[1P-31*] Parallel Cascade Selection Molecular Dynamics/Markov State Model によるタンパク質－タンパク質複合体の結合親和性評価
Protein-protein interactions are crucial for various cellular functions. Thus the characterization of the interactions is important for computationally designing next-generation protein drugs, developing new protein interactions in protein engineering, and many more. Estimating kinetic rates and binding free energy of protein-protein complexes is an important computational technique in accurately understanding the interactions of the complexes. Due to the fact that the time scale of association/dissociation events is much longer than that of molecular simulation, accurate prediction of the kinetic rates is computationally challenging. Previously, Parallel Cascade Selection Molecular Dynamics (PaCS-MD) combined with the Markov State Modeling (MSM), PaCS-MD/MSM, was shown to reproduce the kinetic rates and binding free energy of a protein-peptide complex. In this work, I applied PaCS-MD/MSM to the stable complex of barnase and barstar as the first application to protein-protein complexes. Finally, PaCS-MD/MSM successfully reproduced experimentally-measured dissociation constant rate and binding free energy for the tightly bound protein-protein complex.