Quantum Chemistry for Solvated Molecules on Graphical Processing Units Using Polarizable Continuum Models

Abstract

Abstract:The conductor-like polarization model (C-PCM) with switching/Gaussian smooth discretization is a widely used implicit solvation model in chemical simulations. However, its application in quantum mechanical calculations of large-scale biomolecular systems can be limited by computational expense of both the gas phase electronic structure and the solvation interaction. We have previously used graphical processing units (GPUs) to accelerate the first of these steps. Here, we extend the use of GPUs to accelerate electronic structure calculations including C-PCM solvation. Implementation on the GPU leads to significant acceleration of the generation of the required integrals for C-PCM. We further propose two strategies to improve the solution of the required linear equations: a dynamic convergence threshold and a randomized block-Jacobi preconditioner. These strategies are not specific to GPUs and are expected to be beneficial for both CPU and GPU implementations. We benchmark the performance of the new implementation using over 20 small proteins in solvent environment. Using a single GPU, our method evaluates the C-PCM related integrals and their derivatives more than 10X faster than a conventional CPUbased implementation. Our improvements to the linear solver provide a further 3X acceleration. The overall calculations including C-PCM solvation require typically 20-40% more effort than their gas phase counterparts for moderate basis set and molecule surface discretization level. The relative cost of the C-PCM solvation correction decreases as the basis sets and/or cavity radii increase. Therefore description of solvation with this model should be routine. We also discuss applications to the study of the conformational landscape of an amyloid fibril.Liu, Luehr, Kulik, and Martínez -GPU-based PCM Calculations -Page 2 IntroductionModeling the influence of solvent in quantum chemical calculations is of great importance to understanding solvation effects on electronic properties, nuclear distributions, spectroscopic properties, acidity/basicity, and mechanisms of enzymatic and chemical reactions.

Towards full Quantum‐Mechanics‐based Protein–Ligand Binding Affinities

“…PBEh‐3c is a hybrid DFT composite scheme that is based on a re‐parametrization of PBE0 with a modified def2‐SV(P) basis set, the D3(BJ,ATM) dispersion correction,,, and a damped geometric counter‐poise correction (gCP) . The Hartree–Fock calculations in the HF‐3c optimizations were carried out with a pre‐release version of the TeraChem code ,,. HF‐3c is a minimal‐basis Hartree–Fock based composite method that uses the D3 dispersion correction,, gCP and an empirical short‐range bond length correction.…”

“…This typically leads to as ubsystem with about 1000 atoms, which is fully optimized on the HF-3c level [26] of theory with an implicits olvent model (C-PCMi nw ater) [27] by employing general purpose graphics processing units (GPGPUs). [28] The resulting, minimized structures are then used to calculate the above mentioned contributions: electronic energies are evaluated on the HF-3c level of theory (or with dispersion-corrected hybrid DFT,s ee below), thermostatistical corrections are based on the semi-empirical DFTB3-D3 method, [29][30][31][32] and the solvent contributions are obtained from the COSMO-RS [33][34][35] (DFT) based continuums olvation model. In cases where different binding modes( conformations) are conceivable, those are all treated separately.T his process is repeated for every PL system and once for the empty binding pocket, using the same restraints in all cases (see Figure1).…”

A generalized Poisson and Poisson-Boltzmann solver for electrostatic environments

“…As a test system, we took the protein PDB ID: 1y49 (122 atoms), which was also chosen as a benchmark system in another recent publication on an optimized version of the COSMO 40 Poisson solver for dielectric environments. 41 The approach in this publication is quite different from ours. A small Gaussian type basis set was used, whereas we use a systematic basis set of wavelets.…”

Applications of large-scale density functional theory in biology

“…DFT methods based on all broad classes identified above have been adapted for GPU-based hardware, including real-space grids [68,69], plane-waves [70,71], and GTOs [72,73,74].…”