Elena Pirogova

Advances in Methods for Therapeutic Peptide Discovery, Design and Development

Drug discovery and development are intense, lengthy and interdisciplinary processes. Traditionally, drugs were discovered by synthesizing compounds in time-consuming multi-step experimental investigations followed by in vitro and in vivo biological screening. Promising candidates were then further studied for their pharmacokinetic properties, metabolism and potential toxicity. Today, the process of drug discovery has been revolutionized due to the advances in genomics, proteomics, and bioinformatics. Efficient technologies such as combinatorial chemistry, high throughput screening (HTS), virtual screening, de novo design and structure-based drug design contribute greatly to drug discovery. Peptides are emerging as a novel class of drugs for cancer therapy, and many efforts have been made to develop peptide-based pharmacologically active compounds. This paper presents a review of current advances and novel approaches in experimental and computational drug discovery and design. We also present a novel bioactive peptide analogue, designed using the Resonant Recognition Model (RRM), and discuss its potential use for cancer therapeutics.

Application of ionisation constant of amino acids for protein signal analysis within the resonant recognition model

Biological functions of proteins are determined primarily by the linear sequences of their constitutive elements, i.e. amino acids. The RRM model interprets this linear information using signal analysis methods. Initially, the amino acid sequences are transformed into a numerical series using the electron-ion interaction potential (EIIP) for each amino acid. Here we investigate usage of ionisation constant of amino acids instead of EIIP in the RRM model.

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.

Modification of the RRM model using wavelets transform and ionisation constant to predict protein active sites

The resonant recognition model (RRM) which utilises the Fourier transform on the numerical representation of protein sequences obtained by representing each amino acid by an electron-ion interaction potential, has been shown useful in predicting the key amino acids for a protein sequence. Here, the authors discuss the modification of this model using ionisation constant to represent a protein sequence as a numerical series and continuous wavelet transform to predict active regions in the protein.

Analysis of amino acid parameters in the Resonant Recognition Model

The Resonant Recognition Model (RRM) is a physicomathematical model developed for analysing protein and nucleic acid sequences. Previously, the electron-ion interaction potentials (EIIP) of amino acids have been used to determine the characteristic frequency of biologically related proteins. In this study, the effect of various other amino acid parameters on periodicity, obtained using the RRM, were assessed. Three functionally unrelated protein groups (glucagons, lysozymes and haemoglobins) were used as test examples. However, no single parameter was found to be a good representative of biological activity.

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 bioactivity

The use of ionisation constants of amino acids for protein signal analysis within the resonant recognition model--application to proteases

Biological functions of proteins and their active 3D structures are determined by the linear sequences of amino acids. The resonant recognition model (RRM) is a physico-mathematical model developed for structure/function analysis of protein and DNA sequences. Here, we are comparing results of the RRM analysis [1,2] of protease proteins using the electron-ion interaction potential (EIIP) and ionisation constant (IC) of amino acids. The results obtained reveal that the IC parameter can be successfully used to determine the characteristic patterns of different functional protease subgroups.

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 electronion 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 RRM, 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.