Rxivist logo

Engineering indel and substitution variants of diverse and ancient enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP)

By Gabriel Foley, Ariane Mora, Connie M Ross, Scott Bottoms, Leander Sutzl, Marnie L Lamprecht, Julian Zaugg, Alexandra Essebier, Brad Balderson, Rhys Newell, Raine ES Thomson, Bostjan Kobe, Ross T Barnard, Luke Guddat, Gerhard Schenk, Joerg Carsten, Yosephine Gumulya, Burkhard Rost, Dietmar Haltrich, Volker Sieber, Elizabeth MJ Gillam, Mikael Boden

Posted 31 Dec 2019
bioRxiv DOI: 10.1101/2019.12.30.891457

Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion (indel) events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time.

Download data

  • Downloaded 939 times
  • Download rankings, all-time:
    • Site-wide: 32,669
    • In bioinformatics: 3,527
  • Year to date:
    • Site-wide: None
  • Since beginning of last month:
    • Site-wide: 25,961

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide