Affinity and Correlation in DNA
- There is a statistical difference between the coding and non-coding areas of DNA. The different statistical behavior of these two kinds of sequences can be used to build methods for identifying these areas of the DNA. For example, a fractal method to distinguish coding and non-coding sequences in a complete genome has been proposed . This method shows that, following the thermodynamics formulation of multifractal measures, in a graphic of the analogous of specific heat, points of coding and non-coding sequences of many prokaryote genomes are roughly distributed in different regions.
- This statistical difference is not due to the biological function of DNA. In general, any sequence of letters is quasi-random, and the regularities have nothing to do with the message conveyed, but only with specific properties of the used language. The same letter translated from English in Italian or in French will have other regularities (departures from randomness) due to the different letter usage of Latin origin languages with respect to English language. This happens every time when in the presence of a code.
- DKL(P,Q) > 0, DKL(P,Q) ≠ DKL(Q,P) and DKL(Pi,Qi) = 0 if, and only if, Pi = Qi.
- The propagation of short-range correlations (affinities);
- The large amount of specific sequences in DNA, for example, the Alu sequence, which we will consider later; and
- The hydrogen or disulfide bridges in proteins that directly bind two distant sites in these sequences.
3. Results and Discussion
- Evolutionary reasons could provide information on past DNA and allow us to identify the laws of its evolution. The correlations, therefore, would be a consequence of such laws of genome evolution, and current genomes could be seen as a picture of the ongoing genome evolution process and, assuming that the symmetries on its composition derive from past genomes, could shed some light on the origin of life [46,47,48]. In this case, however, we must take into account that genomes show a great variability of constituent elements as consequence of their rates of mutation, genetic recombination events, horizontal gene transfers and gene losses or gains . For example, two random DNA sequences can show up to 50% identical sequences when gaps are allowed [50,51,52]. As a result, many evolutionary analysis tools work well at short evolutionary distances, but only a few of them have worked well over longer time distances .
- The structural reasons explain how DNA is made. In this case, given that the structure of DNA is determined by physical and system constraints, the latter is dependent on its information content. These authors show that the composition of these codes lie at or around the local minima of the information function. The fact that codes do not evolve towards maximizing the information function leads these authors to assume that there is a mechanism that induces genetic codes to minimize information and that this mechanism is driving the evolution of this code. At some point in the evolution of living organisms, the number of constituents was blocked, and the cell began to develop a genetic code with non-random information content that corresponds to a trend towards one of the lows of the information function, or at least close to such state. This image agrees with most of the considerations about the correlation between the complex biological systems and non-random information content. The authors of  also suggest that based on the complexity of DNA sequences, a model for duplications of DNA sequences can be a fruitful approach to understanding long-range correlations.
- All stacking energies of the dimers are negative, i.e., the dimers are, in all cases, more stable than the two isolated base pairs.
- There are 11 dimers (ApA, ApC, ApG, CpC, CpT, GpA, GpG, GpT, TpA, TpC and TpT) with equal or similar stacking energy. For these dimers, in fact, we have an average stacking energy of 13.56 kcal/mol with a small dispersion in the range [13.22 ÷ 13.96].
- There is only one dimer, ApT, with a stacking energy value lower than that of group (b) and, in particular, with a stacking energy of 11.66 kcal/mol.
- The other four dimers, ApT, CpA (and TpG equal to it) and CpG, have stacking energies higher than that of group (b), with an average value of 16.62 kcal/mol, a dispersion in the range [15.95 ÷ 18.44 and limit values of CpA = TpG = 15.95 kcal/mol and CpG = 18.44 kcal/mol.
Second Chargaff Rule (SCR)
- The difference between human chromosome sequences and their coding parts is relevant both in the relationships between sites and in some specific compositional rules, such as the second Chargaff rule.
- The most important relationship between sites in all the DNA sequences examined is that between two consecutive base pairs, which indicates an energetic stabilization of these couples of base pairs due to the stacking interaction.
- The evidence of the relationship in two successive triplets of DNA-coding sequences demonstrates the existence of a relationship between two successive amino acids in proteins. This is obviously impossible if all the relationships between the sites of a macromolecule are statistical evidence and do not involve causes; in this article, due to stacking interactions and this relationship in coding sequences, we divided the concept of a relationship between sites into two concepts: affinity and correlation, the first with physical causes and the second without.
- The causal relationships, named in this paper affinity, of C1 in all chromosomes and of C3 in the related coding parts, are the most important relationships in DNA. This is in contrast to the current idea that all the relationships between sites of these macromolecules are only statistical evidence, but in perfect agreement with the results of [22,23]. These affinities may be due to different processes, but it is certainly also necessary to consider the energetic interaction between pairs of monomers, which is an interaction not considered in the literature.
- In some large sequences with several Mbp, such as those of some non-coding parts of chromosomes, there is evidence of specific, long-rage correlations that may be related to several large replicate sequences (for example, the Alu sequence).
- The second Chargaff rule is substantially valid for all human chromosomes, but not for their coding parts.
Informed Consent Statement
Conflicts of Interest
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Villani, G. Affinity and Correlation in DNA. J 2022, 5, 214-231. https://doi.org/10.3390/j5020016
Villani G. Affinity and Correlation in DNA. J. 2022; 5(2):214-231. https://doi.org/10.3390/j5020016Chicago/Turabian Style
Villani, Giovanni. 2022. "Affinity and Correlation in DNA" J 5, no. 2: 214-231. https://doi.org/10.3390/j5020016