Literature DB >> 26457994

Accurate Binding Free Energy Predictions in Fragment Optimization.

Thomas B Steinbrecher1, Markus Dahlgren2, Daniel Cappel1, Teng Lin2, Lingle Wang2, Goran Krilov2, Robert Abel2, Richard Friesner3, Woody Sherman2.   

Abstract

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.

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Year:  2015        PMID: 26457994     DOI: 10.1021/acs.jcim.5b00538

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  28 in total

1.  Identification of potential glutaminyl cyclase inhibitors from lead-like libraries by in silico and in vitro fragment-based screening.

Authors:  Mária Szaszkó; István Hajdú; Beáta Flachner; Krisztina Dobi; Csaba Magyar; István Simon; Zsolt Lőrincz; Zoltán Kapui; Tamás Pázmány; Sándor Cseh; György Dormán
Journal:  Mol Divers       Date:  2017-01-09       Impact factor: 2.943

2.  Docking-undocking combination applied to the D3R Grand Challenge 2015.

Authors:  Sergio Ruiz-Carmona; Xavier Barril
Journal:  J Comput Aided Mol Des       Date:  2016-10-05       Impact factor: 3.686

3.  Approaching protein design with multisite λ dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme.

Authors:  Ryan L Hayes; Jonah Z Vilseck; Charles L Brooks
Journal:  Protein Sci       Date:  2018-11       Impact factor: 6.725

4.  Predicting Binding Free Energies in a Large Combinatorial Chemical Space Using Multisite λ Dynamics.

Authors:  Jonah Z Vilseck; Kira A Armacost; Ryan L Hayes; Garrett B Goh; Charles L Brooks
Journal:  J Phys Chem Lett       Date:  2018-06-06       Impact factor: 6.475

5.  Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery.

Authors:  Tai-Sung Lee; Bryce K Allen; Timothy J Giese; Zhenyu Guo; Pengfei Li; Charles Lin; T Dwight McGee; David A Pearlman; Brian K Radak; Yujun Tao; Hsu-Chun Tsai; Huafeng Xu; Woody Sherman; Darrin M York
Journal:  J Chem Inf Model       Date:  2020-09-16       Impact factor: 4.956

6.  Adaptive Landscape Flattening Accelerates Sampling of Alchemical Space in Multisite λ Dynamics.

Authors:  Ryan L Hayes; Kira A Armacost; Jonah Z Vilseck; Charles L Brooks
Journal:  J Phys Chem B       Date:  2017-02-10       Impact factor: 2.991

7.  Relative Binding Free Energy Calculations Applied to Protein Homology Models.

Authors:  Daniel Cappel; Michelle Lynn Hall; Eelke B Lenselink; Thijs Beuming; Jun Qi; James Bradner; Woody Sherman
Journal:  J Chem Inf Model       Date:  2016-11-18       Impact factor: 4.956

8.  Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015.

Authors:  Nanjie Deng; William F Flynn; Junchao Xia; R S K Vijayan; Baofeng Zhang; Peng He; Ahmet Mentes; Emilio Gallicchio; Ronald M Levy
Journal:  J Comput Aided Mol Des       Date:  2016-08-25       Impact factor: 3.686

9.  Discovery of covalent enzyme inhibitors using virtual docking of covalent fragments.

Authors:  Sandipan Roy Chowdhury; Steven Kennedy; Kai Zhu; Rama Mishra; Patrick Chuong; Alyssa-Uyen Nguyen; Stefan G Kathman; Alexander V Statsyuk
Journal:  Bioorg Med Chem Lett       Date:  2018-11-09       Impact factor: 2.823

10.  Validation of tautomeric and protomeric binding modes by free energy calculations. A case study for the structure based optimization of D-amino acid oxidase inhibitors.

Authors:  Zoltán Orgován; György G Ferenczy; Thomas Steinbrecher; Bence Szilágyi; Dávid Bajusz; György M Keserű
Journal:  J Comput Aided Mol Des       Date:  2018-01-15       Impact factor: 3.686

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