This thesis explores the capacity and efficiency of Monte Carlo based methods for structural modeling of protein-protein complexes. I have proposed and implemented new efficient flexible protein-protein docking methods, which offer a wild range of applications to systematically generate realistic models of protein-protein complexes. Furthermore, I have derived an integrative docking solution based on Bayesian inference approach, which is shown to be solid and error tolerant, from the Markov chain Monte Carlo sampling docking.
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This thesis explores the capacity and efficiency of Monte Carlo based methods for structural modeling of protein-protein complexes. I have proposed and implemented new efficient flexible protein-protein docking methods, which offer a wild range of applications to systematically generate realistic models of protein-protein complexes. Furthermore, I have derived an integrative docking solution based on Bayesian inference approach, which is shown to be solid and error tolerant, from the Markov chai...
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