Intelligent Design of 14-3-3 Docking Proteins Utilizing Synthetic Evolution Artificial Intelligence (SYN-AI)

Abstract

The ability to write DNA code from scratch will allow for the discovery of new and interesting chemistries as well as allowing the rewiring of cell signal pathways. Herein, we have utilized synthetic evolution artificial intelligence (SYN-AI) to intelligently design a set of 14-3-3 docking genes. SYN-AI engineers synthetic genes utilizing a parental gene as an evolution template. Wherein, evolution is fast forwarded by transforming template gene sequences to DNA secondary and tertiary codes based upon gene hierarchical structural levels. The DNA secondary code allows identification of genomic building blocks across an orthologous sequence space comprising multiple genomes. Where, the DNA tertiary code allows engineering of super secondary structures. SYN-AI constructed a library of 10 million genes that was reduced to three structurally functional 14-3-3 docking genes by applying natural selection protocols. Synthetic protein identity was verified utilizing Clustal Omega sequence alignments and Phylogeny.fr phylogenetic analysis. Wherein, we were able to confirm three-dimensional structure utilizing I-TASSER and protein ligand interactions utilizing COACH and Cofactor. Conservation of allosteric communications were confirmed utilizing elastic and anisotropic network models. Whereby, we utilized elNemo and ANM2.1 to confirm conservation of the 14-3-3 ζ amphipathic groove. Notably, to the best of our knowledge, we report the first 14-3-3 docking genes to be written from scratch.

Dancing Molecules: Rewiring Cooperative Communications within 14-3-3 ζ Docking Proteins

“…A theory we developed that allows genes to be written from scratch by simulating evolution. [13,14] Synthetic evolution by FTEF allowed us to engineer three functional 14-3-3 ζ docking proteins. Of which we performed normal mode analysis on SYN-AI-1 ζ the most diverged of the three synthetic proteins.…”

“…The evolution force is a compulsion acting at the matter-energy interface that drives molecular diversity and simultaneously promotes conservation of structure and function. [13,14] Effects of the evolution force are manifested at all levels of life and is responsible for such processes as the formation of genes and gene networks. [13,14] FTEF solves for evolution force as a function of four primary evolution engines, (i) evolution conservation, (ii) wobble, (iii) DNA binding state and (iv) periodicity.…”

“…[13,14] Effects of the evolution force are manifested at all levels of life and is responsible for such processes as the formation of genes and gene networks. [13,14] FTEF solves for evolution force as a function of four primary evolution engines, (i) evolution conservation, (ii) wobble, (iii) DNA binding state and (iv) periodicity. [13,14] When applying FTEF, evolution is simulated by assembling genes according to the domain Lego principle.…”

“…[13,14] FTEF solves for evolution force as a function of four primary evolution engines, (i) evolution conservation, (ii) wobble, (iii) DNA binding state and (iv) periodicity. [13,14] When applying FTEF, evolution is simulated by assembling genes according to the domain Lego principle. [15,16] Whereby, gene sequences are transformed into time based DNA codes according to protein hierarchical structural levels.…”

“…[15,16] Whereby, gene sequences are transformed into time based DNA codes according to protein hierarchical structural levels. [13,14] Natural selection is simulated by comparing protein structural fingerprints as well as by limiting selection based upon mutation frequency. Gibb's free energy barriers are also utilized as a selection mechanism, limiting selection to energetically favorable DNA crossovers.…”