- Open Access
Δ ΔPT: a comprehensive toolbox for the analysis of protein motion
© Rodgers et al.; licensee BioMed Central Ltd. 2013
- Received: 15 November 2012
- Accepted: 24 May 2013
- Published: 7 June 2013
Normal Mode Analysis is one of the most successful techniques for studying motions in proteins and macromolecules. It can provide information on the mechanism of protein functions, used to aid crystallography and NMR data reconstruction, and calculate protein free energies.
Δ ΔPT is a toolbox allowing calculation of elastic network models and principle component analysis. It allows the analysis of pdb files or trajectories taken from; Gromacs, Amber, and DL_POLY. As well as calculation of the normal modes it also allows comparison of the modes with experimental protein motion, variation of modes with mutation or ligand binding, and calculation of molecular dynamic entropies.
This toolbox makes the respective tools available to a wide community of potential NMA users, and allows them unrivalled ability to analyse normal modes using a variety of techniques and current software.
- Normal Mode
- Principle Component Analysis
- Lower Frequency Mode
- Normal Mode Analysis
- Minimum Energy Conformation
Normal mode analysis (NMA) is both one of the most commonly used and best suited theoretical methods for studying motions in proteins and other macromolecules. This produces a collection of collective modes which represent the true protein dynamics . The first normal mode studies were performed in the early 1980s [2-4], and they remained restricted to small-size proteins until the mid 1990s. From this time, methodological advances [5-9], simplified protein descriptions [10-13], and faster computer systems allowed them to address increasingly large macromolecular systems. By the early 2000s, entire protein complexes could be addressed, including the whole ribosome [14-16].
Krebs et al. 2002  have analysed more than 3800 experimentally determined protein motions, and have shown that more than half of them can be approximated by applying a perturbation in the direction of at most two low-frequency normal modes of the considered protein; often a single low frequency normal mode is enough, and it is usually one of the three lowest-frequency modes [14, 15]. Conformational changes on ligand binding of proteins have also been represented by motion along low frequency normal modes [8, 14, 15]. This method has also been used in the study of membrane channel opening , the analysis of structural movements of the ribosome , viral capsid maturation , transconformations of the SERCA1 Ca-ATPase [9, 20], tertiary and quaternary conformational changes in aspartate transcarbamylase , mapping G-actin crystal form onto the F-actin crystal form highlighting possible transition pathways , the regulation of the Kv7.1 Potassium Channel by KCNE1 , and the unfolding of Amylosucrases .
B-factors calculated from crystallographic data have been predicted and refined using normal mode analysis [25, 26]. The residue average B-factors (the average over all the heavy atoms, i.e. not including hydrogens) of alpha lytic protease have been well predicted  and extended to examine differences in motion of the S1 binding pocket in either a symmetric or antisymmetric direction. It has been found that the symmetric direction allowed a much large opening of the binding pocket. The diffuse scattering produced by correlated displacements of atoms during X-ray scattering experiments have also been predicted from normal mode analysis for lysozyme . Cryo-EM structures have also been refined using elastic network models .
NMA is most often used to predict conformational changes that proteins undergo to fulfil their function, and can be used to check if a conformational change proposed on the basis of non-structural experimental data is likely to occur. These functional motions have led to the determination of domains within the proteins . For example, Class I major histocompatibility complex molecule fluctuations have been found to be dependant on the conformation of their three domains  and it has been shown that each domain motion has a different function within the molecule. Human growth hormone induces dimerization of its binding protein; it has been shown that this is due to a marked decrease in domain motion after binding .
NMA can also be used to predict entropy changes on ligand binding as each normal mode has a calculable entropy associated with it. This means that for entropically controlled allosteric binding, it would be possible predict changes in the allosteric binding ratios . The free energy of large functional motions can also be predicted by NMA [34, 35]. The vibrational energy of G-actin has been calculated by regarding the molecule as a collection of independent harmonic oscillations (the normal modes) .
The major goal of normal mode analysis is to reduce the complexity of the full dynamics of a complex system and to describe them in a few generalised coordinates. However, if the long range hydrodynamics of water and anharmonicity are important variants to the protein motion then a method that is capable of reducing a complex system to a few general components but is not dependant on a harmonic approximation is needed. This method is principle component analysis (PCA) , and it is a technique used in a wide variety of fields, e.g. from finance to biology.
PCA computes the second moment of a multivariate distribution and describes the deviations from an average in terms of a set of principle components that represent the collective motions of the largest deviations. These principle components are the eigenvectors of a covariance matrix of the motion, whether the system is harmonic, heavily damped, or does not oscillate at all. Like NMA generally, only a small number of the lowest frequency modes are needed to describe most of the protein motion . The lowest frequency modes tend to describe possible conformational changes in the protein while the slightly higher frequency modes describe vibrational, or breathing, motions around the average structure.
For ubiquitin, with molecular dynamics simulations starting from a variety of different X-ray structures, it was found that the first ten quasi-harmonic analysis modes contributed 78% of all the dynamic movement and that these modes described fluctuations of the structures seen with NMR .
PCA need not even be applied to dynamic fluctuations, but can be used to explore a mapping of many different conformers or mutants of a family of proteins. Recent work has explored 40 different X-ray structures of Ras kinase proteins and found that the structural variance can be described by a small number of principal components .
NMA and PCA thus represent a powerful tools with a wide range of applications in structural biology. Due to this there are a number of on-line web servers currently available that can calculate elastic network models, e.g. EL-Nemo  provides the scaled frequency, fluctuations, and shapes of calculated normal modes; ANM web server  provides calculation of the normal modes and allows on-line display of the modes with a Jmol plugin; and FlexServ  provides calculation of normal modes, and also allows simulation by discrete molecular dynamics and Brownian dynamics.
There are also programmatic libraries available for analysis of NMA and PCA, however, these mean the user has to write their own code and integrate the subroutines from these libraries manually; e.g. MMTK  which provides python subroutines for molecular dynamics, NMA, and structural minimisation; and ProDY  which provides python subroutines for PCA and NMA.
We designed Δ ΔPT as a comprehensive, but still easy-to-use toolbox for NMA/PCA, with increased functionality for normal mode analysis over currently available methods, and easier to use than current programmatic libraries. Particular emphasis was put on its ability to analyse data from Elastic Network Models, Gromacs simulations , Amber simulations , and DL_POLY simulations [46, 47] in an interchangeable manner with all the post analysis tools available irrespective of the input data. Due to the modular nature of the software it is also easy to produce additional input or analysis programs to adapt to the needs of most researchers; however, this is not required to use the program.
The eigenvectors of this matrix, e, are the normal modes, and the eigenvalues are the squares of the associated frequencies, ω. The protein movement can then be represented as a superposition of these normal modes, fluctuating around a minimum energy conformation. The normal modes responsible for most of the amplitude of the atomic displacement are associated with the lowest frequencies.
The GENENMM program also allows elastic network models with a varying spring constant, either with an empirical power decay on the interaction (-an flag), with the Hinsen exponential spring constant (-hine) , with Hinsen fitted spring constants (-hin) , or with individually set values between residues (-f file). GNMPROD also allows the production of the one-dimensional Gaussian network model instead of the three-dimensional elastic network modela.
The resulting Hessian can be either fully diagonalised using the DIAGSTD program (not recommended for many more than 1000 sites - although in reality a system this size will only take around 10 minutes to solve on a desktop PC - run serial on an AMD Phenom™II 3.2 GHz Quad Core) or diagonalised using the rotation-translation-block (RTB) approach, DIAGRTB program. The RTB approach groups several atoms into a single point, which is generally achieved by division into residue blocks, or multiple residue blocks. The rigid-body rotations and translations of these ‘super’-sites are used as the new co-ordinate system instead of Cartesian co-ordinates . When a small number of residues per block are used, the approximation has very little effect on the low frequency modes; although the frequencies do increase predictably due to internal block stiffening . Using this approximation, it becomes possible to treat very large proteins, or protein complexes, in an all-atom level of description in reasonable computing time. DIAGRTB can be set to block into groups by a number of residues (-r n), block into the protein secondary structure (-str SECO), or block into custom domains (-str DOMN). The lowest frequency modes mainly depend on the overall shape of the system; they can be captured at extremely high levels of coarse-graining  or by using low-resolution structural data .
The degree of collectivity indicates the fraction of atoms that are significantly affected by a given mode. For modes involving all the atoms, the degree of collectivity tends to be one, whereas for localised motions the degree of collectivity approaches zero (actually 1/N). The first 25-50 low-frequency normal modes tend to have a collectivity of above 0.4 meaning a significant part of the protein is involved in each mode. Low collectivity in the lowest frequency modes is indicative of extended parts of the system, either N- or C- termini or large unstructured loops. These loops cannot be modelled in a meaningful way as they intrinsically adopt multiple conformations, can appear to be invisible in one crystal form but visible in a different crystal form , or can even appear ordered due to crystal packing .
It is common practise to remove these extended parts prior to the normal mode computation. If a RTB approximation is used, there is some advantage to blocking and representing large unstructured loops by one block so they are included but do not dominate the motion.
Adjusting the cutoff value can slightly improve such correlations, and if possible it is recommended that the correlation between the shape of the predicted and the experimental values is iterated upon when setting the cutoff value if no other information is available. The comparison between the shape of the computed and observed crystallographic B-factors provides a measure of how well the protein’s flexibility in its crystal environment is described by the normal modes. This motion tends to echo, but is more restricted (by crystal packing) than the motion in solution.
There are four principal inputs into the toolbox: protein coordinates written in PBD format ; NMA output data from Gromacs; PCA output data from Gromacs or Amber; or a trajectory output from Gromacs, Amber, or DL_POLY.
For the ENM implementation, the PDB file where all ATOM records are read by the GENENMM program is all that is needed to determine the interaction matrix (HETATM records can also be included by using the -het flag, these are commonly used for ligands and provide an easy method of looking at differences on ligand binding). DNA can also be read into the GENENMM using the -DNA flag; this then includes the C4 and C1’ carbon atoms if the -ca flag (for C α only) is used. This interaction matrix is output so that it can be solved directly using either DIAGSTG or blocked in RTBs and solved with DIAGRTB. This simple approach will likely produce useful results when using an original (unprocessed) PDB file, but some modifications of the input data are advisable, e.g. removal of water or buffer molecules. To prevent lumping of residues that are part of separate molecules into one RTB residue, different chain identifiers should be used. Alternate amino-acid conformations should be removed (if present) and hydrogen atoms should be erased, as their presence will have only a minor influence on the results but a large effect on the solution time (using the -ca flag will automatically ignore any atoms that are not C α atoms).
After solving the interaction matrix or covariance matrix from a simulation, the eigenvalues and eigenvectors will be output into a single file. These can be analysed with any of the tools mentioned and the normal modes can be conveniently viewed with the NMWIZ plugin for VMD . The NMWIZWT tool will convert the calculated values into the relevant input file for the NMWIZ plugin.
Δ Δ PT tools
G E N E N M M
Generates interaction matrix for an ENM
D I A G S T D
Diagonalises an interaction matrix
D I A G R T B
Diagonalises an interaction matrix with the RTB approximation
D O M A I N S
Rewrites pdb into custom domain order for domain RTB
G N M P R O D
Calculates the normal modes for a GNM
F R E Q / E N
Calculates the frequencies and energies for a set of normal modes
R M S / C O L
Calculates the motion, B-factors and collectivity for a set of normal modes
C R O S C O R
Calculates the cross correlation for a set of normal modes
O V E R L A P
Calculates the overlap of a set of normal modes
G R O A M E D
Converts Gromacs and Amber output for use with the toolbox
C O V A R
PCA decomposition for Gromacs, Amber, or DL_POLY trajectories
F U L L 2 C A
Reduces outputs for full protein into C α only outputs
T R A J P D B
Converts trajectories from Gromacs, Amber, or DL_POLY into a pbc fixed pdb file
N M W I Z W T
Produces input for the nmwiz plugin for VMD
P R O J E C T
Produces a set of pdb files for each normal mode perturbed from the input structure
E G N P R O J
Plots the trajectory frames onto the eigenvector space
P D B D I F F
Produces the vector between two pdb structures
S P A C I N G
Calculates the inter-atom distances for plotting
M O V E I N G
Calculates the change in atom positions due to the normal modes
P L O T P D B
Writes a file of residue based values onto a pdb for plotting in VMD
NMA is a powerful tool for the study of protein movements, conformational changes, and protein entropy. It compliments experimental techniques such as X-ray crystallography and NMR, has been used extensively in identifying different structural biology domains, and provides new insights into entropy changes on binding.
This toolbox has increased functionality over those of the currently available web servers, e.g. EL-Nemo  and ANM web server . Its main advantages are its abilities to provide the user with tools for analysing elastic network models and molecular dynamics simulations, and for users to add their own extra modules and functions if needed.
This toolbox makes the respective tools available to a wide community of potential NMA users, and allows them unrivalled ability to analyse normal modes using a variety of techniques and current software. With consistent file types, information can be easily exchanged and compared between methods. The availability of a comprehensive and easy-to-use dedicated NMA downloadable software will therefore facilitate further research into this interesting technique.
Project name: Δ Δ P T Project home page:https://sourceforge.net/projects/durham-ddpt/Operating system(s): Platform independentProgramming language: fortran90Other requirements: gfortran 4.4.1 or higher, or ifort 11.1 or higherLicense: GNU GPLAny restrictions to use by non-academics: none
B B-factor -C Cross correlation - D Mass weighted Hessian matrix J mol -1 m -2 Da -1 e Eigenvector m Da -1 F Mass weighted covariance matrix Da m2G Free energy J mol -1 Reduced Plank constant 1.05457148×10-34 m2 kg s -1I Overlap -i Atom number -i Atom number -i Atom number -k Boltzmann constant 8.314 J mol-1 K-1k i j Hookean spring constant J mol -1 m -2m Mass DaN Number of atoms -Q Eigenvalue matrix -R Equilibrium distance between atoms mr Distance between atoms mS Entropy J mol -1 K -1T Temperature Ku Difference from equilibrium distance between atoms mV Potential Energy J mol -1v Normal mode number - x Atomic position matrix mGreek α Collectivity constant - α Direction - β Direction - κ Collectivity - ω Eigenfrequency s-1
aThe Gaussian network model is explicitly represented as , where r is the distance between sites and Γ is the Kirchhoff matrix.bNote that for the ENM, there are always six frequencies that are several orders of magnitude lower than the others (the eigenvalues of these are essentially zero), these correspond to six solid block rotational and translational modes. If more than six very low frequency normal modes are obtained, this means that a group of atoms is at a distance larger that the cut-off radius from the other atoms.cThe B-factors calculated by Equation 10 give only the contribution of the thermal fluctuations while the experimental B-factors also contain contributions from factors .
This work was support by EPSRC grant EP/H051759/1.
- Tama F: Normal mode analysis with simplified models to investigate the global dynamics Of biological systems. Protein Pept Lett. 2003, 10: 119-132. 10.2174/0929866033479077.View ArticlePubMedGoogle Scholar
- Go N, Noguti T, Nishikawa T: Dynamics of a small globular protein in terms of low-frequency vibrational modes. Proc Natl Acad Sci U S A. 1983, 80: 3696-3700. 10.1073/pnas.80.12.3696.PubMed CentralView ArticlePubMedGoogle Scholar
- Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M: CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem. 1983, 4: 187-217. 10.1002/jcc.540040211.View ArticleGoogle Scholar
- Brooks B, Karplus M: Normal modes for specific motions of macromolecules: Application to the hinge-bending mode of lysozyme. Proc Natl Acad Sci U S A. 1985, 82: 4995-4999. 10.1073/pnas.82.15.4995.PubMed CentralView ArticlePubMedGoogle Scholar
- Mouawad L, Perahia D: Diagonalization in a mixed basis: a method to compute low-frequency normal modes for large macromolecules. Biopolymers. 1993, 33: 599-611. 10.1002/bip.360330409.View ArticleGoogle Scholar
- Durand P, Trinquier G, Sanejouand YH: A new approach for determining low-frequency normal modes in macromolecules. Bioploymers. 1994, 34: 759-771. 10.1002/bip.360340608.View ArticleGoogle Scholar
- Marques O, Sanejouand YH: Hinge-bending motion in citrate synthase arising from normal mode calculations. Proteins: Struct Funct Genet. 1995, 23: 557-560. 10.1002/prot.340230410.View ArticleGoogle Scholar
- Tama F, Gadea FX, Marques O, Sanejouand YH: Building-block approach for determining low-frequency normal modes of macromolecules. Proteins: Struct Funct Genet. 2000, 41: 1-7.View ArticleGoogle Scholar
- Li G, Cui Q: A coarse-grained normal mode approach for macromolecules: an efficient implementation and application to Ca2+-ATPase. Biophys J. 2002, 83: 2457-2474. 10.1016/S0006-3495(02)75257-0.PubMed CentralView ArticlePubMedGoogle Scholar
- Tirion KM: Large amplitude elastic motions in proteins from a single-parameter, atomic analysis. Phys Rev Lett. 1996, 77: 1905-1908. 10.1103/PhysRevLett.77.1905.View ArticlePubMedGoogle Scholar
- Bahar I, Atilgan AR, Erman B: Direct evaluation of thermal fluctuations in proteins using a single-parameter harmonic potential. Fold Des. 1997, 2: 173-181. 10.1016/S1359-0278(97)00024-2.View ArticlePubMedGoogle Scholar
- Hinsen K: Analysis of domain motions by approximate normal mode calculations. Proteins: Struct Funct Genet. 1998, 33: 417-429. 10.1002/(SICI)1097-0134(19981115)33:3<417::AID-PROT10>3.0.CO;2-8.View ArticleGoogle Scholar
- Hinsen K, Thomas A, Field MJ: Analysis of domain motion in large proteins. Proteins: Struct Funct Genet. 1999, 34: 369-382. 10.1002/(SICI)1097-0134(19990215)34:3<369::AID-PROT9>3.0.CO;2-F.View ArticleGoogle Scholar
- Tama F: Conformational change of proteins arising from normal mode calculations. Protein Eng. 2001, 14: 1-6. 10.1093/protein/14.1.1.View ArticlePubMedGoogle Scholar
- Delarue M, Sanejouand YH: Simplified normal mode analysis of conformational transitions in DNA-dependent polymerases: the elastic network model. J Mol Biol. 2002, 320: 1011-1024. 10.1016/S0022-2836(02)00562-4.View ArticlePubMedGoogle Scholar
- Tama F, Valle M, Frank J, Brooks III CL: Dynamic reorganization of the functionally active ribosome explored by normal mode analysis and cryo-electron microscopy. Proc Natl Acad Sci U S A. 2003, 100: 9319-9323. 10.1073/pnas.1632476100.PubMed CentralView ArticlePubMedGoogle Scholar
- Krebs WG, Alexandrov V, Wilson CA, Echols N, Yu H, Gerstein M: Normal mode analysis of macromolecular motions in a database framework: developing mode concentration as a useful classifying statistic. Proteins: Struct Funct Genet. 2002, 48: 682-695. 10.1002/prot.10168.View ArticleGoogle Scholar
- Valadié H, Lacapčre JJ, Sanejouand YH, Etchebest C: Dynamical properties of the MscL of escherichia coli: a normal mode analysis. J Mol Biol. 2003, 332: 657-674. 10.1016/S0022-2836(03)00851-9.View ArticlePubMedGoogle Scholar
- Kim MK, Jernigan RL, Chirikjian GS: An elastic network model of HK97 capsid maturation. J Struct Biol. 2003, 143: 107-117. 10.1016/S1047-8477(03)00126-6.View ArticlePubMedGoogle Scholar
- Reuter N, Hinsen K, Lacapère JJ: Transconformations of the SERCA1 Ca-ATPase: a normal mode study. Biophys J. 2003, 85: 2186-2197. 10.1016/S0006-3495(03)74644-X.PubMed CentralView ArticlePubMedGoogle Scholar
- Thomas A, Hinsen K, Field MJ, Perahia D: Tertiary and quaternary conformational changes in aspartate transcarbamylase: a normal mode study. Proteins: Struct Funct Genet. 1999, 34: 96-112. 10.1002/(SICI)1097-0134(19990101)34:1<96::AID-PROT8>3.0.CO;2-0.View ArticleGoogle Scholar
- Tirion M, ben Avraham D, Lorenz M, Holmes K: Normal modes as refinement parameters for the F-actin model. Biophys J. 1995, 68: 5-12. 10.1016/S0006-3495(95)80156-6.PubMed CentralView ArticlePubMedGoogle Scholar
- Gofman Y, Shats S, Attali B, Haliloglu T, Ben-Tal N: How does KCNE1 regulate the Kv7.1 potassium channel? Model-structure, mutations, and dynamics of the Kv7.1-KCNE1 complex. Structure. 2012, 20: 1343-1352. 10.1016/j.str.2012.05.016.View ArticlePubMedGoogle Scholar
- Liu M, Wang S, Sun T, Su J, Zhang Y, Yue J, Sun Z: Insight into the structure, dynamics and the unfolding property of amylosucrases: implications of rational engineering on thermostability. PLoS ONE. 2012, 7: e40441-10.1371/journal.pone.0040441.PubMed CentralView ArticlePubMedGoogle Scholar
- Kidera A, Gō N: Normal mode refinement: crystallographic refinement of protein dynamic structure I. Theory and test by simulated diffraction data. J Mol Biol. 1992, 225: 457-475. 10.1016/0022-2836(92)90932-A.View ArticlePubMedGoogle Scholar
- Diamond R: On the use of normal modes in thermal parameter refinement: theory and application to the bovine pancreatic trypsin inhibitor. Acta Crystallogr A. 1990, 46: 425-435. 10.1107/S0108767390002082.View ArticlePubMedGoogle Scholar
- Miller WW, Agard DA: Enzyme specificity under dynamic control: a normal mode analysis of α-lytic protease. J Mol Biol. 1999, 286: 267-278. 10.1006/jmbi.1998.2445.View ArticlePubMedGoogle Scholar
- Faure P, Micu A, Pérahia D, Doucet J, Smith J, Benoit J: Correlated intramolecular motions and diffuse X-ray scattering in lysozyme. Nat Struct Mol Biol. 1994, 1: 124-128. 10.1038/nsb0294-124.View ArticleGoogle Scholar
- Wang Z, Schröder GF: Real-space refinement with DireX: from global fitting to side-chain improvements. Biopolymers. 2012, 97: 687-697. 10.1002/bip.22046.View ArticlePubMedGoogle Scholar
- Gaillard T, Martin E, San Sebastian E, Cossío FP, Lopez X, Dejaegere A, Stote RH: Comparative normal mode analysis of LFA-1 Integrin I-domains. J Mol Biol. 2007, 374: 231-249. 10.1016/j.jmb.2007.07.006.View ArticlePubMedGoogle Scholar
- Nojima H, Takeda-Shitaka M, Kanou K, Kamiya K, Umeyama H: Dynamic interaction among the platform domain and two MembraneProximal immunoglobulin-like domains of class I major histocompatibility complex: normal mode analysis. Chem Pharm Bull. 2008, 56: 635-641. 10.1248/cpb.56.635.View ArticlePubMedGoogle Scholar
- Kurihara Y, Watanabe T, Nojima H, Takeda-Shitaka M, Sumikawa H, Kamiya K, Umeyama H: Dynamic character of human growth hormone and its receptor: normal mode analysis. Chem Pharm Bull. 2003, 51: 754-758. 10.1248/cpb.51.754.View ArticlePubMedGoogle Scholar
- Rodgers TL, Burnell D, Wilson MR, Pohl E, Cann M, Townsend PD, McLeish TCB, Toncrova H: Modelling allosteric signalling in protein homodimers. Eur Biophys J Biophys Lett. 2011, 40: 121-Google Scholar
- Mouawad L, Perahia D: Motions in hemoglobin studied by normal mode analysis and energy minimization: evidence for the existence of tertiary T-like, Quaternary R-like intermediate structures. J Mol Biol. 1996, 258: 393-410. 10.1006/jmbi.1996.0257.View ArticlePubMedGoogle Scholar
- Tirion MM, ben Avraham D: Normal mode analysis of G-actin. J Mol Biol. 1993, 230: 186-195. 10.1006/jmbi.1993.1135.View ArticlePubMedGoogle Scholar
- Jackson JE: A User’s Guide to Principal Components. 1991, New York: John Wiley & Sons, Inc.View ArticleGoogle Scholar
- Hayward S, Kitao A, Gō N: Harmonic and Anharmonic aspects in the dynamics of BPTI: a normal mode analysis and principal component analysis. Protein Sci. 1994, 3: 936-943.PubMed CentralView ArticlePubMedGoogle Scholar
- Ramanathan A, Agarwal PK: Computational identification of slow conformational fluctuations in proteins. J Phys Chem B. 2009, 113: 16669-16680. 10.1021/jp9077213.PubMed CentralView ArticlePubMedGoogle Scholar
- Gorfe AA, Grant BJ, McCammon JA: Mapping the nucleotide and Isoform-dependent structural and dynamical features of Ras proteins. Structure. 2008, 16: 885-896. 10.1016/j.str.2008.03.009.PubMed CentralView ArticlePubMedGoogle Scholar
- Eyal E, Yang LW, Bahar I: Anisotropic network model: systematic evaluation and a new web interface. Bioinformatics. 2006, 22: 2619-2627. 10.1093/bioinformatics/btl448.View ArticlePubMedGoogle Scholar
- Camps J, Carrillo O, Emperador A, Orellana L, Hospital A, Rueda M, Cicin-Sain D, D’Abramo M, Gelpí JL, Orozco M: FlexServ: an integrated tool for the analysis of protein flexibility. Bioinformatics. 2009, 25: 1709-1710. 10.1093/bioinformatics/btp304.View ArticlePubMedGoogle Scholar
- Hinsen K: The molecular modeling toolkit: a new approach to molecular simulations. J Comput Chem. 2000, 21: 79-85. 10.1002/(SICI)1096-987X(20000130)21:2<79::AID-JCC1>3.0.CO;2-B.View ArticleGoogle Scholar
- Bakan A, Meireles LM, Bahar I: ProDy: protein dynamics inferred from theory and experiments. Bioinformatics. 2011, 27: 1575-1577. 10.1093/bioinformatics/btr168.PubMed CentralView ArticlePubMedGoogle Scholar
- Hess B, Kutzner C, van der Spoel D, Lindahl E: GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput. 2008, 4: 435-447. 10.1021/ct700301q.View ArticlePubMedGoogle Scholar
- Case D, Darden T, Cheatham III T, Simmerling C, Wang J, Duke R, Luo R, Walker R, Zhang W, Merz K, Roberts B, Wang B, Hayik S, Roitberg A, Seabra G, Kolossvai I, Wong K, Paesani F, Vanicek J, Liu J, Wu X, Brozell S, Steinbrecher T, Gohlke H, Cai Q, Ye X, Wang J, Hsieh MJ, Cui G, Roe D, et al: AMBER 11. San Francisco, US: University of California; 2010Google Scholar
- Todorov I, Smith W, Trachenko K, Dove M: DL_POLY_3: new dimensions in molecular dynamics simulations via massive parallelism. J Mater Chem. 2006, 16: 1911-1918. 10.1039/b517931a.View ArticleGoogle Scholar
- Smith W, Todorov IT: A short description of DL POLY. Mol Simul. 2006, 32: 935-943. 10.1080/08927020600939830.View ArticleGoogle Scholar
- Lebon G, Warne T, Edwards PC, Bennett K, Langmead CJ, Leslie AGW, Tate CG: Agonist-bound adenosine A2A receptor structures reveal common features of GPCR activation. Nature. 2011, 474: 521-525. 10.1038/nature10136.PubMed CentralView ArticlePubMedGoogle Scholar
- Frishman D, Argos P: Knowledge-based secondary structure assignment. Proteins: Struct Funct Genet. 1995, 23: 566-579. 10.1002/prot.340230412.View ArticleGoogle Scholar
- Doruker P, Jernigan RL, Bahar I: Dynamics of large proteins through hierarchical levels of coarse-grained structures. J Comput Chem. 2002, 23: 119-127. 10.1002/jcc.1160.View ArticlePubMedGoogle Scholar
- Brink J, Ludtke SJ, Kong Y, Wakil SJ, Ma J, Chiu W: Experimental verification of conformational variation of human fatty acid synthase as predicted by normal mode analysis. Structure. 2004, 12: 185-191.View ArticlePubMedGoogle Scholar
- Dykeman EC, Sankey OF: Normal mode analysis and applications in biological physics. J Phys Condensed Matter. 2010, 22: 423202-10.1088/0953-8984/22/42/423202.View ArticleGoogle Scholar
- Schlitter J: Estimation of absolute and relative entropies of macromolecules using the covariance matrix. Chem Phys Lett. 1993, 215: 617-621. 10.1016/0009-2614(93)89366-P.View ArticleGoogle Scholar
- Brüschweiler R: Collective protein dynamics and nuclear spin relaxation. J Chem Phys. 1995, 102: 3396-3403. 10.1063/1.469213.View ArticleGoogle Scholar
- Pohl E, Holmes RK, Hol WG: (Motion of the DNA-binding domain with respect to the core of the diphtheria toxin repressor (DtxR) revealed in the crystal structures of apo- and holo-DtxR. J Biol Chem. 1998, 273: 22420-22427. 10.1074/jbc.273.35.22420.View ArticlePubMedGoogle Scholar
- Russo S, Schweitzer JE, Polen T, Bott M, Pohl E: Crystal structure of the caseinolytic protease gene regulator, a transcriptional activator in actinomycetes. J Biol Chem. 2009, 284: 5208-5216.View ArticlePubMedGoogle Scholar
- Ichiye T, Karplus M: Collective motions in proteins: a covariance analysis of atomic fluctuations in molecular dynamics and normal mode simulations. Proteins. 1991, 11: 205-217. 10.1002/prot.340110305.View ArticlePubMedGoogle Scholar
- Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The protein data bank. Nucleic Acids Res. 2000, 28: 235-242. 10.1093/nar/28.1.235.PubMed CentralView ArticlePubMedGoogle Scholar
- Hinsen K: Structural flexibility in proteins: impact of the crystal environment. Bioinformatics. 2008, 24: 521-528. 10.1093/bioinformatics/btm625.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.