SAMPL6: calculation of macroscopic pKa values from ab initio quantum mechanical free energies

International audienceMacroscopic pKa values were calculated for all compounds in the SAMPL6 blind prediction challenge, based on quantum chemical calculations with a continuum solvation model and a linear correction derived from a small training set. Microscopic pKa values were derived from the gas-phase free energy difference between protonated and deprotonated forms together with the Conductor-like Polarizable Continuum Solvation Model and the experimental solvation free energy of the proton. pH-dependent microstate free energies were obtained from the microscopic pKas with a maximum likelihood estimator and appropriately summed to yield macroscopic pKa values or microstate populations as function of pH. We assessed the accuracy of three approaches to calculate the microscopic pKas: direct use of the quantum mechanical free energy differences and correction of the direct values for short-comings in the QM solvation model with two different linear models that we independently derived from a small training set of 38 compounds with known pKa. The predictions that were corrected with the linear models had much better accuracy [root-mean-square error (RMSE) 2.04 and 1.95 pKa units] than the direct calculation (RMSE 3.74). Statistical measures indicate that some systematic errors remain, likely due to differences in the SAMPL6 data set and the small training set with respect to their interactions with water. Overall, the current approach provides a viable physics-based route to estimate macroscopic pKa values for novel compounds with reasonable accuracy

Polarizable Force Field Development, and Applications to Conformational Sampling and Free Energy Calculation

The parameters of monovalent ions for the AMOEBA force field were revised. High level quantum mechanics results, relative solvation free energies of monovalent ions, lattice energies and lattice constants of salt crystals were used to calibrate the parameters. The revised parameters were validated against the quantum optimized structures and energies of ion-water dimers and ion-water clusters, and against thermodynamic properties of salt solutions at different concentrations measured in experiments, e.g. mean ionic activity coefficients, self-diffusion coefficients of water. In the simulations the sodium ion is found to qualitatively differ from larger cations in aqueous solution. Direct ionic interactions are predominant for potassium and larger cations, while sodium salt solutions at similar concentrations are dominated by ion-water interactions. A novel stochastic isokinetic integrator proposed by Tuckerman, et al. was extended and generalized in three respects. First, the Nos-Hoover chain algorithm was implemented in the original integrator. Next, the functional form of the isokinetic constraint was generalized so that it was no longer restricted to multiples of kBT. Finally, the isokinetic constraint was extended to be able to constrain the kinetic energies of multi-dimensional velocities, instead of only one degree of freedom as in its original form. An application of conformational sampling with molecular dynamics method, predictions of the binding free energies of cucurbit[8]uril and ligands in the SAMPL6 challenge, is presented. A great improvement in the prediction accuracy was made by more accurate torsional parameters of cucurbit[8]uril and by revised protocols annihilating the intra-molecular van der Waals and key torsions in the ligands. Corresponding methods for all portions of this work have been implemented in the Tinker software package, some of which are also available in the Tinker-OpenMM library

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods

ACCELERATED COMPUTING FOR MOLECULAR DYNAMICS SIMULATION

Molecular dynamics (MD) simulation serves as a computational microscope into the behavior of the biological and chemical macromolecules. At its core, MD models the interactions between atoms at various levels – force fields model the higher quantum level interactions using simpler physics-based models of interaction energies, while periodic boundary conditions model the bulk phase using lattice-based periodic copies of the simulation box. One limitation of the finite size of the simulation box seen during the simulation of membrane bilayers is the artifact of a chemical disequilibrium between the two layers as a drug molecule enters into the bilayer. We have tried to solve this problem by using a periodic boundary condition which has a half screw symmetry. Our results show that the method scales similar to the best-known method for the normal periodic boundary conditions. We have migrated CHARMM to an efficient implementation on the GPUs. These architectures provide thousands of cores on the same chip but require different programming model in order to use the underlying architecture. Our results show that the new CHARMM CUDA engine is efficient in time and accurate in precision. We have also participated in blind prediction challenges organized by SAMPL community to have a fair assessment of the computational chemistry tools. We developed a hybrid QM and MM technique to predict the pKa of drug-like molecules. It avoids the implicit solvent model used by quantum mechanical models and uses explicit solvent molecules. Since modeling explicit solvent molecules is difficult at QM level, they are modeled at the MM level instead. Thermodynamic cycle couples the aqueous Gibbs free energy of deprotonation to simpler components which can be modeled with higher accuracy. We also built a deep learning model to predict the logP of a set of drug-like molecules in a blind fashion. The generated model is robust over a large number of molecules, not just the ones that it was tested for in the SAMPL competition. We expect the method to be interesting for the drug design industry since lipophilicity of a molecule is important to be known even before it has been synthesized