Development of new computational amino acid parameters for protein structure/function analysis within the resonant recognition model

The Resonant Recognition Model (RRM) is a physico-mathematical model developed for analysis of protein and DNA sequences. Biological function of proteins and their 3D structures are determined by the linear sequences of amino acids. Previously, the electron-ion interaction potentials (EIIP) of amino acids have been used to determine the characteristic patterns of different proteins independent of their biological activity. In this study, the effect of various other amino acid parameters on periodicity, obtained using the RRR, were assessed. Here, we are proposing new computational amino acid parameters that could be used successfully for protein analysis instead of EIIP within the RRM

Bioactive peptide design using the Resonant Recognition Model

With a large number of DNA and protein sequences already known, the crucial question is to find out how the biological function of these macromolecules is "written" in the sequence of nucleotides or amino acids. Biological processes in any living organism are based on selective interactions between particular bio-molecules, mostly proteins. The rules governing the coding of a protein's biological function, i.e. its ability to selectively interact with other molecules, are still not elucidated. In addition, with the rapid accumulation of databases of protein primary structures, there is an urgent need for theoretical approaches that are capable of analysing protein structure-function relationships. The Resonant Recognition Model (RRM) [1,2] is one attempt to identify the selectivity of protein interactions within the amino acid sequence. The RRM [1,2] is a physico-mathematical approach that interprets protein sequence linear information using digital signal processing methods. In the RRM the protein primary structure is represented as a numerical series by assigning to each amino acid in the sequence a physical parameter value relevant to the protein's biological activity. The RRM concept is based on the finding that there is a significant correlation between spectra of the numerical presentation of amino acids and their biological activity. Once the characteristic frequency for a particular protein function/interaction is identified, it is possible then to utilize the RRM approach to predict the amino acids in the protein sequence, which predominantly contribute to this frequency and thus, to the observed function, as well as to design de novo peptides having the desired periodicities. As was shown in our previous studies of fibroblast growth factor (FGF) peptidic antagonists [2,3] and human immunodeficiency virus (HIV) envelope agonists [2,4], such de novo designed peptides express desired biological function. This study utilises the RRM computational approach to the analysis of oncogene and proto-oncogene proteins. The results obtained have shown that the RRM is capable of identifying the differences between the oncogenic and proto-oncogenic proteins with the possibility of identifying the "cancer-causing" features within their protein primary structure. In addition, the rational design of bioactive peptide analogues displaying oncogenic or proto-oncogenic-like activity is presented here

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

The resonant recognition model (RRM) is a physicomathematical 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 (ε') and dielectric loss tangent (tan δ) 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 (Δε'), 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

Non-thermal effects of 500 MHz-900MHz microwave radiation on enzyme kinetics

Enzymes are essential for the catalysis of biochemical reactions and in the regulation of metabolic pathways. They function by greatly accelerating the rate of specific chemical reactions that would otherwise be slow. It has been shown that extremely low-power microwaves can influence enzyme activity [1¿5]. This study is focused at investigating the effects of low level microwave exposures ranging from 500MHz to 900MHz on L-Lactate Dehydrogenase (LDH) enzyme activity. The results obtained revealed the increased bioactivity of the LDH upon microwave radiation at two particular frequencies 500MHz and 900MHz

Investigation of the mechanisms of electromagnetic field interaction with proteins

In our earlier work we have proposed that protein activation is electromagnetic in its nature. This prediction is based on the resonant recognition model (RRM) where proteins are analyzed using digital signal processing (DSP) methods applied to the distribution of free electron energies along the protein sequence. This postulate is investigated here by applying the electromagnetic radiation to example of L-lactate dehydrogenase protein and its biological activity is measured before and after the exposures. The concepts presented would lead to the new insights into proteins susceptibility to perturbation by exposure to electromagnetic fields and possibility to program, predict, design and modify proteins and their bioactivit

Application of the resonant recognition model to analysis of interaction between viral and tumor suppressor proteins

Recent findings in cancer research has established a connection between a T-antigen - common virus - and a brain tumor in children. The studies suggested the T-antigen, the viral component of a specific virus, called the JC virus, plays a significant role in the development of the most frequent type of malignant brain tumors by blocking the functionality of tumor suppressor proteins such as p53 and pRb. Here we have investigated the structure and function relationships of T-antigen, p53 and pRb proteins using the Resonant Recognition Model (RRM), a physico-mathematical approach based on digital signal processing methods

Investigation of the structural and functional relationships of oneogene proteins

Proteins are the biomolecular workhorses driving the most biological processes in any living organism. These processes are based on selective interactions between particular proteins. So far, the rules governing the coding of the protein's biological function, i.e. its ability to selectively interact with other biomolecules, have not been elucidated. The resonant recognition model (RRM) is a novel physicomathematical approach established to analyze the interaction between a protein and its target. The RRM assumes that the specificities of protein interactions are based on the resonant electromagnetic energy transfer at the specific frequency for each interaction. One of the main applications of this model is to predict the location of a protein's biological active site(s) using digital signal processing. This paper incorporates the continuous wavelet transform (CWT) into the RRM to predict the active sites, for a chosen protein example. We have investigated the oncogene functional group using digital signal analysis methods, in particular Fourier transform and CWT; determined oncogenes' characteristic frequency and functional active sites; and performed the design of the peptide analogous. The results obtained provide new insights into the structure-function relationships of the analyzed oncogene protein family

On the concept of university competitiveness management

Operating under market economy conditions, higher education institutions should develop a set of strategic measures to ensure their competitiveness. The system of views on solving the problems of achieving and keeping competitive advantage is mainly represented by the concept of managing the competitiveness of higher education institutions. The article is devoted to identifying the features and clarifying the content of this concept. It is determined that its content is a set of goals, principles, functions, methods, and tools that ensure the competitiveness of a university in the educational services market. The aforementioned content components are analysed in detail. Methods of managing university competitiveness are substantiated and classified based on the type of controlling influence. Tools for managing competitiveness are described, including in terms of their impact on end users of scientific and educational products (services)

A bioactive peptide analogue for myxoma virus protein with a targeted cytotoxicity for human skin cancer in vitro

Background - Cancer is an international health problem, and the search for effective treatments is still in progress. Peptide therapy is focused on the development of short peptides with strong tumoricidal activity and low toxicity. In this study, we investigated the efficacy of a myxoma virus peptide analogue (RRM-MV) as a candidate for skin cancer therapy. RRM-MV was designed using the Resonant Recognition Model (RRM) and its effect was examined on human skin cancer and normal human skin cells in vitro

Features of micro- and ultrastructure of low-fat butter and its low-fat analogues

The aim of the research was to study the features of the structure of low-fat butter and butter pastes, which, in terms of composition and properties, more fully meet the requirements of a healthy diet than high-fat types of butter. The objects of research were: butter with fat content of 72.5%; butter with fat content of 55% made with the addition of skimmed milk powder; butter of the same fat content with the addition of stabilizers based on guar and xanthan gums and emulsifiers based on monoand diglycerides of fatty acids; butter pastes with fat content of 45% with similar additives used to increase the stability of the process of butter formation and improve the texture. The microstructure was studied using an MBI-6 microscope, and the ultramicrostructure was studied using a Phillips electron microscope. In the first case, the sample was prepared by crushing the sample, in the second one — by the method of ultrafast freeze-fracture and etching. Researches have shown that the use of the introduced ingredients improves the homogeneity of the structure of the studied products. Due to the ability of milk proteins and stabilizers to retain moisture, it is more evenly distributed and well retained in the fat matrix of the product, formed from crystalline and liquid fat in the form of a continuous phase, which is confirmed by a sufficient penetration depth of the fat-soluble dye. Plasma droplets in butter with fat content of 72.5% and 55% are more isolated than in butter pastes, as indicated by the greater penetration depth of the water-soluble dye. The average diameter of isolated moisture droplets in low-fat products was 3.3–5.4 μm, and the average diameter of the fat globules that form the basis of the crystalline framework was 5.4–7.4 μm, depending on the composition of the product. For butter with fat content of 72.5%, the values of these indicators were 2.8 and 4.0 μm. The results of the study indicate the presence of differences in the sizes of structural elements, but at the same time confirm the uniformity of the structure of low-fat products, allowing them to be attributed to dispersions «water-in-oil»