SASAI Masaki

I. Theoretical Cell Biology

A. Chromatin Structure and Dynamics

  1. Heterogeneous spatial distribution of transcriptional activity in budding yeast nuclei, Naoko Tokuda and Masaki Sasai, Biophys. J. 112:491–504, (2017).
  2. Liquid-like behavior of chromatin, K. Maeshima, S. Ide, K. Hibino, and M. Sasai, Curr. Opin. Genetics and Development. 37, 36-45 (2016).
  3. Dynamical modeling of 3D genome organization in interphase budding yeast, N. Tokuda, T. P. Terada, and M. Sasai, Biophys. J. 102, 296-304 (2012).
  4. Roles of DNA looping in enhancer-blocking activity, N. Tokuda, M. Sasai, and G. Chikenji, Biophys. J. 100, 126-134 (2011).

 

B. Stochastic Gene Expression

  1. Effects of collective histone state dynamics on epigenetic landscape and kinetics of cell reprogramming, S. S. Ashwin and M. Sasai, Scientific Reports 5 , 16746 (2015).
  2. Multiple coupled landscapes and non-diabatic dynamics with applications to self activating genes, C. Chen, K. Zhang, H. Feng, M. Sasai, and J. Wang, Phys. Chem. Chem. Phys. 17, 29036-29044 (2015).
  3. Time scales in epigenetic dynamics and phenotypic heterogeneity of embryonic stem cells, M. Sasai, Y. Kawabata, K. Makishi, K. Itoh, and T. P. Terada, PLoS Comput. Biol. USA 9, e100338 (2013).
  4. Eddy current and coupled landscapes for nonadiabatic and nonequilibrium complex system dynamics, K. Zhang, M. Sasai, and J. Wang, Proc. Natl. Acad. Sci. USA 110, 14930-14935 (2013).
  5. Stable, precise, and reproducible patterning of bicoid and hunchback molecules in the early Drosophila embryo, Y. Okabe-Oho, H. Murakami, S. Oho, and M. Sasai, PLoS Comput. Biol. 5, e1000486 (2009).
  6. Effects of the DNA state fluctuation on single-cell dynamics of self-regulating gene, Y. Okabe, Y. Yagi, and M. Sasai, J. Chem. Phys. 127, 105107 (2007).
  7. Stable stochastic dynamics in yeast cell cycle Y. Okabe and M. Sasai, Biophys. J. 93, 3451-3459 (2007).
  8. Roles of noise in single and coupled multiple genetic oscillators, M. Yoda, T. Ushikubo, W. Inoue, and M. Sasai, J. Chem. Phys. 126, 115101 (2007).
  9. Theoretical research on dynamics of the genetic toggle switch, Ushikubo, W. Inoue, M. Yoda, and M. Sasai, Frontiers of Computational Science (Y. Kaneda, et al. eds. Springer, ISBN-103-540-56373-9) 289-292 (2007).
  10. Testing the transition state theory in stochastic dynamics of a genetic switch, T. Ushikubo, W. Inoue, M. Yoda, and M. Sasai, Chem. Phys. Lett. 430, 139-143 (2006).
  11. Self consistent proteomic field theory of stochastic gene switches, A. M. Walczak, M. Sasai, and P. G. Wolynes, Biophys. J. 88, 828-850 (2005).
  12. Dynamics of repressilator: From noise to coherent oscillation, T. Ushikubo, W. Inoue, and M. Sasai, Genome Informatics 2003, (Gribskov M. et al. eds. ISBN4-946443-82-7), 314-315 (2003).
  13. Stochastic gene expression as a many body problem, M. Sasai and P. G. Wolynes, Proc. Natl. Acad. Sci. USA, 100, 2374-2379 (2003).

 

C. In vitro Circadian Oscillation

  1. Synchronization of Circadian Oscillation of Phosphorylation Level of KaiC in vitro, T. Nagai, T. P. Terada, and M. Sasai, Biophys. J. 98, 2469-2477 (2010).
  2. Mechanism of robust circadian oscillation of KaiC phosphorylation in vitro, K. Eguchi, M. Yoda, T. P. Terada, and M. Sasai, Biophys. J. 95, 1773-1784 (2008).
  3. Monomer-shuffling and allosteric transition in circadian oscillation of KaiC phosphorylation, M. Yoda, K. Eguchi, T. P. Terada, and M. Sasai, PLoS ONE, 2, e408. (2007).

 

D. Eukaryotic Cell Locomotion

  1. Non-Brownian dynamics and strategy of amoeboid cell locomotion, S. I. Nishimura, M. Ueda, and M. Sasai, Phys. Rev. E 85, 041909 (2012).
  2. Cortical factor feedback model of cellular locomotion and cytofission, S. I. Nishimura, M. Ueda, and M. Sasai, PLoS Comp. Biol. 5, e1000310 (2009).
  3. Modulation of the reaction rate of regulating protein induces large morphological and motional change of amoebic cell, S. I. Nishimura and M. Sasai, J. Theor. Biol. 245, 230-237 (2007).
  4. Inertia of amoebic cell locomotion as an emergent collective property of the cellular dynamics, S. I. Nishimura and M. Sasai, Phys. Rev. E 71, 010902 (2005).



II. Proteins

A. Protein Dynamics and Function

  1. Coupling of lever arm swing and biased Brownian motion in actomyosin, Q.-M. Nie, A. Togashi, T. N. Sasaki, M. Takano, M. Sasai, and T. P. Terada, PLoS Comp. Biol. , 10, e1003552 (2014).
  2. Conformational flexibility of loops of myosin enhances global bias in the actin-myosin interaction landscape, Q.-M. Nie, M. Sasai, and T. P. Terada, Phys. Chem. Chem. Phys. , 16, 6441-6447 (2014).
  3. Entropic mechanism of allosteric communication in conformational transitions of dihydrofolate reductase, T. P. Terada, T. Kimura and M. Sasai, J. Phys. Chem. B, 117, 12864-12877 (2013).
  4. Statistical mechanics of protein allostery: Roles of backbone and side-chain structural fluctuations, K. Itoh and M. Sasai, J. Chem. Phys., 134, 125102 (2011).
  5. Unidirectional Brownian motion observed in an in silico single molecule experiment of an actomyosin motor, M. Takano, T. P. Terada, and M. Sasai, Proc. Natl. Acad. Sci. USA, 107, 7769-7774 (2010).
  6. Entropic mechanism of large fluctuation in allosteric transition, K. Itoh and M. Sasai, Proc. Natl. Acad. Sci. USA, 107, 7775-7780 (2010).
  7. Dynamical transition and proteinquake in photoactive yellow protein, K. Itoh and M. Sasai, Proc. Natl. Acad. Sci. USA , 101, 14736-14741 (2004).
  8. Coupling of functioning and folding: Photoactive yellow protein as an example system, K. Itoh and M. Sasai, Chem. Phys. 307 121-127 (2004).
  9. On the model granularity to simulate protein dynamics: A biological physics view on biomolecular computing, M. Takano, J. Higo, H.K. Nakamura, M. Sasai, Natural Computing 4, 377-393 (2004).
  10. Conformational change of actomyosin complex drives the multiple stepping movement, T. P. Terada, M. Sasai, and T. Yomo, Proc. Natl. Acad. Sci. USA 99, 9202-9206 (2002).


B. Protein Folding

  1. Cooperativity and modularity in protein folding, Masaki Sasai, George Chikenji, and Tomoki P. Terada, Biophysics and Physicobiology 13: 281-293 (2016).
  2. Folding pathway of a multidomain protein depends on its topology of domain connectivity, T. Inanami, T. P. Terada, and M. Sasai, Proc. Natl. Acad. Sci. USA, 111, 15969-15974 (2014).
  3. Multi-dimensional theory of protein folding, K. Itoh and M. Sasai, J. Chem. Phys., 130, 145104 (2009).
  4. Cooperativity, connectivity, and folding pathways of multidomain proteins, K. Itoh and M. Sasai, Proc. Natl. Acad. Sci. USA, 105, 13865-13870 (2008).
  5. Flexibly varying folding mechanism of a nearly symmetrical protein: B domain of protein A, K. Itoh and M. Sasai, Proc. Natl. Acad. Sci. USA, 103, 7298-7303 (2006).
  6. Correlation between evolutionary structural development and protein folding, C. Nagao, T. P. Terada, T. Yomo, and M. Sasai, Proc. Natl. Acad. Sci. USA, 102, 18950-18955 (2005).
  7. Scrutinizing the squeezed exponential kinetics observed in the folding simulation of an off-lattice Go-like protein model, H. K. Nakamura, M. Sasai, and M. Takano, Chem. Phys. 307 259-267 (2004).
  8. Squeezed exponential kinetics to describe a nonglassy downhill folding as observed in a lattice protein model, H. K. Nakamura, M. Sasai, and M. Takano, Proteins: Structure, Function, and Bioinformatics, 55, 99-106 (2004).
  9. Correlation between the conformation space and the sequence space of peptide chain, T. N. Sasaki and M. Sasai, J. Biol. Phys. 28, 483-492 (2002).
  10. Population analyses of kinetic partitioning in protein folding, H.K. Nakamura and M. Sasai, Proteins: Structure, Function, and Genetics, 43, 280-291 (2001).
  11. Strange kinetics and complex landscapes in a lattice model of protein folding, H.K. Nakamura, T.N. Sasaki, and M. Sasai, Chem. Phys. Lett. 347, 247-254 (2001).
  12. Kinetic consistency in protein folding process, S. Saito and M. Sasai, J. Mol. Struct.-THEOCHEM, 462, 503-521 (1999).
  13. Gradual development of protein-like global structures through functional selection, T. Yomo, S. Saito, and M. Sasai, Nature Structural Biology, 6, 743-746 (1999).
  14. Hierarchy and connectivity in the folding funnel, H.K. Nakamura and M. Sasai, Old and New Views of Protein Folding ed. by K. Kuwajima and M. Arai, (Elsevier, Amsterdam) 125-131 (1999).
  15. Gradual development of folding ability through functional selection, S. Saito, M. Sasai, and T. Yomo, Biological Physics, ed. by H. Frauenfelder, G. Hummer, and R. Garcia, (American Institute of Physics, New York) 69-76 (1999).
  16. Protein folding simulation and self-consistent potential functions, F. Takazawa, M. Sasaki, M. Kanehisa, Genome Informatics 1998, S. ed. by Miyano, and T. Takagi (Universal Academy Press, Tokyo, 1998) 308-309.
  17. Evolution of the folding ability of proteins through functional selection, S. Saito, M. Sasai, and T. Yomo, Proc. Natl. Acad. Sci. USA 94, 11324-11328 (1997).
  18. Conformation, energy, and folding ability of selected amino acid sequences, M. Sasai, Proc. Natl. Acad. Sci. USA 92, 8438-8442 (1995).
  19. Unified theory of collapse, folding and glass transitions in associative memory Hamiltonian models of proteins, M. Sasai and P.G. Wolynes, Phys. Rev. A 46, 7979-7997 (1992).
  20. Molecular theory of associative memory hamiltonian models of protein folding, M. Sasai and P.G. Wolynes, Phys. Rev. Lett. 65, 2740-2743 (1990).


C. Protein Structure Prediction

  1. The Fragment-based consistency score in model quality assessment for de novo prediction of protein structures, H. Cetin, T. N. Sasaki, and M. Sasai, Chem-Bio Informatics Journal 11, 63-81 (2011).
  2. De novo protein structure prediction by dynamic fragment assembly and conformational space annealing, J. Lee, J. Lee, T. N. Sasaki, M. Sasai, C. Seok, and J. Lee, Proteins: Structure, Function, and Bioinformatics. 79, 2403-2417 (2011).
  3. A coarse-grained Langevin molecular dynamics approach to de novo protein structure prediction, T. N. Sasaki, H. Cetin, and M. Sasai, Biochem. Biophys. Research Comm. 369, 500-506 (2008).
  4. A coarse-grained Langevin molecular dynamics approach to protein structure reproduction, T. N. Sasaki and M. Sasai, Chem. Phys. Lett. 402, 102-106 (2005).


D. Protein-Ligand Interaction

  1. Importance of consensus region of multiple-ligand templates in a virtual screening method, T. Okuno, K. Kato, S.Minami, T. P. Terada, M. Sasai, and G. Chikenji, Biophysics and Physicobiology 13: 149–156 (2016).
  2. VS-APPLE: A virtual screening algorithm using promiscuous protein-ligand complexes, T. Okuno, K. Kato, T. P. Terada, M. Sasai, and G. Chikenji, J. Chemical Information and Modeling 55, 1108-1119 (2015).


III. Liquid Water

  1. Fluctuating hydration structure around nanometer-size hydrophobic solutes II - Caging and drying around single-wall carbon nanotubes -, T. Hotta, and M. Sasai, J. Phys. Chem. C 111, 2861-2871 (2007).
  2. Hydrophobic Hydration on Nanometer Length Scale, T. Hotta and M. Sasai, Frontiers of Computational Science (Y. Kaneda, et al. eds. Springer, ISBN-103-540-56373-9) 239-242 (2007).
  3. Fluctuating hydration structure around nanometer-size hydrophobic solutes I - Caging and drying around C60 and C60H60 spheres -, T. Hotta, A. Kimura, and M. Sasai, J. Phys. Chem. B 109, 18600-18608 (2005).
  4. Energy landscape picture of supercooled liquids: Application of a generalized random energy model, M. Sasai, J. Chem. Phys. 118, 10651-10662 (2003).
  5. Energy landscape perspective of kinetic slowing down in liquid water, M. Sasai, New Kinds of Phase Transitions: Transformations in Disordered Substances (Proc. NATO Advanced Research Workshop, Volga River), ed. by V. V. Brazhkin. S. V. Buldyrev, V. N. Ryzhov, and H. E. Stanley, (Kluwer, Dordecht) 121-130 (2002).
  6. Large vortex-like structure of dipole field in computer models of liquid water and Dipole-Bridge between Biomolecules, J. Higo, M. Sasai, H. Shirai, H. Nakamura, and T. Kugimiya, Proc. Natl. Acad. Sci. USA, 98,  5961-5964 (2001).
  7. Spatiotemporal heterogeneity and rugged energy landscape in liquid water, M. Sasai, Physica A 285, 315-324 (2000).
  8. Spatiotemporal inhomogeneity and rugged energy landscape in liquid water, M. Sasai, Science and Technology of High Pressure, ed. by Murli H. Manghnani, William J. Nellis and Malcolm F. Nicol. (Universities Press, Hyderabad, India) 122-124 (2000).
  9. Molecular scale precursor of the liquid-liquid phase transition of water, E. Shiratani and M. Sasai, J. Chem. Phys 108, 3264-3276 (1998).
  10. Growth and collapse of structural patterns in the hydrogen bond network in liquid water, E. Shiratani and M. Sasai, J. Chem. Phys. 104, 7671-7680 (1996).
  11. Random graph models of metastable liquid water, M. Sasai, Physical Chemistry of Aqueous Systems, ed. by H. J. White, Jr. et al. (Begell House, New York) 370-377 (1995).
  12. The random graph model of hydrogen bond network, M. Sasai, Bull. Chem. Soc. Jpn. 66, 3362-3371 (1993).
  13. Functional integral model of liquid water and field theoretic description of hydration, M. Sasai, Water-Biomolecule Interaction, ed. by M.U.Palma et al. (Italian Physical Soc) 285-288 (1993).
  14. Long time fluctuation of liquid water - 1/f spectrum of energy fluctuation in hydrogen bond network rearrangement dynamics -, M. Sasai, I. Ohmine and R. Ramaswamy, J. Chem. Phys. 96, 3045-3053 (1992).
  15. Fluctuations of hydrogen bond network in liquid water, M. Sasai, Slow Dynamics in Condensed Matter, ed. by K. Kawasaki et al. (American Institute of Physics, New York), 513-514 (1992).
  16. Relaxations, fluctuations, phase transitions and chemical reactions in liquid water, I. Ohmine and M. Sasai, Prog. Theor. Phys. Suppl. 103, 61-91 (1991).
  17. Instabilities of hydrogen bond network in liquid water, M. Sasai, J. Chem. Phys. 93, 7329-7341 (1990).