Background
Messenger RNA was originally not expected to have any secondary structure, because it was simply supposed to pass through the ribosomes (as a magnetic tape passes a tape-recorder)
The secondary structure and complexity of mRNA became an important issue because it influences the accessibility of the mRNA to regulatory molecules (proteins, microRNA), its stability and its level of expression. In addition, new theoretical considerations and experimental evidence suggest that mRNAs may even play role in carrying and providing structural information for translated proteins and might serve as chaperons.
It is suggested that mRNA secondary structures are conserved and play important roles in (a) splicing
The aim of this work is to study the relationship between mRNA primary and secondary structure, base composition and structural complexity (measured as folding energy). Special attention is paid to the role of wobble bases in modifying mRNA thermodynamics.
Methods
Ninety coding sequences of proteins with known 3D structures were selected from the PDB. This selection contained 26,370 codons. Care was taken to avoid very similar structures in the selections. The propensity towards alpha helices was monitored during selection and structures with very high and very low alpha helix contents were also selected to ensure a wide range of structural representations. The possible influence of different kingdom (prokaryotes and eukaryotes) or variations in environmental conditions on mRNA folding stability were not considered in this study.
Single-stranded RNA molecules can form local secondary structures through the interactions of complementary segments. Watson-Crick (WC) base pair formation lowers the average free energy, d
I used a nucleic acid secondary structure predicting tool, mfold
A JAVA tool called SeqForm, developed by us, was used to select sequence residues in predefined phases (every third in our case) and for residue replacements
Linear regression analyses and Student's
Results and discussion
Folding free energy of coding sequences
There is disagreement in the literature regarding the positional (free) folding energy of coding sequences. It has been shown that mRNAs have greater negative folding energies than shuffled or codon choice-randomized sequences
Effect of wobble bases on the dG of CDS
Effect of wobble bases on the dG of CDS. The TFE of mRNA is indicated in native sequences (CDS), after residue randomization (shuffle) and after the indicated manipulation of the wobble bases. (For details see the text). Each column represent the mean ± S.E.M.; n is indicated in the columns.
The simple measurement of global folding energy is often criticized and is not regarded as a reliable measure of mRNA structure conservation
The potential role of wobble basis in determining and regulating the folding energy of coding sequences
Synonymous codons are not used with equal frequency in redundant codons; however, the frequency pattern of codon usage is well conserved in the same species. The cause or reason for this bias is not known, nor is it known whether the bias has any effect on biological functions. Many biological parameters are known to correlate with codon bias, for example (a) translational selection, (b) GC composition, (c) strand-specific mutational bias, (d) amino acid conservation, (e) protein hydropathy, (f) transcriptional selection, (g) structural elements in the coded protein, (h) tRNA copy number, (i) speed of translation and even (j) RNA stability or (k) gene length (for review see
Indeed, modifications of synonymous codon usage have very pronounced effects on the dG values of coding sequences (Figure
The literature also suggests, along with our previous results
Correlation between dG of CDS and Structural Elements of the Coded Protein
Correlation between dG of CDS and Structural Elements of the Coded Protein. The relative frequencies of the two main structural elements in 77 proteins are plotted against the folding energy of their coding sequences.
Correlation between mRNA sequence composition and total folding energy
There is a strong, negative, linear correlation between the length (L) of a protein coding sequence (CDS) and its dG as well as its G+C (guanine + cytosine) content and folding energy (Figure
Correlation between the Length (a), G+C content (b) and the TFE of mRNAs
Correlation between the Length (a), G+C content (b) and the TFE of mRNAs.
Distribution of C+G and Folding Energy in Codon-Related Subsequences of mRNA
Distribution of C+G and Folding Energy in Codon-Related Subsequences of mRNA. Codons were identified in intact mRNA sequences and phase-selected for subsequences containing only the first (1_1), second (2_2), third (3_3) codon letters or combinations of these subsequences. Subsequence 1_2 means, for example, that the 3rd codon letters were removed from the original mRNA. The G+C content was counted and the dGs were measured by
The TFE represents the 3D complexity of the structures formed by nucleic acids where Watson-Crick base pairs play a fundamental role. There are 3 H bonds between C and G (dG = -1524 kcal/1000 bases) but only two between A and T (dG = -365 kcal/1000 bases). This more than 4-fold dG difference explains the dominance of C+G over A+T in determining TFE.
Two major factors determine the folding energies of nucleic acids: first, the G+C content or (G+C)/(A+T) ratio; second, the availability of bases for Watson-Crick base pairing, which may be characterized by the 1000–2|G-C|-2|A-T| values. The |G-C| and |A-T| values are the absolute difference between WC pairs, i.e. the non-pairing fraction. The value 1000 – (non-pairing fractions) will give the maximum possible number of WC pairs in a 1000 base-long sequence.
There are strong linear correlations between these values (based on the primary sequence of the mRNA) and the free energy (Figure
Correlation between nucleotide composition and Folding Energy in mRNA sequences
Correlation between nucleotide composition and Folding Energy in mRNA sequences. The wobble base compositions of 78 mRNA sequences (ORIG) were modified to contain equal numbers of synonymous codons (EQUAL); only one, the most frequent synonymous codon (1 MAX); or A or C, but not T or G (A OR C). Predictions of dG were made using the different equations in the figure and plotted against the
Contribution of synonymous codons to the folding energy of coding sequences
To analyze the role of individual codons on the mRNA structure further, I designed 64 different artificial nucleic acid sequences. Each sequence contained a 100-fold repetition of a single codon, i.e. they were repeating poly-codons. The sequences (64 × 64) were hybridized using the DINAMelt Server two-stage hybridization tool
Accumulated Affinity between Codons
CF
1
1
2
2
2
2
2
2
2
2
2
3
3
4
4
4
4
4
6
6
6
AA
Met
Trp
Lys
Glu
Phe
Asn
Gln
His
Tyr
Asp
Cys
End
Ile
Pro
Gly
Thr
Ala
Val
Leu
Arg
Ser
1
Met
3.2
3.2
3.3
3.6
3.1
3.1
3.2
3.7
3.4
3.4
3.2
3.6
3.7
3.4
3.9
1
Trp
3.1
3.2
3.3
3.7
3.6
3.2
3.4
3.5
3.9
3.0
3.9
3.8
3.0
4.0
3.7
3.7
2
Lys
3.8
3.1
3.3
3.3
3.4
3.1
3.4
3.9
3.1
3.9
2
Glu
3.9
3.6
3.4
3.4
3.5
3.9
3.5
3.5
3.5
4.2
3.6
4.2
2
Phe
3.2
3.1
3.8
3.9
3.4
3.3
3.5
3.6
3.1
4.0
3.5
3.3
3.3
4.0
3.2
2
Asn
3.2
3.2
3.4
3.5
3.2
3.7
3.6
3.6
3.2
2.9
3.0
3.9
4.0
3.2
3.8
2
Gln
3.3
3.3
3.3
3.2
3.2
3.3
4.0
3.6
3.3
3.5
2.9
3.9
4.0
4.1
3.7
3.9
2
His
3.6
3.7
3.1
3.6
3.7
3.6
3.8
3.2
4.0
3.1
3.6
4.0
3.7
3.9
3.7
2
Tyr
3.1
3.6
3.3
3.4
3.5
3.2
2.7
3.5
3.2
3.2
3.9
3.7
3.9
3.4
3.3
4.0
3.7
3.8
4.0
2
Asp
3.2
3.5
3.2
3.3
3.7
3.2
3.2
3.5
3.4
3.8
3.7
3.4
3.5
3.5
4.0
4.0
3.9
4.2
2
Cys
3.1
3.4
3.3
3.4
3.7
4.0
3.6
3.2
3.5
3.5
3.7
3.6
3.6
4.0
3.9
4.2
3.8
3.7
4.1
3.9
3
End
3.2
3.6
3.6
3.6
3.8
3.9
3.4
3.7
3.8
4.0
3.7
3.3
3.9
3.8
3.9
4.3
3.8
4.2
3
Ile
3.7
3.5
3.4
3.5
3.1
3.6
3.2
3.7
3.8
3.6
4.0
3.9
3.8
3.6
3.2
4.0
4.0
3.8
4.0
4
Pro
3.4
3.9
3.1
3.9
3.3
3.7
3.6
3.7
3.2
4.5
3.3
4.1
4.2
3.6
4.3
3.9
4
Gly
3.0
4.0
3.2
3.5
4.0
3.9
3.4
4.0
3.3
3.8
4.5
3.5
4.2
4.3
4.0
4.5
4.2
4.4
4
Thr
3.4
3.9
2.9
3.5
3.5
3.7
3.4
3.5
3.9
3.9
3.6
3.3
4.2
3.2
3.9
4.4
4.2
4.2
4.3
4
Ala
3.2
3.8
3.5
3.3
3.0
3.9
3.6
3.3
3.5
4.2
3.8
3.2
4.1
4.3
3.9
4.5
4.4
4.3
4.4
4.3
4
Val
3.6
3.0
3.4
3.5
3.3
3.9
4.0
4.0
4.0
4.0
3.8
3.9
4.0
4.2
4.0
4.4
4.4
3.6
4.3
4.4
4.1
6
Leu
3.7
4.0
3.9
4.2
0.0
4.0
4.1
3.7
3.7
4.0
3.7
4.3
4.0
3.6
4.5
4.2
4.3
4.3
4.0
4.5
4.3
6
Arg
3.4
3.7
3.1
3.6
4.0
3.2
3.7
3.9
3.8
3.9
4.1
3.8
3.8
4.3
4.2
4.2
4.4
4.4
4.5
4.5
4.6
6
Ser
4.0
3.8
3.9
4.2
3.2
3.7
3.9
3.5
3.8
4.2
4.0
4.2
3.8
3.8
4.5
4.2
4.4
4.2
4.3
4.6
4.1
Each value is the accumulated affinity (dG) between synonymous codons corresponding to the indicated amino acids (log-kcal/mol);
CF: codon frequency/number of synonymous codons/amino acid. Highest (>4) and lowest (<3) values are indicated.
Fifty-one of the 400 possible amino acid pairs are coded by codons with no affinity for each other. The accumulated dG values of the codons encoding the remaining 349 amino acid pairs show statistically highly significant positive correlations to the calculated and real frequencies of amino acid pairs in real proteins (Figure
Correlation between the affinity (dG) of artificial poly-codons and the propensity for the coded amino acid pairs
Correlation between the affinity (dG) of artificial poly-codons and the propensity for the coded amino acid pairs. The affinities of artificial poly-codons for each other (64 × 64 = 4096 dG) were determined. Synonymous codons were combined and added to 20 × 20 = 400 subgroups corresponding to the possible pairs formed by the respective coded amino acids. Frequency of calculated amino acid (AA)-pairs= sqrt [(# of synonymous codon of amino acid A)x(# of synonymous codon of amino acid B)]. Frequencies of real AA-pairs were calculated from the real propensity towards amino acids in the proteins examined using the equation = sqrt [(propensity of amino acid A)x(propensity of amino acid B)]. Frequencies of co-locating AA-pairs were measured from crystallographic structures using the SeqX tool. Two linear correlation coefficients were calculated, one excluding and the other () including dG = 0 values.
This chain of correlations indicates that synonymous codon frequency has a strong positive effect on the accumulated affinity of codons in the nucleic acid (mRNA structure) and the relative frequencies of amino acids in the coded protein generally and co-locating amino acids especially (protein structure). I interpret this result as supporting the view that the effects of synonymous codons on nucleic acid structure and protein composition are additive and they are not interchangeable with each other in these respects.
An additional chain of evidence that suggests the non-equality of synonymous codons arises from studies on single-nucleotide polymorphisms (SNPs). Synonymous SNPs do not produce altered coding sequences, so they are not expected to change the function of the protein in which they occur. However, they often do. Synonymous SNPs in the Multi-drug Resistance 1 (MDR1) gene alter the conformation of the protein product, the P-glycoprotein (P-gp), while the mRNA and protein levels remain similar in wild-type and polymorphic P-gp
There are numerous theories and speculations regarding the Genetic Code because of its redundancy. The 64>20 canonical code contains 3-times more information than it is necessary for determining amino acids. The excess information is stored in the wobble bases. A useful property of the Genetic Code is the minimization of the effects of frame-shift translation errors. However it is more and more accepted that coding sequences convey, in addition to the protein-coding information, several different biologically meaningful signals at the same time. These "parallel codes" include binding sequences for regulatory
My study at hand is a specific case where the folding free energy of an mRNA is used as a measure for adapting structural features and the suggestions that wobble bases are important in carrying parallel (structural) codes are not entirely novel. However it is an additional indication that the interchangeability of synonymous codons is rather restricted: the universal genetic code is optimal as it is.
The reported correlations are not backed up with an evolutionary model and doesn't show that the RNA secondary structure has exerted a selection pressure on the evolution of the canonical genetic code. Therefore these correlations are not necessarily genuine and they may simply be secondary by-product of other optimization processes. However, the existence of these correlations
Conclusion
The genetic code is redundant in regard to the meaning of codons in translation. However, this redundancy seems to have its own meaning. Synonymous codons are interchangeable in regard to amino acid coding, but their effect is individual and additive in respect of their role in determining the FEs of coding sequences and the relative frequencies of amino acids in the translated protein sequences and co-locating amino acid pairs. This might explain why wobble base modifications often have a large effect on the efficiency of gene expression and folding of the protein product
Competing interests
The author declares that he has no competing interests.
Authors' contributions
JB is the only author.