Deep learning techniques have significantly impacted protein structure prediction and protein design
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Protein structure prediction and design can be regarded as two inverse processes governed by the same folding principle. Although progress remained stagnant over the past two decades, the recent application of deep neural networks to spatial constraint prediction and end-to-end model training has significantly improved the accuracy of protein structure prediction, largely solving the problem at the fold level for single-domain proteins. The field of protein design has also witnessed dramatic improvement, where noticeable examples have shown that information stored in neural-network models can be used to advance functional protein design. Thus, incorporation of deep learning techniques into different steps of protein folding and design approaches represents an exciting future direction and should continue to have a transformative impact on both fields.
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Existing protein structure prediction methods fall far short of atomic or experimental accuracy, especially when no homologous structure is available, limiting their utility for many biological applications.
“Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available.”
From Highly accurate protein structure prediction with AlphaFold (2021) · cites this paper as [14]
The Conclusion and Future Directions section says nearly one-third of AlphaFold2 single-domain models were below experimental accuracy, and the AlphaFold2 section identifies targets without templates as the hardest to model. But the paper does not characterize existing methods as uniformly falling far short: it reports that about two-thirds of AlphaFold2 models were comparable to low-to-medium-resolution experimental models and that it correctly folded the core regions of nearly all template-free targets. The cited passages have no page numbers in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredDeep learning has already been exploited in structural modeling and design, analysis, and linking these to function.
“Deep learning has also been already exploited in structural modeling and design60,194−196 and analysis191 and linking these to function.197”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [195]
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Comparative Protein Modelling by Satisfaction of Spatial Restraints
Šali, A., & Blundell, T. L. (1993) · Journal of Molecular Biology · doi:10.1006/jmbi.1993.1626 · Cited by 12,977 · see citing works
I-TASSER: a unified platform for automated protein structure and function prediction
Roy, A., Küçükural, A., & Zhang, Y. (2010) · Nature Protocols · doi:10.1038/nprot.2010.5 · Cited by 6,093 · see citing works
Self-citationImproved protein structure prediction using potentials from deep learning
Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1923-7 · Cited by 3,378 · see citing works
Scoring function for automated assessment of protein structure template quality
Zhang, Y., & Skolnick, J. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20264 · Cited by 2,392 · see citing works
Self-citationProtein homology detection by HMM–HMM comparison
Söding, J. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bti125 · Cited by 2,381 · see citing works
Protein Structure Prediction Using Rosetta
Rohl, C. A., Strauss, C. E. M., Misura, K. M. S., & Baker, D. A. (2004) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(04)83004-0 · Cited by 1,711 · see citing works
Design of a Novel Globular Protein Fold with Atomic-Level Accuracy
Kuhlman, B. A., Dantas, G. A., Ireton, G. C., Varani, G., Stoddard, B. L., & Baker, D. A. (2003) · Science · doi:10.1126/science.1089427 · Cited by 1,666 · see citing works
The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design
Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O’Meara, M. J., DiMaio, F. P., Park, H., Shapovalov, M. V., Renfrew, P. D., Mulligan, V. K., Kappel, K., et al. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00125 · Cited by 1,532 · see citing works
Morcos, F., Pagnani, A., Lunt, B., Bertolino, A., Marks, D. S., Sander, C., Zecchina, R., Onuchic, J. N., Hwa, T., & Weigt, M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1111471108 · Cited by 1,434 · see citing works
Improved protein structure prediction using predicted interresidue orientations
Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., & Baker, D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1914677117 · Cited by 1,421 · see citing works
How significant is a protein structure similarity with TM-score = 0.5?
Xu, J., & Zhang, Y. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq066 · Cited by 1,142 · see citing works
Self-citationHigh-Resolution Comparative Modeling with RosettaCM
Song, Y., DiMaio, F., Wang, R. Y., Kim, D. E., Miles, C., Brunette, T., Thompson, J., & Baker, D. A. (2013) · Structure · doi:10.1016/j.str.2013.08.005 · Cited by 1,079 · see citing works
Identification of direct residue contacts in protein–protein interaction by message passing
Weigt, M., White, R. A., Szurmant, H., Hoch, J. A., & Hwa, T. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0805923106 · Cited by 1,025 · see citing works
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005324 · Cited by 931 · see citing works
Correlated mutations and residue contacts in proteins
Göbel, U., Sander, C., Schneider, R., & Valencia, A. (1994) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340180402 · Cited by 863 · see citing works
Xu, D. H., & Zhang, Y. (2012) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24065 · Cited by 853 · see citing works
Self-citationJones, D. T., Buchan, D. W. A., Cozzetto, D., & Pontil, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr638 · Cited by 760 · see citing works
De novo design of picomolar SARS-CoV-2 miniprotein inhibitors
Cao, L., Goreshnik, I., Coventry, B., Case, J. B., Miller, L., Kozodoy, L., Chen, R. E., Carter, L., Walls, A. C., Park, Y.-J., et al. (2020) · Science · doi:10.1126/science.abd9909 · Cited by 742 · see citing works
Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models
Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M., & Aurell, E. (2013) · Physical Review E · doi:10.1103/physreve.87.012707 · Cited by 661 · see citing works
De novo protein design by deep network hallucination
Anishchenko, I., Chidyausiku, T. M., Ovchinnikov, S., Pellock, S. J., & Baker, D. (2020) · doi:10.1101/2020.07.22.211482 · Cited by 629 · see citing works
Proof of principle for epitope-focused vaccine design
Correia, B. E., Bates, J. T., Loomis, R. J., Baneyx, G., Carrico, C., Jardine, J. G., Rupert, P., Correnti, C., Kalyuzhniy, O., Vittal, V., et al. (2014) · Nature · doi:10.1038/nature12966 · Cited by 555 · see citing works
De novo design of potent and selective mimics of IL-2 and IL-15
Silva, D.-A., Yu, S., Ulge, U. Y., Spangler, J. B., Jude, K. M., Labão-Almeida, C., Ali, L. R., Quijano-Rubio, A., Ruterbusch, M., Leung, I., et al. (2019) · Nature · doi:10.1038/s41586-018-0830-7 · Cited by 531 · see citing works
Cyclic coordinate descent: A robotics algorithm for protein loop closure
Canutescu, A. A., & Dunbrack, R. L. (2003) · Protein Science · doi:10.1110/ps.0242703 · Cited by 527 · see citing works
Progress and challenges in protein structure prediction
Zhang, Y. (2008) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2008.02.004 · Cited by 507 · see citing works
Self-citationCritical assessment of methods of protein structure prediction (CASP)—Round XIII
Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25823 · Cited by 506 · see citing works
‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures
Callaway, E. (2020) · Nature · doi:10.1038/d41586-020-03348-4 · Cited by 499 · cited 1× · see citing works
“5 for all domains (except for one whose structure was solved by NMR) in the CASP14 experiment, marking the solution of the single-domain protein structure prediction problem at a fold level [68].”
BackgroundMassively parallel de novo protein design for targeted therapeutics
Chevalier, A., Silva, D.-A., Rocklin, G. J., Hicks, D. R., Vergara, R., Murapa, P., Bernard, S. M., Zhang, L., Lam, K.-H., Yao, G., et al. (2017) · Nature · doi:10.1038/nature23912 · Cited by 453 · see citing works
Distance-based protein folding powered by deep learning
Xu, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1821309116 · Cited by 401 · see citing works
Wu, S., & Zhang, Y. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21945 · Cited by 375 · see citing works
Self-citationRosettaRemodel: A Generalized Framework for Flexible Backbone Protein Design
Huang, P., Ban, Y. A., Richter, F. M., André, I., Vernon, R. M., Schief, W. R., & Baker, D. A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024109 · Cited by 373 · see citing works
De novo design of a fluorescence-activating β-barrel
Dou, J., Vorobieva, A. A., Sheffler, W., Doyle, L. A., Park, H., Bick, M. J., Mao, B., Foight, G. W., Lee, M. Y., Gagnon, L. A., et al. (2018) · Nature · doi:10.1038/s41586-018-0509-0 · Cited by 351 · see citing works
Automated structure prediction of weakly homologous proteins on a genomic scale
Zhang, Y., & Skolnick, J. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0305695101 · Cited by 344 · see citing works
Self-citationDe novo design of protein homo-oligomers with modular hydrogen-bond network–mediated specificity
Boyken, S. E., Chen, Z., Groves, B., Langan, R. A., Oberdorfer, G., Ford, A., Gilmore, J. M., Xu, C., DiMaio, F., Pereira, J. H., et al. (2016) · Science · doi:10.1126/science.aad8865 · Cited by 298 · see citing works
High thermodynamic stability of parametrically designed helical bundles
Huang, P.-S., Oberdorfer, G., Xu, C., Pei, X. Y., Nannenga, B. L., Rogers, J. M., DiMaio, F., Gonen, T., Luisi, B., & Baker, D. (2014) · Science · doi:10.1126/science.1257481 · Cited by 277 · see citing works
De novo design of a four-fold symmetric TIM-barrel protein with atomic-level accuracy
Huang, P., Feldmeier, K., Parmeggiani, F., Fernández‐Velasco, D. A., Höcker, B., & Baker, D. A. (2015) · Nature Chemical Biology · doi:10.1038/nchembio.1966 · Cited by 253 · see citing works
Zhang, J., & Zhang, Y. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015386 · Cited by 237 · see citing works
Self-citationDe novo design of protein logic gates
Chen, Z., Kibler, R. D., Hunt, A. C., Büsch, F., Pearl, J. R., Jia, M., VanAernum, Z. L., Wicky, B. I. M., Dods, G., Liao, H., et al. (2020) · Science · doi:10.1126/science.aay2790 · Cited by 209 · see citing works
Deep‐learning contact‐map guided protein structure prediction in CASP13
Zheng, W., Li, Y., Zhang, C., Pearce, R., Mortuza, S. M., & Zhang, Y. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25792 · Cited by 208 · see citing works
Self-citationToward optimal fragment generations for ab initio protein structure assembly
Xu, D. H., & Zhang, Y. (2012) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24179 · Cited by 201 · see citing works
Self-citationDe novo protein design enables the precise induction of RSV-neutralizing antibodies
Sesterhenn, F., Yang, C., Bonet, J., Cramer, J. T., Wen, X., Wang, Y., Chiang, C.-I., Abriata, L. A., Kucharska, I., Castoro, G., et al. (2020) · Science · doi:10.1126/science.aay5051 · Cited by 191 · see citing works
Mapping pathways of allosteric communication in GroEL by analysis of correlated mutations
Kass, I., & Horovitz, A. (2002) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10180 · Cited by 186 · see citing works
Zhang, C., Zheng, W. H., Mortuza, S. M., Li, Y., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz863 · Cited by 182 · see citing works
Self-citationBowie, J. U., & Eisenberg, D. S. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.10.4436 · Cited by 174 · see citing works
Programmable design of orthogonal protein heterodimers
Chen, Z., Boyken, S. E., Jia, M., Büsch, F., Flores‐Solis, D., Bick, M. J., Lu, P., VanAernum, Z. L., Sahasrabuddhe, A., Langan, R. A., et al. (2018) · Nature · doi:10.1038/s41586-018-0802-y · Cited by 171 · see citing works
Protein sequence design with a learned potential
Anand, N., Eguchi, R., Mathews, I. I., Perez, C. P., Derry, A., Altman, R. B., & Huang, P.-S. (2022) · Nature Communications · doi:10.1038/s41467-022-28313-9 · Cited by 171 · see citing works
Li, Y., Hu, J., Zhang, C., Yu, D.-J., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz291 · Cited by 158 · see citing works
Self-citationA defined structural unit enables de novo design of small-molecule–binding proteins
Polizzi, N. F., & DeGrado, W. F. (2020) · Science · doi:10.1126/science.abb8330 · Cited by 147 · see citing works
Protein sequence design by conformational landscape optimization
Norn, C., Wicky, B. I. M., Juergens, D., Liu, S., Kim, D., Tischer, D., Koepnick, B., Anishchenko, I., Players, F., Baker, D., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2017228118 · Cited by 144 · see citing works
Baldassi, C., Zamparo, M., Feinauer, C., Procaccini, A., Zecchina, R., Weigt, M., & Pagnani, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0092721 · Cited by 140 · see citing works
Zheng, W. H., Zhang, C., Wuyun, Q., Pearce, R., Li, Y., & Zhang, Y. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz384 · Cited by 138 · see citing works
Self-citationDesign of metalloproteins and novel protein folds using variational autoencoders
Greener, J. G., Moffat, L., & Jones, D. T. (2018) · Scientific Reports · doi:10.1038/s41598-018-34533-1 · Cited by 128 · see citing works
EvoEF2: accurate and fast energy function for computational protein design
Huang, X., Pearce, R., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz740 · Cited by 119 · see citing works
Self-citationHuang, X., Pearce, R., & Zhang, Y. (2020) · Aging · doi:10.18632/aging.103416 · Cited by 113 · see citing works
Self-citationProtein structure prediction using Rosetta in CASP12
Ovchinnikov, S., Park, H., Kim, D. E., DiMaio, F., & Baker, D. A. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25390 · Cited by 105 · see citing works
Zhang, C., Mortuza, S. M., He, B., Wang, Y., & Zhang, Y. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25414 · Cited by 93 · see citing works
Self-citationSPIN2: Predicting sequence profiles from protein structures using deep neural networks
O'Connell, J., Li, Z., Hanson, J., Heffernan, R., Lyons, J., Paliwal, K. K., Dehzangi, A., Yang, Y., & Zhou, Y. (2018) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25489 · Cited by 92 · see citing works
(2021) · doi:10.1371/journal.pcbi.1008865 · Cited by 85 · see citing works
Self-citationPearce, R., Huang, X., Setiawan, D., & Zhang, Y. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.02.028 · Cited by 83 · see citing works
Self-citationLi, Z., Yang, Y., Faraggi, E., Zhan, J., & Zhou, Y. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24620 · Cited by 74 · see citing works
Wei, K. Y., Moschidi, D., Bick, M. J., Nerli, S., McShan, A. C., Carter, L. P., Huang, P.-S., Fletcher, D. A., Sgourakis, N. G., Boyken, S. E., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1914808117 · Cited by 57 · see citing works
Protein Sequence Design with a Learned PotentialPreprint
Anand, N., Eguchi, R. R., Mathews, I. I., Perez, C. P., Derry, A., Altman, R. B., & Huang, P. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.06.895466 · Cited by 40 · see citing works
Design of proteins presenting discontinuous functional sites using deep learningPreprint
Tischer, D., Lisanza, S. L., Wang, J., Dong, R., Anishchenko, I. V., Milles, L. F., Ovchinnikov, S., & Baker, D. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.29.402743 · Cited by 36 · see citing works
Sesterhenn, F., Galloux, M., Vollers, S. S., Csepregi, L., Yang, C., Descamps, D., Bonet, J., Friedensohn, S., Gaínza, P., Corthésy, P., et al. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000164 · Cited by 35 · see citing works
Protein sequence design by explicit energy landscape optimizationPreprint
Norn, C., Wicky, B. I. M., Juergens, D., Liu, S., Kim, D. E., Koepnick, B., Anishchenko, I. V., Players, F., Baker, D. A., & Ovchinnikov, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.23.218917 · Cited by 33 · see citing works
Improving accuracy of protein contact prediction using balanced network deconvolution
Sun, H.-P., Huang, Y., Wang, X.-F., Zhang, Y., & Shen, H.-B. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24744 · Cited by 25 · see citing works
Self-citationOng, E., Huang, X., Pearce, R., Zhang, Y., & He, Y. O. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.12.039 · Cited by 23 · see citing works
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Flagged first, then by citations
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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) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Zhou, X., Zheng, W. H., Li, Y., Pearce, R., Zhang, C., Bell, E. W., Zhang, G., & Zhang, Y. (2022) · Nature Protocols · doi:10.1038/s41596-022-00728-0 · Cited by 524 · see citing works
Self-citationImpact of protein conformational diversity on AlphaFold predictions
Saldaño, T., Escobedo, N., Marchetti, J., Zea, D. J., Mac Donagh, J., Velez Rueda, A. J., Gonik, E., García Melani, A., Novomisky Nechcoff, J., Salas, M. N., et al. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac202 · Cited by 196 · see citing works
AlphaFold, Artificial Intelligence (AI), and Allostery
Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190 · see citing works
Adhav, V. A., & Saikrishnan, K. (2023) · ACS Omega · doi:10.1021/acsomega.3c00205 · Cited by 183 · see citing works
Game changers in science and technology - now and beyond
Betz, U. A. K., Arora, L., Assal, R. A., de Azevedo, H. F. Z., Baldwin, J., Becker, M. S., Bostock, S., Cheng, V. W. T., Egle, T., Ferrari, N., et al. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122588 · Cited by 146 · see citing works
Toward the solution of the protein structure prediction problem
Pearce, R., & Zhang, Y. (2021) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2021.100870 · Cited by 120 · see citing works
Self-citationDeep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER
Zheng, W. H., Wuyun, Q., Li, Y., Liu, Q., Zhou, X., Peng, C., Zhu, Y., Freddolino, L., & Zhang, Y. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02654-4 · Cited by 115 · see citing works
Self-citationGe, S., Sun, S., Xu, H., Cheng, Q. S., & Ren, Z. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf136 · Cited by 89 · see citing works
Deep language models for interpretative and predictive materials science
Hu, Y., & Buehler, M. J. (2023) · APL Machine Learning · doi:10.1063/5.0134317 · Cited by 80 · see citing works
Pearce, R., Omenn, G. S., & Zhang, Y. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.15.491755 · Cited by 71 · see citing works
Self-citationCritical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes
Wodak, S. J., Vajda, S., Lensink, M. F., Kozakov, D., & Bates, P. A. (2023) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-102622-084607 · Cited by 71 · see citing works
Computer-Aided Drug Discovery for Undruggable Targets
Sun, Q., Wang, H., Xie, J., Wang, L., Mu, J., Li, J., Ren, Y., & Lai, L. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00969 · Cited by 69 · see citing works
Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods
Mostofian, B., Martin, H., Razavi, A. M., Patel, S., Allen, B. K., Sherman, W., & Izaguirre, J. A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00603 · Cited by 68 · see citing works
Fungal biotechnology: From yesterday to tomorrow
Roth, M. G., Westrick, N. M., & Baldwin, T. T. (2023) · Frontiers in Fungal Biology · doi:10.3389/ffunb.2023.1135263 · Cited by 48 · see citing works
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Lupas, A. N., Pereira, J., Alva, V., Merino, F., Coles, M. P., & Hartmann, M. D. (2021) · Biochemical Journal · doi:10.1042/bcj20200963 · Cited by 64 · see citing works
AlphaFold 3: an unprecedent opportunity for fundamental research and drug development
Fang, Z., Ran, H., Zhang, Y., Chen, C., Ping, L., Zhang, X., & Wu, M. (2025) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbaf015 · Cited by 58 · see citing works
Self-citationAppadurai, R., Koneru, J. K., Bonomi, M., Robustelli, P., & Srivastava, A. K. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00224 · Cited by 54 · see citing works
Integrative Structural Biology in the Era of Accurate Structure Prediction
Masrati, G., Landau, M., Ben-Tal, N., Lupas, A., Kosloff, M., & Kosinski, J. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167127 · Cited by 53 · see citing works
Misiura, M., Shroff, R., Thyer, R., & Kolomeisky, A. B. (2022) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26311 · Cited by 51 · see citing works
Luu, R. K., Wysokowski, M., & Buehler, M. J. (2023) · Applied Physics Letters · doi:10.1063/5.0155890 · Cited by 51 · see citing works
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Gillani, M., & Pollastri, G. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.04.032 · Cited by 52 · see citing works
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Buscajoni, L., Martinetz, M. C., Berkemeyer, M., & Brocard, C. (2022) · Biotechnology Advances · doi:10.1016/j.biotechadv.2022.108050 · Cited by 48 · see citing works
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Defresne, M., Barbe, S., & Schiex, T. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222111741 · Cited by 44 · see citing works
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Pearce, R., Li, Y., Omenn, G. S., & Zhang, Y. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010539 · Cited by 44 · see citing works
Self-citationRecent Progress of Protein Tertiary Structure Prediction
Wuyun, Q., Chen, Y., Shen, Y., Cao, Y., Hu, G., Cui, W., Gao, J., & Zheng, W. H. (2024) · Molecules · doi:10.3390/molecules29040832 · Cited by 42 · see citing works
Polonsky, K., Pupko, T., & Freund, N. T. (2023) · The Journal of Immunology · doi:10.4049/jimmunol.2300150 · Cited by 41 · see citing works
Protein structure prediction in the deep learning era
Peng, Z., Wang, W., Han, R., Zhang, F., & Yang, J. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102495 · Cited by 37 · see citing works
Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design
Moffat, L., Greener, J. G., & Jones, D. T. (2021) · doi:10.1101/2021.08.24.457549 · Cited by 33 · see citing works
Zhou, B., Zheng, L., Wu, B., Yi, K., Zhong, B., Tan, Y., Liu, Q., Lió, P., & Hong, L. (2024) · Cell Discovery · doi:10.1038/s41421-024-00728-2 · Cited by 35 · see citing works
Artificial intelligence driven innovations in biochemistry: A review of emerging research frontiers
Junaid, M. A. L. (2025) · Biomolecules and Biomedicine · doi:10.17305/bb.2024.11537 · Cited by 32 · see citing works
Dou, Z., Sun, Y., Jiang, X., Wu, X., Li, Y., Gong, B., & Wang, L. (2023) · Acta Biochimica et Biophysica Sinica · doi:10.3724/abbs.2023033 · Cited by 30 · see citing works
AI-assisted food enzymes design and engineering: a critical review
Wang, X., Yang, P., Zhao, B., & Liu, S. (2022) · Systems Microbiology and Biomanufacturing · doi:10.1007/s43393-022-00138-z · Cited by 28 · see citing works
Han, J., Ullah, M., Andoh, V., Khan, M. N., Feng, Y., Guo, Z., & Chen, H. (2024) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.4c06856 · Cited by 27 · see citing works
Du, H., Jiang, D., Gao, J., Zhang, X., Jiang, L., Zeng, Y., Wu, Z., Shen, C., Xu, L., Cao, D., et al. (2022) · Research · doi:10.34133/2022/9873564 · Cited by 25 · see citing works
Evolution of a chordate-specific mechanism for myoblast fusion
Zhang, H., Shang, R., Kim, K., Zheng, W. H., Johnson, C. J., Sun, L., Niu, X., Liu, L., Zhou, J., Liu, L., et al. (2022) · Science Advances · doi:10.1126/sciadv.add2696 · Cited by 22 · see citing works
Self-citationBeyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction
Genc, A. G., & McGuffin, L. J. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4196-5_7 · Cited by 22 · see citing works
Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing
Shi, Z., Liu, P., Liao, X., Mao, Z., Zhang, J., Wang, Q., Sun, J., Ma, H., & Ma, Y. (2022) · BioDesign Research · doi:10.34133/2022/9898461 · Cited by 22 · see citing works
Learned features of antibody-antigen binding affinity
Miller, N. L., Clark, T., Raman, R., & Sasisekharan, R. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1112738 · Cited by 22 · see citing works
Mani, H., Chang, C., Hsu, H., Yang, C.-H., Yen, J.-H., & Liou, J.-W. (2023) · Bioengineering · doi:10.3390/bioengineering10091004 · Cited by 20 · see citing works
Multimeric protein interaction and complex prediction: Structure, dynamics and function
Lu, D., Yu, S., Huang, Y., & Gong, X. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.05.009 · Cited by 20 · see citing works
Deep metric learning for accurate protein secondary structure prediction
Yang, W. P., Liu, Y., & Xiao, C. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108356 · Cited by 21 · see citing works
Banerjee, A., Saha, S., Tvedt, N. C., Yang, L., & Bahar, İ. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102517 · Cited by 19 · see citing works
De novo protein fold design through sequence-independent fragment assembly simulations
Pearce, R., Huang, X., Omenn, G. S., & Zhang, Y. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2208275120 · Cited by 16 · see citing works
Self-citationImpact of protein conformational diversity on AlphaFold predictions
Saldaño, T., Escobedo, N., Marchetti, J., Zea, D. J., Mac Donagh, J., Velez Rueda, A. J., Gonik, E., Melani, A. G., Novomisky Nechcoff, J., Salas, M. N., et al. (2021) · doi:10.1101/2021.10.27.466189 · Cited by 196 · see citing works
Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading
Bhattacharya, S., Roche, R., Shuvo, M. H., & Bhattacharya, D. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.643752 · Cited by 16 · see citing works
Artificial intelligence for template-free protein structure prediction: a comprehensive review
Mufassirin, M. M. M., Newton, M. A. H., & Sattar, A. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10350-x · Cited by 19 · see citing works
Buehler, M. J. (2023) · Patterns · doi:10.1016/j.patter.2023.100692 · Cited by 17 · see citing works
Using machine‐learning‐driven approaches to boost hot‐spot's knowledge
Rosário‐Ferreira, N., Bonvin, A. M. J. J., & Moreira, I. S. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1602 · Cited by 14 · see citing works
Application of computational methods for class A GPCR Ligand discovery
Szwabowski, G. L., Baker, D. L., & Parrill, A. L. (2023) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2023.108434 · Cited by 13 · see citing works
Liu, Z., Zhang, R., Zhang, W., & Xu, Y. (2022) · Critical Reviews in Biotechnology · doi:10.1080/07388551.2022.2054770 · Cited by 13 · see citing works
Zhang, M., Deng, Y., Zhou, Q., Gao, J., Zhang, D., & Pan, X. (2024) · Environmental Science Processes & Impacts · doi:10.1039/d4em00662c · Cited by 12 · see citing works
The physics-AI dialogue in drug design
Vargas-Rosales, P. A., & Caflisch, A. (2025) · RSC Medicinal Chemistry · doi:10.1039/d4md00869c · Cited by 13 · see citing works
Deep learning in nuclear medicine: from imaging to therapy
Zhang, M.-X., Liu, P., Zhang, M.-D., Su, P., Shang, H.-S., Zhu, J., Wang, D., Ji, X., & Liao, Q.-M. (2025) · Annals of Nuclear Medicine · doi:10.1007/s12149-025-02031-w · Cited by 11 · see citing works
The Role of Artificial Intelligence in Theranostics
Currie, G. M., & ROHREN, E. M. (2025) · Journal of Nuclear Medicine Technology · doi:10.2967/jnmt.125.270251 · Cited by 11 · see citing works
Computational protein design with data‐driven approaches: Recent developments and perspectives
Liu, H., & Chen, Q. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1646 · Cited by 10 · see citing works
A primer on predictive techniques for food and bioresources transformation processes
Sicard, J., Barbe, S., Boutrou, R., Bouvier, L., Delaplace, G., Lashermes, G., Théron, L., Vitrac, O., & Tonda, A. P. (2023) · Journal of Food Process Engineering · doi:10.1111/jfpe.14325 · Cited by 10 · see citing works
George, D. M., Ramadoss, R., Mackey, H. R., & Vincent, A. S. (2022) · Biotechnology Reports · doi:10.1016/j.btre.2022.e00775 · Cited by 10 · see citing works
Wang, H., Wu, Y., & Wang, Y. (2022) · Foods · doi:10.3390/foods11131954 · Cited by 10 · see citing works
A panel of multivalent nanobodies broadly neutralizing Omicron subvariants and recombinant
Zheng, L., Wang, H., Liu, X., Xu, C., Tian, M., Shi, G., Bai, C., Li, Z., Wang, J., & Liu, S. (2024) · Journal of Medical Virology · doi:10.1002/jmv.29528 · Cited by 9 · see citing works
Toward the assessment of predicted inter-residue distance
Du, Z., Peng, Z., & Yang, J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab781 · Cited by 9 · see citing works
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Siddiqui, S. S., Loganathan, S., Elangovan, V. R., & Ali, M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-89925-3.00020-4 · Cited by 10 · see citing works
Go, E. B., Lee, J. H., Cho, J. H., Kwon, N. H., Choi, J., & Kwon, I. (2024) · Journal of Biological Engineering · doi:10.1186/s13036-024-00418-3 · Cited by 8 · see citing works
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Currie, G. M., & Hawk, K. E. (2025) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2025.05.005 · Cited by 8 · see citing works
Programmable design of synthetic plant immune receptors for pathogen protein recognition
Zhu, H., Jiang, D., Zhang, Q., Zhang, K., Zhao, K. T., Qiu, J., & Gao, C. (2026) · Science · doi:10.1126/science.aee1792 · Cited by 8 · see citing works
Appadurai, R., Koneru, J. K., Bonomi, M., Robustelli, P., & Srivastava, A. K. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.11.516231 · Cited by 8 · see citing works
Kumawat, P., Agarwal, L. K., & Sharma, K. R. (2024) · Current Microbiology · doi:10.1007/s00284-024-03671-3 · Cited by 7 · see citing works
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Naderiyan, Z., & Shoari, A. (2025) · International Journal of Translational Medicine · doi:10.3390/ijtm5030028 · Cited by 7 · see citing works
Kwon, N. H., Lee, J. H., & Kwon, I. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14091769 · Cited by 7 · see citing works
Sharifi, F., Sharifi, I., Babaei, Z., Alahdin, S., & Afgar, A. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100190 · Cited by 7 · see citing works
Inter-domain distance prediction based on deep learning for domain assembly
Ge, F., Peng, C., Cui, X., Xia, Y., & Zhang, G. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad100 · Cited by 7 · see citing works
Dynamic energy conversion in protein catalysis: From brownian motion to enzymatic function
Niazi, S. K. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.07.050 · Cited by 6 · see citing works
Cui, H., Wang, Y., Fu, Y., Yu, X., Li, W., Lin, F., & Han, W. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01372 · Cited by 6 · see citing works
MoEPH: an adaptive fusion-based LLM for predicting phage-host interactions in health informatics
Chen, Q., Zhao, Z., Li, M., Song, W., Xiao, M., & Fang, M. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1634705 · Cited by 6 · see citing works
Nagar, N., Tubiana, J., Loewenthal, G., Wolfson, H. J., Ben‐Tal, N., & Pupko, T. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168155 · Cited by 7 · see citing works
Assessing Fairness of AlphaFold2 Prediction of Protein 3D Structures
Abbas, U. L., Chen, J., & Shao, Q. (2023) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3584371.3612943 · Cited by 6 · see citing works
Molecular investigation of TSHR gene in Bangladeshi congenital hypothyroid patients
Begum, M. N., Mahtarin, R., Islam, M. T., Ahmed, S., Konika, T. K., Mannoor, K., Akhteruzzaman, S., & Qadri, F. (2023) · PLOS ONE · doi:10.1371/journal.pone.0282553 · Cited by 6 · see citing works
Saharkhiz, S., Mostafavi, M., Birashk, A., Karimian, S., Khalilollah, S., Jaferian, S., Yazdani, Y., Alipourfard, I., Huh, Y. S., Farani, M. R., et al. (2024) · Topics in Current Chemistry · doi:10.1007/s41061-024-00469-6 · Cited by 6 · see citing works
Zhang, M., Wan, Y., Wang, J., Li, S., & Li, H. (2025) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2025.101437 · Cited by 5 · see citing works
Applications of Bioinformatics on Genomics and Proteomics Levels Highlighted in Brucella Pathogen
Elrashedy, A., Nayel, M. A., Salama, A. A., & Hasan, M. E. (2024) · Journal of Current Veterinary Research · doi:10.21608/jcvr.2024.352697 · Cited by 5 · see citing works
Talluri, S. (2022) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2022.01.003 · Cited by 5 · see citing works
Modeling Protein Structure Using Geometric Vector Field NetworksPreprint
Mao, W., Zhu, M., Chen, H., & Shen, C. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.07.539736 · Cited by 5 · see citing works
Yang, P., & Ning, K. (2022) · iMeta · doi:10.1002/imt2.9 · Cited by 4 · see citing works
Protein Dihedral Angle Prediction: The State of the Art
Gogoi, C. R., Rahman, A., Saikia, B., & Baruah, A. (2023) · ChemistrySelect · doi:10.1002/slct.202203427 · Cited by 4 · see citing works
An interactive visualization tool for educational outreach in protein contact map overlap analysis
Baker, K., Hughes, N., & Bhattacharya, S. (2024) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2024.1358550 · Cited by 5 · see citing works
Wang, R., Zhang, Z., Wang, Q., Gu, P., Yang, Y., Wang, B., & Teng, Y. (2026) · International Journal of Nanomedicine · doi:10.2147/ijn.s580526 · Cited by 5 · see citing works
Aloliqi, A. A. (2024) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2024.126373 · Cited by 3 · see citing works
Barhoon, M., & Mahdiuni, H. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01029 · Cited by 3 · see citing works
Homologues not needed: Structure prediction from a protein language model
Ben‐Tal, N., & Kolodny, R. (2022) · Structure · doi:10.1016/j.str.2022.07.002 · Cited by 3 · see citing works
Chandra, A., Goyal, N., Behera, A., Samanta, J., & Sharma, T. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-16013-4.00016-6 · Cited by 3 · see citing works
Zhong, J., Zou, Z., Qiu, J., & Wang, S. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf156 · Cited by 2 · see citing works
DivPro: diverse protein sequence design with direct structure recovery guidance
Zhou, X., Shen, G., Chen, Y.-C., Chen, G., & Heng, P. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf258 · Cited by 2 · see citing works
Nallasamy, V., & Seshiah, M. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07868-0 · Cited by 2 · see citing works
Drogalin, A., Monteiro, L. S., Alves, M. J., & Castro, T. G. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2209184 · Cited by 2 · see citing works
Zheng, W. H., Wuyun, Q., & Zhang, Y. (2024) · Clinical and Translational Medicine · doi:10.1002/ctm2.1689 · Cited by 2 · see citing works
Self-citationBangash, S. H., Ibrahim, M., Ali, A., Wei, C., Hussain, A., Riaz, M., Rehman, M. F. U., Ahmed, F., Al‐Salahi, R. A., & Tang, W. (2025) · RSC Advances · doi:10.1039/d5ra00505a · Cited by 2 · see citing works
Quintana, F. M., Kong, Z., He, L., & Chen, B. Y. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.22.456843 · Cited by 2 · see citing works
ALPHAFOLD: DERİN ÖĞRENME VE SİNİR AĞLARI YOLUYLA PROTEİN KATLAMASINDA DEVRİM YARATMAK
Tekin, B., & Gurbanov, R. (2023) · İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi · doi:10.55071/ticaretfbd.1323165 · Cited by 1 · see citing works
- Authors
- Pearce, R., & Zhang, Y.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Zhang, Yang
- Venue
- Current Opinion in Structural Biology
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 68
- Pages
- 194-207
- ISSN
- 0959-440X (ISSN-L)
- PMC
- PMC8222070
- OpenAlex
- W3132448805
- Cited by
- 127
- References
- 66
- Citations by year
- 2026: 8 · 2025: 29 · 2024: 21 · 2023: 29 · 2022: 29 · 2021: 11
- FWCI
- 2.36
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 268 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Current Opinion in Structural Biology (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (DBI2030790, IIS1901191, MTM2025426)
- National Institute of General Medical Sciences (GM136422, S10OD026825)
- National Institute of Allergy and Infectious Diseases (AI134678)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein structure prediction, protein design, deep learning, deep neural network, protein folding, single-domain proteins
- MeSH terms
- Deep Learning, Proteins, Neural Networks, Computer, Protein Folding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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