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Enzyme engineering enables the modification of wild-type proteins to meet industrial and research demands by enhancing catalytic activity, stability, binding affinities, and other properties.
Kamtekar, S., Berman, A.J., Wang, J., Lázaro, J.M., de Vega, M., Blanco, L., Salas, M., Steitz, T.A.: Insights into strand displacement and processivity from the crystal structure of the protein-primed DNA polymerase of bacteriophage φ \varphi 29. Molecular Cell 16
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Eddy, S.R.: Accelerated profile hmm searches. PLoS Computational Biology 7
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Manrao, E.A., Derrington, I.M., Laszlo, A.H., Langford, K.W., Hopper, M.K., Gillgren, N., Pavlenok, M., Niederweis, M., Gundlach, J.H.: Reading DNA at single-nucleotide resolution with a mutant mspa nanopore and phi29 DNA polymerase. Nature Biotechnology 30
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Hopf, T.A., Schärfe, C.P., Rodrigues, J.P., Green, A.G., Kohlbacher, O., Sander, C., Bonvin, A.M., Marks, D.S.: Sequence co-evolution gives 3D contacts and structures of protein complexes. eLife 3
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Wang, J., Bever, C.R., Majkova, Z., Dechant, J.E., Yang, J., Gee, S.J., Xu, T., Hammock, B.D.: Heterologous antigen selection of camelid heavy chain single domain antibodies against tetrabromobisphenol a. Analytical Chemistry 86
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Povilaitis, T., Alzbutas, G., Sukackaite, R., Siurkus, J., Skirgaila, R.: In vitro evolution of phi29 DNA polymerase using isothermal compartmentalized self replication technique. Protein Engineering, Design and Selection 29
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Hopf, T.A., Ingraham, J.B., Poelwijk, F.J., Schärfe, C.P., Springer, M., Sander, C., Marks, D.S.: Mutation effects predicted from sequence co-variation. Nature Biotechnology 35
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Riesselman, A.J., Ingraham, J.B., Marks, D.S.: Deep generative models of genetic variation capture the effects of mutations. Nature Methods 15
2018
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Laine, E., Karami, Y., Carbone, A.: Gemme: a simple and fast global epistatic model predicting mutational effects. Molecular Biology and Evolution 36
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2020
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Frazer, J., Notin, P., Dias, M., Gomez, A., Min, J.K., Brock, K., Gal, Y., Marks, D.S.: Disease variant prediction with deep generative models of evolutionary data. Nature 599
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Jing, B., Eismann, S., Suriana, P., Townshend, R.J.L., Dror, R.: Learning from protein structure with geometric vector perceptrons. In: International Conference on Learning Representations (2021)
2021
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Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., Rives, A.: Language models enable zero-shot prediction of the effects of mutations on protein function. Advances in Neural Information Processing Systems 34
2021
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Rao, R.M., Liu, J., Verkuil, R., Meier, J., Canny, J., Abbeel, P., Sercu, T., Rives, A.: MSA transformer. In: International Conference on Machine Learning. pp. 8844–8856. PMLR (2021)
2021
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Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., et al.: Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences 118
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Hsu, C., Verkuil, R., Liu, J., Lin, Z., Hie, B., Sercu, T., Lerer, A., Rives, A.: Learning inverse folding from millions of predicted structures. In: International Conference on Machine Learning. pp. 8946–8970. PMLR (2022)
2022
Tan, Y., Zhou, B., Zheng, L., Fan, G., Hong, L.: Semantical and topological protein encoding toward enhanced bioactivity and thermostability. bioRxiv pp. 2023–12 (2023)
2023
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Yang, K.K., Zanichelli, N., Yeh, H.: Masked inverse folding with sequence transfer for protein representation learning. Protein Engineering, Design and Selection 36
2023
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Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A.J., Bambrick, J., et al.: Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature pp. 1–3 (2024)
2024
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Iovino, B.G., Tang, H., Ye, Y.: Protein domain embeddings for fast and accurate similarity search. In: International Conference on Research in Computational Molecular Biology. pp. 421–424. Springer (2024)
2024
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Cited alongside, same era.
Lu, H., Diaz, D.J., Czarnecki, N.J., Zhu, C., Kim, W., Shroff, R., Acosta, D.J., Alexander, B.R., Cole, H.O., Zhang, Y., et al.: Machine learning-aided engineering of hydrolases for PET depolymerization. Nature 604
2022
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Marquet, C., Heinzinger, M., Olenyi, T., Dallago, C., Erckert, K., Bernhofer, M., Nechaev, D., Rost, B.: Embeddings from protein language models predict conservation and variant effects. Human Genetics 141
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Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., Steinegger, M.: Colabfold: making protein folding accessible to all. Nature Methods 19
2022
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Notin, P., Dias, M., Frazer, J., Hurtado, J.M., Gomez, A.N., Marks, D., Gal, Y.: Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. In: International Conference on Machine Learning. pp. 16990–17017. PMLR (2022)
2022
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Notin, P., Van Niekerk, L., Kollasch, A.W., Ritter, D., Gal, Y., Marks, D.S.: Trancepteve: Combining family-specific and family-agnostic models of protein sequences for improved fitness prediction. NeurIPS 2022 Workshop on Learning Meaningful Representations of Life (2022)
2022
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Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al.: Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379
2023
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Madani, A., Krause, B., Greene, E.R., Subramanian, S., Mohr, B.P., Holton, J.M., Olmos, J.L., Xiong, C., Sun, Z.Z., Socher, R., et al.: Large language models generate functional protein sequences across diverse families. Nature Biotechnology 41
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Su, J., Han, C., Zhou, Y., Shan, J., Zhou, X., Yuan, F.: SaProt: protein language modeling with structure-aware vocabulary. In: The Twelfth International Conference on Learning Representations (2023)
2023
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Kang, L., Wu, B., Zhou, B., Tan, P., Kang, Y., Yan, Y., Zong, Y., Li, S., Liu, Z., Hong, L.: Ai-enabled alkaline-resistant evolution of protein to apply in mass production. bioRxiv pp. 2024–09 (2024)
2024
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Li, M., Tan, Y., Ma, X., Zhong, B., Yu, H., Zhou, Z., Ouyang, W., Zhou, B., Hong, L., Tan, P.: ProSST: Protein language modeling with quantized structure and disentangled attention. bioRxiv pp. 2024–04 (2024)
2024
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Notin, P., Kollasch, A., Ritter, D., Van Niekerk, L., Paul, S., Spinner, H., Rollins, N., Shaw, A., Orenbuch, R., Weitzman, R., et al.: ProteinGym: large-scale benchmarks for protein fitness prediction and design. In: Advances in Neural Information Processing Systems. vol. 36 (2024)
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2024
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2024
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Van Kempen, M., Kim, S.S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C.L., Söding, J., Steinegger, M.: Fast and accurate protein structure search with Foldseek. Nature Biotechnology 42
2024
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Zhao, J., Zhang, C., Luo, Y.: Contrastive fitness learning: Reprogramming protein language models for low-n learning of protein fitness landscape. In: International Conference on Research in Computational Molecular Biology. pp. 470–474. Springer (2024)
2024
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Zhou, B., Zheng, L., Wu, B., Tan, Y., Lv, O., Yi, K., Fan, G., Hong, L.: Protein engineering with lightweight graph denoising neural networks. Journal of Chemical Information and Modeling (2024)
2024
Closest in time.