Creating de novo overlapped genes

Dominic Y. Logel, Paul R. Jaschke*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Citations (Scopus)

Abstract

Future applications of synthetic biology will rely on deploying engineered cells outside of lab environments for long periods of time. Currently, a significant roadblock to this application is the potential for deactivating mutations in engineered genes. A recently developed method to protect engineered coding sequences from mutation is called Constraining Adaptive Mutations using Engineered Overlapping Sequences (CAMEOS). In this chapter we provide a workflow for utilizing CAMEOS to create synthetic overlaps between two genes, one essential (infA) and one non-essential (aroB), to protect the non-essential gene from mutation and loss of protein function. In this workflow we detail the methods to collect large numbers of related protein sequences, produce multiple sequence alignments (MSAs), use the MSAs to generate hidden Markov models and Markov random field models, and finally generate a library of overlapping coding sequences through CAMEOS scripts. To assist practitioners with basic coding skills to try out the CAMEOS method, we have created a virtual machine containing all the required packages already installed that can be downloaded and run locally.

Original languageEnglish
Title of host publicationComputational biology and machine learning for metabolic engineering and synthetic biology
EditorsKumar Selvarajoo
Place of PublicationNew York, NY
PublisherHumana Press Inc.
Chapter6
Pages95-120
Number of pages26
ISBN (Electronic)9781071626177
ISBN (Print)9781071626160
DOIs
Publication statusPublished - 2023

Publication series

NameMethods in Molecular Biology
Volume2553
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Keywords

  • Deep learning
  • Machine learning
  • Generative model
  • Markov random field
  • Overlapping genes
  • Multiple sequence alignments
  • Protein design
  • Genome compression
  • Synthetic genomes
  • Synthetic biology

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  • COESB: ARC Centre of Excellence in Synthetic Biology

    Paulsen, I., Filipovska, A., Parker, R., Nielsen, L. K., Neilan, B. A., Alexandrov, K., Jackson , C., Wodak, J., Rackham, O., Marcellin, E., Gillings, M., Rogers, W., Lee, L., Packer, N., O'Hara, I. M., Speight, R., Vickers, C. E., Beliaev, A., Scott, C., Lacey, J., Mankad, A., Calvert, J., Thomas, G., Rodriguez-Concepcion, M., Fleishman, S., Koepke, M., Ball, M., Turner, N. J., Borneman, A. R., Holowko, M., Goold, H., Ellis, T., Mitchell, L. A. & Dai, J.

    9/11/208/11/27

    Project: Research

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