1. Protein Folding Theory

Proteins self-organize themselves to fold into unique native structures. This fascinating process is studied by statistical mechanical theories and computer simulations. Complex folding processes involving intermediate states or multiple pathways are analyzed in terms of multi-dimensional free energy landscape.
M. Sasai, "Conformation, energy, and folding ability of selected amino acid sequences", Proc. Natl. Acad. Sci. USA 92(18) 8438-8442 (1995).
H.K. Nakamura & M. Sasai, "Population analyses of kinetic partitioning in protein folding", Prot. Struct. Func. Genet. 43(3) 280-291 (2001).
K. Itoh & M. Sasai, "Flexibly varying folding mechanism of a nearly symmetrical protein: B domain of protein A", Proc. Natl. Acad. Sci. USA 103(19) 7298-7303 (2006).

2. Simulated Evolution of Proteins  (From random sequences to proteins)

Can proteins evolve from random sequences? When the local steric configuration at the active-site is chosen to have specific functional form, random sequences can evolve into the protein-like fordable sequences. We can monitor in computer the process of simulated protein evolution.

In the figure below, each rank represents three conformations resulted from three independent folding simulations. Red parts are helices. Balls represent the active site. At the 10th generation of evolutionary selection, simulated folding trajectories reach randomly different structures, while at the 200th generation, folding trajectories converge to a unique native-like conformation. This simulated evolution showed that sequence selection according to a functional requirement suffices to select the foldable sequence.
T. Yomo, S. Saito & M. Sasai, "Gradual development of protein-like global structures through functional selection", Nat. Struct. Biol. 6(8) 743-746 (1999).
T.N. Sasaki & M. Sasai, "Correlation Between the Conformation Space and the Sequence Space of Peptide Chain", J. Biol. Phys. 28(3) 483-492 (2002).
C. Nagao, T.P. Terada, T. Yomo & M. Sasai, Proc. Natl. Acad. Sci. USA 102(52) 18950-18955 (2005).

3. Proteins as Soft Machines I (Molecular motors and allostery)

Large scale structural change in protein functioning process often takes place with the time scale of msec to sec. In such large scale structural change, disorder-order transitions can be involved. The energy landscape which facilitates such deformation process should be called "functional funnel". Precise motion in motor proteins can be described as trajectories in functional funnel. Borrowing theories and computational methods from protein folding study, protein functioning processes are analyzed.
The figure below shows the trajectories of motion of a myosin head sliding along the actin filament. x is the position along the actin filament, t is time, rho is the order parameter describing partial unfolding/folding of myosin head. After ATP hydrolysis, myosin takes multiple steps of sliding motion with the unit step of 5.5nm which corresponds to the diameter of actin monomer. Myosin works as a soft dynamical material.
T.P. Terada, M. Sasai & T. Yomo, "Conformational Change of Actomyosin Complex Drives the Multiple Stepping Movement", Proc. Natl. Acad. Sci. USA 99 (14) 9202 (2002).

4. Proteins as Soft Machines II (Signal transduction and allostery)

Local change due to the reception of signal or ligands can cause the global large scale structural deformation of proteins. Such allostery is based on dynamical flexible response. In photoactive yellow protein, for example, chromophore colored with light blue in the figure absorbs a photon, which triggers unfolding of the N-terminal cap. Partial unfolding/folding controls the lifetime of the important intermediate in photo cycle.
K. Itoh & M. Sasai, "Dynamical transition and proteinquake in photoactive yellow protein", Proc. Natl. Acad. Sci. USA 101(41) 14736-14741 (2004).
K. Itoh & M. Sasai, "Coupling of functioning and folding: photoactive yellow protein as an example system", Chem. Phys. 307(2-3) 121-127 (2004).

5. Protein Structure Prediction

Using a multi-body potential derived from database of structures, the Langevin molecular dynamics simulation is performed to predict protein structures. Structures of several tens or hundred residue long were successfully predicted with this method. Consistency among local fragmental structures and the global conformation underlies the success of prediction method.
T.N. Sasaki & M. Sasai, "A coarse-grained langevin molecular dynamics approach to protein structure reproduction", Chem. Phys. Lett. 402(1-3) 102-106 (2005).

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