A rationale for the symmetries by base substitutions of degeneracy in the genetic code

Reviewing evidence for systematic transcriptional deletions, nucleotide exchanges, and expanded codons, and peptide clusters in human mitochondria

Polymerization sometimes transforms sequences by (a) systematic deletions of mono-, dinucleotides after trinucleotides, or (b) 23 systematic nucleotide exchanges (9 symmetric, X<>Y, e.g. G<>T, 14 asymmetric, X > Y > Z > X, e.g. A > G > T > A), producing del- and swinger RNAs. Some peptides correspond to del- and swinger RNA translations, also according to tetracodons, codons expanded by a silent nucleotide. Here new analyzes assume different proteolytic patterns, partially alleviating false negative peptide detection biases, expanding noncanonical mitoproteome profiles. Mito-genomic, -transcriptomic and -proteomic evidence for noncanonical transcriptions and translations are reviewed and clusters of del- and swinger peptides (also along tetracodons) are described. Noncanonical peptide clusters indicate regulated expression of cryptically encoded mitochondrial protein coding genes. These candidate noncanonical proteins don't resemble known proteins.

Natural mitochondrial proteolysis confirms transcription systematically exchanging/deleting nucleotides, peptides coded by expanded codons

Protein sequences have higher linguistic complexities than human languages. This indicates undeciphered multilayered, overprinted information/genetic codes. Some superimposed genetic information is revealed by detections of transcripts systematically (a) exchanging nucleotides (nine symmetric, e.g. A<->C, fourteen asymmetric, e.g. A->C->G->A, swinger RNAs) translated according to tri-, tetra- and pentacodons, and (b) deleting mono-, dinucleotides after each trinucleotide (delRNAs). Here analyses of two independent proteomic datasets considering natural proteolysis confirm independently translation of these non-canonical RNAs, also along tetra- and pentacodons, increasing coverage of putative, cryptically encoded proteins. Analyses assuming endoproteinase GluC and elastase digestions (cleavages after residues D, E, and A, L, I, V, respectively) detect additional peptides colocalizing with detected non-canonical RNAs. Analyses detect fewer peptides matching GluC-, elastase- than trypsin-digestions: artificial trypsin-digestion outweighs natural proteolysis. Results suggest occurrences of complete proteins entirely matching non-canonical, superimposed encoding(s). Protein-coding after bijective transformations could explain genetic code symmetries, such as along Rumer's transformation.

Unbiased Mitoproteome Analyses Confirm Non-canonical RNA, Expanded Codon Translations

Proteomic MS/MS mass spectrometry detections are usually biased towards peptides cleaved by experimentally added digestion enzyme(s). Hence peptides resulting from spontaneous degradation and natural proteolysis usually remain undetected. Previous analyses of tryptic human proteome data (cleavage after K, R) detected non-canonical tryptic peptides translated according to tetra- and pentacodons (codons expanded by silent mono- and dinucleotides), and from transcripts systematically (a) deleting mono-, dinucleotides after trinucleotides (delRNAs), (b) exchanging nucleotides according to 23 bijective transformations. Nine symmetric and fourteen asymmetric nucleotide exchanges (X ↔ Y, e.g. A ↔ C; and X → Y → Z → X, e.g. A → C → G → A) produce swinger RNAs. Here unbiased reanalyses of these proteomic data detect preferentially non-canonical tryptic peptides despite assuming random cleavage. Unbiased analyses couldn't reconstruct experimental tryptic digestion if most detected non-canonical peptides were false positives. Detected non-tryptic non-canonical peptides map preferentially on corresponding, previously described non-canonical transcripts, as for tryptic non-canonical peptides. Hence unbiased analyses independently confirm previous trypsin-biased analyses that showed translations of del- and swinger RNA and expanded codons. Accounting for natural proteolysis completes trypsin-biased mitopeptidome analyses, independently confirms non-canonical transcriptions and translations.

Hidden in plain view: degeneracy in complex systems

Degeneracy is a word with two meanings. The popular usage of the word denotes deviance and decay. In scientific discourse, degeneracy refers to the idea that different pathways can lead to the same output. In the biological sciences, the concept of degeneracy has been ignored for a few key reasons. Firstly, the word "degenerate" in popular culture has negative, emotionally powerful associations that do not inspire scientists to consider its technical meaning. Secondly, the tendency of searching for single causes of natural and social phenomena means that scientists can overlook the multi-stranded relationships between cause and effect. Thirdly, degeneracy and redundancy are often confused with each other. Degeneracy refers to dissimilar structures that are functionally similar while redundancy refers to identical structures. Degeneracy can give rise to novelty in ways that redundancy cannot. From genetic codes to immunology, vaccinology and brain development, degeneracy is a crucial part of how complex systems maintain their functional integrity. This review article discusses how the scientific concept of degeneracy was imported into genetics from physics and was later introduced to immunology and neuroscience. Using examples of degeneracy in immunology, neuroscience and linguistics, we demonstrate that degeneracy is a useful way of understanding how complex systems function. Reviewing the history and theoretical scope of degeneracy allows its usefulness to be better appreciated, its coherency to be further developed, and its application to be more quickly realized.

A method to predict edge strands in beta-sheets from protein sequences

There is a need for rules allowing three-dimensional structure information to be derived from protein sequences. In this work, consideration of an elementary protein folding step allows protein sub-sequences which optimize folding to be derived for any given protein sequence. Classical mechanics applied to this system and the energy conservation law during the elementary folding step yields an equation whose solutions are taken over the field of rational numbers. This formalism is applied to beta-sheets containing two edge strands and at least two central strands. The number of protein sub-sequences optimized for folding per amino acid in beta-strands is shown in particular to predict edge strands from protein sequences. Topological information on beta-strands and loops connecting them is derived for protein sequences with a prediction accuracy of 75%. The statistical significance of the finding is given. Applications in protein structure prediction are envisioned such as for the quality assessment of protein structure models.

Optimization models and the structure of the genetic code

The codon assignment of the quasi-universal genetic code can be assumed to have resulted from the evolutionary pressures that prevailed when the code was still evolving. Here, we review studies of the structure of the genetic code based on optimization models. We also review studies that, from the structure of the code, attempt to derive aspects of the primordial circumstances in which the genetic code froze. Different rationales are summarized, compared with experimental data, discussed in the context of the transition from a RNA world to a DNA-protein world, and linked to the emergence of the last universal common ancestor.