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Proteins power a vast array of functional processes in living cells.
P-SEA: a new efficient assignment of secondary structure from C α C_{\alpha} trace of proteins
Labesse, G., Colloc’h, N., Pothier, J., and Mornon, J.-P · 1997
Earlier work this paper cites.
The protein data bank
Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., and Bourne, P. E · 2000
Earlier work this paper cites.
Design of a novel globular protein fold with atomic-level accuracy
Kuhlman, B., Dantas, G., Ireton, G. C., Varani, G., Stoddard, B. L., and Baker, D · 2003
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Scoring function for automated assessment of protein structure template quality
Zhang, Y. and Skolnick, J · 2004
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Directed evolution: new parts and optimized function
Dougherty, M. J. and Arnold, F. H · 2009
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How significant is a protein structure similarity with TM-score= 0.5?
Xu, J. and Zhang, Y · 2010
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Discrete frenet frame, inflection point solitons, and curve visualization with applications to folded proteins
Hu, S., Lundgren, M., and Niemi, A. J · 2011
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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SCOPe: Structural classification of proteins—extended, integrating SCOP and ASTRAL data and classification of new structures
Fox, N. K., Brenner, S. E., and Chandonia, J.-M · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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The coming of age of de novo protein design
Huang, P.-S., Boyken, S. E., and Baker, D · 2016
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Generative modeling for protein structures
Anand, N. and Huang, P.-S · 2018
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End-to-end differentiable learning of protein structure
AlQuraishi, M · 2019
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Fully differentiable full-atom protein backbone generation
Anand, N., Eguchi, R. R., and Huang, P.-S · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Improved protein structure prediction using predicted interresidue orientations
Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., and Baker, D · 2020
Cited alongside, same era.
De novo protein design by deep network hallucination
Anishchenko, I., Pellock, S. J., Chidyausiku, T. M., Ramelot, T. A., Ovchinnikov, S., Hao, J., Bafna, K., Norn, C., Kang, A., Bera, A. K., DiMaio, F., Carter, L., Chow, C. M., Montelione, G. T., and Baker, D · 2021
Cited alongside, same era.
Accurate prediction of protein structures and interactions using a three-track neural network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al · 2021
Cited alongside, same era.
Single-sequence protein structure prediction using a language model and deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., Sorger, P. K., and AlQuraishi, M · 2022
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Robust deep learning–based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., Leung, P. J. Y., Huddy, T. F., Pellock, S., Tischer, D., Chan, F., Koepnick, B., Nguyen, H., Kang, A., Sankaran, B., Bera, A. K., King, N. P., and Baker, D · 2022
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Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation
Eguchi, R. R., Choe, C. A., and Huang, P.-S · 2022
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A high-level programming language for generative protein design
Hie, B., Candido, S., Lin, Z., Kabeli, O., Rao, R., Smetanin, N., Sercu, T., and Rives, A · 2022
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Illuminating protein space with a programmable generative model
Ingraham, J., Baranov, M., Costello, Z., Frappier, V., Ismail, A., Tie, S., Wang, W., Xue, V., Obermeyer, F., Beam, A., and Grigoryan, G · 2022
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Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
Learning from protein structure with geometric vector perceptrons
Jing, B., Eismann, S., Suriana, P., Townshend, R. J., and Dror, R. O · 2021
Cited alongside, same era.
Highly accurate protein structure prediction with AlphaFold
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. 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., and Hassabis, D · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Nichol, A. and Dhariwal, P · 2021
Cited alongside, same era.
E(n) equivariant graph neural networks
Satorras, V. G., Hoogeboom, E., and Welling, M · 2021
Cited alongside, same era.
Openfold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
Ahdritz, G., Bouatta, N., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T. J., Berenberg, D., Fisk, I., Zanichelli, N., Zhang, B., Nowaczynski, A., Wang, B., Stepniewska-Dziubinska, M. M., Zhang, S., Ojewole, A., Guney, M. E., Biderman, S., Watkins, A. M., Ra, S., Lorenzo, P. R., Nivon, L., Weitzner, B., Ban, Y.-E. A., Sorger, P. K., Mostaque, E., Zhang, Z., Bonneau, R., and AlQuraishi, M · 2022
Cited alongside, same era.
Protein structure and sequence generation with equivariant denoising diffusion probabilistic models
Anand, N. and Achim, T · 2022
Cited alongside, same era.
Later among the works it cites.
Language models of protein sequences at the scale of evolution enable accurate structure prediction
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., dos Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., et al · 2022
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Hierarchical text-conditional image generation with CLIP latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Trippe, B. L., Yim, J., Tischer, D., Baker, D., Broderick, T., Barzilay, R., and Jaakkola, T · 2022
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AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al · 2022
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Scaffolding protein functional sites using deep learning
Wang, J., Lisanza, S., Juergens, D., Tischer, D., Watson, J. L., Castro, K. M., Ragotte, R., Saragovi, A., Milles, L. F., Baek, M., Anishchenko, I., Yang, W., Hicks, D. R., Expòsit, M., Schlichthaerle, T., Chun, J.-H., Dauparas, J., Bennett, N., Wicky, B. I. M., Muenks, A., DiMaio, F., Correia, B., Ovchinnikov, S., and Baker, D · 2022
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Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Hanikel, N., Pellock, S. J., Courbet, A., Sheffler, W., Wang, J., Venkatesh, P., Sappington, I., Torres, S. V., Lauko, A., De Bortoli, V., Mathieu, E., Barzilay, R., Jaakkola, T. S., DiMaio, F., Baek, M., and Baker, D · 2022
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Tackling the generative learning trilemma with denoising diffusion GANs
Xiao, Z., Kreis, K., and Vahdat, A · 2022
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SE(3) diffusion model with application to protein backbone generation
Yim, J., Trippe, B. L., De Bortoli, V., Mathieu, E., Doucet, A., Barzilay, R., and Jaakkola, T · 2023
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