ABSTRACT: We describe the development and application of a computational method for the prediction and rationalization of pKa values of ionizable residues in proteins, based on ab initio quantum mechanics (QM) and the effective fragment potential (EFPs) method (a hybrid QM/MM method). The theoretical developments include (1) a covalent boundary method based on frozen localized orbitals, (2) divide-and-conquer methods for the ab initio computation of protein EFPs consisting of multipoles up to octupoles and dipole polarizability tensors, (3) a method for computing vibrational free energies for a localized molecular region, and (4) solutions of the polarized continuum model of bulk solvation equations for protein-sized systems. The QM-based pKa prediction method is one of the most accurate methods currently available and can be used in cases where other pKa prediction methods fail. Preliminary analysis of the computed results indicate that many pKa values (1) are primarily determined by hydrogen bonds rather than long-range charge-charge interactions and (2) are relatively insensitive to large-scale dynamical fluctuations of the protein structure.

A promising approach to developing improved potential functions for modeling macromolecular interactions consists of combining protein structural analysis, quantum mechanical calculations on small molecule models, and molecular mechanics potential decomposition. Here we apply this approach to the interactions of pairs of ring-containing amino acids in proteins. We find reasonable qualitative agreement between molecular mechanics and quantum chemistry calculations, both over one-dimensional projections of the binding free energy landscape for amino acid homodimers and over a set of homodimers and heterodimers from experimentally observed protein crystal structures. The molecular mechanics landscapes are a sum of charge-charge and Lennard-Jones contributions; short-range quantum mechanical effects such as charge transfer appear not to be significant in ring side chain interactions. We also find a reasonable degree of correlation between the molecular mechanics energy landscapes and the distributions of dimer geometries observed in protein structures, suggesting that the intrinsic dimer interaction energies do contribute to packing of side chains in proteins rather than being overwhelmed by the numerous interactions with other protein atoms and solvent. These results demonstrate that interactions involving aromatic residues and proline can be fairly well modeled using current molecular mechanics force fields, but there is still room for improvement, particularly for interactions involving proline and tyrosine.