Description |
Migration is an observed phenomenon in birds' life which is crucial to the species' survival [1,2]. In several migrating avian species such as European blackcaps, willow warbler, or Eurasian cuckoo, migration was found to have a genetic basis as an inherited trait that received selective evolutionary pressure rather than a learned behavior [3,4]. Some migratory birds use the inclination of the magnetic field of the earth as its magnetic compass for navigation [5]. The most promising candidate that is responsible for the magnetoreception in avian navigation is the photoreceptor protein cryptochrome which is located in the outer segment of the photoreceptor cells [6].
Inside the cryptochrome, a radical pair [FAD∙-TrpH∙-] forms after a photon excites the FAD cofactor that is associated with the signaling of the protein. An electron transfer chain is then formed among tryptophans to create a radical pair with FAD. The rates of the individual electron transfer processes affect the sensitivity of the species to the magnetic field [7]. Previous experiments have shown that different species have varying magnetic field sensitivity [8]. This makes it important to rationalize the rates of the electron transfers for each of the different cryptochromes in different migratory birds. A common approach done is to model the protein in different redox states of the radical pair formation using molecular dynamics and estimate associated the free energy [8,9]. This method however is computationally taxing. In this talk, we propose a method to use generative models to model cryptochrome using training data from molecular dynamics simulations. We use the available data for cryptochrome from a European robin (ErCry4) as a training set and generate different protein conformations using generative adversarial networks and variational autoencoders. From the trained model, our goal is to generate protein conformations of cryptochrome of other species such as pigeon or chicken. The latent space distribution is modeled conditioned on the given protein structure of the European robin so that training can be transferred to a new protein structure of another species. This method can be used to quickly generate protein conformations such that energy and the electron transfer rates could be estimated for other cryptochrome structures such are those found in other migratory birds and in fishes. [1] Liedvogel, M., Åkesson, S., & Bensch, S. (2011). The genetics of migration on the move. Trends in ecology & evolution, 26(11), 561-569. https://doi.org/10.1016/j.tree.2011.07.009 [2] Dingle, H., & Drake, V. A. (2007). What is migration?. Bioscience, 57(2), 113-121. https://doi.org/10.1641/B570206 [3] Merlin, C., & Liedvogel, M. (2019). The genetics and epigenetics of animal migration and orientation:birds, butterflies and beyond. Journal of Experimental Biology, 222(Suppl_1), jeb191890. https://doi.org/10.1242/jeb.191890 [4] Lundberg, M., Liedvogel, M., Larson, K., Sigeman, H., Grahn, M., Wright, A., ... & Bensch, S. (2017). Genetic differences between willow warbler migratory phenotypes are few and cluster in large haplotype blocks. Evolution Letters, 1(3), 155-168. https://doi.org/10.1002/evl3.15 [5] Wiltschko, W., Munro, U., Ford, H., & Wiltschko, R. (1993). Magnetic inclination compass: a basis for the migratory orientation of birds in the Northern and Southern Hemisphere. Experientia, 49, 167-170. https://doi.org/10.1007/BF01989423 [6] Ritz, T., Adem, S., & Schulten, K. (2000). A model for photoreceptor-based magnetoreception in birds. Biophysical journal, 78(2), 707-718. https://doi.org/10.1016/S0006-3495(00)76629-X [7] Timmel, C. R., Till, U., Brocklehurst, B., Mclauchlan, K. A., & Hore, P. J. (1998). Effects of weak magnetic fields on free radical recombination reactions. Molecular Physics, 95(1), 71-89. https://doi.org/10.1080/00268979809483134 [8] Xu, J., Jarocha, L. E., Zollitsch, T., Konowalczyk, M., Henbest, K. B., Richert, S., ... & Hore, P. J. (2021). Magnetic sensitivity of cryptochrome 4 from a migratory songbird. Nature, 594(7864), 535-540. https://doi.org/10.1038/s41586-021-03618-9 [9] Schuhmann, F., Kattnig, D. R., & Solov’yov, I. A. (2021). Exploring post-activation conformational changes in pigeon cryptochrome 4. The Journal of Physical Chemistry B, 125(34), 9652-9659. https://doi.org/10.1021/acs.jpcb.1c02795 |
Modeling Cryptochromes using Generative Models
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