A two-dimensional histogram-matching method for protein phase refinement and extension

Nieh, Yeu-Perng; Zhang, Kam Y. J.

doi:10.1107/s0907444999010987

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…The initial phases and electron density map was obtained by SAD phasing using SOLVE (Terwilliger and Berendzen, 1999). Subsequent density modification involved solvent flattening (Wang, 1985) and histogram matching (Nieh and Zhang, 1999) as implemented in the Crystallography and NMR System (Brunger et al, 1998). Exploiting the positions of selenomethionine as anchors, the initial electron density maps allowed unambiguous chain tracing for most of the protein backbone, side chain, and GTP.…”

Section: Experimental Procedures Expression and Purification Of Rab6-r1pmentioning

confidence: 99%

Structural Basis for Recruitment of Rab6-Interacting Protein 1 to Golgi via a RUN Domain

et al. 2009

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Small GTPase Rab6 regulates vesicle trafficking at the level of Golgi via recruitment of numerous and unrelated effectors. The crystal structure of Rab6a(GTP) in complex with a 378-residue internal fragment of the effector Rab6IP1 was solved at 3.2 angstroms resolution. This Rab6IP1 region encompasses an all alpha-helical RUN domain followed in tandem by a PLAT domain that adopts a beta sandwich fold. The structure reveals that the first and last alpha helices of the RUN domain mediate binding to switch I, switch II, and the interswitch region of Rab6. It represents the largest Rab-effector complex determined to date. Comparisons with the recent structure of Rab6 in complex with an unrelated effector, human golgin GCC185, reveals significant conformational changes in the conserved hydrophobic triad of Rab6. Flexibility in the switch and interswitch regions of Rab6 mediates recognition of compositionally distinct alpha-helical coiled coils, thereby contributing to Rab6 promiscuity in effector recruitment.

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Section: Experimental Procedures Expression and Purification Of Rab6-r1pmentioning

confidence: 99%

Structural Basis for Recruitment of Rab6-Interacting Protein 1 to Golgi via a RUN Domain

et al. 2009

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“…In X-ray crystallography, the histograms of the ED maps of proteins are determined by the effective B factor of the diffraction data sets used to compute them (Nieh & Zhang, 1999). The B factor of a data set determines its resolution, and its value can be estimated by applying Wilson-plot methods to the data (Wang, 2017).…”

Section: The Identification Of Mg 2+ Cations In An Experimental Esp Mapmentioning

confidence: 99%

Identification of ions in experimental electrostatic potential maps

Wang

Liu

Frank

et al. 2018

IUCrJ

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“…They calculated the case i=T, we have not considered it in equation (9) since the addition of all the arrows is always 0 when the sets have the same number of elements. We have defined our new distance between signatures similarly to the distance between histograms as follows,…”

Section: Ordinal Distancementioning

confidence: 99%

Signatures versus histograms: Definitions, distances and algorithms

Serratosa

Sanfeliu

2006

Pattern Recognition

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Abstract. The aim of this paper is to present a new method to compare histograms. The main advantage is that there is an important time-complexity reduction respect the methods presented before. This reduction is statistically and analytically demonstrated in the paper. The distances between histograms that we present are defined on a structure called signature, which is a lossless representation of histograms. Moreover, the type of the elements of the sets that the histograms represent are ordinal, nominal and modulo. We show that the computational cost of these distances is O(z') for the ordinal and nominal types and O(z'2 ) for the modulo one, being z' the number of non-empty bins of the histograms. The computational cost of the algorithms presented in the literature depends on the number of bins of the histograms. In most of the applications, the obtained histograms are sparse, then considering only the non-empty bins makes the time consuming of the comparison drastically decrease. The distances and algorithms presented in this paper are experimentally validated on the comparison of images obtained from public databases and positioning of mobile robots through the recognition of indoor scenes (captured in a learning stage).

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A two-dimensional histogram-matching method for protein phase refinement and extension

Cited by 8 publications

References 13 publications

Structural Basis for Recruitment of Rab6-Interacting Protein 1 to Golgi via a RUN Domain

Structural Basis for Recruitment of Rab6-Interacting Protein 1 to Golgi via a RUN Domain

Identification of ions in experimental electrostatic potential maps

Signatures versus histograms: Definitions, distances and algorithms

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