A model of molecular vector machine of proteins

Significance of genetic code module structure in gene expression and GC content enhancement in RNA sequences

The existent algebraic models of the genetic code contribute to the understanding of the physio-chemical characteristics of the amino acids. However, the process of translating a gene into a phenotype is highly complex. Moreover, the intricacy of gene expression gets further multiplied due to the biases in the codon usage. This paper explores an algebraic structure called module on the set of codons as well as on that of RNA sequences. We study the potential implications of these structures on gene expression and the GC content of an RNA sequence. The base order {C,U,G,A} appears to possess greater biological significance than many of the orders previously studied. We have developed a novel algorithm to generate RNA sequences with high GC content, aiming to enhance the thermostability of biomolecules. The insights gained from this investigation may have applications in biomolecular modeling and docking, protein engineering, drug development, and related fields.

The Canonical Table of the Genetic Code as a periodic system of triplets

The Canonical Table of the Genetic Code (CTGC) is constructed theoretically on the basis of the similarity of PFs (PF) of proteins with the conformation of 4-arc chain graphs (Karasev, 2019). Of the 64 conformations of the graph, specified by the position of the connectivity edges, and the matrices of 6 variables (x₁ … x₆), x_i = (0, 1), 4 blocks of 16 elements each were formed. Then they were coded in the form of triplets based on the correspondence of pairs of variables to four letters of the code: 00 = C, 01 = U, 10 = G, 11 = A, and supplemented based on the known triplet-amino acid assignment. The resulting table is compared with the Periodic Table of Chemical Elements (PTCE). As in the PTCE, this CTGC has an initial element - a triplet that encodes graphs with zero number of connected edges. Within each block, vacancies are filled with connectivity edges in two alternative ways, both in rows and in the columns. As we move from the initial block 00 to the final block 11, there is a sequential filling of vacancies for variables x₃x₄: 00, 01, 10, 11. In general, the CTGC can be considered as a periodic system of triplets. Comparison with the previously described variety of tables of the genetic code made it possible to conclude that the CTGC more adequately reflects the properties of the genetic code. Prospects for the possible application of this table are being discussed.

Study of the Fabrication Technology of Hybrid Microfluidic Biochips for Label-Free Detection of Proteins

A study of the peculiarities and a comparative analysis of the technologies used for the fabrication of elements of novel hybrid microfluidic biochips for express biomedical analysis have been carried out. The biochips were designed with an incorporated microfluidic system, which enabled an accumulation of the target compounds in a biological fluid to be achieved, thus increasing the biochip system's sensitivity and even implementing a label-free design of the detection unit. The multilevel process of manufacturing a microfluidic system of a given topology for label-free fluorometric detection of protein structures is presented. The technological process included the chemical modification of the working surface of glass substrates by silanization using (3-aminopropyl) trimethoxysilane (APTMS), formation of the microchannels, for which SU-8 technologies and a last generation dry film photoresist were studied and compared. The solid-state phosphor layers were deposited using three methods: drop application; airbrushing; and mechanical spraying onto the adhesive surface. The processes of sealing the system, installing input ports, and packaging using micro-assembly technologies are described. The technological process has been optimized and the biochip was implemented and tested. The presented system can be used to design novel high-performance diagnostic tools that implement the function of express detection of protein markers of diseases and create low-power multimodal, highly intelligent portable analytical decision-making systems in medicine.

Data on the application of the molecular vector machine model: A database of protein pentafragments and computer software for predicting and designing secondary protein structures

Based on ideas about the molecular vector machine of proteins [1], a database of protein pentafragments has been created and algorithms have been proposed for predicting the secondary structure of proteins according to their primary structure and for designing the primary protein structure for a given secondary structure that it takes on. A comprehensive software suite (Predicto @ Designer) has been developed using the pentafragments database and the said algorithms. For the proteins used to create the pentafragments database, a high accuracy (close to 100%) in predicting the secondary protein structure as well as good prospects for its use for designing secondary structures of proteins have been demonstrated.

A model of molecular vector machine of proteins

Based on the analysis of N_iH⋯O_i-4C bond region of the proteins pentafragments using the quantum chemistry representations, a group of 20 vectors of action has been isolated. These vectors of action are reconstructed by a group of 20 side chains of amino acids being the irreducible representations of vectors. As a result, a model of molecular vector machine of proteins (MVM) has been proposed. This machine includes proteins pentafragments, a system of 20 vectors inside dodecahedron coming out of the O_i-4 atom as from the center and tetrahedral α_i-carbon atom with the side chains of amino acids that change as the polypeptide chain grows. A model with the structured canonical set of amino acids based on the principles of antisymmetry was transferred onto the dodecahedron as an MVM element (Karasev et al., 2005; Karasev et al., 2007). The perspective of MVM application for predicting and designing the secondary structure of proteins has been discussed.