Publications

Selected Publications

Kim, P., Noble, A.J., Cheng, A., and Bepler, T. Learning to automate cryo-electron microscopy data collection with Ptolemy. (2022). https://arxiv.org/abs/2112.01534

Cheng, A., Kim, P., Kuang, H., Mendez, J.H., Chua, E.Y.D., Maruthi, K., Wei, H., Sawh, A., Aragon, M.F., Serbynovskyi, V., Neselu, K., Eng, E.T., Potter, C.S., Carragher, B., Bepler, T., and Noble, A.J. Fully Automated Multi-Grid Cryo-EM Screening using Smart Leginon. (2022). https://www.biorxiv.org/content/10.1101/2022.07.23.501225v1

Ram, S., and Bepler, T. Few Shot Protein Generation. (2022). https://arxiv.org/abs/2204.01168

Chen, C.H., Bepler, T., Pepper, K., Fu, D., and Lu, T.K. Synthetic molecular evolution of antimicrobial peptides. Current Opinion in Biotechnology 75 (2022). Link

Bepler, T., and Berger, B. Learning the protein language: evolution, structure, and function. Cell Systems 12, 654-669 (2021). Link

Zhong, E., Bepler, T., Berger, B., and Davis, J. CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks. Nat Methods 18, 176–185 (2021). Link

Bepler, T., Kelley, K., Noble, A.J., and Berger, B. Topaz-Denoise: general deep denoising models for cryoEM and cryoET. Nat Commun 11, 5208 (2020). Link

Zhong, E., Bepler, T., Davis, J., and Berger, B. Reconstructing continuous distributions of 3D protein structure from cryo-EM images. 8th International Conference on Learning Representations (2020). Link

Bepler, T., Zhong, E., Kotaro, K., Brignole, E., and Berger, B. Explicitly disentangling image content from translation and rotation with spatial-VAE. Advances in Neural Information Processing Systems (2019). Link

Bepler, T., Morin, A., Rapp, M. et al. Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Nat Methods 16, 1153–1160 (2019). Link

Brasch, J., Goodman, K.M., Noble, A.J. et al. Visualization of clustered protocadherin neuronal self-recognition complexes. Nature 569, 280–283 (2019). Link

Bepler, T., and Berger, B. Learning protein sequence embeddings using information from structure. 7th International Conference on Learning Representations (2019). Link