Modified 2S albumins with improved tryptophan content are correctly expressed in transgenic tobacco plants

Marcellino, Lucília Helena; Neshich, Goran; Sá, Maria Fátima Grossi de; Krebbers, Enno; Gander, Eugen S.

doi:10.1016/0014-5793(96)00375-4

Cited by 20 publications

(13 citation statements)

References 22 publications

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“…A construct “nanoenvironment” was previously defined in our earlier work regarding the characterization for the specificity of ligand binding, protein function preservation in mutants and the specificity of enzymes based on the type of interface residues (Fernandez et al, 2003 [29]; Marcellino et al, 1996 [30] and Ribeiro et al, 2010 [31], respectively). As defined by Murakami et al (2005)[2], His12 and His47 are the key catalytic residues, followed by the metal ion coordinating residues Glu32, Asp34 and Asp91.…”

Section: Methodsmentioning

confidence: 99%

Identification of New Sphingomyelinases D in Pathogenic Fungi and Other Pathogenic Organisms

Dias-Lopes

Neshich

et al. 2013

PLoS ONE

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Sphingomyelinases D (SMases D) or dermonecrotic toxins are well characterized in Loxosceles spider venoms and have been described in some strains of pathogenic microorganisms, such as Corynebacterium sp. After spider bites, the SMase D molecules cause skin necrosis and occasional severe systemic manifestations, such as acute renal failure. In this paper, we identified new SMase D amino acid sequences from various organisms belonging to 24 distinct genera, of which, 19 are new. These SMases D share a conserved active site and a C-terminal motif. We suggest that the C-terminal tail is responsible for stabilizing the entire internal structure of the SMase D Tim barrel and that it can be considered an SMase D hallmark in combination with the amino acid residues from the active site. Most of these enzyme sequences were discovered from fungi and the SMase D activity was experimentally confirmed in the fungus Aspergillus flavus. Because most of these novel SMases D are from organisms that are endowed with pathogenic properties similar to those evoked by these enzymes alone, they might be associated with their pathogenic mechanisms.

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Section: Methodsmentioning

confidence: 99%

Identification of New Sphingomyelinases D in Pathogenic Fungi and Other Pathogenic Organisms

Dias-Lopes

Neshich

et al. 2013

PLoS ONE

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“…Brazil nut 2S sequences driven by the analogous 2S promoter from A. thaliana caused a 20% increase in methionine content in transgenic A. thaliana (Conceição et al, 1994). However, whenever 2S-BN sequences were expressed under the control of the 35S CaMV promoter, the expression levels observed were insufficient to alter the methionine content in the target plants, e.g., S. tuberosum (Tu et al, 1994), V. narbonensis and N. tabacum (Marcellino et al, 1996).…”

Section: Amino Acid Composition Of Seedsmentioning

confidence: 99%

Expression of a methionine-rich storage albumin from the Brazil nut (Bertholletia excelsa H.B.K., Lecythidaceae) in transgenic bean plants (Phaseolus vulgaris L., Fabaceae)

et al. 1999

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Bean (Phaseolus vulgaris), an important component in the diet of people in developing countries, has low levels of the essential amino acid, methionine. We have attempted to correct this deficiency by introducing a transgene coding for a methioninerich storage albumin from the Brazil nut via biolistic methods. The transgene's coding sequence was driven by a doubled 35S CaMV promoter and AMV enhancer sequences. The transgene was stable and correctly expressed in homozygous R 2 to R 5 seeds. In two of the five transgenic lines the methionine content was significantly increased (14 and 23%) over the values found in untransformed plants.

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“…BlueStar STING has been used previously for predicting enzyme class [14], protein-ligand analysis [15], [16], protein mutant analysis [17], [18], and protein-protein interaction pattern analysis [19], mostly because BlueStar STING offers easy access to a very rich repository of protein characteristics. The analysis reported in this paper uses only the information in the three-dimensional structure for predicting protein interfaces.…”

Section: Introductionmentioning

confidence: 99%

Improving Predictions of Protein-Protein Interfaces by Combining Amino Acid-Specific Classifiers Based on Structural and Physicochemical Descriptors with Their Weighted Neighbor Averages

et al. 2014

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Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now integrated into the BlueStar STING suite of programs. Consequently, the prediction of protein-protein interfaces for all proteins available in the PDB is possible through STING_interfaces module, accessible at the following website: (http://www.cbi.cnptia.embrapa.br/SMS/predictions/index.html).

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Modified 2S albumins with improved tryptophan content are correctly expressed in transgenic tobacco plants

Cited by 20 publications

References 22 publications

Identification of New Sphingomyelinases D in Pathogenic Fungi and Other Pathogenic Organisms

Identification of New Sphingomyelinases D in Pathogenic Fungi and Other Pathogenic Organisms

Expression of a methionine-rich storage albumin from the Brazil nut (Bertholletia excelsa H.B.K., Lecythidaceae) in transgenic bean plants (Phaseolus vulgaris L., Fabaceae)

Improving Predictions of Protein-Protein Interfaces by Combining Amino Acid-Specific Classifiers Based on Structural and Physicochemical Descriptors with Their Weighted Neighbor Averages

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