MENU

Contribution

---------------------------------------

The LoFT protocol

The LoFT cell lysis method is a relatively mild method for creating a cell-free protein production mix. We used the protocol published by Fujiwara[1]. We downscaled the culture volume from 1L to 250mL which was more feasible in our small school lab setting. We extended the freezing time from the original 1 hour to 24 hour after noticing longer freezing times yielded more protein. We have documented the specific details of the conditions we used when we carried out the experiment in the PDF below.

In our project, we used the LoFT method to prepare protein mixes for CF3HBD and NitR mutants. We then used His-tag purification to isolate the proteins from the mix.

Figure 1, LoFT Process.

Dry Lab

Previously, our team attempted to protein redesigning of the 3-hydroxybutyrate dehydrogenase (CF3HBD) to facilitate the cyclization of 5-aminovaleric acid (5AVA) and selected mutants were modelled by RosettaFold and further refined by Backrub. Molecular Dynamics (MD) was performed for the mutants however none of the mutants showed significant result after the preliminary in silico analysis.

Our team has altered our approach to redesigning 3-hydroxybutyrate dehydrogenase (CF3HBD) this year. Moreover, we also included lactam-binding NitR in the dry lab section as the other potential redesigning target since it takes a critical role in the ligand fishing of our project.

All proteins were modelled by RosettaFold [2] or AlphaFold2 [3] [4] via ColabFold [5] or I-TASSER [6] [7] [8] and all structures are further refined with Backrub [9]. Here, several folding approaches were considered in parallel as different algorithms are utilized and energy minimization were performed in Rosetta for all the generated structure.

With reference to the folding funnel hypothesis, energy minimization was performed iteratively, in which the lowest-scoring structure was further processed with Backrub [9]. The lowest scoring structure is considered as it is suggested that the lower the Rosetta Energy Unit (REU) score, the more stable the produced structure. Since Backrub accounts for the bond rotations of the Calpaha-Cbeta which gives a more refined structure for ligand docking, it was used to further refine the structure before the ligand docking.

Autodock Vina [10] [11]via Pyrx [12]is then used to dock 5AVA to the refined structures of NitR and CF3HBD. With reference to the critical residues suggested by Yeom et al.[13] (Q91, S139, H141 & Y152 for CF3HB and L117 for NitR), binding modes that resembles the suggested structures were selected to proceed to the QM/MM.

The cyclisation of 5AVA was modelled using the quantum chemistry and solid-state physics software package CP2K [14]. To save computational power and increase efficiency, the reaction is only modelled on Chain B, which is a reasonable choice as docking suggests the ligand binds either to Chain B or D. The ligand, 5AVA, were parasitised using antechamber [15] and parmchk2 [16] [17].

By comparing the relative one-dimensional energy as a function of the distance difference, candidates with a lower activation energy would be considered a better candidate and prioritized to be tested in the wet lab.

Reference:

  1. Fujiwara, K.; Doi, N. Biochemical Preparation of Cell Extract for Cell-Free Protein Synthesis without Physical Disruption. PLoS ONE 2016, 11 (4)., https://doi.org/10.1371/journal.pone.0154614..
  2. Baek, M.; DiMaio, F.; Anishchenko, I.; Dauparas, J.; Ovchinnikov, S.; Lee, G. R.; Wang, J.; Cong, Q.; Kinch, L. N.; Schaeffer, R. D.; Millán, C.; Park, H.; Adams, C.; Glassman, C. R.; DeGiovanni, A.; Pereira, J. H.; Rodrigues, A. V.; van Dijk, A. A.; Ebrecht, A. C.; Opperman, D. J.; Sagmeister, T.; Buhlheller, C.; Pavkov-Keller, T.; Rathinaswamy, M. K.; Dalwadi, U.; Yip, C. K.; Burke, J. E.; Garcia, K. C.; Grishin, N. V.; Adams, P. D.; Read, R. J.; Baker, D. Accurate Prediction of Protein Structures and Interactions Using a Three-Track Neural Network. Science 2021, 373 (6557), 871–876.
  3. Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S. A.; Ballard, A. J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A. W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly Accurate Protein Structure Prediction with Alphafold. Nature 2021, 596 (7873), 583–589.
  4. Evans, R.; O’Neill, M.; Pritzel, A.; Antropova, N.; Senior, A.; Green, T.; Žídek, A.; Bates, R.; Blackwell, S.; Yim, J.; Ronneberger, O.; Bodenstein, S.; Zielinski, M.; Bridgland, A.; Potapenko, A.; Cowie, A.; Tunyasuvunakool, K.; Jain, R.; Clancy, E.; Kohli, P.; Jumper, J.; Hassabis, D. Protein Complex Prediction with Alphafold-Multimer. 2021.
  5. Mirdita, M.; Schütze, K.; Moriwaki, Y.; Heo, L.; Ovchinnikov, S.; Steinegger, M. Colabfold: Making Protein Folding Accessible to All. Nature Methods 2022, 19 (6), 679–682.
  6. Zhang, Y. I-Tasser Server for Protein 3D Structure Prediction. BMC Bioinformatics 2008, 9 (1).
  7. Yang, J.; Yan, R.; Roy, A.; Xu, D.; Poisson, J.; Zhang, Y. The I-Tasser Suite: Protein Structure and Function Prediction. Nature Methods 2014, 12 (1), 7–8.
  8. Roy, A.; Kucukural, A.; Zhang, Y. I-Tasser: A Unified Platform for Automated Protein Structure and Function Prediction. Nature Protocols 2010, 5 (4), 725–738.
  9. Smith, C. A., & Kortemme, T. (2008). Backrub-like backbone simulation recapitulates natural protein conformational variability and improves mutant side-chain prediction. Journal of Molecular Biology, 380(4), 742–756. https://doi.org/10.1016/j.jmb.2008.05.023
  10. Eberhardt, J.; Santos-Martins, D.; Tillack, A. F.; Forli, S. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. Journal of Chemical Information and Modeling 2021, 61 (8), 3891–3898.
  11. Trott, O.; Olson, A. J. Autodock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. Journal of Computational Chemistry 2009.
  12. Dallakyan, S.; Olson, A. J. Small-Molecule Library Screening by Docking with Pyrx. Methods in Molecular Biology 2014, 243–250.
  13. Yeom, S.-J.; Kim, M.; Kwon, K. K.; Fu, Y.; Rha, E.; Park, S.-H.; Lee, H.; Kim, H.; Lee, D.-H.; Kim, D.-M.; Lee, S.-G. A Synthetic Microbial Biosensor for High-Throughput Screening of Lactam Biocatalysts. Nature Communications 2018, 9 (1).
  14. Kühne, T. D.; Iannuzzi, M.; Del Ben, M.; Rybkin, V. V.; Seewald, P.; Stein, F.; Laino, T.; Khaliullin, R. Z.; Schütt, O.; Schiffmann, F.; Golze, D.; Wilhelm, J.; Chulkov, S.; Bani-Hashemian, M. H.; Weber, V.; Borštnik, U.; Taillefumier, M.; Jakobovits, A. S.; Lazzaro, A.; Pabst, H.; Müller, T.; Schade, R.; Guidon, M.; Andermatt, S.; Holmberg, N.; Schenter, G. K.; Hehn, A.; Bussy, A.; Belleflamme, F.; Tabacchi, G.; Glöß, A.; Lass, M.; Bethune, I.; Mundy, C. J.; Plessl, C.; Watkins, M.; VandeVondele, J.; Krack, M.; Hutter, J. CP2K: An Electronic Structure and Molecular Dynamics Software Package - Quickstep: Efficient and Accurate Electronic Structure Calculations. The Journal of Chemical Physics 2020, 152 (19), 194103.
  15. Wang, J.; Wang, W.; Kollman, P. A.; Case, D. A. Automatic Atom Type and Bond Type Perception in Molecular Mechanical Calculations. Journal of Molecular Graphics and Modelling 2006, 25 (2), 247–260.
  16. Case, D. A.; Cheatham, T. E.; Darden, T.; Gohlke, H.; Luo, R.; Merz, K. M.; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R. J. The Amber Biomolecular Simulation Programs. Journal of Computational Chemistry 2005, 26 (16), 1668–1688.
  17. D.A. Case, D. M. Y., I. Y. Ben-Shalom, S. R. Brozell, D. S. Cerutti, T. E. Cheatham, III, V. W. D. Cruzeiro, T. A. Darden, R. E. Duke, D. Ghoreishi, M. K. Gilson, H. Gohlke, A. W. Goetz, D. Greene, R. Harris, N. Homeyer, S. Izadi, A. Kovalenko, T. Kurtzman, T. S. Lee, S. LeGrand, P. Li, C. Lin, J. Liu, T. Luchko, R. Luo, D. J. Mermelstein, K. M. Merz, Y. Miao, G. Monard, C. Nguyen, H. Nguyen, I. Omelyan, A. Onufriev, F. Pan, R. Qi, D. R. Roe, A. Roitberg, C. Sagui, S. Schott-Verdugo, J. Shen, C. L. Simmerling, J. Smith, R. Salomon-Ferrer, J. Swails, R. C. Walker, J. Wang, H. Wei, R. M. Wolf, X. Wu, L. Xiao, & P.A. Kollman . (2018). AMBER 2018. University of California, San Francisco. Retrieved from http://ambermd.org/CiteAmber.php
Access Wiki Tools View Source Code