Accurate Binding Free Energy Predictions in Fragment Optimization

被引:106
作者
Steinbrecher, Thomas B. [1 ]
Dahlgren, Markus [2 ]
Cappel, Daniel [1 ]
Lin, Teng [2 ]
Wang, Lingle [2 ]
Krilov, Goran [2 ]
Abel, Robert [2 ]
Friesner, Richard [3 ]
Sherman, Woody [2 ]
机构
[1] Schrodinger GmbH, D-68165 Baden Wurttemberg, Germany
[2] Schrodinger Inc, New York, NY 10036 USA
[3] Columbia Univ, Dept Chem, New York, NY 10027 USA
关键词
LIGAND-BINDING; LEAD DISCOVERY; PHEROMONE BINDING; STRUCTURAL BASIS; NONPOLAR CAVITY; DRUG DISCOVERY; T4; LYSOZYME; PROTEIN; MODEL; DOCKING;
D O I
10.1021/acs.jcim.5b00538
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Predicting protein-ligand binding free energies is a central aim of computational structure-based drug design (SBDD) - improved accuracy in binding free energy predictions could significantly reduce costs and accelerate project timelines in lead discovery and optimization. The recent development and validation of advanced free energy calculation methods represents a major step toward this goal. Accurately predicting the relative binding free energy changes of modifications to ligands is especially valuable in the field of fragment-based drug design, since fragment screens tend to deliver initial hits of low binding affinity that require multiple rounds of synthesis to gain the requisite potency for a project. In this study, we show that a free energy perturbation protocol, FEP+, which was previously validated on drug-like lead compounds, is suitable for the calculation of relative binding strengths of fragment-sized compounds as well. We study several pharmaceutically relevant targets with a total of more than 90 fragments and find that the FEP+ methodology, which uses explicit solvent molecular dynamics and physics-based scoring with no parameters adjusted, can accurately predict relative fragment binding affinities. The calculations afford R-2-values on average greater than 0.5 compared to experimental data and RMS errors of ca. 1.1 kcal/mol overall, demonstrating significant improvements over the docking and MM-GBSA methods tested in this work and indicating that FEP+ has the requisite predictive power to impact fragment-based affinity optimization projects.
引用
收藏
页码:2411 / 2420
页数:10
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