Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration

Abstract

We report the implementation of the thermodynamic integration method on the pmemd module of the AMBER 16 package on GPUs (pmemdGTI). The pmemdGTI code typically delivers over 2 orders of magnitude of speed-up relative to a single CPU core for the calculation of ligand-protein binding affinities with no statistically significant numerical differences and thus provides a powerful new tool for drug discovery applications.

Figure 1

Schematic view of the pmemdGTI software design and execution flow (GPU context is terminology referring to a GPU program unit representing the executing instance on the GPU.) Upper panel (software design): The original AMBER GPU context is packaged as a base class and contains all simulation data structures, including all coordinates, forces, energy terms, simulation parameters, and settings. The MD GPU simulation constant set is a collection of fast-access mathematical and physical constants and the pointers to the data structures of the GPU context and is packaged as a base class as well. Lower panel (execution flow): The CUDA multiple-stream capability is utilized, and the original AMBER MD code runs on a separate stream from the newly added TI code. Color codes: light green: the original AMBER modules; yellow: the add-on to encapsulate AMBER modules; light orange: the new TI add-on modules.

Figure 2

Binding affinities of various ligands with Factor Xa calculated from pmemdGTI (GPU) and pmemd (CPU). Ligands are from ref , and their structures are provided in the Supporting Information. Binding affinities were calculated with TI using 11 evenly spaced λ-windows ranging from 0.0 to 1.0 and integrated with Simpson’s rule. Listed are average ΔΔG’s values from 10 independent runs with 4 ns of data collection for each λ-window. The ΔΔG is the difference of ΔG_Complex and ΔG_Soln (see text). The softcore approach^, was used for all TI calculations, except a → bt* where no softcore potential was used. All numbers are in kcal/mol. Left Panel: The plot of ΔΔG results from GPU (x-axis) and CPU (y-axis) with the associated standard errors as the error bars. The squared correlation coefficient (R²) between these two sets of data is 0.996. The dotted line is the ideal y = x regression line. Right Panel: The numerical values of ΔΔG and the associated standard errors (in parentheses), with the mutated chemical function groups shown with softcore atoms colored red.