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

Sleep onset estimator: evaluation of parameters

The electroencephalographic (EEG) alterations during the human sleep onset (falling asleep period) has been evaluated by several studies in the past. However, the analysis part has been limited due to standard signal processing methods. This paper has attempted to evaluate a number of advanced parameters for improved sleep onset estimation, such as EEG non-parametric coherence, power frequency and spectral band power. These parameters can be utilised in an on-line algorithm design for neurofeedback applications

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

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 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

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

Influence of sinusoidally modulated visual stimuli at extremely low frequency range on the human EEG Activity

The purpose of this study was to investigate whether sinusoidally modulated visual stimuli at extremely low frequencies (ELF) of 50, 16.66, 13, 10, 8.33 and 4Hz could influence the changes in EEG activity in 33 human subjects. An improved design of visual stimulator system has addressed an issue of electrical interference from electrical signals driven by LED arrays onto simultaneously recorded EEG. A comparison between 1 and 3-Way ANOVA was performed in order to evaluate whether visual stimuli at ELFs could influence the EEG in humans to compliment the currently active medical research in seasonal affective disorder (SAD) and photic driving. The results revealed that under evaluation of 1 and 3-Way repeated-measures ANOVA tests, the Theta, Alpha2 and Gamma EEG bands exhibited a common significant difference between ELF visual stimuli

Decomposition of evoked potentials using peak detection and the discrete wavelet transform

A new method of viewing evoked potential data is described. This method, called the peak detection method, is based on singularity detection using the discrete wavelet transform. The peaks and troughs of raw visual evoked potential data are identified and characterized using the algorithms of this method, resulting in a linear decomposition of the recording into sets of individual peaks. The individual peaks are then added together, averaged and compared to the ensemble average signal. The peak detection method correlates strongly to the ensemble average showing that this method retains the same evoked potential signal profil

Alterations in human EEG activity caused by extremely low frequency electromagnetic fields

This study has investigated whether extremely low frequency (ELF) electromagnetic fields (EMFs) can alter human brain activity. Linearly polarised magnetic flux density of 20Â-T (rms) was generated using a standard double Helmholtz coils and applied to the human head over a sequence of 1 minute stimulations followed by one minute without stimulation in the following order of frequencies 50, 16.66, 13, 10, 8.33 and 4Hz. We collected recordings on 33 human volunteers under double-blind counter-balanced conditions. Each stimulation lasted for two minutes followed by one minute post-stimulation EEG recording. The same procedure was repeated for the EMF control sessions, where the order of control and exposure sessions was determined randomly according to the subject's ID number. The rest period between two conditions (exposure and control) was 30 minutes. The results indicate that there was a significant increase in Alpha1, Alpha2, and Beta1 at the frontal brain region, and a significant decrease in Alpha2 band in parietal and occipital region due to EMF exposure

ECG R-R peak detection on mobile phones.

Mobile phones have become an integral part of modern life. Due to the ever increasing processing power, mobile phones are rapidly expanding its arena from a sole device of telecommunication to organizer, calculator, gaming device, web browser, music player, audio/video recording device, navigator etc. The processing power of modern mobile phones has been utilized by many innovative purposes. In this paper, we are proposing the utilization of mobile phones for monitoring and analysis of biosignal. The computation performed inside the mobile phone's processor will now be exploited for healthcare delivery. We performed literature review on RR interval detection from ECG and selected few PC based algorithms. Then, three of those existing RR interval detection algorithms were programmed on JavaTM platform. Performance monitoring and comparison studies were carried out on three different mobile devices to determine their application on a realtime telemonitoring scenario