Citation: Davide Sala, Andrea Giachetti, Antonio Rosato. Molecular dynamics simulations of metalloproteins: A folding study of rubredoxin from Pyrococcus furiosus[J]. AIMS Biophysics, 2018, 5(1): 77-96. doi: 10.3934/biophy.2018.1.77
[1] | Xiaoxue Zhao, Zhuchun Li . Synchronization of a Kuramoto-like model for power grids with frustration. Networks and Heterogeneous Media, 2020, 15(3): 543-553. doi: 10.3934/nhm.2020030 |
[2] | Tingting Zhu . Synchronization of the generalized Kuramoto model with time delay and frustration. Networks and Heterogeneous Media, 2023, 18(4): 1772-1798. doi: 10.3934/nhm.2023077 |
[3] | Seung-Yeal Ha, Yongduck Kim, Zhuchun Li . Asymptotic synchronous behavior of Kuramoto type models with frustrations. Networks and Heterogeneous Media, 2014, 9(1): 33-64. doi: 10.3934/nhm.2014.9.33 |
[4] | Tingting Zhu . Emergence of synchronization in Kuramoto model with frustration under general network topology. Networks and Heterogeneous Media, 2022, 17(2): 255-291. doi: 10.3934/nhm.2022005 |
[5] | Seung-Yeal Ha, Jaeseung Lee, Zhuchun Li . Emergence of local synchronization in an ensemble of heterogeneous Kuramoto oscillators. Networks and Heterogeneous Media, 2017, 12(1): 1-24. doi: 10.3934/nhm.2017001 |
[6] | Seung-Yeal Ha, Jeongho Kim, Jinyeong Park, Xiongtao Zhang . Uniform stability and mean-field limit for the augmented Kuramoto model. Networks and Heterogeneous Media, 2018, 13(2): 297-322. doi: 10.3934/nhm.2018013 |
[7] | Seung-Yeal Ha, Hansol Park, Yinglong Zhang . Nonlinear stability of stationary solutions to the Kuramoto-Sakaguchi equation with frustration. Networks and Heterogeneous Media, 2020, 15(3): 427-461. doi: 10.3934/nhm.2020026 |
[8] | Seung-Yeal Ha, Se Eun Noh, Jinyeong Park . Practical synchronization of generalized Kuramoto systems with an intrinsic dynamics. Networks and Heterogeneous Media, 2015, 10(4): 787-807. doi: 10.3934/nhm.2015.10.787 |
[9] | Vladimir Jaćimović, Aladin Crnkić . The General Non-Abelian Kuramoto Model on the 3-sphere. Networks and Heterogeneous Media, 2020, 15(1): 111-124. doi: 10.3934/nhm.2020005 |
[10] | Young-Pil Choi, Seung-Yeal Ha, Seok-Bae Yun . Global existence and asymptotic behavior of measure valued solutions to the kinetic Kuramoto--Daido model with inertia. Networks and Heterogeneous Media, 2013, 8(4): 943-968. doi: 10.3934/nhm.2013.8.943 |
Synchronization in complex networks has been a focus of interest for researchers from different disciplines[1,2,4,8,15]. In this paper, we investigate synchronous phenomena in an ensemble of Kuramoto-like oscillators which is regarded as a model for power grid. In [9], a mathematical model for power grid is given by
$ Pisource=I¨θi˙θi+KD(˙θi)2−N∑l=1ailsin(θl−θi),i=1,2,…,N, $ | (1) |
where
By denoting
$ (˙θi)2=ωi+KNN∑l=1sin(θl−θi),˙θi>0,i=1,2,…,N. $ | (2) |
Here, the setting
If
$ (˙θi)2=ωi+KNN∑l=1sin(θl−θi+α),˙θi>0,i=1,2,…,N. $ | (3) |
We will find a trapping region such that any nonstationary state located in this region will evolve to a synchronous state.
The contributions of this paper are twofold: First, for identical oscillators without frustration, we show that the initial phase configurations located in the half circle will converge to complete phase and frequency synchronization. This extends the analytical results in [5] in which the initial phase configuration for synchronization needs to be confined in a quarter of circle. Second, we consider the nonidentical oscillators with frustration and present a framework leading to the boundness of the phase diameter and complete frequency synchronization. To the best of our knowledge, this is the first result for the synchronization of (3) with nonidentical oscillators and frustration.
The rest of this paper is organized as follows. In Section 2, we recall the definitions for synchronization and summarize our main results. In Section 3, we give synchronization analysis and prove the main results. Finally, Section 4 is devoted to a concluding summary.
Notations. We use the following simplified notations throughout this paper:
$ νi:=˙θi,i=1,2,…,N,ω:=(ω1,ω2,…,ωN),ˉω:=max1≤i≤Nωi,ω_:=min1≤i≤Nωi,D(ω):=ˉω−ω_,θM:=max1≤i≤Nθi,θm:=min1≤i≤Nθi,D(θ):=θM−θm,νM:=max1≤i≤Nνi,νm:=min1≤i≤Nνi,D(ν):=νM−νm,θνM∈{θj|νj=νM},θνm∈{θj|νj=νm}. $ |
In this paper, we consider the system
$ (˙θi)2=ωi+KNN∑l=1sin(θl−θi+α),˙θi>0,α∈(−π4,π4),θi(0)=θ0i,i=1,2,…,N. $ | (4) |
Next we introduce the concepts of complete synchronization and conclude this introductory section with the main result of this paper.
Definition 2.1. Let
1. it exhibits asymptotically complete phase synchronization if
$ limt→∞(θi(t)−θj(t))=0,∀i≠j. $ |
2. it exhibits asymptotically complete frequency synchronization if
$ limt→∞(˙θi(t)−˙θj(t))=0,∀i≠j. $ |
For identical oscillators without frustration, we have the following result.
Theorem 2.2. Let
$ θ0∈A:={θ∈[0,2π)N:D(θ)<π}, $ |
then there exits
$ D(θ(t))≤D(θ0)e−λ1t,t≥0. $ | (5) |
and
$ D(ν(t))≤D(ν(t0))e−λ2(t−t0),t≥t0. $ | (6) |
Next we introduce the main result for nonidentical oscillators with frustration. For
$ K_{c}: = \frac{D({\omega})\sqrt{2\bar{\omega}}}{1-\sqrt{2\bar{\omega}}\sin|\alpha|} > 0. $ |
For suitable parameters, we denote by
$ sinD∞1=sinD∞∗:=√ˉω+K(D(ω)+Ksin|α|)K√ω_−K,0<D∞1<π2<D∞∗<π. $ |
Theorem 2.3. Let
$ θ0∈B:={θ∈[0,2π)N|D(θ)<D∞∗−|α|}, $ |
then for any small
$ D(ν(t))≤D(ν(T))e−λ3(t−T),t≥T. $ | (7) |
Remark 1. If the parametric conditions in Theorem 2.3 are fulfilled, the reference angles
$ D(ω)√2ˉω1−√2ˉωsin|α|<K,1−√2ˉωsin|α|>0. $ |
This implies
$ √2ˉω(D(ω)+Ksin|α|)K<1. $ |
Then, by
$ sinD∞1=sinD∞∗:=√ˉω+K(D(ω)+Ksin|α|)K√ω_−K≤√2ˉω(D(ω)+Ksin|α|)K<1. $ |
Remark 2. In order to make
In this subsection we consider the system (4) with identical natural frequencies and zero frustration:
$ (˙θi)2=ω0+KNN∑l=1sin(θl−θi),˙θi>0,i=1,2,…,N. $ | (8) |
To obtain the complete synchronization, we need to derive a trapping region. We start with two elementary estimates for the transient frequencies.
Lemma 3.1. Suppose
$ (˙θi−˙θj)(˙θi+˙θj)=2KNN∑l=1cos(θl−θi+θj2)sinθj−θi2. $ |
Proof. It is immediately obtained by (8).
Lemma 3.2. Suppose
$ ˙θi≤√ω0+K. $ |
Proof. It follows from (8) and
$ (˙θi)2=ω0+KNN∑l=1sin(θl−θi)≤ω0+K. $ |
Next we give an estimate for trapping region and prove Theorem 2.2. For this aim, we will use the time derivative of
Lemma 3.3. Let
Proof. For any
$ T:={T∈[0,+∞)|D(θ(t))<D∞,∀t∈[0,T)}. $ |
Since
$ D(θ(t))<D∞,t∈[0,η). $ |
Therefore, the set
$ T∗=∞. $ | (9) |
Suppose to the contrary that
$ D(θ(t))<D∞,t∈[0,T∗),D(θ(T∗))=D∞. $ |
We use Lemma 3.1 and Lemma 3.2 to obtain
$ 12ddtD(θ(t))2=D(θ(t))ddtD(θ(t))=(θM−θm)(˙θM−˙θm)=(θM−θm)1˙θM+˙θm2KNN∑l=1cos(θl−θM+θm2)sin(θm−θM2)≤(θM−θm)1˙θM+˙θm2KNN∑l=1cosD∞2sin(θm−θM2)≤(θM−θm)1√ω0+KKNN∑l=1cosD∞2sin(θm−θM2)=−2KcosD∞2√ω0+KD(θ)2sinD(θ)2≤−KcosD∞2π√ω0+KD(θ)2,t∈[0,T∗). $ |
Here we used the relations
$ −D∞2<−D(θ)2≤θl−θM2≤0≤θl−θm2≤D(θ)2<D∞2 $ |
and
$ xsinx≥2πx2,x∈[−π2,π2]. $ |
Therefore, we have
$ ddtD(θ)≤−KcosD∞2π√ω0+KD(θ),t∈[0,T∗), $ | (10) |
which implies that
$ D(θ(T∗))≤D(θ0)e−KcosD∞2π√ω0+KT∗<D(θ0)<D∞. $ |
This is contradictory to
Now we can give a proof for Theorem 2.2.
Proof of Theorem 2.2.. According to Lemma 3.3, we substitute
On the other hand, by (5) there exist
$ ˙νi=K2NνiN∑l=1cos(θl−θi)(νl−νi). $ |
Using Lemma 3.2, we now consider the temporal evolution of
$ ddtD(ν)=˙νM−˙νm=K2NνMN∑l=1cos(θl−θνM)(νl−νM)−K2NνmN∑l=1cos(θl−θνm)(νl−νm)≤Kcosδ2NνMN∑l=1(νl−νM)−Kcosδ2NνmN∑l=1(νl−νm)≤K2Ncosδ√ω0+KN∑l=1(νl−νM)−K2Ncosδ√ω0+KN∑l=1(νl−νm)=Kcosδ2N√ω0+KN∑l=1(νl−νM−νl+νm)=−Kcosδ2√ω0+KD(ν),t≥t0. $ |
This implies that
$ D(ν(t))≤D(ν(t0))e−Kcosδ2√ω0+K(t−t0),t≥t0, $ |
and proves (6) with
Remark 3. Theorem 2.2 shows, as long as the initial phases are confined inside an arc with geodesic length strictly less than
In this subsection, we prove the main result for nonidentical oscillators with frustration.
Lemma 3.4. Let
$ (˙θi−˙θj)(˙θi+˙θj)≤D(ω)+KNN∑l=1[sin(θl−θi+α)−sin(θl−θj+α)]. $ |
Proof. By (4) and for any
$ (˙θi−˙θj)(˙θi+˙θj)=(˙θi)2−(˙θj)2, $ |
the result is immediately obtained.
Lemma 3.5. Let
$ ˙θi∈[√ω_−K,√ˉω+K],∀i=1,2,…,N. $ |
Proof. From (4), we have
$ ω_−K≤(˙θi)2≤ˉω+K,∀i=1,2,…,N, $ |
and also because
Lemma 3.6. Let
Proof. We define the set
$ T:={T∈[0,+∞)|D(θ(t))<D∞∗−|α|,∀t∈[0,T)},T∗:=supT. $ |
Since
$ T∗=∞. $ |
Suppose to the contrary that
$ D(θ(t))<D∞∗−|α|,t∈[0,T∗),D(θ(T∗))=D∞∗−|α|. $ |
We use Lemma 3.4 to obtain
$ 12ddtD(θ)2=D(θ)ddtD(θ)=D(θ)(˙θM−˙θm)≤D(θ)1˙θM+˙θm[D(ω)+KNN∑l=1(sin(θl−θM+α)−sin(θl−θm+α))]⏟I. $ |
For
$ I=D(ω)+KcosαNN∑l=1[sin(θl−θM)−sin(θl−θm)]+KsinαNN∑l=1[cos(θl−θM)−cos(θl−θm)]. $ |
We now consider two cases according to the sign of
(1)
$ I≤D(ω)+KcosαsinD(θ)ND(θ)N∑l=1[(θl−θM)−(θl−θm)]+KsinαNN∑l=1[1−cosD(θ)]=D(ω)−K[sin(D(θ)+α)−sinα]=D(ω)−K[sin(D(θ)+|α|)−sin|α|]. $ |
(2)
$ I≤D(ω)+KcosαsinD(θ)ND(θ)N∑l=1[(θl−θM)−(θl−θm)]+KsinαNN∑l=1[cosD(θ)−1]=D(ω)−K[sin(D(θ)−α)+sinα]=D(ω)−K[sin(D(θ)+|α|)−sin|α|]. $ |
Here we used the relations
$ sin(θl−θM)θl−θM,sin(θl−θm)θl−θm≥sinD(θ)D(θ), $ |
and
$ cosD(θ)≤cos(θl−θM),cos(θl−θm)≤1,l=1,2,…,N. $ |
Since
$ I≤D(ω)−K[sin(D(θ)+|α|)−sin|α|] $ | (11) |
$ ≤D(ω)+Ksin|α|−KsinD∞∗D∞∗(D(θ)+|α|). $ | (12) |
By (12) and Lemma 3.5 we have
$ 12ddtD(θ)2≤D(θ)1˙θM+˙θm(D(ω)+Ksin|α|−KsinD∞∗D∞∗(D(θ)+|α|))=D(ω)+Ksin|α|˙θM+˙θmD(θ)−KsinD∞∗D∞∗(˙θM+˙θm)D(θ)(D(θ)+|α|)≤D(ω)+Ksin|α|2√ω_−KD(θ)−KsinD∞∗D∞∗2√ˉω+KD(θ)(D(θ)+|α|),t∈[0,T∗). $ |
Then we obtain
$ ddtD(θ)≤D(ω)+Ksin|α|2√ω_−K−KsinD∞∗2D∞∗√ˉω+K(D(θ)+|α|),t∈[0,T∗), $ |
i.e.,
$ ddt(D(θ)+|α|)≤D(ω)+Ksin|α|2√ω_−K−KsinD∞∗2D∞∗√ˉω+K(D(θ)+|α|)=KsinD∞∗2√ˉω+K−KsinD∞∗2D∞∗√ˉω+K(D(θ)+|α|),t∈[0,T∗). $ |
Here we used the definition of
$ D(θ(t))+|α|≤D∞∗+(D(θ0)+|α|−D∞∗)e−KsinD∞∗2D∞∗√ˉω+Kt,t∈[0,T∗), $ |
Thus
$ D(θ(t))≤(D(θ0)+|α|−D∞∗)e−KsinD∞∗2D∞∗√ˉω+Kt+D∞∗−|α|,t∈[0,T∗). $ |
Let
$ D(θ(T∗))≤(D(θ0)+|α|−D∞∗)e−KsinD∞∗2D∞∗√ˉω+KT∗+D∞∗−|α|<D∞∗−|α|, $ |
which is contradictory to
$ T∗=∞. $ |
That is,
$ D(θ(t))≤D∞∗−|α|,∀t≥0. $ |
Lemma 3.7. Let
$ ddtD(θ(t))≤D(ω)+Ksin|α|2√ω_−K−K2√ˉω+Ksin(D(θ)+|α|),t≥0. $ |
Proof. It follows from (11) and Lemma 3.5, Lemma 3.6 and that we have
$ 12ddtD(θ)2=D(θ)ddtD(θ)≤D(θ)1˙θM+˙θm[D(ω)−K(sin(D(θ)+|α|)−sin|α|)]=D(ω)+Ksin|α|˙θM+˙θmD(θ)−Ksin(D(θ)+|α|)˙θM+˙θmD(θ)≤D(ω)+Ksin|α|2√ω_−KD(θ)−Ksin(D(θ)+|α|)2√ˉω+KD(θ),t≥0. $ |
The proof is completed.
Lemma 3.8. Let
$ D(θ(t))<D∞1−|α|+ε,t≥T. $ |
Proof. Consider the ordinary differential equation:
$ ˙y=D(ω)+Ksin|α|2√ω_−K−K2√ˉω+Ksiny,y(0)=y0∈[0,D∞∗). $ | (13) |
It is easy to find that
$ |y(t)−y∗|<ε,t≥T. $ |
In particular,
$ D(θ(t))+|α|<D∞1+ε,t≥T, $ |
which is the desired result.
Remark 4. Since
$ sinD∞1≥D(ω)K+sin|α|>sin|α|, $ |
we have
Proof of Theorem 2.3. It follows from Lemma 3.8 that for any small
$ supt≥TD(θ(t))<D∞1−|α|+ε<π2. $ |
We differentiate the equation (4) to find
$ ˙νi=K2NνiN∑l=1cos(θl−θi+α)(νl−νi),νi>0. $ |
We now consider the temporal evolution of
$ ddtD(ν)=˙νM−˙νm=K2NνMN∑l=1cos(θl−θνM+α)(νl−νM)−K2NνmN∑l=1cos(θl−θνm+α)(νl−νm)≤K2NνMN∑l=1cos(D∞1+ε)(νl−νM)−K2NνmN∑l=1cos(D∞1+ε)(νl−νm)≤Kcos(D∞1+ε)2N√ˉω+KN∑l=1(νl−νM−νl+νm)=−Kcos(D∞1+ε)2√ˉω+KD(ν),t≥T, $ |
where we used
$ cos(θl−θνM+α),cos(θl−θνm+α)≥cos(D∞1+ε),andνM,νm≤√ˉω+K. $ |
Thus we obtain
$ D(ν(t))≤D(ν(T))e−Kcos(D∞1+ε)2√ˉω+K(t−T),t≥T, $ |
and proves (7) with
In this paper, we presented synchronization estimates for the Kuramoto-like model. We show that for identical oscillators with zero frustration, complete phase synchronization occurs exponentially fast if the initial phases are confined inside an arc with geodesic length strictly less than
We would like to thank the anonymous referee for his/her comments which helped us to improve this paper.
[1] |
Klepeis JL, Lindorff-Larsen K, Dror RO, et al. (2009) Long-timescale molecular dynamics simulations of protein structure and function. Curr Opin Struct Biol 19: 120–127. doi: 10.1016/j.sbi.2009.03.004
![]() |
[2] |
Stone JE, Phillips JC, Freddolino PL, et al. (2007) Accelerating molecular modeling applications with graphics processors. J Comput Chem 28: 2618–2640. doi: 10.1002/jcc.20829
![]() |
[3] |
Perez A, Morrone JA, Simmerling C, et al. (2016) Advances in free-energy-based simulations of protein folding and ligand binding. Curr Opin Struct Biol 36: 25–31. doi: 10.1016/j.sbi.2015.12.002
![]() |
[4] |
Lane TJ, Shukla D, Beauchamp KA, et al. (2013) To milliseconds and beyond: Challenges in the simulation of protein folding. Curr Opin Struct Biol 23: 58–65. doi: 10.1016/j.sbi.2012.11.002
![]() |
[5] |
Freddolino PL, Harrison CB, Liu Y, et al. (2010) Challenges in protein-folding simulations. Nat Phys 6: 751–758. doi: 10.1038/nphys1713
![]() |
[6] |
Best RB (2012) Atomistic molecular simulations of protein folding. Curr Opin Struct Biol 22: 52–61. doi: 10.1016/j.sbi.2011.12.001
![]() |
[7] | Piana S, Lindorff-Larsen K, Shaw DE (2012) Protein folding kinetics and thermodynamics from atomistic simulation. P Natl Acad Sci USA 109: 17845–17850. |
[8] |
Suárez E, Lettieri S, Zwier MC, et al. (2014) Simultaneous computation of dynamical and equilibrium information using a weighted ensemble of trajectories. J Chem Theory Comput 10: 2658–2667. doi: 10.1021/ct401065r
![]() |
[9] |
Pierce LCT, Salomon-Ferrer R, Augusto F. De Oliveira C, et al. (2012) Routine access to millisecond time scale events with accelerated molecular dynamics. J Chem Theory Comput 8: 2997–3002. doi: 10.1021/ct300284c
![]() |
[10] |
Kubelka J, Hofrichter J, Eaton WA (2004) The protein folding "speed limit." Curr Opin Struct Biol 14: 76–88. doi: 10.1016/j.sbi.2004.01.013
![]() |
[11] |
Lindorff-Larsen K, Piana S, Dror RO, et al. (2011) How fast-folding proteins fold. Science 334: 517–520. doi: 10.1126/science.1208351
![]() |
[12] | Putignano V, Rosato A, Banci L, et al. (2018) MetalPDB in 2018: a database of metal sites in biological macromolecular structures. Nucleic Acids Res 41: 459–464. |
[13] |
Li W, Wang J, Zhang J, et al. (2015) Molecular simulations of metal-coupled protein folding. Curr Opin Struct Biol 30: 25–31. doi: 10.1016/j.sbi.2014.11.006
![]() |
[14] |
Bentrop D, Bertini I, Iacoviello R, et al. (1999) Structural and dynamical properties of a partially unfolded Fe4S4 protein: Role of the cofactor in protein folding. Biochemistry 38: 4669–4680. doi: 10.1021/bi982647q
![]() |
[15] |
Blake PR, Summers MF, Park JB, et al. (1991) Determinants of protein hyperthermostability: purification and amino acid sequence of rubredoxin from the hyperthermophilic archaebacterium pyrococcus furiosus and secondary structure of the zinc adduct by NMR. Biochemistry 30: 10885–10895. doi: 10.1021/bi00109a012
![]() |
[16] | Prakash S, Sundd M, Guptasarma P (2014) The key to the extraordinary thermal stability of P. furiosus holo-rubredoxin: Iron binding-guided packing of a core aromatic cluster responsible for high kinetic stability of the native structure. PLoS One 9: e89703. |
[17] |
Hernandez G, Jenney FE, Adams MW, et al. (2000) Millisecond time scale conformational flexibility in a hyperthermophile protein at ambient temperature. Proc Natl Acad Sci USA 97: 3166–3170. doi: 10.1073/pnas.97.7.3166
![]() |
[18] | Rader AJ (2010) Thermostability in rubredoxin and its relationship to mechanical rigidity. Phys Biol 7: 016002. |
[19] |
Bonomi F, Iametti S, Ferranti P, et al. (2008) "Iron priming" guides folding of denatured aporubredoxins. J Biol Inorg Chem 13: 981–991. doi: 10.1007/s00775-008-0385-4
![]() |
[20] |
Zartler ER, Jenney FE, Terrell M, et al. (2001) Structural basis for thermostability in aporubredoxins from Pyrococcus furiosus and Clostridium pasteurianum. Biochemistry 40: 7279–7290. doi: 10.1021/bi0026831
![]() |
[21] |
Cavagnero S, Debe DA, Zhou ZH, et al. (1998) Kinetic role of electrostatic interactions in the unfolding of hyperthermophilic and mesophilic rubredoxins. Biochemistry 37: 3369–3376. doi: 10.1021/bi9721795
![]() |
[22] |
Strop P, Mayo SL (1999) Rubredoxin variant folds without iron. J Am Chem Soc 121: 2341–2345. doi: 10.1021/ja9834780
![]() |
[23] |
Hamelberg D, Mongan J, McCammon JA (2004) Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules. J Chem Phys 120: 11919–11929. doi: 10.1063/1.1755656
![]() |
[24] |
Doshi U, Hamelberg D (2015) Towards fast, rigorous and efficient conformational sampling of biomolecules: Advances in accelerated molecular dynamics. BBA-Gen Subjects 1850: 878–888. doi: 10.1016/j.bbagen.2014.08.003
![]() |
[25] |
Miao Y, Feixas F, Eun C (2015) Accelerated molecular dynamics simulations of protein folding. J Comput Chem 36: 1536–1549. doi: 10.1002/jcc.23964
![]() |
[26] | Case DA, Cerutti DS, Cheatham TE, et al. (2017) Amber 2017, University of California, San Francisco. |
[27] |
Carvalho ATP, Teixeira AFS, Ramos MJ (2013) Parameters for molecular dynamics simulations of iron-sulfur proteins. J Comput Chem 34: 1540–1548. doi: 10.1002/jcc.23287
![]() |
[28] |
Bertini I, Case DA, Ferella L, et al. (2011) A grid-enabled web portal for NMR structure refinement with AMBER. Bioinformatics 27: 2384–2390. doi: 10.1093/bioinformatics/btr415
![]() |
[29] |
Wassenaar TA, van Dijk M, Loureiro-Ferreira N, et al. (2012) WeNMR: Structural biology on the grid. J Grid Comput 10: 743–767. doi: 10.1007/s10723-012-9246-z
![]() |
[30] |
Prompers JJ, Brüschweiler R, Bruschweiler R (2002) General framework for studying the dynamics of folded and nonfolded proteins by NMR relaxation spectroscopy and MD simulation. J Am Chem Soc 124: 4522–4534. doi: 10.1021/ja012750u
![]() |
[31] |
Korzhnev DM, Billeter M, Arseniev AS, et al. (2001) NMR studies of Brownian tumbling and internal motions in proteins. Prog Nucl Mag Res Sp 38: 197–266. doi: 10.1016/S0079-6565(00)00028-5
![]() |
[32] |
Kabsch W, Sander C (1983) Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22: 2577–2637. doi: 10.1002/bip.360221211
![]() |
[33] |
Rost B, Sander C (1993) Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 232: 584–599. doi: 10.1006/jmbi.1993.1413
![]() |
[34] |
Li DW, Brüschweiler R (2012) PPM: A side-chain and backbone chemical shift predictor for the assessment of protein conformational ensembles. J Biomol NMR 54: 257–265. doi: 10.1007/s10858-012-9668-8
![]() |
[35] |
Hiller R, Zhou ZH, Adams MW, et al. (1997) Stability and dynamics in a hyperthermophilic protein with melting temperature close to 200 degrees C. Proc Natl Acad Sci USA 94: 11329–11332. doi: 10.1073/pnas.94.21.11329
![]() |
[36] |
Ishima R, Torchia DA (2000) Protein dynamics from NMR. Nat Struct Biol 7: 740–743. doi: 10.1038/78963
![]() |
[37] |
Jarymowycz VA, Stone MJ (2006) Fast time scale dynamics of protein backbones: NMR relaxation methods, applications, and functional consequences. Chem Rev 106: 1624–1671. doi: 10.1021/cr040421p
![]() |
[38] |
LeMaster DM (1999) NMR relaxation order parameter analysis of the dynamics of protein side chains. J Am Chem Soc 121: 1726–1742. doi: 10.1021/ja982988r
![]() |
[39] |
Ruschak AM, Kay LE (2010) Methyl groups as probes of supra-molecular structure, dynamics and function. J Biomol NMR 46: 75–87. doi: 10.1007/s10858-009-9376-1
![]() |
[40] |
Bougault CM, Eidsness MK, Prestegard JH (2003) Hydrogen bonds in rubredoxins from mesophilic and hyperthermophilic organisms. Biochemistry 42: 4357–4372. doi: 10.1021/bi027264d
![]() |
[41] |
Prestegard JH, Bougault CM, Kishore AI (2004) Residual dipolar couplings in structure determination of biomolecules. Chem Rev 104: 3519–3540. doi: 10.1021/cr030419i
![]() |
[42] |
Cho-Chung YS, Pitot HC (1968) Regulatory effects of nicotinamide on tryptophan pyrrolase synthesis in rat liver in vivo. Eur J Biochem 3: 401–406. doi: 10.1111/j.1432-1033.1967.tb19543.x
![]() |
[43] |
Blasie CA, Berg JM (2002) Structur e-based thermodynamic analysis of a coupled metal binding-protein folding reaction involving a zinc finger peptide. Biochemistry 41: 15068–15073. doi: 10.1021/bi026621h
![]() |
[44] |
Weinkam P, Romesberg FE, Wolynes PG (2009) Chemical frustration in the protein folding landscape: Grand canonical ensemble simulations of cytochrome c. Biochemistry 48: 2394–2402. doi: 10.1021/bi802293m
![]() |
[45] |
Devereux M, Gresh N, Piquemal JP, et al. (2014) A supervised fitting approach to force field parametrization with application to the SIBFA polarizable force field. J Comput Chem 35: 1577–1591. doi: 10.1002/jcc.23661
![]() |
[46] |
Wu R, Lu Z, Cao Z, et al. (2011) A transferable nonbonded pairwise force field to model zinc interactions in metalloproteins. J Chem Theory Comput 7: 433–443. doi: 10.1021/ct100525r
![]() |
[47] |
Sakharov DV, Lim C (2005) Zn protein simulations including charge transfer and local polarization effects. J Am Chem Soc 127: 4921–4929. doi: 10.1021/ja0429115
![]() |
[48] |
Chakravorty DK, Wang B, Lee CW, et al. (2012) Simulations of allosteric motions in the zinc sensor CzrA. J Am Chem Soc 134: 3367–3376. doi: 10.1021/ja208047b
![]() |
[49] |
Chakravorty DK, Parker TM, Guerra AJ, et al. (2013) Energetics of zinc-mediated interactions in the allosteric pathways of metal sensor proteins. J Am Chem Soc 135: 30–33. doi: 10.1021/ja309170g
![]() |
[50] |
Reyes-Caballero H, Campanello GC, Giedroc DP (2011) Metalloregulatory proteins: Metal selectivity and allosteric switching. Biophys Chem 156: 103–114. doi: 10.1016/j.bpc.2011.03.010
![]() |
[51] |
Andrews CT, Elcock AH (2013) Molecular dynamics simulations of highly crowded amino acid solutions: comparisons of eight different force field combinations with experiment and with each other. J Chem Theory Comput 9: 4585–4602. doi: 10.1021/ct400371h
![]() |
[52] |
Abriata LA, Dal Peraro M (2015) Assessing the potential of atomistic molecular dynamics simulations to probe reversible protein-protein recognition and binding. Sci Rep 5: 10549. doi: 10.1038/srep10549
![]() |
![]() |
![]() |
1. | Sha Xu, Xiaoyue Huang, Hua Zhang, 2024, Synchronization of a Kuramoto-like Model with Time Delay and Phase Shift, 978-9-8875-8158-1, 5299, 10.23919/CCC63176.2024.10662837 | |
2. | Sun-Ho Choi, Hyowon Seo, Inertial power balance system with nonlinear time-derivatives and periodic natural frequencies, 2024, 129, 10075704, 107695, 10.1016/j.cnsns.2023.107695 |