Poster Presentation 26th Annual Lorne Proteomics Symposium 2021

Using smartphones to explore protein structures in XR (#92)

Neblina Sikta 1 , Stuart Anderson 2 , Christian Stolte 1 , Sandeep Kaur 1 3 , Bosco Ho 1 , Nicola Bordin 4 , Matt Adcock 2 , Andrea Schafferhans 5 6 , Sean O'Donoghue 1 2 3
  1. Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
  2. CSIRO Data61, Sydney, Australia
  3. School of Biotechnology and Biomolecular Sciences (UNSW), Sydney, Australia
  4. Institute of Structural and Molecular Biology, University College London, UK
  5. Department of Bioengineering Sciences, Weihenstephan-Tr. University of Applied Sciences, Freising, Germany
  6. Department of Informatics, Bioinformatics & Computational Biology, Technical University of Munich, Germany

Proteins fold into intricate 3D shapes that can often be difficult to navigate and understand. Thus, it was recognized already in the 1970’s that virtual reality (VR) has potential to help in protein research, leading to decades of research prototypes (Brooks 2014). Still today, however, the use of VR in molecular graphics is primarily limited to demonstration systems that require specialist hardware. However, this may be able to change, thanks to the advent of ‘augmented reality’ (AR), in which virtual objects are displayed interactively with the physical world. AR and VR are closely related technologies - here, we use the term ‘extended reality’ (XR) to encompass both. Most iOS and Android smartphones on the market today have impressive XR capabilities; in this project, we have created a production application that lets researchers use smartphones to explore protein structures in XR. Our application is a completely redesigned version of Aquaria (O’Donoghue et al. 2015), a web-based molecular graphics system with >100 million pre-calculated protein structure models, based on systematically matching all SwissProt sequences against all PDB structures. These 3D models can be mapped with features, which can be either user-defined, or predefined in CATH (Dawson et al. 2017), COSMIC (Tate et al. 2019), PredictProtein (Yachdav et al. 2014), SNAP2 (Hecht, Bromberg, and Rost 2015), or UniProt (The UniProt Consortium 2019). With the redesigned version (https://aquaria.app), it is now easy to use a smartphone to find structural models of interest, color them using mapped sequence features, then explore the colored models in XR. All structures related to a protein can be found just by specifying a gene name (e.g., https://aquaria.app/Human/WT1); mutations can also be specified directly in the URL (e.g., https://aquaria.app/Human/WT1?Arg370Leu). Our goal was to make it easy for researchers to experience using XR to explore structures of direct relevance to their work - and even mapped with their own feature data. This experience is now possible with most smartphones currently in use. Our application also supports more specialist hardware, such as Microsoft HoloLens or other devices compatible with Windows Mixed Reality.

  1. Brooks, Frederick P. 2014. “Impressions by a Dinosaur – Summary of Faraday Discussion 169: Molecular Simulations and Visualization.” Faraday Discussions 169 (September): 521–27. https://doi.org/10.1039/C4FD00130C.
  2. Dawson, Natalie L., Tony E. Lewis, Sayoni Das, Jonathan G. Lees, David Lee, Paul Ashford, Christine A. Orengo, and Ian Sillitoe. 2017. “CATH: An Expanded Resource to Predict Protein Function through Structure and Sequence.” Nucleic Acids Research 45 (D1): D289–95. https://doi.org/10.1093/nar/gkw1098.
  3. Hecht, Maximilian, Yana Bromberg, and Burkhard Rost. 2015. “Better Prediction of Functional Effects for Sequence Variants.” BMC Genomics 16 (8): S1. https://doi.org/10.1186/1471-2164-16-S8-S1.
  4. O’Donoghue, Seán I., Kenneth S. Sabir, Maria Kalemanov, Christian Stolte, Benjamin Wellmann, Vivian Ho, Manfred Roos, et al. 2015. “Aquaria: Simplifying Discovery and Insight from Protein Structures.” Nature Methods 12 (2): 98–99. https://doi.org/10.1038/nmeth.3258.
  5. Tate, John G, Sally Bamford, Harry C Jubb, Zbyslaw Sondka, David M Beare, Nidhi Bindal, Harry Boutselakis, et al. 2019. “COSMIC: The Catalogue Of Somatic Mutations In Cancer.” Nucleic Acids Research 47 (D1): D941–47. https://doi.org/10.1093/nar/gky1015.
  6. The UniProt Consortium. 2019. “UniProt: A Worldwide Hub of Protein Knowledge.” Nucleic Acids Research 47 (D1): D506–15. https://doi.org/10.1093/nar/gky1049.
  7. Yachdav, Guy, Edda Kloppmann, Laszlo Kajan, Maximilian Hecht, Tatyana Goldberg, Tobias Hamp, Peter Hönigschmid, et al. 2014. “PredictProtein—an Open Resource for Online Prediction of Protein Structural and Functional Features.” Nucleic Acids Research 42 (W1): W337–43. https://doi.org/10.1093/nar/gku366.