Protein Model Refinement and Flexible Docking by Constrained Free Energy Minimization

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This project focuses on two fundamental problems. The first is the development of flexible protein docking algorithms for the case in which the association occurs with substantial conformational change involving side chains and loop regions. The second problem is the development of con-formational search algorithms for refining low resolution protein models with 6 to 10 A RMSD from the native structure, obtained by homology modeling, fold recognition. or ab initio structure prediction. In both applications, free energy potentials, combining molecular mechanics with em-pirical solvation and entropic terms, will be used as target functions. It has been shown that such potentials can be more effective in decoy discrimination than purely empirical functions. However even with a normalization procedure to reduce the noise in the van der Waals energy, the combined potentials exhibit a multicomponent/multifrequency behavior, and are very difficult to minimize. The free energy is regarded as the sum of three functions: a smooth component that includes elec-trostatic, solvation, and entropic contributions, an intermediate frequency component of internal (bonded) energy terms, and the van der Waals term which is essentially a high frequency noise, and carries little information about the distance from the native state.Most established methods of finding the global minimum of a multicomponent/multifrequency potential generate a large number of trial conformations and then rank them according to their free energies. This project will use a radically different approach that mimics native folding or association pathways. The basic idea of the method is performing minimization of the smooth free energy components, but restricting the search to regions of the conformational space with low van der Waals energy. Since this group has already shown that the combined potential has good discriminatory power, the key to a successful docking or model refinement is the ability to generate enough near-native conformations. It is expected that exploiting the gradient of the smooth free energy components will substantially increase the efficiency of sampling, and the resulting methods will be useful to the broad molecular biology/biochemistry community.