iGNM: a database of protein functional motions based on Gaussian Network Model

Yang, Lee‐Wei; Liu, Xiong; Jursa, Christopher Jon; Holliman, Mark; Rader, Andrew J.; Karimi, Hassan A.; Bahar, İvet

doi:10.1093/bioinformatics/bti469

Cited by 119 publications

(121 citation statements)

References 54 publications

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“…This makes it possible to systematically perform NMA for many PDB structures. Using this advantage, several databases and online servers delivering NMA have been developed to reveal the dynamic features of proteins (Echols et al 2003;Suhre and Sanejouand 2004;Wako et al 2004;Yang et al 2005Yang et al , 2006Skjaerven et al 2014;Tiwari et al 2014;Eyal et al 2015). ProMode, ProMode-Oligomer, and ProModeElastic are examples of such databases developed by the authors (Wako et al 2004;Endo 2012, 2013).…”

Section: Introductionmentioning

confidence: 99%

Normal mode analysis as a method to derive protein dynamics information from the Protein Data Bank

Wako

Endo

2017

Biophys Rev

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Normal mode analysis (NMA) can facilitate quick and systematic investigation of protein dynamics using data from the Protein Data Bank (PDB). We developed an elastic network model-based NMA program using dihedral angles as independent variables. Compared to the NMA programs that use Cartesian coordinates as independent variables, key attributes of the proposed program are as follows: (1) chain connectivity related to the folding pattern of a polypeptide chain is naturally embedded in the model; (2) the full-atom system is acceptable, and owing to a considerably smaller number of independent variables, the PDB data can be used without further manipulation; (3) the number of variables can be easily reduced by some of the rotatable dihedral angles; (4) the PDB data for any molecule besides proteins can be considered without coarse-graining; and (5) individual motions of constituent subunits and ligand molecules can be easily decomposed into external and internal motions to examine their mutual and intrinsic motions. Its performance is illustrated with an example of a DNA-binding allosteric protein, a catabolite activator protein. In particular, the focus is on the conformational change upon cAMP and DNA binding, and on the communication between their binding sites remotely located from each other. In this illustration, NMA creates a vivid picture of the protein dynamics at various levels of the structures, i.e., atoms, residues, secondary structures, domains, subunits, and the complete system, including DNA and cAMP. Comparative studies of the specific protein in different states, e.g., apo-and holo-conformations, and free and complexed configurations, provide useful information for studying structurally and functionally important aspects of the protein.

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Section: Introductionmentioning

confidence: 99%

Normal mode analysis as a method to derive protein dynamics information from the Protein Data Bank

Wako

Endo

2017

Biophys Rev

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“…By reducing the number of degrees of freedom, coarse-grained models of proteins can drastically reduce the intrinsic cost of simulating a time step, as well as increasing the duration of each step. The strategy has been pursued for many different problems over the years from protein folding to aggregation to conformational change (8,(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27). Although coarse-grained models fail to capture atomistic detail and may have limited biochemical accuracy, recent work may permit the use of simplified ensembles in accelerating atomistic sampling (28,29).…”

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confidence: 99%

Efficient and verified simulation of a path ensemble for conformational change in a united-residue model of calmodulin

Zhang

Jasnow

Zuckerman

2007

Proc. Natl. Acad. Sci. U.S.A.

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163

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The computational sampling of rare, large-scale, conformational transitions in proteins is a well appreciated challenge-for which a number of potentially efficient path-sampling methodologies have been proposed. Here, we study a large-scale transition in a united-residue model of calmodulin using the ''weighted ensemble'' (WE) approach of Huber and Kim. Because of the model's relative simplicity, we are able to compare our results with bruteforce simulations. The comparison indicates that the WE approach quantitatively reproduces the brute-force results, as assessed by considering (i) the reaction rate, (ii) the distribution of event durations, and (iii) structural distributions describing the heterogeneity of the paths. Importantly, the WE method is readily applied to more chemically accurate models, and by studying a series of lower temperatures, our results suggest that the WE method can increase efficiency by orders of magnitude in more challenging systems.transition path ͉ path sampling ͉ weighted ensemble ͉ conformational transition

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“…The iGNM server, at http://ignm.ccbb.pitt.edu, provides normal mode calculations based on the Gaussian Network Model described above [15]. Another server, at http://igs-server.cnrs-mrs.fr/elnemo, performs calculations for a given structure based on a 3-dimensional elastic network model of Sanejouand, el al [11].…”

Section: Using Web Resources For Normal Mode Analysismentioning

confidence: 99%

Using normal mode analysis in teaching mathematical modeling to biology students

Kondrashov

2011

Math. Model. Nat. Phenom.

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Abstract. Linear oscillators are used for modeling a diverse array of natural systems, for instance acoustics, materials science, and chemical spectroscopy. In this paper I describe simple models of structural interactions in biological molecules, known as elastic network models, as a useful topic for undergraduate biology instruction in mathematical modeling. These models use coupled linear oscillators to model the fluctuations of molecular structures around the equilibrium state. I present many learning activities associated with building and understanding these models, ranging from analytical to computational. I provide a number of web resources where students can obtain structural data, perform calculations, and suggest research directions for independent projects.

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iGNM: a database of protein functional motions based on Gaussian Network Model

Cited by 119 publications

References 54 publications

Normal mode analysis as a method to derive protein dynamics information from the Protein Data Bank

Normal mode analysis as a method to derive protein dynamics information from the Protein Data Bank

Efficient and verified simulation of a path ensemble for conformational change in a united-residue model of calmodulin

Using normal mode analysis in teaching mathematical modeling to biology students

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