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AssigneeDanmarks Tekniske Universitet
InventorsHenrik Toft Simonsen, Christopher Workman, Kyle Rothschild-Mancinelli
Estimating the term…
The present disclosure relates to a method for generating a synthetic promoter and/or terminator, the method comprising: obtaining gene sequences, such as functional sequences and/or coding sequences, of genomes of multiple closely related species; identifying activity regulating features, by calculating a correlation coefficient between parameters of the statistical model and features scores of the FMS, and recognizing said activity regulating features as model features of which said correlation coefficient is above a predetermined threshold, such as 0.5, optimizing, for each HMM, the starting consensus sequence, by, for each activity regulating feature, calculating the gene statistics upon inserting/replacing the activity regulating feature into the starting consensus sequence or a partially optimized consensus sequence, and where said insertion/replacement leads to an improvement in the gene statistics, such as an increase by a predetermined threshold, accepting said insertion/replacement, otherwise said insertion/replacement is discarded.
A computer-implemented method for generating a synthetic promoter and/or terminator for expressing a protein of interest in a cell, the method comprising:
The method according to any one of the preceding claims, wherein the promoter and/or terminator is intended for being synthesized and/or for expressing a protein of interest in a cell.
The method according to any one of the preceding claims, wherein gene sequences of at least 10 closely related species are obtained.
The method according to any one of the preceding claims, wherein the genomes are obtained from databases, such as genome assemblies, or sequenced de novo.
The method according to any one of the preceding claims, wherein the sequences are annotated.
The method according to any one of the preceding claims, wherein the closely related species are of the same genus or related genera within a family.
The method according to any one of the preceding claims, wherein the species are selected from any one of Aspergillus, Saccharomyces, Kluyveromyces, Komagataella, Barnettozyma, Cyberlindnera, Phaffomyces, Physcomitrium, Starmera, Wickerhamomyces, Yarrowia or Trichoderma.
The method according to any one of the preceding claims, wherein the species are fungal species, such as belonging to Aspergillus, Saccharomyces, Yarrowia and/or Trichoderma.
The method according to any one of the preceding claims, wherein the number of homolog groups is at least 80% of the number of coding genes in any one of the species, more preferably at least 90%, yet more preferably at least 95%.
The method according to any one of the preceding claims, wherein the gene statistics is calculated for each gene sequence of the homolog groups.
The method according to any one of the preceding claims, wherein the gene statistics is, for each gene sequence, a linear combination of the tRNA adaptation index and the number of effective codons.
The method according to any one of the preceding claims, wherein the expected length of the promoter and/or expected length of the terminator is at least 800 bps for eukaryotes.
The method according to any one of the preceding claims, wherein for each iteration of the HMM, the HMM is used to create an updated MSA that is used to fit an updated HMM.
The method according to any one of the preceding claims, wherein the fitting is repeated until convergence that is defined as no change of the consensus sequence, such as the most likely outcome of the HMM, for a predetermined number of iterations, such as five.
The method according to any one of the preceding claims, wherein the regions of high conservation are defined as regions of high information content in the match states of the model.
The method according to any one of the preceding claims, wherein the regions of high information content in the match states of the model are identified by:
The method according to any one of the preceding claims, wherein merged regions of high information content in the match states of the model are identified by merging regions of high information content in the match states of the model that are separated by at maximum a predetermined number of positions, such as 3.
The method according to any one of the preceding claims, wherein the model features are identified by summarizing the consensus sequence of the regions of high information content and/or the merged regions of high information content in the match states of the model.
The method according to any one of the preceding claims, wherein the FMS is generated by identifying the maximum score for each model feature in each HMM model.
The method according to any one of the preceding claims, wherein the statistical model is configured to predict performance statistics, such as functionality and/or strength, of promoters or terminators.
The method according to any one of the preceding claims, wherein optimization of the starting consensus sequence is carried out from the activity regulating feature with the highest correlation to the parameters of the statistical model to the activity regulating feature with the lowest correlation to the parameters of the statistical model.
The method according to any one of the preceding claims, wherein the activity regulating feature is tested for insertion/replacement at the position of the FMS and 85% of the FMS of that activity regulating feature.
The method according to any one of the preceding claims, wherein the statistical model is a partial least square regression model.
The method according to any one of the preceding claims, further comprising a step of:
The method according to any one of the preceding claims, wherein the strength of the synthetic promoter or of the synthetic terminator is at least 100% of the strength of the native promoter or of the native terminator, respectively, such as at least 110%, such as at least 120%, such as at least 130%, such as at least 140%, such as at least 150%, such as at least 160%, such as at least 170%, such as at least 180%, such as at least 190%, such as at least 200%, such as at least 250%, such as at least 300%, such as at least 350%, such as at least 400%, such as at least 450%, such as at least 500%.
Dependencies are inferred from the claim text.