Observation of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msup><mml:mi>B</mml:mi><mml:mn>0</mml:mn></mml:msup><mml:mo>→</mml:mo><mml:mi>p</mml:mi><mml:mover accent="true"><mml:mi>Λ</mml:mi><mml:mo>¯</mml:mo></mml:mover><mml:msup><mml:mi>π</mml:mi><mml:mo>−</mml:mo></mml:msup></mml:math>

Mz, Wang; Yj, Lee; K, Abe; T, Abe; Aihara, H.; Akatsu, M.; Asano, Yoshihiro; T, Aso; Aushev, T.; Bakich, AM; Ban, Y.; Banaś, E.; Bay, A.; Behera, P. K.; Bizjak, I.; Bondar, A.; Bożek, A.; Bračko, M.; Browder, T. E.; Bc, Casey; Mc, Chang; Chang, P.; Chào, Y.; Chen, Kai-Feng; Cheon, B.G.; Chistov, R.; Choi, Y.; Choi, Youngjoon; Drutskoy, A.; Eidelman, S.; Eiges,; Fukunaga, C.; Gabyshev, N.; Garmash, A.; Gershon, T.; Golob, B.; Guo, R. S.; Hagner, C.; Hara, T.; Hazumi, M.; Hojo, T.; Hokuue, T.; Hoshi, Y.; Hou, W.-S.; Hsiung, Y.; Huang, Hsuan‐Cheng; Igaki, T.; Igarashi, Y.; Iijima, T.; Inami, K.; Ishikawa, A.; Itoh, R.; Iwasaki, H.; Iwasaki, Y.; Hk, Jang; Kang, Joon Ho; Kang, J. S.; Katayama, N.; Kawai, Hiroyuki; Kawasaki, T.; Kichimi, H.; Dw, Kim; Kim, Hyung Jun; Kim, Jae-Hoon; Kinoshita, K.; Kobayashi, S.; Krokovny, P.; Kuzmin, A.; Kwon, Y.-J.; Sh, Lee; Li, Jian; Lin, S.-W.; Liventsev, D.; MacNaughton, J.; Majumder, G.; Mandl, F.; Matsuishi, T.; Matsumoto, S.; Matsumoto, T.; Mitaroff, W.; Miyata, H.; Moloney, G. R.; Mori, Takashi; Nagamine, T.; Nagasaka, Y.; Nakano, E.; Nakao, M.; Nakazawa, H.; Jw, Nam; Natkaniec, Z.; Nishida, S.; Nitoh, O.; Ogawa, S.; Ohshima, T.; Okabe, T.; Okuno, S.; Olsen, S. L.; Ostrowicz, W.; Ozaki, H.; Pakhlov, P.; Park, H; Ks, Park; Peters, M.; Piilonen, L. E.; Różańska, M.; Rybicki, K.; Sagawa, H.; Saitoh, Saburou; Sakai, Y.; Satapathy, M.; Satpathy, A.; Schneider, O.; Schrenk, S.; Schümann, J.; Aj, Schwartz; Semenov, S.; Sevior, M. E.; Shibuya, H.; Shwartz, B.; Sidorov,; Singh, J. B.; Stanič, S.; Starič, M.; Sugi, A.; Sumiyoshi, T.; Suzuki, Satoshi; Takahashi, Tohru; Takasaki, F.; Tamai, K.; Tanaka, M.; Taylor, G. N.; Teramoto, Y.; Tokuda, S.; Tsuboyama, T.; Tsukamoto, T.; Uehara, S.; Ueno, K.; Uno, S.; Varner, G.; Varvell, K. E.; Cc, Wang; Wang, CH; Watanabe, Y.; Won, E.; Yabsley, B.; Yamada, Y.; Yamashita, Y.; Yamauchi, M.; Yanai, Haruo; Yeh, P.; Zhang, ZP; Žontar, D.

doi:10.1103/physrevlett.90.201802

Cited by 66 publications

(15 citation statements)

References 18 publications

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“…The funneled shape toward the native binding state guarantees that binding will be stable against environmental and evolutionary fluctuations. The funnel concept was previously used to explain different binding mechanisms, 8,22,[34][35][36] enzyme pathway and allostery, 37,38 binding selectivity and specificity, 39 and the role of water-mediated interactions in enhancing recognition in binding. 40 In the present work, we combine analytical and simulation approaches to examine the properties of dimeric proteins that either fold only upon binding (2-state binding) or that fold via a monomeric intermediate (3-state binding).…”

Section: Introductionmentioning

confidence: 99%

Energy Landscape Analysis of Protein Dimers

Levy¹,

Papoian²,

Onuchic³

et al. 2004

Israel Journal of Chemistry

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Many cellular functions are carried out by proteins that are bound together in multiprotein complexes. The binding between two highly flexible proteins to form homodimers is studied here using energy landscape theory and simulations based on a perfectly funneled energy landscape. With the aim to survey the range of binding mechanisms, two sets of homodimers were selected based on the experimental knowledge of whether stable monomers are needed for binding to take place. We find that the binding mechanism can be predicted based on the structure of the complex subunits alone. On average, the theory predicts a lower stability for subunits that are less compact and less hydrophobic, indicating, in agreement with their experimental classification, that their folding will be coupled to their binding. On the other hand, when a monomeric intermediate is experimentally found, the predicted stability of the monomers is comparable to that of known folded proteins. Furthermore, when dimerization is coupled to monomer folding, the interface is more hydrophobic.

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Section: Introductionmentioning

confidence: 99%

Energy Landscape Analysis of Protein Dimers

Levy¹,

Papoian²,

Onuchic³

et al. 2004

Israel Journal of Chemistry

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“…The ideas of potential landscape have been introduced for uncovering global principles in biology for protein dynamics [30], interactions [31,32], and gene networks [33][34][35]. For a non-equilibrium open systems such as the p53-Mdm2 oscillator that exchanges energies and informations with outside environments, the potential and non-equilibrium probability flux are able to reveal insights for the global robustness and physical mechanisms of the non-equilibrium interactions.…”

Section: B Potential Landscape and Probability Fluxmentioning

confidence: 99%

Bifurcation analysis and potential landscapes of the p53-Mdm2 module regulated by the co-activator programmed cell death 5

Yang

Zhuge

et al. 2015

Chaos: An Interdisciplinary Journal of Nonlinear Science

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The dynamics of p53 play important roles in the regulation of cell fate decisions in response to various stresses, and programmed cell death 5 (PDCD5) functions as a co-activator of p53 that modulates p53 dynamics. In the present paper, we investigated how p53 dynamics are modulated by PDCD5 during the deoxyribose nucleic acid damage response using methods of bifurcation analysis and potential landscape. Our results revealed that p53 activities display rich dynamics under different PDCD5 levels, including monostability, bistability with two stable steady states, oscillations, and the coexistence of a stable steady state (or two states) and an oscillatory state. The physical properties of the p53 oscillations were further demonstrated by the potential landscape in which the potential force attracts the system state to the limit cycle attractor, and the curl flux force drives coherent oscillation along the cyclic trajectory. We also investigated the efficiency with which PDCD5 induced p53 oscillations. We show that Hopf bifurcation can be induced by increasing the PDCD5 efficiency and that the system dynamics exhibited clear transition features in both barrier height and energy dissipation when the efficiency was close to the bifurcation point.

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“…[102,106,115,116] Recent work using SBM investigated the effect of symmetry on the uniqueness of native states for several proteins, [113,[117][118][119] the folding of complex topologies such as knotted proteins, [120][121][122] the binding of Zn-finger proteins to DNA, [123] or proteinÀprotein association. [124][125][126][127] As an example, the complex topology of protein knots is conserved within several protein families and is assumed to have the important physiological role of additional fold stabilization. Different knotted proteins are identified and classified on the basis of their knot types.…”

Section: Protein Foldingmentioning

confidence: 99%

Simulating Biomolecular Folding and Function by Native‐Structure‐Based/Go‐Type Models

Sinner¹,

Lutz²,

John³

et al. 2014

Israel Journal of Chemistry

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The 2013 Nobel Prize in Chemistry highlights how crucial computer simulations have become for many scientific and engineering fields. Nowadays, scientific progress is not only driven by the interplay of new experimental measurements and increasingly sophisticated theoretical frameworks, but also by an incredible toolbox of complex computational models meeting ubiquitously available computing power and data storage facilities. Quantum mechanical (QM) calculations can be condensed into molecular mechanics (MM) force fields and coupled QM/MM calculations can derive atomic and molecular properties of biomolecular or materials science systems with high accuracy. Pure MM simulations driven by Monte Carlo or molecular dynamics algorithms are widely applied in biological chemistry/physics and can investigate large biomolecular systems, such as proteins, DNA, or RNA. One coarse‐grained class of these models, native‐structure‐based or Go models, are based on energy landscape theory and the principle of minimal frustration. Herein, an ensemble of converging pathways guide protein folding on a funnel‐like shape of the entire energy landscape towards the native state. Simulations based on these ideas have been tremendously successful in explaining protein folding and function. Their history and recent application highlights are reviewed.

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Observation ofB0→pΛ¯π−

Cited by 66 publications

References 18 publications

Energy Landscape Analysis of Protein Dimers

Energy Landscape Analysis of Protein Dimers

Bifurcation analysis and potential landscapes of the p53-Mdm2 module regulated by the co-activator programmed cell death 5

Simulating Biomolecular Folding and Function by Native‐Structure‐Based/Go‐Type Models

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