Biological processes in any living organism are based on selective interactions between particular biomolecules. In most cases, these interactions involve and are driven by proteins which are the main conductors of any living process within the organism. The physical nature of these interactions is still not well known. This paper represents a whole new view to biomolecular interactions, in particular protein-protein and protein-DNA interactions, based on the assumption that these interactions are electromagnetic in their nature. This new approach is incorporated in the Resonant Recognition Model (RRM), which was developed over the last 10 years. It has been shown initially that certain periodicities within the distribution of energies of delocalized electrons along a protein molecule are critical for protein biological function, i.e., interaction with its target. If protein conductivity was introduced, then a charge moving through protein backbone can produce electromagnetic irradiation or absorption with spectral characteristics corresponding to energy distribution along the protein. The RRM enables these spectral characteristics, which were found to be in the range of infrared and visible light, to be calculated. These theoretically calculated spectra were proved using experimentally obtained frequency characteristics of some light-induced biological processes. Furthermore, completely new peptides with desired spectral characteristics, and consequently corresponding biological activities, were designed.

Informational content of linear macromolecules (DNA, RNA, and proteins) is analyzed by the method of digital signal processing. Each element (amino acid or nucleotide) of macromolecules is represented by the corresponding value of electron-ion interaction potential. This numerical representation of the primary structure of macromolecules is subjected to digital signal processing in order to extract the information corresponding to biological function. It is shown that the multiple cross spectrum of functionally related sequences exhibits significant peak frequencies. In the case of functionally unrelated sequences such peaks were not found. Peak frequencies are different for different biological functions. Based on this, we conjecture that the peak frequencies in the multiple cross spectrum of sequences with the same boilogical function are related to this biological function.

A protein's chemical properties, the chain conformation, the function of the protein and its species specificity are determined by the information contained in the amino acid sequence. Proteins of similar functions have at some level sequential identical amino acid sequences. The closer the phylogenetic relationship, the more similar are the sequences. To find the similarities between two or more protein sequences is of great importance for protein sequence analysis. The differences in the amino acid sequences permit the construction of a family tree of evolution. In this work, a comparison method was devised that is capable of analysing a protein sequence `hierarchically', i.e. it can examine a protein sequence at different spatial resolutions. Based on a wavelet decomposition of protein sequences and a cross-correlation study, a sequence–scale similarity concept is proposed for generating a similarity vector, which renders the comparison of two sequences feasible at different spatial resolutions (scales). This new similarity concept is an expansion of the conventional sequence similarity, which only takes into account the local pairwise amino acid match and ignores the information contained in coarser spatial resolutions.