On Characterization of Dose Variations of 2-D Proteomics Maps by Matrix Invariants

Randić, Milan; Novič, and Marjana; Vračko, Marjan

doi:10.1021/pr0100117

Cited by 31 publications

(22 citation statements)

References 27 publications

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“…recognized different behavior of the response curve for small concentrations and large concentrations and concluded that the low-dose and high-dose effects of peroxisome proliferates could be qualitatively different. Similarly in a subsequent analysis of the same data, the work of Randić et al, one can notice a larger scatter of points in the dose−response curve for small concentrations of LY171883. Now it is clear that at least in the case of the later study, the information on proteome was obscured to some degree by including data on proteomics maps, that is, data on the proteins mass and the relative charge, which was incorporated in multivariate statistical analysis, including use of principal component analysis (PCA) but which is not essential for evaluation of proteome as such.…”

Section: Discussionmentioning

confidence: 57%

“…Observe that in this analysis we have focused on cell proteome by simply considering only the information on the variation in the abundance of protein spots of the proteomics maps, and hence we are not trying to characterize the changes in the proteome maps, which would include information on the x and y coordinate of proteins spots. This appears to have been the objective of Anderson et al as well as of more recent analysis of the same data based on the construction of special distance matrix for a set of most intensive protein spots …”

Section: Methodsmentioning

confidence: 99%

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Order from Chaos: Observing Hormesis at the Proteome Level

Randić

Estrada

2005

J. Proteome Res.

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We report on an observed regularity in the overall response of cell proteome under influence of different doses of a peroxisome proliferator. Our analysis for the first time demonstrates presence of hormesis at the cellular level. It is shown that despite the fact that the response of individual proteins remains unpredictable, the perturbation of proteome as a whole (measured as the average departure of protein abundances from the corresponding values of the control group) shows regularity: It initially decreases, reaches a minimum, and then increases as the concentration of the used proliferator continue to increase. The work is based on the available data of Anderson at al. on the effects of peroxisome proliferator LY171883 on protein abundances in mouse liver when administrated at different concentrations.

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Section: Discussionmentioning

confidence: 57%

Section: Methodsmentioning

confidence: 99%

Order from Chaos: Observing Hormesis at the Proteome Level

Randić

Estrada

2005

J. Proteome Res.

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“…For example, it has been shown that rats exposed to small doses of irradiation have better survival rate than the control group, which was not exposed to such radiation, when both are subject to large doses of irradiation , none of which revealed the presence of hormesis at the proteome level. The discovery of hormesis awaited development of an alternative approach to characterization of proteomics maps, which was focused on proteome (abundance of proteins spots in a 2-D gel) rather then on proteomics maps (which includes information on mass and the charge of proteins).…”

Section: Introductionmentioning

confidence: 99%

Quantitative Characterizations of Proteome: Dependence on the Number of Proteins Considered

Randić

2006

J. Proteome Res.

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We consider the sensitivity of numerical characterizations of proteome on the number of proteins considered in the analysis. We examined data on proteomics maps belonging to the liver cells of mice subject to four proliferators. We varied the number of proteins considered for quantitative analysis from 25 up to 1000 proteins. For each case, we have compared the similarity/dissimilarity results when different number of proteins has been considered. We found that proteins maps based on a set of about 300 most abundant proteins spots suffice for satisfactory numerical characterization of corresponding proteome.

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“…In the first experiment (Experiment 1), we show the use in an experimental example to use 2D-Lattice electrostatic parameters to numerically characterize protein sequences and seek a model to predict RNase III function without relying on alignment. Different classes of 2D graphs representations of DNA, RNA, protein sequence, or proteomic maps have been used by other researchers [87,91,92,[154][155][156][157][158][159][160][161][162][163][164]. We subsequently developed three different classifiers (one for each type of TIs) to connect protein sequence information (represented by TIs values) with the classification of sequences as RNase III or not.…”

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confidence: 99%

QSAR for RNases and theoretic–experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein

et al. 2009

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The toxicity and low success of current treatments for Leishmaniosis determines the search of new peptide drugs and/or molecular targets in Leishmania pathogen species (L. infantum and L. major). For example, Ribonucleases (RNases) are enzymes relevant to several biologic processes; then, theoretical and experimental study of the molecular diversity of Peptide Mass Fingerprints (PMFs) of RNases is useful for drug design. This study introduces a methodology that combines QSAR models, 2D-Electrophoresis (2D-E), MALDI-TOF Mass Spectroscopy (MS), BLAST alignment, and Molecular Dynamics (MD) to explore PMFs of RNases. We illustrate this approach by investigating for the first time the PMFs of a new protein of L. infantum. Here we report and compare new versus old predictive models for RNases based on Topological Indices (TIs) of Markov Pseudo-Folding Lattices. These group of indices called Pseudo-folding Lattice 2D-TIs include: Spectral moments pi ( k )(x,y), Mean Electrostatic potentials xi ( k )(x,y), and Entropy measures theta ( k )(x,y). The accuracy of the models (training/cross-validation) was as follows: xi ( k )(x,y)-model (96.0%/91.7%)>pi ( k )(x,y)-model (84.7/83.3) > theta ( k )(x,y)-model (66.0/66.7). We also carried out a 2D-E analysis of biological samples of L. infantum promastigotes focusing on a 2D-E gel spot of one unknown protein with M<20, 100 and pI <7. MASCOT search identified 20 proteins with Mowse score >30, but not one >52 (threshold value), the higher value of 42 was for a probable DNA-directed RNA polymerase. However, we determined experimentally the sequence of more than 140 peptides. We used QSAR models to predict RNase scores for these peptides and BLAST alignment to confirm some results. We also calculated 3D-folding TIs based on MD experiments and compared 2D versus 3D-TIs on molecular phylogenetic analysis of the molecular diversity of these peptides. This combined strategy may be of interest in drug development or target identification.

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On Characterization of Dose Variations of 2-D Proteomics Maps by Matrix Invariants

Cited by 31 publications

References 27 publications

Order from Chaos: Observing Hormesis at the Proteome Level

Order from Chaos: Observing Hormesis at the Proteome Level

Quantitative Characterizations of Proteome: Dependence on the Number of Proteins Considered

QSAR for RNases and theoretic–experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein

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