Molecular mechanics force fields, which are commonly used in biomolecular modeling and computer-aided drug design, typically treat nonbonded interactions using a limited library of empirical parameters that are developed for small molecules. This approach does not account for polarization in larger molecules or proteins, and the parametrization process is labor-intensive. Using linear-scaling density functional theory and atoms-in-molecule electron density partitioning, environment-specific charges and Lennard-Jones parameters are derived directly from quantum mechanical calculations for use in biomolecular modeling of organic and biomolecular systems. The proposed methods significantly reduce the number of empirical parameters needed to construct molecular mechanics force fields, naturally include polarization effects in charge and Lennard-Jones parameters, and scale well to systems comprised of thousands of atoms, including proteins. The feasibility and benefits of this approach are demonstrated by computing free energies of hydration, properties of pure liquids, and the relative binding free energies of indole and benzofuran to the L99A mutant of T4 lysozyme.
The quality of the 1.14*CM1A and 1.20*CM5 charge models was evaluated for calculations of free energies of hydration. For a set of 426 neutral molecules, 1.14*CM1A and 1.20*CM5 yield MADs of 1.26 and 1.20 kcal/mol, respectively. The 1.14*CM1A charges, which can be readily obtained for large systems, exhibit large deviations only for a subset of functional groups. The results for these cases were systematically improved using Localized Bond Charge Corrections (LBCC) by which off-setting adjustments are made to the partial charges for atoms in specified bond types. Only 19 LBCCs were needed to yield 1.14*CM1A-LBCC charges that reduce the errors for the 426 ΔGhyd values to only 0.61 kcal/mol. The modified charge method was also tested in computation of heats of vaporization and densities for pure organic liquids, yielding average errors of 1.40 kcal/mol and 0.024 g/cm3, similar to those for 1.14*CM1A.
Multisite λ dynamics (MSλD) is a powerful emerging method in free energy calculation that allows prediction of relative free energies for a large set of compounds from very few simulations. Calculating free energy differences between substituents that constitute large volume or flexibility jumps in chemical space is difficult for free energy methods in general, and for MSλD in particular, due to large free energy barriers in alchemical space. This study demonstrates that a simple biasing potential can flatten these barriers and introduces an algorithm that determines system specific biasing potential coefficients. Two sources of error, deep traps at the endpoints and solvent disruption by hard-core potentials, are identified. Both scale with the size of the perturbed substituent and are removed by sharp biasing potentials and a new soft-core implementation, respectively. MSλD with landscape flattening is demonstrated on two sets of molecules: derivatives of the heat shock protein 90 inhibitor geldanamycin and derivatives of benzoquinone. In the benzoquinone system, landscape flattening leads to two orders of magnitude improvement in transition rates between substituents and robust solvation free energies. Landscape flattening opens up new applications for MSλD by enabling larger chemical perturbations to be sampled with improved precision and accuracy.
The recently developed Charge Model 5 (CM5) is tested for its utility in condensed-phase simulations. The CM5 approach, which derives partial atomic charges from Hirshfeld population analyses, provides excellent results for gas-phase dipole moments and is applicable to all elements of the periodic table. Herein, the adequacy of scaled CM5 charges for use in modeling aqueous solutions has been evaluated by computing free energies of hydration (ΔGhyd) for 42 neutral organic molecules via Monte Carlo statistical mechanics. An optimal scaling factor for the CM5 charges was determined to be 1.27, resulting in a mean unsigned error (MUE) of 1.1 kcal/mol for the free energies of hydration. Testing for an additional 20 molecules gave an MUE of 1.3 kcal/mol. The high precision of the results is confirmed by free energy calculations using both sequential perturbations and complete molecular annihilation. Performance for specific functional groups is discussed; sulfur-containing molecules yield the largest errors. In addition, the scaling factor of 1.27 is shown to be appropriate for CM5 charges derived from a variety of density functional methods and basis sets. Though the average errors from the 1.27*CM5 results are only slightly lower than those using 1.14*CM1A charges, the broader applicability and easier access to CM5 charges via the Gaussian program are additional attractive features. The 1.27*CM5 charge model can be used for an enormous variety of applications in conjunction with many fixed-charge force fields and molecular modeling programs.
The estimation of changes in free energy upon mutation is central to the problem of protein design. Modern protein design methods have had remarkable success over a wide range of design targets, but are reaching their limits in ligand binding and enzyme design due to insufficient accuracy in mutational free energies. Alchemical free energy calculations have the potential to supplement modern design methods through more accurate molecular dynamics based prediction of free energy changes, but suffer from high computational cost. Multisite λ dynamics (MSλD) is a particularly efficient and scalable free energy method with potential to explore combinatorially large sequence spaces inaccessible with other free energy methods. This work aims to quantify the accuracy of MSλD and demonstrate its scalability. We apply MSλD to the classic problem of calculating folding free energies in T4 lysozyme, a system with a wealth of experimental measurements. Single site mutants considering 32 mutations show remarkable agreement with experiment with a Pearson correlation of 0.914 and mean unsigned error of 1.19 kcal/mol. Multisite mutants in systems with up to five concurrent mutations spanning 240 different sequences show comparable agreement with experiment. These results demonstrate the promise of MSλD in exploring large sequence spaces for protein design.
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