Compositions and methods for expressing genes of interest in host cells
Provided herein are compositions and methods for stabilizing RNA, increasing protein expression, and combinations thereof. Also provided are compositions and methods for utilizing stabilized RNA or increased protein levels to generate chordate proteins in a host cell, such as a plant cell.
1. A method for selecting a nucleic acid sequence, said method comprising the steps of:
a) providing data on a plurality of nucleic acid sequences that encode the same amino acid sequence;
b) predicting secondary structure of the plurality of nucleic acid sequences with a plurality of RNA folding models, such that each nucleic acid sequence in the plurality of nucleic acid sequences is associated with at least two predicted secondary structures;
c) determining a structural similarity score, the determining comprising quantifying differences between the at least two predicted secondary structures associated with each nucleic acid sequence, that were predicted in step b);
d) selecting a nucleic acid sequence with a higher structural similarity score than at least one other nucleic acid sequence in the plurality of nucleic acid sequences wherein the selected nucleic acid sequence accumulates at higher levels when expressed in a host cell compared to accumulation of at least one of the other nucleic acids from the plurality of nucleic acids, said at least one other nucleic acid having a lower structural similarity score than the selected nucleic acid; and
e) manufacturing the selected nucleic acid sequence.
2. The method of claim 1 , wherein at least one of the plurality of RNA folding models employs machine learning.
3. The method of claim 1 , wherein the plurality of nucleic acid sequences encode amino acids sharing at least 95% sequence identity.
4. The method of claim 1 , comprising: f) transforming a host cell with the manufactured nucleic acid sequence.
5. The method of claim 1 , comprising: f) expressing the manufactured nucleic acid in a host cell.
6. The method of claim 1 , wherein the nucleic acid sequence encodes for a messenger RNA.
7. The method of claim 1 , wherein the plurality of RNA folding models comprise an analysis selected from the group consisting of Cocke-Younger Kasami, inside and outside, loop-based energy, minimum free energy, suboptimal folding, centroid, and any combination thereof.
8. The method of claim 1 , wherein the at least two predicted secondary structures are a minimum free energy structure and a centroid structure.
9. The method of claim 1 , wherein the structural similarity score is determined via tool selected from the group consisting of Consan, Dynalign, PMcomp, Stemloc, Foldalign, locARNA, SPARSE, MARNA, FoldAlignM, Murlet, CARNA, RAF, RNAforester, RNAdistance, RNAStrAt, RNApdist, and any combination thereof.
10. The method of claim 1 , wherein the structural similarity score is a ranking of the plurality of nucleic acid sequences based on the relative similarity of each nucleic acid sequences' predicted secondary structures.
11. The method of claim 1 , wherein the structural similarity score is based on degree of curve overlap in a graph depicting number of base pairs at each position of the predicted secondary structures of each nucleic acid sequence.
12. The method of claim 1 , wherein the structure similarity score is based on the degree of curve overlap of the predicted secondary structures of each nucleic acid sequence, plotted in a mountain plot.
13. The method of claim 1 , wherein the structural similarity score is based on the correlation of curves representing the predicted secondary structures for each nucleic acid sequence in a graph depicting number of base pairs at each position.
14. The method of claim 11 , wherein the degree of curve overlap is calculated by methodology selected from the group consisting of least squares, curve length measure, and any combination thereof.