Prediction of ⪡ hot spots ⪢ in interleukin-2 based on informational spectrum characteristics of growth- regulating factors. Comparison with experimental data

Cosic, Irena; Pavlović, Mirjana; Vojisavljevic, Vuk

doi:10.1016/0300-9084(89)90005-9

Cited by 34 publications

(16 citation statements)

References 37 publications

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“…The spectral function is defined by discrete Fourier transformation. The detailed calculation procedures used in the resonant recognition model are described in [6,7,9, 111. These procedures were applied here to the group of 25 repressors and 46 oncogene-related proteins (listed in Appendix) with the aim of defining characteristic frequencies of these two functional groups of proteins.…”

Section: Resonant Recognition Modelmentioning

confidence: 99%

Studies on protein‐DNA interactions using the resonant recognition model

Cosic

Hearn

1992

European Journal of Biochemistry

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The structural features of protein-DNA interactions have been evaluated using a new information theory algorithm for the analysis of protein structure/function dependence: the so-called resonant recognition model. The physicochemical basis of this analysis was firstly validated with the trprepressor -operator interaction as a well-defined example. The amino acid and structural features predicted by these procedures to be crucial for repressor-operator interaction were found to be clustered around the known three-dimensional structure of the active site of the trp repressor, Similar methods of analysis have been extended to the less-well-defined example of the Ha-ras p21 protein family. The results of this analysis have indicated two distinct interactive regions in p21, one associated with the guanine-nucleotide-binding site, whilst the second is proposed to be associated with a binding site for an activator protein. These studies indicate that the p22 protein, besides the ability to function as a plasma-membrane-associated guanine-nucleotide-binding regulatory protein and bind free guanine nucleotides in the cytoplasm, has the structural ability to bind guanine incorporated in DNA. Thus, p21-related protcins may have the potential to function as an DNA-binding and regulating protein with the mode of upstream DNA binding closely related to their oncogenic function.In recent years, substantial scientific effort has been directed towards unravelling the cellular and molecular mechanisms which lead to uncontrolled proliferation associated with cell transformation. Regulation of gene expression represents the central role in these cellular processes of proliferation, differentiation and transformation. Specific protein-DNA interactions are known to pkay a critical role in gene regulation. Investigations in this field have largely been directed towards understanding these interactions through identification of the structural features of the proteins and DNA segments involved in these processes [l -41. As a generally accepted model. selective protein-DNA recognition is often discussed in terms of mutual structural complementarity, with the protein and DNA fitting together like a lock and key. Although, this model has been used [3 -51 prospectively with a number of examples of protein-DNA interactions, the key-and-lock model cannot predict thc critical shapes which allow the interaction, identify & I I O V D the amino acids or nucleotides which are crucial for defining this shape, or anticipate the overall physicochemical parameters which characterise the molecular forces responsiblc for selective rccognition betwcen proteins and certain sequence arrangements of DNA. Thus, this keyand-lock model has important empirical, but not predictive, capabilities relevant to the submolecular basis of protein-DNA recognition.To gain insight into the physicochemical and structural requirements which may characterise protein-DNA interacCorrespondence to M. T. W. Itearn, Department of Biochcmislry and Centre for Bioprocess Technology,...

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Section: Resonant Recognition Modelmentioning

confidence: 99%

Studies on protein‐DNA interactions using the resonant recognition model

Cosic

Hearn

1992

European Journal of Biochemistry

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“…Once the characteristic frequency for a particular protein function (interaction) is identified, it is then possible to utilize the RRM approach to predict the amino acids in the sequence that predominantly contribute to this frequency and are crucial for the observed function [5]. Finally, it is possible to design peptides having the desired periodicities [6]. Such peptides have been shown to express the desired biological function; examples shown in preliminary work include FGF peptidic antagonists [7] and HIV envelope agonists [8].…”

Section: A the Resonant Recognition Modelmentioning

confidence: 99%

“…In general terms, to determine dielectric properties of solids, a film is produced and its capacitance and conductance measured between two conducting parallel plates as a function of frequency, with the dielectric constant and dielectric loss being defined by (5) (6) In this case is the empty capacitance, the capacitance of the empty cell, with the thickness being the same as the sample, and is given by (7) where and are the sample area and thickness, respectively, and is the permittivity of free space. However, to measure the properties of amino acid solutions, a different electrode was used in this work-interdigitated comb electrodes, in which the electrode are patterned onto an inert substrate (usually polymer) surface.…”

Section: Techniquementioning

confidence: 99%

Investigation of the applicability of dielectric relaxation properties of amino acid solutions within the resonant recognition model

Pirogova

Simon

Cosic

2003

IEEE Trans.on Nanobioscience

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Abstract-The resonant recognition model (RRM) is a physico-mathematical approach used to analyze the interactions of a protein and its target, using digital signal processing methods. The RRM is based on the finding that there is a significant correlation between the spectra of numerical presentation of protein sequences and their biological activities. Initially, the electron-ion interaction potential was used to represent each amino acid in the protein sequences. In this paper, the dielectric constant ( ) and dielectric loss tangent (tan ) parameters have been determined for their possible use in the RRM. These parameters are based on the values of capacitance and conductance obtained experimentally for 20 amino acid solutions using dielectric spectroscopy for the case of the real component of dielectric permittivity; the parameter used is the dielectric increment (1 ), the difference between dielectric constant of the amino acid solution and that of the solvent alone. The results of multiple cross-spectral analyses have shown that parameters analyzed generate in the consensus spectrum one dominant peak corresponding to the common biological activity of proteins studied, allowing the conclusion that these new parameters are suitable for use in the RRM approach.

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“…By identifying the characteristic frequency of a particular protein, it is possible to predict which amino acids in the sequence predominantly contribute to the frequency and consequently to the observed function [1], [2], [7], [8]. Since the characteristic frequency correlates with the biological function, the positions of the amino acids that are most affected by the change of amplitude at the particular frequency can be defined as hot spots for the corresponding biological function.…”

Section: B "Hot Spots" In Terms Of the Rrm And 3-d Protein Structuresmentioning

confidence: 99%

“…These hot spots are related to this frequency and to the corresponding biological function. The procedure described was used in a number of examples: IL-2 [8]; hemoglobins, myoglobins, and lysozymes [7]; chymotrypsins [11]; glucagons and tumor necrosis factors [9]; EGFs, fibroblast growth factors (FGFs), etc. [2].…”

Section: B "Hot Spots" In Terms Of the Rrm And 3-d Protein Structuresmentioning

confidence: 99%

Investigation of the structural and functional relationships of oncogene proteins

Pirogova¹,

Fang²,

Akay

et al. 2002

Proc. IEEE

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Prediction of ⪡ hot spots ⪢ in interleukin-2 based on informational spectrum characteristics of growth- regulating factors. Comparison with experimental data

Cited by 34 publications

References 37 publications

Studies on protein‐DNA interactions using the resonant recognition model

Studies on protein‐DNA interactions using the resonant recognition model

Investigation of the applicability of dielectric relaxation properties of amino acid solutions within the resonant recognition model

Investigation of the structural and functional relationships of oncogene proteins

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