Regularities in mutational variability in selected protein families and the Markovian model of amino acid replacement

Genome Attractors as Places of Evolution and Oases of Life

So far, much effort has been made to understand evolution and life phenomena. However, the more we know, the more new puzzles appear. This article introduces some new approaches to understanding what drives evolution. Organism evolution has been examined using artificial neural networks and a semihomologous approach based on the sequences of cytochrome c. To realize this task, three and four-layer neural networks have been designed and then taught. It has been shown that the four-layer neural network more clearly recognizes evolutionary similarities, usually indicating greater (comparing to the three-layer network) similarities to the organisms that were used to train the neural networks. It has been noted that unified cell bioenergetics allows describing the manner in which the main engine that drives evolution works. Reasons for some diseases have been also interpreted to present considerations in a broader and more holistic view. The presented results point out that the evolution of organisms can be considered as a discontinuous process taking place mainly in genome attractors that define and stabilize organisms.

Evolutionary and functional lessons from human-specific amino acid substitution matrices

Human genetic variation in coding regions is fundamental to the study of protein structure and function. Most methods for interpreting missense variants consider substitution measures derived from homologous proteins across different species. In this study, we introduce human-specific amino acid (AA) substitution matrices that are based on genetic variations in the modern human population. We analyzed the frequencies of >4.8M single nucleotide variants (SNVs) at codon and AA resolution and compiled human-centric substitution matrices that are fundamentally different from classic cross-species matrices (e.g. BLOSUM, PAM). Our matrices are asymmetric, with some AA replacements showing significant directional preference. Moreover, these AA matrices are only partly predicted by nucleotide substitution rates. We further test the utility of our matrices in exposing functional signals of experimentally-validated protein annotations. A significant reduction in AA transition frequencies was observed across nine post-translational modification (PTM) types and four ion-binding sites. Our results propose a purifying selection signal in the human proteome across a diverse set of functional protein annotations and provide an empirical baseline for interpreting human genetic variation in coding regions.

Study on attractors during organism evolution

The important question that arises during determining the evolution of organisms is whether evolution should be treated as a continuous process or whether groups of organisms fall into 'local' attractors during evolution. A similar question arises during considering the development of cells after cancer transformation. Answers to these questions can provide a better understanding of how normal and transformed organisms evolve. So far, no satisfactory answers have been found to these questions. To find the answers and demonstrate that organisms during evolution get trapped in 'local' attractors, an artificial neural network supported by a semihomologous approach and unified cell bioenergetics concept have been used in this work. A new universal model of cancer transformation and cancer development has been established and presented to highlight the differences between the development of transformed cells and normal organisms. An unequivocal explanation of cancer initialization and development has not been discovered so far, thus the proposed model should shed new light on the evolution of transformed cells.

Comparative Study on Sequence-Structure-Function Relationship of the Human Short-chain Dehydrogenases/Reductases Protein Family

Human short-chain dehydrogenases/reductases (SDRs) protein family has been the subject of recent studies for its critical role in human metabolism. Studies also found that single nucleotide polymorphisms of the SDR protein family were responsible for a variety of genetic diseases, including type II diabetes. This study reports the effect of sequence variation on the structural and functional integrities of human SDR protein family using phylogenetics and correlated mutation analysis tools. Our results indicated that (i) tyrosine, serine, and lysine are signature protein residues that have direct contribution to the structural and functional stabilities of the SDR protein family, (ii) subgroups of SDR protein family have their own signature protein combination that represent their unique functionality, and (iii) mutations of the human SDR protein family showed high correlation in terms of evolutionary history. In combination, the results inferred that over evolutionary history, the SDR protein family was able to diverge itself in order to adapt with the changes in human nutritional demands. Our study reveals understanding of structural and functional scaffolds of specific SDR subgroups that may facilitate the design of specific inhibitor.

Study on phylogenetic relationships, variability, and correlated mutations in M2 proteins of influenza virus A

M2 channel, an influenza virus transmembrane protein, serves as an important target for antiviral drug design. There are still discordances concerning the role of some residues involved in proton transfer as well as the mechanism of inhibition by commercial drugs. The viral M2 proteins show high conservativity; about 3/4 of the positions are occupied by one residue in over 95%. Nine M2 proteins from the H3N2 strain and possibly two proteins from H2N2 strains make a phylogenic cluster closely related to 2RLF. The variability range is limited to 4 residues/position with one exception. The 2RLF protein stands out by the presence of 2 serines at the positions 19 and 50, which are in most other M2 proteins occupied by cysteines. The study of correlated mutations shows that there are several positions with significant mutational correlation that have not been described so far as functionally important. That there are 5 more residues potentially involved in the M2 mechanism of action. The original software used in this work (Consensus Constructor, SSSSg, Corm, Talana) is freely accessible as stand-alone offline applications upon request to the authors. The other software used in this work is freely available online for noncommercial purposes at public services on bioinformatics such as ExPASy or NCBI. The study on mutational variability, evolutionary relationship, and correlated mutation presented in this paper is a potential way to explain more completely the role of significant factors in proton channel action and to clarify the inhibition mechanism by specific drugs.