Analysis of motion features for molecular dynamics simulation of proteins

Kamada, Masao; Toda, Mikito; Sekijima, Masakazu; Takata, Masami; Joe, Kazuki

doi:10.1016/j.cplett.2010.12.028

Cited by 11 publications

(10 citation statements)

References 29 publications

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“…59 Using this approach, Kamada and co-workers revealed that the clusters of the collective motions of Thermomyces lanuginosa lipase drastically change during the time evolution, involving single or multiple secondary structures. 39 Still one more future direction would be using our approach to identify the degree of sharpness of temporal changes in vibrational energy distributions associated with formation of key contacts in a protein's history. Coupling the power of wavelets with the capability of a clustering algorithm in the timefrequency domain would provide a new pathway to deeper understanding of the dynamics of a complex system.…”

Section: Discussionmentioning

confidence: 99%

“…A second development would be to extract time-localized coordinates (or their conjugate momenta) from the pattern of the multiresolution decomposition of the original Cartesian coordinates (or momenta). This can be done with singular value decomposition of the wavelet coefficient matrix, 39 in analogy with the conventional spectral density matrix. 59 Using this approach, Kamada and co-workers revealed that the clusters of the collective motions of Thermomyces lanuginosa lipase drastically change during the time evolution, involving single or multiple secondary structures.…”

Section: Discussionmentioning

confidence: 99%

“…We demonstrate the method by applying it to time series derived from MD simulations of a coarse-grained minimalist protein model which is a 3-color, 46-bead model protein whose potential and free energy landscapes, kinetics, and dynamics have been well studied. [30][31][32][33][34] While earlier studies that used the wavelet transform to analyze MD trajectories of proteins focused on identifying or clustering conformational states, [35][36][37][38][39] we use the approach to identify vibrational states and measure time scales for equipartitioning vibrational energy. To this end, we first apply the principal component analysis (PCA) [40][41][42][43] to decompose the various space scale contributions from the original multivariate MD trajectory.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal hierarchy in the dynamics of a minimalist protein model

Matsunaga

Baba²,

et al. 2013

The Journal of Chemical Physics

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A method for time series analysis of molecular dynamics simulation of a protein is presented. In this approach, wavelet analysis and principal component analysis are combined to decompose the spatiotemporal protein dynamics into contributions from a hierarchy of different time and space scales. Unlike the conventional Fourier-based approaches, the time-localized wavelet basis captures the vibrational energy transfers among the collective motions of proteins. As an illustrative vehicle, we have applied our method to a coarse-grained minimalist protein model. During the folding and unfolding transitions of the protein, vibrational energy transfers between the fast and slow time scales were observed among the large-amplitude collective coordinates while the other small-amplitude motions are regarded as thermal noise. Analysis employing a Gaussian-based measure revealed that the time scales of the energy redistribution in the subspace spanned by such large-amplitude collective coordinates are slow compared to the other small-amplitude coordinates. Future prospects of the method are discussed in detail.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal hierarchy in the dynamics of a minimalist protein model

Matsunaga

Baba²,

et al. 2013

The Journal of Chemical Physics

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“…The obtained modes are also used as the axis of the free-energy surface. Moreover, various other analysis methods have been proposed, such as full correlation analysis (Lange and Grubmüller 2007 ), subspace joint approximate diagonalization of eigenmatrices (Sakuraba et al 2010 ), and wavelet analysis (Kamada et al 2011 ), among others (Moritsugu et al 2015 ; Matsunaga et al 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Relaxation mode analysis for molecular dynamics simulations of proteins

Mitsutake

Takano

2018

Biophys Rev

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Molecular dynamics simulation is a powerful method for investigating the structural stability, dynamics, and function of biopolymers at the atomic level. In recent years, it has become possible to perform simulations on time scales of the order of milliseconds using special hardware. However, it is necessary to derive the important factors contributing to structural change or function from the complicated movements of biopolymers obtained from long simulations. Although some analysis methods for protein systems have been developed using increasing simulation times, many of these methods are static in nature (i.e., no information on time). In recent years, dynamic analysis methods have been developed, such as the Markov state model and relaxation mode analysis (RMA), which was introduced based on spin and homopolymer systems. The RMA method approximately extracts slow relaxation modes and rates from trajectories and decomposes the structural fluctuations into slow relaxation modes, which characterize the slow relaxation dynamics of the system. Recently, this method has been applied to biomolecular systems. In this article, we review RMA and its improved versions for protein systems.

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“…They divided protein motions into intra-substate and inter-substate motions. Various other analysis methods have also been proposed, such as full correlation analysis [39], subspace joint approximate diagonalization of eigenmatrices [40], wavelet analysis [41], and others [42,43] (See also Ref. 44).…”

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confidence: 99%

Analysis of molecular dynamics simulations of 10-residue peptide, chignolin, using statistical mechanics: Relaxation mode analysis and three-dimensional reference interaction site model theory

2019

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Molecular dynamics simulation is a fruitful tool for investigating the structural stability, dynamics, and functions of biopolymers at an atomic level. In recent years, simulations can be performed on time scales of the order of milliseconds using specialpurpose systems. Since the most stable structure, as well as meta-stable structures and intermediate structures, is included in trajectories in long simulations, it is necessary to develop analysis methods for extracting them from trajectories of simulations. For these structures, methods for evaluating the stabilities, including the solvent effect, are also needed. We have developed relaxation mode analysis to investigate dynamics and kinetics of simulations based on statistical mechanics. We have also applied the three-dimensional reference interaction site model theory to investigate stabilities with solvent effects. In this paper, we review the results for designing amino-acid substitution of the 10-residue peptide, chignolin, to stabilize the misfolded structure using these developed analysis methods.It is important to develop analysis methods for molecular simulations of protein systems. We have developed relaxation mode analysis to investigate dynamics and kinetics of simulations. We also have applied the three-dimensional reference interaction site model theory to investigate stability with solvent effects. Here, we review the results for designing amino-acid substitution of 10-residue peptide, chignolin, to stabilize the misfolded structure using these analysis methods based on statistical mechanics. Background of mode decomposition methods for protein simulationsAs longer and larger MD simulations are performed, it has become increasingly important to develop methods to extract the "essential" information from the trajectory. The reduction in the large number of degrees of freedom of coordinates to a few collective ones is an active field of theo retical research. In NMA, the normal modes near the minimum potential energy of the protein molecule are obtained [3,24,25]. LMA investigates modes around a minimumenergy state, including the water effect [4,6,26,27]. An elastic network model and a Gaussian network model approximately calculate normal modes with large amplitudes by using the harmonic potential of coarse-grained models [28][29][30][31][32]. This method extracts collective modes with large amplitudes for large protein systems like viruses, because such proteins have rigid-like motions [33]. PCA, also called quasiharmonic analysis or the essential dynamics method [6,7,[34][35][36][37][38], is one of the most popular methods for analyzing the structural fluctuations around the average structure. The modes with large structure fluctuations are extracted and are considered cooperative movement, and the relation of these fluctuations with function has been widely examined. The obtained modes are also used as the axis of the free-energy surface. The JAM model was introduced for treating multiple-hierarchy free-energy landscapes [9]. They divided protei...

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Analysis of motion features for molecular dynamics simulation of proteins

Cited by 11 publications

References 29 publications

Spatio-temporal hierarchy in the dynamics of a minimalist protein model

Spatio-temporal hierarchy in the dynamics of a minimalist protein model

Relaxation mode analysis for molecular dynamics simulations of proteins

Analysis of molecular dynamics simulations of 10-residue peptide, chignolin, using statistical mechanics: Relaxation mode analysis and three-dimensional reference interaction site model theory

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