Computational methods for synthetic gene design
View Patent ↗The present invention is drawn to methods for designing synthetic nucleotide sequences encoding polypeptides of interest. The methods involve organizing a database of sequences as a set of N-length oligomer sequences and compiling a list of probability scores for each N-length sequence. The probability scores are used to substitute one or more higher-scoring sequences into the parent nucleotide sequence to generate an optimized sequence. The nucleotide sequence of interest may be further optimized by removing either or both of unintended open reading frames or undesired short DNA elements, and/or substituting oligomer sequences to achieve a specific G:C content. These methods may be used for optimizing expression of heterologous genes in any organism, particularly in plants. The method generates synthetic sequences with a composition similar to that of a target database. These synthetic sequences may be used, for example, for regulating pesticidal activity or herbicide resistance in organisms, particularly plants or plant cells.
1. A method of optimizing a nucleotide sequence for expression in a target organism comprising:
a) providing a nucleotide sequence of interest;
b) providing a target database of nucleotide sequences, wherein the database is specific to a genus of the target organism;
c) compiling a list of all possible N-length nucleotide sequences (NLS) for a given length of N, wherein N is less than the length of the shortest nucleotide sequence in the database, and wherein N is constant for each NLS;
d) obtaining an observed frequency value for each NLS by counting the number of times each NLS is represented in the database and dividing by the total number of NLSs observed in the database;
e) calculating the probability score for each NLS identified in step (c) with respect to the database provided in step (b) to generate an NLS Statistics Table, wherein the probability score represents the observed frequency of each NLS relative to its expected frequency of occurrence within the target database;
f) identifying for one or more N-length region(s) (NLRs) of the nucleotide sequence of interest a corresponding set of NLSs that does not alter the amino acid sequence encoded by the nucleotide sequence of interest, wherein an NLS that does not alter the amino acid sequence encoded by the nucleotide sequence of interest is considered a silent NLS (SNLS), and wherein the length of N in the NLR is equal to the length of N in the NLS; g) calculating a delta score for each SNLS, wherein the delta score represents the difference between the probability score of the NLR of the nucleotide sequence of interest and each corresponding SNLS;
h) substituting one or more NLRs in the nucleotide sequence of interest with the corresponding SNLS having the largest delta score to generate an optimized sequence; and
i) chemically synthesizing a nucleic acid molecule consisting of the optimized nucleotide sequence for expression in the target organism.
2. The method of claim 1 , wherein step (h) further comprises:
1) scanning the optimized sequence generated in step (h) to identify any spurious open reading frames (ORF);
2) substituting one or more NLRs in the optimized sequence with a corresponding SNLS that disrupts the spurious ORF; and,
3) optionally repeating steps (1) and (2) until all spurious ORFs are removed from the sequence.
3. The method of claim 2 , wherein step (h) further comprises:
4) setting a target G:C content for the optimized sequence of step (2) or step (3);
5) substituting one or more NLRs in the optimized sequence generated in step (2) or step (3) with a corresponding SNLS that drives the G:C content closer to the target G:C content; and,
6) optionally repeating steps (4) and (5) until the target G:C content is reached.
4. The method of claim 2 , wherein step (h) further comprises:
4) scanning the optimized sequence generated in step (2) or (3) to identify any undesired short DNA elements;
5) substituting one or more NLRs in the optimized sequence with a corresponding SNLS that removes the undesired short DNA element; and,
6) optionally repeating steps (4) and (5) until all undesired short DNA elements are removed from the sequence.
5. The method of claim 4 , wherein step (h) further comprises:
7) setting a target G:C content for the optimized sequence of step (6);
8) substituting one or more NLRs in the optimized sequence with a corresponding SNLS that drives the G:C content closer to the target G:C content; and,
9) optionally repeating steps (7) and (8) until the target G:C content is reached.
6. The method of claim 1 , wherein step (h) further comprises:
1) scanning the optimized sequence generated in step (h) to identify any undesired short DNA elements;
2) substituting one or more NLRs in the optimized sequence with a corresponding SNLS that removes the undesired short DNA element; and,
3) optionally repeating steps (1) and (2) until all undesired short DNA elements are removed from the sequence.
7. The method of claim 6 , wherein step (h) further comprises:
4) setting a target G:C content for the optimized sequence of step (2) or step (3);
5) substituting one or more NLRs in the optimized sequence generated in step (2) or step (3) with a corresponding SNLS that drives the G:C content closer to the target G:C content; and,
6) optionally repeating steps (4) and (5) until the target G:C content is reached.
8. The method of claim 1 , wherein step (h) further comprises:
1) setting a target G:C content for the optimized sequence of step (h);
2) substituting one or more NLRs in the optimized sequence with a corresponding SNLS that drives the G:C content closer to the target G:C content; and,
3) optionally repeating steps (1) and (2) until the target G:C content is reached.
9. The method of claim 1 , wherein said target database is specific to an organism of interest.
10. The method of claim 1 , wherein said N-length is between 4 and 10.
11. The method of claim 10 , wherein said N-length is between 5 and 7.