TY - GEN
T1 - The Properties of the Standard Genetic Code and Its Selected Alternatives in Terms of the Optimal Graph Partition
AU - Aloqalaa, Daniyah A.
AU - Kowalski, Dariusz R.
AU - Błażej, Paweł
AU - Wnȩtrzak, Małgorzata
AU - Mackiewicz, Dorota
AU - Mackiewicz, Paweł
N1 - Funding Information:
Supported by the National Science Centre, Poland (Narodowe Centrum Nauki, Polska) under Grants number UMO-2017/27/N/NZ2/00403 and UMO-2017/25/B/ST6/02553.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - The standard genetic code (SGC) is a system of rules, which assigns 20 amino acids and stop translation signal to 64 codons, i.e triplets of nucleotides. The structure of the SGC shows some properties suggesting that this code evolved to minimize deleterious effects of mutations and translational errors. To analyse this issue, we presented the structure of the SGC and its natural alternative versions as a graph, in which vertices corresponded to codons and edges to point mutations between these codons. The mutations were weighted according to the mutation type, i.e. transitions and transversions. Under this representation, each genetic code is a partition of the set of vertices into 21 disjoint subsets, while its resistance to the mutation consequences can be reformulated into the optimal graph clustering task. In order to investigate this problem, we developed an appropriate clustering algorithm, which searched for the codes showing the minimum average calculated for the set conductance of codon groups. The algorithm found three best codes for various ranges of the weights for the mutations. The average weighted-conductance of the studied genetic codes was the most similar to that of the best codes in the range of weights corresponding to the observed transversion/transition ratio in natural mutational pressures. However, it should be noted that the optimization of the codes was not as perfect as the best codes and many alternative genetic codes performed better than the SGC. These results may suggest that the evolution of the SGC was driven not only by the selection for the robustness to mutations or mistranslations.
AB - The standard genetic code (SGC) is a system of rules, which assigns 20 amino acids and stop translation signal to 64 codons, i.e triplets of nucleotides. The structure of the SGC shows some properties suggesting that this code evolved to minimize deleterious effects of mutations and translational errors. To analyse this issue, we presented the structure of the SGC and its natural alternative versions as a graph, in which vertices corresponded to codons and edges to point mutations between these codons. The mutations were weighted according to the mutation type, i.e. transitions and transversions. Under this representation, each genetic code is a partition of the set of vertices into 21 disjoint subsets, while its resistance to the mutation consequences can be reformulated into the optimal graph clustering task. In order to investigate this problem, we developed an appropriate clustering algorithm, which searched for the codes showing the minimum average calculated for the set conductance of codon groups. The algorithm found three best codes for various ranges of the weights for the mutations. The average weighted-conductance of the studied genetic codes was the most similar to that of the best codes in the range of weights corresponding to the observed transversion/transition ratio in natural mutational pressures. However, it should be noted that the optimization of the codes was not as perfect as the best codes and many alternative genetic codes performed better than the SGC. These results may suggest that the evolution of the SGC was driven not only by the selection for the robustness to mutations or mistranslations.
KW - Code degeneracy
KW - Graph theory
KW - Mutation
KW - Set conductance
KW - Standard genetic code
KW - Transition
KW - Transversion
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U2 - 10.1007/978-3-030-46970-2_9
DO - 10.1007/978-3-030-46970-2_9
M3 - Conference contribution
AN - SCOPUS:85085029316
SN - 9783030469696
T3 - Communications in Computer and Information Science
SP - 170
EP - 191
BT - Biomedical Engineering Systems and Technologies - 12th International Joint Conference, BIOSTEC 2019, Revised Selected Papers
A2 - Roque, Ana
A2 - Gamboa, Hugo
A2 - Tomczyk, Arkadiusz
A2 - De Maria, Elisabetta
A2 - Putze, Felix
A2 - Moucek, Roman
A2 - Fred, Ana
PB - Springer
T2 - 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019
Y2 - 22 February 2019 through 24 February 2019
ER -