b00275. Epub 2017 Jun 13.
7
Predicting Binding Free Energies: Frontiers and Benchmarks.预测结合自由能:前沿和基准。
Annu Rev Biophys. 2017 May 22;46:531-558. doi: 10.1146/annurev-biophys-070816-033654. Epub 2017 Apr 7.
8
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.
9
Overview of the SAMPL5 host-guest challenge: Are we doing better?SAMPL5主客体挑战概述:我们是否取得了进步?
J Comput Aided Mol Des. 2017 Jan;31(1):1-19. doi: 10.1007/s10822-016-9974-4. Epub 2016 Sep 22.
10
The SAMPL5 host-guest challenge: computing binding free energies and enthalpies from explicit solvent simulations by the attach-pull-release (APR) method.SAMPL5主客体挑战:通过附着-拉动-释放(APR)方法从显式溶剂模拟计算结合自由能和焓。
J Comput Aided Mol Des. 2017 Jan;31(1):133-145. doi: 10.1007/s10822-016-9970-8. Epub 2016 Sep 16.

Bind3P:基于主客体结合数据的水模型优化。

Bind3P: Optimization of a Water Model Based on Host-Guest Binding Data.

机构信息

Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , La Jolla , California 92093 , United States.

OpenEye Scientific Software , 9 Bisbee Court , Santa Fe , New Mexico 87508 , United States.

出版信息

J Chem Theory Comput. 2018 Jul 10;14(7):3621-3632. doi: 10.1021/acs.jctc.8b00318. Epub 2018 Jun 22.

DOI:10.1021/acs.jctc.8b00318
PMID:29874074
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6039243/
Abstract

We report a water model, Bind3P (Version 0.1), which was obtained by using sensitivity analysis to readjust the Lennard-Jones parameters of the TIP3P model against experimental binding free energies for six host-guest systems, along with pure liquid properties. Tests of Bind3P against >100 experimental binding free energies and enthalpies for host-guest systems distinct from the training set show a consistent drop in the mean signed error, relative to matched calculations with TIP3P. Importantly, Bind3P also yields some improvement in the hydration free energies of small organic molecules and preserves the accuracy of bulk water properties, such as density and the heat of vaporization. The same approach can be applied to more sophisticated water models that can better represent pure water properties. These results lend further support to the concept of integrating host-guest binding data into force field parametrization.

摘要

我们报告了一个水模型,Bind3P(版本 0.1),该模型是通过使用敏感性分析来重新调整 TIP3P 模型的 Lennard-Jones 参数,以适应六个主体-客体系统的实验结合自由能,以及纯液体性质。Bind3P 对超过 100 个主体-客体系统的实验结合自由能和焓的测试表明,与使用 TIP3P 进行的匹配计算相比,平均符号误差明显下降。重要的是,Bind3P 还改善了小分子的水合自由能,同时保持了诸如密度和汽化热等大量水性质的准确性。这种方法可以应用于更复杂的水模型,以更好地表示纯水性质。这些结果进一步支持了将主体-客体结合数据纳入力场参数化的概念。