Computational Methods for Calculation of Ligand-Receptor Binding Affinities Involving Protein and Nucleic Acid Complexes
Abstract: The ability to accurately predict binding free energies from computer simulations is an invaluable resource in understanding biochemical processes and drug action. Several methods based on microscopic molecular dynamics simulations exist, and in this thesis the validation, application, and development of the linear interaction energy (LIE) method is presented.For a test case of several hydrophobic ligands binding to P450cam it is found that the LIE parameters do not change when simulations are performed with three different force fields. The nonpolar contribution to binding of these ligands is best reproduced with a constant offset and a previously determined scaling of the van der Waals interactions.A new methodology for prediction of binding free energies of protein-protein complexes is investigated and found to give excellent agreement with experimental results. In order to reproduce the nonpolar contribution to binding, a different scaling of the van der Waals interactions is neccesary (compared to small ligand binding) and found to be, in part, due to an electrostatic preorganization effect not present when binding small ligands.A new treatment of the electrostatic contribution to binding is also proposed. In this new scheme, the chemical makeup of the ligand determines the scaling of the electrostatic ligand interaction energies. These scaling factors are calibrated using the electrostatic contribution to hydration free energies and proposed to be applicable to ligand binding.The issue of codon-anticodon recognition on the ribosome is adressed using LIE. The calculated binding free energies are in excellent agreement with experimental results, and further predict that the Leu2 anticodon stem loop is about 10 times more stable than the Ser stem loop in complex with a ribosome loaded with the Phe UUU codon. The simulations also support the previously suggested roles of A1492, A1493, and G530 in the codon-anticodon recognition process.
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