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| Self Assembling Genes from a Massive Computation | |
| University of California Irvine professor and CODA co-founder Rick Lathrop spent over 15 years in the area of artificial intelligence and machine learning at MIT. As a pioneer in protein threading algorithms and structure prediction, Rick has numerous publications and is a recognized leader in the field.
Intrigued by the specific annealing properties of DNA, Rick followed his instincts and designed a unique computational solution which generates a “buy list” of standard commercial oligonucleotides. When combined, these oligos assemble into a well behaved gene with a unique thermodynamic address at any point on the construct. The resulting Computationally Optimized DNA CODA’s process enables high quality, economical, client-specified library creation as minimal efforts are required to produce 10’s to 1000’s of variants. This innovative solution requires massive parallel processing power to compute a unique solution with constraint variations as needed for a particular protein of interest. This extensive optimization also controls thermodynamic parameters required for the correct self-assembly of a single gene that will reliably express the desired protein. This invention provides a new means of solving protein expression problems from increased yield to solubility and even enhanced activity or stability, as required.
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Every CODA optimized gene requires computation utilizing
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