(226h) Locating Optimum Orientations of Adsorbed Protein on a Solid Surface Using a Hybrid Genetic Algorithm and Spatial Grid Method | AIChE

(226h) Locating Optimum Orientations of Adsorbed Protein on a Solid Surface Using a Hybrid Genetic Algorithm and Spatial Grid Method


Wei, T. - Presenter, Lamar University
Mu, S. - Presenter, Yokogawa Engineering Asia Pte Ltd
Nakano, A. - Presenter, University of Southern California
Shing, K. - Presenter, University of Southern California

Atomistic simulation of protein adsorption on a solid surface in aqueous environment is very computationally demanding and the determination of preferred protein orientations on the solid surface usually serves as an initial step in the simulation study. We have developed an efficient method to calculate the adsorption energy of a protein molecule on a solid surface and to search for low energy orientations. In the hybrid genetic algorithm/spatial grid method, the surface and the protein molecule are treated as rigid bodies, whereas the bulk fluid space is gridded. For each bulk space grid point, an effective interaction region in the surface is defined (such as by a cutoff distance), and the possible interaction energy between an atom at the grid point and the surface is calculated and recorded. The protein is rotated as a rigid body (the protein configuration in water at pH7.4 was previously deduced by a molecular-dynamic simulation), while maintaining constant minimum protein-surface distance, and the orientation-dependent energy is then obtained using the generated database of grid energies. A hybrid-GA loop search (Hybrid GA) procedure consists of a genetic algorithm to identify promising regions for the global energy minimum and a local optimizer with derivative-free Nelder-Mead method to search for the lowest-energy orientation within the identified regions. Similarity Coefficient (SC) analysis is used to adaptive control to whether to terminate search in the loop. We test the method for lysozyme adsorption on a hydrophobic hydrogen-terminated silicon (110) surface in implicit water (i.e., with a distance-dependent dielectric constant). Hybrid GA is compared with the standard GA and a brute-force search. The results show that the Hybrid search method has faster convergence, greater exploration capability and better solution accuracy. The optimum orientation corresponds to maximization of the hydrophobic interactions between the surface and the lysozyme molecule.


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