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Blinded prediction of protein–ligand binding affinity using Amber thermodynamic integration for the 2018 D3R grand challenge 4

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Abstract

In the framework of the 2018 Drug Design Data Resource grand challenge 4, blinded predictions on relative binding free energy were performed for a set of 39 ligands of the Cathepsin S protein. We leveraged the GPU-accelerated thermodynamic integration of Amber 18 to advance our computational prediction. When our entry was compared to experimental results, a good correlation was observed (Kendall’s τ: 0.62, Spearman’s ρ: 0.80 and Pearson’s R: 0.82). We designed a parallelized transformation map that placed ligands into several groups based on common alchemical substructures; TI transformations were carried out for each ligand to the relevant substructure, and between substructures. Our calculations were all conducted using the linear potential scaling scheme in Amber TI because we believe the softcore potential/dual-topology approach as implemented in current Amber TI is highly fault-prone for some transformations. The issue is illustrated by using two examples in which typical preparation for the dual-topology approach of Amber TI fails. Overall, the high accuracy of our prediction is a result of recent advances in force fields (ff14SB and GAFF), as well as rapid calculation of ensemble averages enabled by the GPU implementation of Amber. The success shown here in a blinded prediction strongly suggests that alchemical free energy calculation in Amber is a promising tool for future commercial drug design.

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Acknowledgements

This work was supported by NIH Grant GM107104 to CS and USPH NIH Grant GM078114 to D. Raleigh. We gratefully acknowledge support from Henry and Marsha Laufer and the Laufer Center.

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Authors and Affiliations

  1. Department of Chemistry, Stony Brook University, Stony Brook, NY, 11794-3400, USA

    Junjie Zou, Chuan Tian & Carlos Simmerling

  2. Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794-3400, USA

    Junjie Zou, Chuan Tian & Carlos Simmerling

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Correspondence to Carlos Simmerling.

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(1) ligand IDs in each group as shown in Fig. 1; (2) initial state, end state and number of steps of each transition in Fig. 1; (3) calculated and experimental ∆∆G values and the standard deviation of the calculated ∆∆G values from the three independent runs; (4) raw data and figure for Fig. 5.

10822_2019_223_MOESM2_ESM.pdf

Demonstrates two examples at where the softcore TI/dual-topology approach in Amber is problematic. Folder 2d-sketch contains figures showing the 2d structures of ligands in Cathepsin S free energy data set. Folder input contains pmemd input files for conducting minimization, equilibration and production run with λ = 0.00922. Input files are the same for other λ windows except for the clambda flag. Folder mol contains mol2 files for all ligands and substructures. Folder prm7rst7 contains all Amber parm7 and rst7 files for the TI calculations using pmemdGTI. Folder lib and folder frcmod contain library files and force field modification files used by Amber tleap. Folder pdb contains the pdb files loaded into Amber tleap for the protein-ligand complexes and the monomeric ligands. Folder smile4sub contains the smile strings defining the substructures. Folder prmLA contains Parmed scripts used for generating parm7 files for Amber TI. Folder TIexample contains an example TI calculation for the transformation of ligand 004 to substructure A.

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Zou, J., Tian, C. & Simmerling, C. Blinded prediction of protein–ligand binding affinity using Amber thermodynamic integration for the 2018 D3R grand challenge 4. J Comput Aided Mol Des 33, 1021–1029 (2019). https://doi.org/10.1007/s10822-019-00223-x

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