Accurate prediction of binding free energies of protein-ligand system has long been a focus area for theoretical and computational studies; with important implications in fields like pharmaceuticals, enzyme-redesign, etc. The aim of this project was to develop such a predictive model for calculating binding free energies of protein-ligand systems based on the LIE-SASA methods. Many models have been successfully fit to experimental data, but a general predictive model, not reliant on experimental values, would make LIE-SASA a more powerful and widely applicable method. The model was developed such that There is no significant increase in computational time No increase in complexity of system setup No increase in the number of empirical parameters. The method was tested on a small number of protein-ligand systems, selected with certain constraints. This was our training set, from which we obtain the complete expression for binding free energy. Expectedly, there was good agreement with experimental values for the training set On applying our model to a similar sized validation set, with the same selection constraints as for the training set, we achieved even better agreement with experimental results, with lower standard errors. Finally, the model was tested by applying it to a set of systems without such selection constraints, and again found good agreement with experimental values. In terms of accuracy, the model was comparable to a system specific empirical fit that was performed on this set. These encouraging results could be an indicator of generality.

A central goal of computational biophysics and biochemistry is to understand the behavior, interactions, and reactions of molecules, and to interpret and facilitate experimental design. The objective of this thesis research is to use the molecular modeling and simulation techniques to advance our understanding of principles in molecular structure properties, recognition and interaction at the atomic level. First, a physical molecular mechanics model is built to study the conformational properties of depsipeptide, which shows potential for engineered protein mimetics with controllable structure and function. We explore the possible kinase-substrate binding modes and the likelihood of an [alpha]-helix docking interaction within a kinase active site. Finally, efficient physical models based on a polarizable potential function are developed to describe the structural properties and calculate protein-ligand binding affinities accurately for both trypsin and matrix metalloproteinase. / text