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Proteins, essential to biological systems, perform functions intricately linked to their three-dimensional structures.
The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain
Anfinsen, C. B., Haber, E., Sela, M., and White Jr, F · 1961
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Are there pathways for protein folding?
Levinthal, C · 1968
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How to fold graciously
Levinthal, C · 1969
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Gapped blast and psi-blast: a new generation of protein database search programs
Altschul, S. F., Madden, T. L., Schäffer, A. A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D. J · 1997
Earlier work this paper cites.
Exploring zipping and assembly as a protein folding principle
Voelz, V. A. and Dill, K. A · 2007
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HMMER web server: interactive sequence similarity searching
Finn, R. D., Clements, J., and Eddy, S. R · 2011
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How fast-folding proteins fold
Lindorff-Larsen, K., Piana, S., Dror, R. O., and Shaw, D. E · 2011
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HHblits: lightning-fast iterative protein sequence searching by hmm-hmm alignment
Remmert, M., Biegert, A., Hauser, A., and Söding, J · 2012
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Proteins: structure and function
Whitford, D · 2013
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The nature of protein folding pathways
Englander, S. W. and Mayne, L · 2014
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Deep generative models of genetic variation capture mutation effects
Riesselman, A. J., Ingraham, J. B., and Marks, D. S · 2017
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Variational auto-encoding of protein sequences
Sinai, S., Kelsic, E., Church, G. M., and Nowak, M. A · 2017
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Attention is all you need
Vaswani, A · 2017
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Rosettaantibodydesign (rabd): A general framework for computational antibody design
Adolf-Bryfogle, J., Kalyuzhniy, O., Kubitz, M., Weitzner, B. D., Hu, X., Adachi, Y., Schief, W. R., and Dunbrack Jr, R. L · 2018
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Unified rational protein engineering with sequence-based deep representation learning
Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., and Church, G. M · 2019
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Learning protein sequence embeddings using information from structure
Bepler, T. and Berger, B · 2019
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Deciphering protein evolution and fitness landscapes with latent space models
Ding, X., Zou, Z., and Brooks III, C. L · 2019
Cited alongside, same era.
rawMSA: End-to-end deep learning using raw multiple sequence alignments
Mirabello, C. and Wallner, B · 2019
Cited alongside, same era.
ProGen: Language modeling for protein generation
Madani, A., McCann, B., Naik, N., Keskar, N. S., Anand, N., Eguchi, R. R., Huang, P.-S., and Socher, R · 2020
Cited alongside, same era.
Long-range correlation in protein dynamics: Confirmation by structural data and normal mode analysis
Tang, Q.-Y. and Kaneko, K · 2020
Cited alongside, same era.
On layer normalization in the transformer architecture
Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., and Liu, T · 2020
Cited alongside, same era.
Learning inverse folding from millions of predicted structures
Hsu, C., Verkuil, R., Liu, J., Lin, Z., Hie, B., Sercu, T., Lerer, A., and Rives, A · 2022
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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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Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval
Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A. N., Marks, D., and Gal, Y · 2022
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High-resolution de novo structure prediction from primary sequence
Wu, R., Ding, F., Wang, R., Shen, R., Zhang, X., Luo, S., Su, C., Wu, Z., Xie, Q., Berger, B., et al · 2022
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PEER: a comprehensive and multi-task benchmark for protein sequence understanding
Xu, M., Zhang, Z., Lu, J., Zhu, Z., Zhang, Y., Chang, M., Liu, R., and Tang, J · 2022
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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.
Structure-based protein function prediction using graph convolutional networks
Gligorijević, V., Renfrew, P. D., Kosciolek, T., Leman, J. K., Berenberg, D., Vatanen, T., Chandler, C., Taylor, B. C., Fisk, I. M., Vlamakis, H., et al · 2021
Cited alongside, same era.
Combining evolutionary and assay-labelled data for protein fitness prediction
Hsu, C., Nisonoff, H., Fannjiang, C., and Listgarten, J · 2021
Cited alongside, same era.
Iterative refinement graph neural network for antibody sequence-structure co-design
Jin, W., Wohlwend, J., Barzilay, R., and Jaakkola, T. S · 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., et al · 2021
Cited alongside, same era.
Language models enable zero-shot prediction of the effects of mutations on protein function
Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., and Rives, A · 2021
Cited alongside, same era.
Msa transformer
Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J. F., Abbeel, P., Sercu, T., and Rives, A · 2021
Cited alongside, same era.
Protein representation learning by geometric structure pretraining
Zhang, Z., Xu, M., Jamasb, A., Chenthamarakshan, V., Lozano, A., Das, P., and Tang, J · 2022
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Structure-aware protein self-supervised learning
Chen, C., Zhou, J., Wang, F., Liu, X., and Dou, D · 2023
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Pre-training antibody language models for antigen-specific computational antibody design
Gao, K., Wu, L., Zhu, J., Peng, T., Xia, Y., He, L., Xie, S., Qin, T., Liu, H., He, K., et al · 2023
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Evolutionary-scale prediction of atomic-level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al · 2023
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Reprogramming pretrained language models for antibody sequence infilling
Melnyk, I., Chenthamarakshan, V., Chen, P.-Y., Das, P., Dhurandhar, A., Padhi, I., and Das, D · 2023
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Saprot: protein language modeling with structure-aware vocabulary
Su, J., Han, C., Zhou, Y., Shan, J., Zhou, X., and Yuan, F · 2023
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Masked inverse folding with sequence transfer for protein representation learning
Yang, K. K., Zanichelli, N., and Yeh, H · 2023
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Uni-mol: A universal 3d molecular representation learning framework
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., and Ke, G · 2023
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Simulating 500 million years of evolution with a language model
Hayes, T., Rao, R., Akin, H., Sofroniew, N. J., Oktay, D., Lin, Z., Verkuil, R., Tran, V. Q., Deaton, J., Wiggert, M., et al · 2024
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Fast and accurate protein structure search with foldseek
Van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L., Söding, J., and Steinegger, M · 2024
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Alphafold protein structure database in 2024: providing structure coverage for over 214 million protein sequences
Varadi, M., Bertoni, D., Magana, P., Paramval, U., Pidruchna, I., Radhakrishnan, M., Tsenkov, M., Nair, S., Mirdita, M., Yeo, J., et al · 2024
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