Author: bugman
Date: Fri Feb 5 18:14:30 2010
New Revision: 10669
URL: http://svn.gna.org/viewcvs/relax?rev=10669&view=rev
Log:
Updated the d'Auvergne and Gooley, 2008 references to have the full medline
info.
Modified:
1.3/docs/latex/bibliography.bib
Modified: 1.3/docs/latex/bibliography.bib
URL:
http://svn.gna.org/viewcvs/relax/1.3/docs/latex/bibliography.bib?rev=10669&r1=10668&r2=10669&view=diff
==============================================================================
--- 1.3/docs/latex/bibliography.bib (original)
+++ 1.3/docs/latex/bibliography.bib Fri Feb 5 18:14:30 2010
@@ -1337,90 +1337,179 @@
year = 2007
}
-@article{dAuvergneGooley08a,
+@Article{dAuvergneGooley08a,
Author = {d'Auvergne, E. J. and Gooley, P. R.},
-title = "{Optimisation of NMR dynamic models I. Minimisation algorithms and
their
-performance within the model-free and Brownian rotational diffusion
-spaces}",
-journal = jbnmr,
-volume = "40",
-number = "2",
-pages = "107-109",
-year = "2008",
-pmid = "18085410",
-abstract = "{
-The key to obtaining the model-free description of the dynamics of a
-macromolecule is the optimisation of the model-free and Brownian
-rotational diffusion parameters using the collected R (1), R (2) and
-steady-state NOE relaxation data. The problem of optimising the
-chi-squared value is often assumed to be trivial, however, the long chain
-of dependencies required for its calculation complicates the model-free
-chi-squared space. Convolutions are induced by the Lorentzian form of the
-spectral density functions, the linear recombinations of certain spectral
-density values to obtain the relaxation rates, the calculation of the NOE
-using the ratio of two of these rates, and finally the quadratic form of
-the chi-squared equation itself. Two major topological features of the
-model-free space complicate optimisation. The first is a long, shallow
-valley which commences at infinite correlation times and gradually
-approaches the minimum. The most severe convolution occurs for motions on
-two timescales in which the minimum is often located at the end of a long,
-deep, curved tunnel or multidimensional valley through the space. A large
-number of optimisation algorithms will be investigated and their
-performance compared to determine which techniques are suitable for use in
-model-free analysis. Local optimisation algorithms will be shown to be
-sufficient for minimisation not only within the model-free space but also
-for the minimisation of the Brownian rotational diffusion tensor. In
-addition the performance of the programs Modelfree and Dasha are
-investigated. A number of model-free optimisation failures were
-identified: the inability to slide along the limits, the singular matrix
-failure of the Levenberg-Marquardt minimisation algorithm, the low
-precision of both programs, and a bug in Modelfree. Significantly, the
-singular matrix failure of the Levenberg-Marquardt algorithm occurs when
-internal correlation times are undefined and is greatly amplified in
-model-free analysis by both the grid search and constraint algorithms. The
-program relax ( http://www.nmr-relax.com ) is also presented as a new
-software package designed for the analysis of macromolecular dynamics
-through the use of NMR relaxation data and which alleviates all of the
-problems inherent within model-free analysis.}",
-}
-
-@article{dAuvergneGooley08b,
+ Title = {Optimisation of {NMR} dynamic models {I}.
+ {M}inimisation algorithms and their performance within
+ the model-free and {B}rownian rotational diffusion
+ spaces.},
+ Journal = {J Biomol NMR},
+ Volume = {40},
+ Number = {2},
+ Pages = {107-19},
+ abstract = {The key to obtaining the model-free description of the
+ dynamics of a macromolecule is the optimisation of the
+ model-free and Brownian rotational diffusion parameters
+ using the collected R (1), R (2) and steady-state NOE
+ relaxation data. The problem of optimising the
+ chi-squared value is often assumed to be trivial,
+ however, the long chain of dependencies required for
+ its calculation complicates the model-free chi-squared
+ space. Convolutions are induced by the Lorentzian form
+ of the spectral density functions, the linear
+ recombinations of certain spectral density values to
+ obtain the relaxation rates, the calculation of the NOE
+ using the ratio of two of these rates, and finally the
+ quadratic form of the chi-squared equation itself. Two
+ major topological features of the model-free space
+ complicate optimisation. The first is a long, shallow
+ valley which commences at infinite correlation times
+ and gradually approaches the minimum. The most severe
+ convolution occurs for motions on two timescales in
+ which the minimum is often located at the end of a
+ long, deep, curved tunnel or multidimensional valley
+ through the space. A large number of optimisation
+ algorithms will be investigated and their performance
+ compared to determine which techniques are suitable for
+ use in model-free analysis. Local optimisation
+ algorithms will be shown to be sufficient for
+ minimisation not only within the model-free space but
+ also for the minimisation of the Brownian rotational
+ diffusion tensor. In addition the performance of the
+ programs Modelfree and Dasha are investigated. A number
+ of model-free optimisation failures were identified:
+ the inability to slide along the limits, the singular
+ matrix failure of the Levenberg-Marquardt minimisation
+ algorithm, the low precision of both programs, and a
+ bug in Modelfree. Significantly, the singular matrix
+ failure of the Levenberg-Marquardt algorithm occurs
+ when internal correlation times are undefined and is
+ greatly amplified in model-free analysis by both the
+ grid search and constraint algorithms. The program
+ relax ( http://www.nmr-relax.com ) is also presented as
+ a new software package designed for the analysis of
+ macromolecular dynamics through the use of NMR
+ relaxation data and which alleviates all of the
+ problems inherent within model-free analysis.},
+ authoraddress = {Department of NMR-based Structural Biology, Max Planck
+ Institute for Biophysical Chemistry, Am Fassberg 11,
+ D-37077, Goettingen, Germany. edward@xxxxxxxxxxxxx},
+ keywords = {*Algorithms ; Cytochromes c2/chemistry ; Diffusion ;
+ *Models, Molecular ; Nuclear Magnetic Resonance,
+ Biomolecular/*methods ; Rhodobacter
+ capsulatus/chemistry ; *Rotation},
+ language = {eng},
+ medline-aid = {10.1007/s10858-007-9214-2 [doi]},
+ medline-crdt = {2007/12/19 09:00},
+ medline-da = {20080109},
+ medline-dcom = {20080917},
+ medline-dep = {20071218},
+ medline-edat = {2007/12/19 09:00},
+ medline-fau = {d'Auvergne, Edward J ; Gooley, Paul R},
+ medline-is = {0925-2738 (Print) ; 0925-2738 (Linking)},
+ medline-jid = {9110829},
+ medline-jt = {Journal of biomolecular NMR},
+ medline-lr = {20091118},
+ medline-mhda = {2008/09/18 09:00},
+ medline-oid = {NLM: PMC2758376},
+ medline-own = {NLM},
+ medline-phst = {2007/02/23 [received] ; 2007/11/06 [accepted] ;
+ 2007/12/18 [aheadofprint]},
+ medline-pl = {Netherlands},
+ medline-pmc = {PMC2758376},
+ medline-pmid = {18085410},
+ medline-pst = {ppublish},
+ medline-pt = {Journal Article},
+ medline-rn = {9035-43-2 (Cytochromes c2)},
+ medline-sb = {IM},
+ medline-so = {J Biomol NMR. 2008 Feb;40(2):107-19. Epub 2007 Dec 18.},
+ medline-stat = {MEDLINE},
+ url =
{http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18085410},
+ year = 2008
+}
+
+@Article{dAuvergneGooley08b,
Author = {d'Auvergne, E. J. and Gooley, P. R.},
- Title = {Optimisation of NMR dynamic models II. A new methodology for the
dual
-optimisation of the model-free parameters and the Brownian rotational
-diffusion tensor},
-journal = jbnmr,
-volume = "40",
-number = "2",
-pages = "121-123",
-year = "2008",
-pmid = "18085411",
-abstract = "{
-Finding the dynamics of an entire macromolecule is a complex problem as
-the model-free parameter values are intricately linked to the Brownian
-rotational diffusion of the molecule, mathematically through the
-autocorrelation function of the motion and statistically through model
-selection. The solution to this problem was formulated using set theory as
-an element of the universal set [Formula: see text]-the union of all
-model-free spaces (d'Auvergne EJ and Gooley PR (2007) Mol BioSyst 3(7),
-483-494). The current procedure commonly used to find the universal
-solution is to initially estimate the diffusion tensor parameters, to
-optimise the model-free parameters of numerous models, and then to choose
-the best model via model selection. The global model is then optimised and
-the procedure repeated until convergence. In this paper a new methodology
-is presented which takes a different approach to this diffusion seeded
-model-free paradigm. Rather than starting with the diffusion tensor this
-iterative protocol begins by optimising the model-free parameters in the
-absence of any global model parameters, selecting between all the
-model-free models, and finally optimising the diffusion tensor. The new
-model-free optimisation protocol will be validated using synthetic data
-from Schurr JM et al. (1994) J Magn Reson B 105(3), 211-224 and the
-relaxation data of the bacteriorhodopsin (1-36)BR fragment from Orekhov VY
-(1999) J Biomol NMR 14(4), 345-356. To demonstrate the importance of this
-new procedure the NMR relaxation data of the Olfactory Marker Protein
-(OMP) of Gitti R et al. (2005) Biochem 44(28), 9673-9679 is reanalysed.
-The result is that the dynamics for certain secondary structural elements
-is very different from those originally reported.}",
+ Title = {Optimisation of {NMR} dynamic models {II}. {A} new
+ methodology for the dual optimisation of the model-free
+ parameters and the {B}rownian rotational diffusion
+ tensor.},
+ Journal = {J Biomol NMR},
+ Volume = {40},
+ Number = {2},
+ Pages = {121-33},
+ abstract = {Finding the dynamics of an entire macromolecule is a
+ complex problem as the model-free parameter values are
+ intricately linked to the Brownian rotational diffusion
+ of the molecule, mathematically through the
+ autocorrelation function of the motion and
+ statistically through model selection. The solution to
+ this problem was formulated using set theory as an
+ element of the universal set [formula: see text]-the
+ union of all model-free spaces (d'Auvergne EJ and
+ Gooley PR (2007) Mol BioSyst 3(7), 483-494). The
+ current procedure commonly used to find the universal
+ solution is to initially estimate the diffusion tensor
+ parameters, to optimise the model-free parameters of
+ numerous models, and then to choose the best model via
+ model selection. The global model is then optimised and
+ the procedure repeated until convergence. In this paper
+ a new methodology is presented which takes a different
+ approach to this diffusion seeded model-free paradigm.
+ Rather than starting with the diffusion tensor this
+ iterative protocol begins by optimising the model-free
+ parameters in the absence of any global model
+ parameters, selecting between all the model-free
+ models, and finally optimising the diffusion tensor.
+ The new model-free optimisation protocol will be
+ validated using synthetic data from Schurr JM et al.
+ (1994) J Magn Reson B 105(3), 211-224 and the
+ relaxation data of the bacteriorhodopsin (1-36)BR
+ fragment from Orekhov VY (1999) J Biomol NMR 14(4),
+ 345-356. To demonstrate the importance of this new
+ procedure the NMR relaxation data of the Olfactory
+ Marker Protein (OMP) of Gitti R et al. (2005) Biochem
+ 44(28), 9673-9679 is reanalysed. The result is that the
+ dynamics for certain secondary structural elements is
+ very different from those originally reported.},
+ authoraddress = {Department of NMR-based Structural Biology, Max Planck
+ Institute for Biophysical Chemistry, Am Fassberg 11,
+ Goettingen, D-37077, Germany. edward@xxxxxxxxxxxxx},
+ keywords = {Algorithms ; Amides/chemistry ;
+ Bacteriorhodopsins/chemistry ; Crystallography, X-Ray ;
+ Diffusion ; *Models, Molecular ; Nuclear Magnetic
+ Resonance, Biomolecular/*methods ; Olfactory Marker
+ Protein/chemistry ; Peptide Fragments/chemistry ;
+ Protein Structure, Secondary ; *Rotation},
+ language = {eng},
+ medline-aid = {10.1007/s10858-007-9213-3 [doi]},
+ medline-crdt = {2007/12/19 09:00},
+ medline-da = {20080109},
+ medline-dcom = {20080917},
+ medline-dep = {20071218},
+ medline-edat = {2007/12/19 09:00},
+ medline-fau = {d'Auvergne, Edward J ; Gooley, Paul R},
+ medline-is = {0925-2738 (Print) ; 0925-2738 (Linking)},
+ medline-jid = {9110829},
+ medline-jt = {Journal of biomolecular NMR},
+ medline-lr = {20091118},
+ medline-mhda = {2008/09/18 09:00},
+ medline-oid = {NLM: PMC2758375},
+ medline-own = {NLM},
+ medline-phst = {2007/02/23 [received] ; 2007/11/06 [accepted] ;
+ 2007/12/18 [aheadofprint]},
+ medline-pl = {Netherlands},
+ medline-pmc = {PMC2758375},
+ medline-pmid = {18085411},
+ medline-pst = {ppublish},
+ medline-pt = {Journal Article},
+ medline-rn = {0 (Amides) ; 0 (Olfactory Marker Protein) ; 0 (Peptide
+ Fragments) ; 53026-44-1 (Bacteriorhodopsins)},
+ medline-sb = {IM},
+ medline-so = {J Biomol NMR. 2008 Feb;40(2):121-33. Epub 2007 Dec 18.},
+ medline-stat = {MEDLINE},
+ url =
{http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=18085411},
+ year = 2008
}
@Article{DellwoWand89,
r10669 - /1.3/docs/latex/bibliography.bib