Coding

Part:BBa_K4790061:Design

Designed by: Yi Zheng   Group: iGEM23_NNU-CHINA   (2023-08-14)


ASR1(node 59)-W120R


Assembly Compatibility:
  • 10
    INCOMPATIBLE WITH RFC[10]
    Illegal EcoRI site found at 799
    Illegal PstI site found at 100
    Illegal PstI site found at 232
  • 12
    INCOMPATIBLE WITH RFC[12]
    Illegal EcoRI site found at 799
    Illegal NheI site found at 721
    Illegal PstI site found at 100
    Illegal PstI site found at 232
  • 21
    INCOMPATIBLE WITH RFC[21]
    Illegal EcoRI site found at 799
  • 23
    INCOMPATIBLE WITH RFC[23]
    Illegal EcoRI site found at 799
    Illegal PstI site found at 100
    Illegal PstI site found at 232
  • 25
    INCOMPATIBLE WITH RFC[25]
    Illegal EcoRI site found at 799
    Illegal PstI site found at 100
    Illegal PstI site found at 232
  • 1000
    COMPATIBLE WITH RFC[1000]


Design Notes

We should select direction of mutation which is predicted the highest possibility. Mutation should refer to Codon preference in E.coli.


Source

It does not come from any genomic sequence. Node 59 comes from Ancestral Sequence Reconstruction (ASR). The mutation site is predicted by machine learning.

References

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2. Guzzetti, D.; Lebrun, A.; Subileau, M.; Grousseau, E.; Dubreucq, E.; Drone, J., A Catalytically Competent Terpene Synthase Inferred Using Ancestral Sequence Reconstruction Strategy. ACS Catalysis 2016, 6 (8), 5345-5349.

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6. Yoshida, S.; Hiraga, K.; Takehana, T.; Taniguchi, I.; Yamaji, H.; Maeda, Y.; Toyohara, K.; Miyamoto, K.; Kimura, Y.; Oda, K., A bacterium that degrades and assimilates poly(ethylene terephthalate). Science 2016, 351 (6278), 1196-1199.

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8. Meier, J.; Rao, R.; Verkuil, R.; Liu, J.; Sercu, T.; Rives, A. In Language models enable zero-shot prediction of the effects of mutations on protein function, 35th Conference on Neural Information Processing Systems (NeurIPS), Electr Network, 2021.

9. Hopf, T. A.; Ingraham, J. B.; Poelwijk, F. J.; Scharfe, C. P. I.; Springer, M.; Sander, C.; Marks, D. S., Mutation effects predicted from sequence co-variation.Nature Biotechnology 2017 , 35 (2), 128-135.

10. Hsu, C.; Nisonoff, H.; Fannjiang, C.; Listgarten, J., Learning protein fitness models from evolutionary and assay-labeled data. Nature Biotechnology 2022, 40 (7), 1114.

11. Hie, B. L. L.; Shanker, V. R. R.; Xu, D.; Bruun, T. U. J.; Weidenbacher, P. A. A.; Tang, S.; Wu, W.; Pak, J. E. E.; Kim, P. S. S., Efficient evolution of human antibodies from general protein language models. Nature Biotechnology 2023.

12. Shashkova, T. I. I.; Umerenkov, D.; Salnikov, M.; Strashnov, P. V. V.; Konstantinova, A. V. V.; Lebed, I.; Shcherbinin, D. N. N.; Asatryan, M. N. N.; Kardymon, O. L. L.; Ivanisenko, N. V. V., SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning. Frontiers in Immunology 2022, 13.

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