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Combinatorial codon scrambling enables scalable gene synthesis and amplification of repetitive proteins


Most genes are synthesized using seamless assembly methods that rely on the polymerase chain reaction1,2,3 (PCR). However, PCR of genes encoding repetitive proteins either fails or generates nonspecific products. Motivated by the need to efficiently generate new protein polymers through high-throughput gene synthesis, here we report a codon-scrambling algorithm that enables the PCR-based gene synthesis of repetitive proteins by exploiting the codon redundancy of amino acids and finding the least-repetitive synonymous gene sequence. We also show that the codon-scrambling problem is analogous to the well-known travelling salesman problem4, and obtain an exact solution to it by using De Bruijn graphs5 and a modern mixed integer linear programme solver. As experimental proof of the utility of this approach, we use it to optimize the synthetic genes for 19 repetitive proteins, and show that the gene fragments are amenable to PCR-based gene assembly and recombinant expression.

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Figure 1: Computational analysis of codon scrambling.
Figure 2: Gene assembly of a diverse set of repetitive proteins.
Figure 3: Protein expression of a diverse set of repetitive proteins.


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We thank S. Mukherjee for valuable advice on mathematical optimization, and K. Dooley for useful discussions on soluble protein expression. This work was financially supported by the NIH through grant no. GM061232 to A.C. and by the NSF through the Research Triangle MRSEC (NSF DMR-11-21107). N.C.T. was supported by an NIH Biotechnology Training Grant (T32 GM008555).

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N.C.T. designed and performed experiments, developed algorithms, and prepared the manuscript. A.C. designed experiments and prepared the manuscript.

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Correspondence to Ashutosh Chilkoti.

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The authors declare no competing financial interests.

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Tang, N., Chilkoti, A. Combinatorial codon scrambling enables scalable gene synthesis and amplification of repetitive proteins. Nature Mater 15, 419–424 (2016).

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