Fundamental Theory of the Evolution Force: Gene Engineering utilizing Synthetic Evolution Artificial Intelligence
The effects of the evolution force are observable in nature at all structural levels ranging from small molecular systems to conversely enormous biospheric systems. However, the evolution force and work associated with formation of biological structures has yet to be described mathematically or theoretically. In addressing the conundrum, we consider evolution from a unique perspective and in doing so we introduce the “Fundamental Theory of the Evolution Force: FTEF”. We utilized synthetic evolution artificial intelligence (SYN-AI) to identify genomic building blocks and to engineer 14-3-3 ζ docking proteins by transforming gene sequences into time-based DNA codes derived from protein hierarchical structural levels. The aforementioned served as templates for random DNA hybridizations and genetic assembly. The application of hierarchical DNA codes allowed us to fast forward evolution, while dampening the effect of point mutations. Natural selection was performed at each hierarchical structural level and mutations screened using Blosum 80 mutation frequency-based algorithms. Notably, SYN-AI engineered a set of three architecturally conserved docking proteins that retained motion and vibrational dynamics of native Bos taurus 14-3-3 ζ.
[1] Capra J, Singh M, John A (2007) Predicting functionally important residues from sequence conservation. Bioinformatics 23:1875 – 1882.
[2] Capra J, Laskowshi R, Thornton J, Singh M, Funkhouser T ( 2009) Predicting protein ligand sites by combining sequence conservation and 3D structure. PLoS Comput. Biol. 5(12): e1000585.doc10.1371/journal.pcbi.1000585.
[3] Lawrie D, Petrov D (2014) Comparative population genomics: power and principles for the inference of functionality. Trends Genet. 30(4): 133 – 139.
[4] Ponting C (2017) Biological function in the twilight zone of sequence conversion. BMC Biol. 15 (71). https://doi.org/10.1186/s12915-017-0411-5.
[5] Weinhold N, Sander O, Dominques FS, Sommer L (2008) Local function conservation in sequence and structure space. PLoS Comput. Biol. 4(7). https://doi.org/10.1371/journal.pcbi.1000105
[6] Bulmer M (1987) Coevolution of codon usage and transfer RNA abundance. Nature 325: 728–730.
[7] Bulmer M (1991) The selection-mutation-drift theory of synonymous codon usage. Genetics 129: 897–907.
[8] Crick F (1966) Codon – anticodon pairing: The wobble hypothesis. Mol. Biol. 19: 548 – 555.
[9] Diwan D, Agashe D (2018) Wobbling forth and drifting Back: The evolutionary history and impact of bacterial tRNA modifications. Mol. Biol. Evol. 35 (8): 2046 – 2059. https://doi.org/10.1093/molbev/msy110
[10] Hong Y, Qi L (2011) Mutation and selection on the wobble nucleotide in tRNA anticodons in marine bivalve mitochondrial genomes. PLoS One 6(1):e16147. https://doi.org/10.1371/journal.pone.0016147
[11] Tong KL, Wong JT (2004) Anticodon and wobble evolution. Gene 333:169 – 177.
[12] Xia X (2005) Mutation and selection on the anticodon of tRNA genes in vertebrate mitochondrial genomes. Gene 345: 13 – 20.
[13] Boger D, Fink B, Brunette S, Winston T, Hedrick M (2001) A simple, high-resolution method for establishing DNA binding affinity and sequence selectivity. J. Am. Chem. Soc. 123(25):5878 – 5891.
[14] Davis L (2014). Engineering cellulosic bioreactors by template assisted DNA shuffling and in vitro recombination (TADSir). Biosystems 124: 95 – 104.
[15] Moore G, Maranas C, Lutz S, Benkovic J (2000) Predicting crossover generation in DNA shuffling. P. Natl. Acad. Sci. U.S.A. https://www.ncbi.nlm.nih.gov/pubmed/11248060 98(6):3226 – 3231.
[16] Moore G, Maranas C (2002) Predicting out-of-sequence reassembly in DNA shuffling. Theor. Biol. 219: 9 – 17.
[17] Bellesia G, Jewett A, Shea J (2009) Sequence periodicity and secondary structure propensity in model proteins. Protein Sci. 19:141 – 154.
[18] Xiong H, Buckwalter B, Shieh H, Hecht M (1995) Periodicity of polar and nonpolar amino acids is the major determinant of secondary structure in self-assembling oligomeric proteins. P. Natl. Acad. Sci. U.S.A.https://www.ncbi.nlm.nih.gov/pubmed/11248060 92:6349 – 6353.
[19] Leonov H, Arkin I (2005) A periodicity analysis of transmembrane helices. Bioinformatics 21(11):2604 – 2610.
[20] Woese C (1998) The universal ancestor. P. Natl. Acad. Sci. U.S.A. https://www.ncbi.nlm.nih.gov/pubmed/11248060 95:6854 – 6859.
[21] Glansdorf N, Xu Y, Labedan B (2008) The Last Universal Common Ancestor: emergence, constitution and genetic legacy of an elusive forerunner. Biology 3(29). https://doi.org/10.1186/1745-6150-3-29.
[22] Doolittle R (1995) The multiplicity of domains in proteins. Annul. Rev. Biochem. 64: 287 – 314.
[23] Henikoff S, Greene EA, Pietrokovsk S, Bork P, Attwood TK, Hood L (1997) Gene families: the taxonomy of protein paralogs and chimeras. Science 278(5338):609 – 614.
[24] Chang YL, Tsai HK, Kao CY, Hu YJ, Yang JM (2008) Evolutionary conservation of DNA-contact residues in DNA-binding domains. BMC Bioinformatics 9: 53 – 62.
[25] Guharoy M, Chakrabarti P (2005) Conservation and relative importance of residues across protein-protein interfaces. P. Natl. Acad. Sci. U.S.A. https://www.ncbi.nlm.nih.gov/pubmed/11248060 102 (43): 15447 – 15452.
[26] Frywell K (1996) The coevolution of gene family trees. Trends Genet. 12 (9): 364 – 369.
[27] Jordan K, Mariño-Ramírez L, Wolf Y, Koonin E (2004) Conservation and Coevolution in the Scale-Free Human Gene Coexpression Network. Mol. Biol. Evol. 21 (11): 2058 – 2070. https://doi.org/10.1093/molbev/msh222
[28] Crick FHC (1968) The origin of the genetic code. Mol. Biol. 38 (3): 367 – 379.
[29] Wu HL, Elsen J, Bagby S (2005) Evolution of the genetic triplet code via two types of doublet codons. Mol. Evol. 61: 54 – 64.
[30] Fukai S, Nureki O, Sekine S, Shimada A, Vassylyev D, Yokoyama S (2003) Mechanism of molecular interactions for tRNA (Val) recognition by valyl-tRNA synthetase. RNA 9 (1): 100 – 111.
[31] Wong J (1975) A Co-evolution theory of the genetic code. Proc. Natl. Acad. Sci. U.S.A. 72 (5): 1909 – 1912.
[32] Bacher J, Reiss B, Ellington A (2002) Anticipatory evolution and DNA shuffling. Genome Biol. 3 (8): REVIEWS1021. doi:10.1186/gb-2002-3-8-reviews1021.
[33] Schubert I (2007) Chromosome evolution. Curr. Opin. Plant Biol. 10: 109 – 115.
[34] Zhang J (2003) Evolution by gene duplication: an update. Trends Ecol. Evol. 18: 292 – 298.
[35] Hughes A (2002) Adaptive evolution after gene duplication. Trends Genet. 18: 433 – 434.
[36] Lynch M, Katju V (2004) The altered evolutionary trajectories of gene duplicates. Trends Genet. 20: 544 – 549.
[37] Wetmur J, Davidson N (1968) Kinetics of renaturation of DNA. Mol. Biol. 31: 349 – 370.
[38] SantaLucia J (1998) A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. P. Natl. Acad. Sci. U.S.A. https://www.ncbi.nlm.nih.gov/pubmed/11248060 95:1460 – 1465.
[39] Wang L, Stein L (2010) Localizing triplet periodicity in DNA and cDNA sequences. BMC Bioinformatics 11: 550 – 557.
[40] Shah K, Krishnamachari A (2012) On the origin of three base periodicity in genomes. Biosystems 107; 142 – 144.
[41] Shipman PD, Newell AC (2004) Phyllotactic patterns on plants. Phys. Rev. Lett. 92(16):168102 – 168101.
[42] Aravind L, Walker R, Koonin EV (1999) Conserved domains in DNA repair proteins and evolution of repair systems. Nucleic Acids Res. 27 (5): 1223 – 1242.
[43] Davis L.(2019) Intelligent design of 14-3-3 docking proteins utilizing Synthetic Evolution Artificial Intelligence (SYN-AI). ACS Omega 4 (21), 18948-18960. DOI: 10.1021/acsomega.8b03100
[44] Petosa, C.; Masters, S. C.; Bankston, L. A.; Pohli, J.; Wang, B.; Fu, H.; Liddington, R. C. 14-3-3z binds a phosphorylated raf peptide and an unphosphorylated peptide via its conserved amphipathic groove. J. Biol. Chem. 1998, 273, 16305−16310.
[45] Davis L (unpublished) Dancing molecules: Rewiring cooperative communications within 14-3-3 ζ docking proteins. doi: https://doi.org/10.1101/683466
[46] Ghosh A, Vishveshwara S (2008) Variations in clique and community patterns in protein structures during allosteric communication: investigation of dynamically equilibrated structures of methionyl tRNA synthetase complexes. Biochemistry – U.S. 47 (44): 11398 – 11407.
[47] Eyal E, Lum G, Bahar I (2015) The anisotropic Network Model web server at 2015 (ANM 2.0). Bioinformatics 31: 1487 – 1489.
[48] Clawitter CJ. The Ark of Mathematics, Part 5 Vector Calculus Electricity and Magnetism. Clawitter;2013. 38 p.
[1] Capra J, Singh M, John A (2007) Predicting functionally important residues from sequence conservation. Bioinformatics 23:1875 – 1882.
[2] Capra J, Laskowshi R, Thornton J, Singh M, Funkhouser T ( 2009) Predicting protein ligand sites by combining sequence conservation and 3D structure. PLoS Comput. Biol. 5(12): e1000585.doc10.1371/journal.pcbi.1000585.
[3] Lawrie D, Petrov D (2014) Comparative population genomics: power and principles for the inference of functionality. Trends Genet. 30(4): 133 – 139.
[4] Ponting C (2017) Biological function in the twilight zone of sequence conversion. BMC Biol. 15 (71). https://doi.org/10.1186/s12915-017-0411-5.
[5] Weinhold N, Sander O, Dominques FS, Sommer L (2008) Local function conservation in sequence and structure space. PLoS Comput. Biol. 4(7). https://doi.org/10.1371/journal.pcbi.1000105
[6] Bulmer M (1987) Coevolution of codon usage and transfer RNA abundance. Nature 325: 728–730.
[7] Bulmer M (1991) The selection-mutation-drift theory of synonymous codon usage. Genetics 129: 897–907.
[8] Crick F (1966) Codon – anticodon pairing: The wobble hypothesis. Mol. Biol. 19: 548 – 555.
[9] Diwan D, Agashe D (2018) Wobbling forth and drifting Back: The evolutionary history and impact of bacterial tRNA modifications. Mol. Biol. Evol. 35 (8): 2046 – 2059. https://doi.org/10.1093/molbev/msy110
[10] Hong Y, Qi L (2011) Mutation and selection on the wobble nucleotide in tRNA anticodons in marine bivalve mitochondrial genomes. PLoS One 6(1):e16147. https://doi.org/10.1371/journal.pone.0016147
[11] Tong KL, Wong JT (2004) Anticodon and wobble evolution. Gene 333:169 – 177.
[12] Xia X (2005) Mutation and selection on the anticodon of tRNA genes in vertebrate mitochondrial genomes. Gene 345: 13 – 20.
[13] Boger D, Fink B, Brunette S, Winston T, Hedrick M (2001) A simple, high-resolution method for establishing DNA binding affinity and sequence selectivity. J. Am. Chem. Soc. 123(25):5878 – 5891.
[14] Davis L (2014). Engineering cellulosic bioreactors by template assisted DNA shuffling and in vitro recombination (TADSir). Biosystems 124: 95 – 104.
[15] Moore G, Maranas C, Lutz S, Benkovic J (2000) Predicting crossover generation in DNA shuffling. P. Natl. Acad. Sci. U.S.A. https://www.ncbi.nlm.nih.gov/pubmed/11248060 98(6):3226 – 3231.
[16] Moore G, Maranas C (2002) Predicting out-of-sequence reassembly in DNA shuffling. Theor. Biol. 219: 9 – 17.
[17] Bellesia G, Jewett A, Shea J (2009) Sequence periodicity and secondary structure propensity in model proteins. Protein Sci. 19:141 – 154.
[18] Xiong H, Buckwalter B, Shieh H, Hecht M (1995) Periodicity of polar and nonpolar amino acids is the major determinant of secondary structure in self-assembling oligomeric proteins. P. Natl. Acad. Sci. U.S.A.https://www.ncbi.nlm.nih.gov/pubmed/11248060 92:6349 – 6353.
[19] Leonov H, Arkin I (2005) A periodicity analysis of transmembrane helices. Bioinformatics 21(11):2604 – 2610.
[20] Woese C (1998) The universal ancestor. P. Natl. Acad. Sci. U.S.A. https://www.ncbi.nlm.nih.gov/pubmed/11248060 95:6854 – 6859.
[21] Glansdorf N, Xu Y, Labedan B (2008) The Last Universal Common Ancestor: emergence, constitution and genetic legacy of an elusive forerunner. Biology 3(29). https://doi.org/10.1186/1745-6150-3-29.
[22] Doolittle R (1995) The multiplicity of domains in proteins. Annul. Rev. Biochem. 64: 287 – 314.
[23] Henikoff S, Greene EA, Pietrokovsk S, Bork P, Attwood TK, Hood L (1997) Gene families: the taxonomy of protein paralogs and chimeras. Science 278(5338):609 – 614.
[24] Chang YL, Tsai HK, Kao CY, Hu YJ, Yang JM (2008) Evolutionary conservation of DNA-contact residues in DNA-binding domains. BMC Bioinformatics 9: 53 – 62.
[25] Guharoy M, Chakrabarti P (2005) Conservation and relative importance of residues across protein-protein interfaces. P. Natl. Acad. Sci. U.S.A. https://www.ncbi.nlm.nih.gov/pubmed/11248060 102 (43): 15447 – 15452.
[26] Frywell K (1996) The coevolution of gene family trees. Trends Genet. 12 (9): 364 – 369.
[27] Jordan K, Mariño-Ramírez L, Wolf Y, Koonin E (2004) Conservation and Coevolution in the Scale-Free Human Gene Coexpression Network. Mol. Biol. Evol. 21 (11): 2058 – 2070. https://doi.org/10.1093/molbev/msh222
[28] Crick FHC (1968) The origin of the genetic code. Mol. Biol. 38 (3): 367 – 379.
[29] Wu HL, Elsen J, Bagby S (2005) Evolution of the genetic triplet code via two types of doublet codons. Mol. Evol. 61: 54 – 64.
[30] Fukai S, Nureki O, Sekine S, Shimada A, Vassylyev D, Yokoyama S (2003) Mechanism of molecular interactions for tRNA (Val) recognition by valyl-tRNA synthetase. RNA 9 (1): 100 – 111.
[31] Wong J (1975) A Co-evolution theory of the genetic code. Proc. Natl. Acad. Sci. U.S.A. 72 (5): 1909 – 1912.
[32] Bacher J, Reiss B, Ellington A (2002) Anticipatory evolution and DNA shuffling. Genome Biol. 3 (8): REVIEWS1021. doi:10.1186/gb-2002-3-8-reviews1021.
[33] Schubert I (2007) Chromosome evolution. Curr. Opin. Plant Biol. 10: 109 – 115.
[34] Zhang J (2003) Evolution by gene duplication: an update. Trends Ecol. Evol. 18: 292 – 298.
[35] Hughes A (2002) Adaptive evolution after gene duplication. Trends Genet. 18: 433 – 434.
[36] Lynch M, Katju V (2004) The altered evolutionary trajectories of gene duplicates. Trends Genet. 20: 544 – 549.
[37] Wetmur J, Davidson N (1968) Kinetics of renaturation of DNA. Mol. Biol. 31: 349 – 370.
[38] SantaLucia J (1998) A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. P. Natl. Acad. Sci. U.S.A. https://www.ncbi.nlm.nih.gov/pubmed/11248060 95:1460 – 1465.
[39] Wang L, Stein L (2010) Localizing triplet periodicity in DNA and cDNA sequences. BMC Bioinformatics 11: 550 – 557.
[40] Shah K, Krishnamachari A (2012) On the origin of three base periodicity in genomes. Biosystems 107; 142 – 144.
[41] Shipman PD, Newell AC (2004) Phyllotactic patterns on plants. Phys. Rev. Lett. 92(16):168102 – 168101.
[42] Aravind L, Walker R, Koonin EV (1999) Conserved domains in DNA repair proteins and evolution of repair systems. Nucleic Acids Res. 27 (5): 1223 – 1242.
[43] Davis L.(2019) Intelligent design of 14-3-3 docking proteins utilizing Synthetic Evolution Artificial Intelligence (SYN-AI). ACS Omega 4 (21), 18948-18960. DOI: 10.1021/acsomega.8b03100
[44] Petosa, C.; Masters, S. C.; Bankston, L. A.; Pohli, J.; Wang, B.; Fu, H.; Liddington, R. C. 14-3-3z binds a phosphorylated raf peptide and an unphosphorylated peptide via its conserved amphipathic groove. J. Biol. Chem. 1998, 273, 16305−16310.
[45] Davis L (unpublished) Dancing molecules: Rewiring cooperative communications within 14-3-3 ζ docking proteins. doi: https://doi.org/10.1101/683466
[46] Ghosh A, Vishveshwara S (2008) Variations in clique and community patterns in protein structures during allosteric communication: investigation of dynamically equilibrated structures of methionyl tRNA synthetase complexes. Biochemistry – U.S. 47 (44): 11398 – 11407.
[47] Eyal E, Lum G, Bahar I (2015) The anisotropic Network Model web server at 2015 (ANM 2.0). Bioinformatics 31: 1487 – 1489.
[48] Clawitter CJ. The Ark of Mathematics, Part 5 Vector Calculus Electricity and Magnetism. Clawitter;2013. 38 p.
@article{"International Journal of Biological, Life and Agricultural Sciences:79982", author = "L. K. Davis", title = "Fundamental Theory of the Evolution Force: Gene Engineering utilizing Synthetic Evolution Artificial Intelligence", abstract = "The effects of the evolution force are observable in nature at all structural levels ranging from small molecular systems to conversely enormous biospheric systems. However, the evolution force and work associated with formation of biological structures has yet to be described mathematically or theoretically. In addressing the conundrum, we consider evolution from a unique perspective and in doing so we introduce the “Fundamental Theory of the Evolution Force: FTEF”. We utilized synthetic evolution artificial intelligence (SYN-AI) to identify genomic building blocks and to engineer 14-3-3 ζ docking proteins by transforming gene sequences into time-based DNA codes derived from protein hierarchical structural levels. The aforementioned served as templates for random DNA hybridizations and genetic assembly. The application of hierarchical DNA codes allowed us to fast forward evolution, while dampening the effect of point mutations. Natural selection was performed at each hierarchical structural level and mutations screened using Blosum 80 mutation frequency-based algorithms. Notably, SYN-AI engineered a set of three architecturally conserved docking proteins that retained motion and vibrational dynamics of native Bos taurus 14-3-3 ζ.", keywords = "14-3-3 docking genes, synthetic protein design, time based DNA codes, writing DNA code from scratch.", volume = "14", number = "11", pages = "148-15", }
