1.Thompson, M. C., Yeates, T. O. & Rodriguez, J. A. Advances in methods for atomic resolution macromolecular structure determination. F1000Res. 9, 667 (2020).
2.Bai, X.-C., McMullan, G. & Scheres, S. H. W. How cryo-EM is revolutionizing structural biology. Trends Biochem. Sci. 40, 49–57 (2015).
3.Jaskolski, M., Dauter, Z. & Wlodawer, A. A brief history of macromolecular crystallography, illustrated by a family tree and its Nobel fruits. FEBS J. 281, 3985–4009 (2014).
4.Wüthrich, K. The way to NMR structures of proteins. Nat. Struct. Biol. 8, 923–925 (2001).
5.wwPDB Consortium. Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res. 47, D520–D528 (2018).
6.Mitchell, A. L. et al. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 48, D570–D578 (2020).
7.Steinegger, M., Mirdita, M. & Söding, J. Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold. Nat. Methods 16, 603–606 (2019).
8.Dill, K. A., Ozkan, S. B., Shell, M. S. & Weikl, T. R. The protein folding problem. Annu. Rev. Biophys. 37, 289–316 (2008).
9.Anfinsen, C. B. Principles that govern the folding of protein chains. Science 181, 223–230 (1973).
10.Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020).
11.Wang, S., Sun, S., Li, Z., Zhang, R. & Xu, J. Accurate de novo prediction of protein contact map by ultra-deep learning model. PLOS Comput. Biol. 13, e1005324 (2017).
12.Zheng, W. et al. Deep-learning contact-map guided protein structure prediction in CASP13. Proteins 87, 1149–1164 (2019).
13.Abriata, L. A., Tamò, G. E. & Dal Peraro, M. A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments. Proteins 87, 1100–1112 (2019).
14.Pearce, R. & Zhang, Y. Deep learning techniques have significantly impacted protein structure prediction and protein design. Curr. Opin. Struct. Biol. 68, 194–207 (2021).
15.Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T. & Topf, M. Critical assessment of techniques for protein structure prediction, fourteenth round. CASP 14 Abstract Book https://www.predictioncenter.org/casp14/doc/CASP14_Abstracts.pdf (2020).
16.Brini, E., Simmerling, C. & Dill, K. Protein storytelling through physics. Science 370, eaaz3041 (2020).
17.Sippl, M. J. Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins. J. Mol. Biol. 213, 859–883 (1990).
18.Šali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).
19.Roy, A., Kucukural, A. & Zhang, Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat. Protocols 5, 725–738 (2010).
20.Altschuh, D., Lesk, A. M., Bloomer, A. C. & Klug, A. Correlation of co-ordinated amino acid substitutions with function in viruses related to tobacco mosaic virus. J. Mol. Biol. 193, 693–707 (1987).
21.Shindyalov, I. N., Kolchanov, N. A. & Sander, C. Can three-dimensional contacts in protein structures be predicted by analysis of correlated mutations? Protein Eng. 7, 349–358 (1994).
22.Weigt, M., White, R. A., Szurmant, H., Hoch, J. A. & Hwa, T. Identification of direct residue contacts in protein–protein interaction by message passing. Proc. Natl Acad. Sci. USA 106, 67–72 (2009).
23.Marks, D. S. et al. Protein 3D structure computed from evolutionary sequence variation. PLoS ONE 6, e28766 (2011).
24.Jones, D. T., Buchan, D. W. A., Cozzetto, D. & Pontil, M. PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics 28, 184–190 (2012).
25.Moult, J., Pedersen, J. T., Judson, R. & Fidelis, K. A large-scale experiment to assess protein structure prediction methods. Proteins 23, ii–iv (1995).
26.Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. & Moult, J. Critical assessment of methods of protein structure prediction (CASP)-round XIII. Proteins 87, 1011–1020 (2019).
27.Zhang, Y. & Skolnick, J. Scoring function for automated assessment of protein structure template quality. Proteins 57, 702–710 (2004).
28.Tu, Z. & Bai, X. Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1744–1757 (2010).
29.Carreira, J., Agrawal, P., Fragkiadaki, K. & Malik, J. Human pose estimation with iterative error feedback. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 4733–4742 (2016).
30.Mirabello, C. & Wallner, B. rawMSA: end-to-end deep learning using raw multiple sequence alignments. PLoS ONE 14, e0220182 (2019).
31.Huang, Z. et al. CCNet: criss-cross attention for semantic segmentation. In Proc. IEEE/CVF International Conference on Computer Vision 603–612 (2019).
32.Hornak, V. et al. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65, 712–725 (2006).
33.Zemla, A. LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Res. 31, 3370–3374 (2003).
34.Mariani, V., Biasini, M., Barbato, A. & Schwede, T. lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics 29, 2722–2728 (2013).
35.Xie, Q., Luong, M.-T., Hovy, E. & Le, Q. V. Self-training with noisy student improves imagenet classification. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 10687–10698 (2020).
36.Mirdita, M. et al. Uniclust databases of clustered and deeply annotated protein sequences and alignments. Nucleic Acids Res. 45, D170–D176 (2017).

1.Thompson, M. C., Yeates, T. O. & Rodriguez, J. A. Advances in methods for atomic resolution macromolecular structure determination. F1000Res. 9, 667 (2020).
2.Bai, X.-C., McMullan, G. & Scheres, S. H. W. How cryo-EM is revolutionizing structural biology. Trends Biochem. Sci. 40, 49–57 (2015).
3.Jaskolski, M., Dauter, Z. & Wlodawer, A. A brief history of macromolecular crystallography, illustrated by a family tree and its Nobel fruits. FEBS J. 281, 3985–4009 (2014).
4.Wüthrich, K. The way to NMR structures of proteins. Nat. Struct. Biol. 8, 923–925 (2001).
5.wwPDB Consortium. Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res. 47, D520–D528 (2018).
6.Mitchell, A. L. et al. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 48, D570–D578 (2020).
7.Steinegger, M., Mirdita, M. & Söding, J. Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold. Nat. Methods 16, 603–606 (2019).
8.Dill, K. A., Ozkan, S. B., Shell, M. S. & Weikl, T. R. The protein folding problem. Annu. Rev. Biophys. 37, 289–316 (2008).
9.Anfinsen, C. B. Principles that govern the folding of protein chains. Science 181, 223–230 (1973).
10.Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020).
11.Wang, S., Sun, S., Li, Z., Zhang, R. & Xu, J. Accurate de novo prediction of protein contact map by ultra-deep learning model. PLOS Comput. Biol. 13, e1005324 (2017).
12.Zheng, W. et al. Deep-learning contact-map guided protein structure prediction in CASP13. Proteins 87, 1149–1164 (2019).
13.Abriata, L. A., Tamò, G. E. & Dal Peraro, M. A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments. Proteins 87, 1100–1112 (2019).
14.Pearce, R. & Zhang, Y. Deep learning techniques have significantly impacted protein structure prediction and protein design. Curr. Opin. Struct. Biol. 68, 194–207 (2021).
15.Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T. & Topf, M. Critical assessment of techniques for protein structure prediction, fourteenth round. CASP 14 Abstract Book https://www.predictioncenter.org/casp14/doc/CASP14_Abstracts.pdf (2020).
16.Brini, E., Simmerling, C. & Dill, K. Protein storytelling through physics. Science 370, eaaz3041 (2020).
17.Sippl, M. J. Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins. J. Mol. Biol. 213, 859–883 (1990).
18.Šali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).
19.Roy, A., Kucukural, A. & Zhang, Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat. Protocols 5, 725–738 (2010).
20.Altschuh, D., Lesk, A. M., Bloomer, A. C. & Klug, A. Correlation of co-ordinated amino acid substitutions with function in viruses related to tobacco mosaic virus. J. Mol. Biol. 193, 693–707 (1987).
21.Shindyalov, I. N., Kolchanov, N. A. & Sander, C. Can three-dimensional contacts in protein structures be predicted by analysis of correlated mutations? Protein Eng. 7, 349–358 (1994).
22.Weigt, M., White, R. A., Szurmant, H., Hoch, J. A. & Hwa, T. Identification of direct residue contacts in protein–protein interaction by message passing. Proc. Natl Acad. Sci. USA 106, 67–72 (2009).
23.Marks, D. S. et al. Protein 3D structure computed from evolutionary sequence variation. PLoS ONE 6, e28766 (2011).
24.Jones, D. T., Buchan, D. W. A., Cozzetto, D. & Pontil, M. PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics 28, 184–190 (2012).
25.Moult, J., Pedersen, J. T., Judson, R. & Fidelis, K. A large-scale experiment to assess protein structure prediction methods. Proteins 23, ii–iv (1995).
26.Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. & Moult, J. Critical assessment of methods of protein structure prediction (CASP)-round XIII. Proteins 87, 1011–1020 (2019).
27.Zhang, Y. & Skolnick, J. Scoring function for automated assessment of protein structure template quality. Proteins 57, 702–710 (2004).
28.Tu, Z. & Bai, X. Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1744–1757 (2010).
29.Carreira, J., Agrawal, P., Fragkiadaki, K. & Malik, J. Human pose estimation with iterative error feedback. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 4733–4742 (2016).
30.Mirabello, C. & Wallner, B. rawMSA: end-to-end deep learning using raw multiple sequence alignments. PLoS ONE 14, e0220182 (2019).
31.Huang, Z. et al. CCNet: criss-cross attention for semantic segmentation. In Proc. IEEE/CVF International Conference on Computer Vision 603–612 (2019).
32.Hornak, V. et al. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65, 712–725 (2006).
33.Zemla, A. LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Res. 31, 3370–3374 (2003).
34.Mariani, V., Biasini, M., Barbato, A. & Schwede, T. lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics 29, 2722–2728 (2013).
35.Xie, Q., Luong, M.-T., Hovy, E. & Le, Q. V. Self-training with noisy student improves imagenet classification. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 10687–10698 (2020).
36.Mirdita, M. et al. Uniclust databases of clustered and deeply annotated protein sequences and alignments. Nucleic Acids Res. 45, D170–D176 (2017).

1.Thompson, M. C., Yeates, T. O. & Rodriguez, J. A. Advances in methods for atomic resolution macromolecular structure determination. F1000Res. 9, 667 (2020).
2.Bai, X.-C., McMullan, G. & Scheres, S. H. W. How cryo-EM is revolutionizing structural biology. Trends Biochem. Sci. 40, 49–57 (2015).
3.Jaskolski, M., Dauter, Z. & Wlodawer, A. A brief history of macromolecular crystallography, illustrated by a family tree and its Nobel fruits. FEBS J. 281, 3985–4009 (2014).
4.Wüthrich, K. The way to NMR structures of proteins. Nat. Struct. Biol. 8, 923–925 (2001).
5.wwPDB Consortium. Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res. 47, D520–D528 (2018).
6.Mitchell, A. L. et al. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 48, D570–D578 (2020).
7.Steinegger, M., Mirdita, M. & Söding, J. Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold. Nat. Methods 16, 603–606 (2019).
8.Dill, K. A., Ozkan, S. B., Shell, M. S. & Weikl, T. R. The protein folding problem. Annu. Rev. Biophys. 37, 289–316 (2008).
9.Anfinsen, C. B. Principles that govern the folding of protein chains. Science 181, 223–230 (1973).
10.Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020).
11.Wang, S., Sun, S., Li, Z., Zhang, R. & Xu, J. Accurate de novo prediction of protein contact map by ultra-deep learning model. PLOS Comput. Biol. 13, e1005324 (2017).
12.Zheng, W. et al. Deep-learning contact-map guided protein structure prediction in CASP13. Proteins 87, 1149–1164 (2019).
13.Abriata, L. A., Tamò, G. E. & Dal Peraro, M. A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments. Proteins 87, 1100–1112 (2019).
14.Pearce, R. & Zhang, Y. Deep learning techniques have significantly impacted protein structure prediction and protein design. Curr. Opin. Struct. Biol. 68, 194–207 (2021).
15.Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T. & Topf, M. Critical assessment of techniques for protein structure prediction, fourteenth round. CASP 14 Abstract Book https://www.predictioncenter.org/casp14/doc/CASP14_Abstracts.pdf (2020).
16.Brini, E., Simmerling, C. & Dill, K. Protein storytelling through physics. Science 370, eaaz3041 (2020).
17.Sippl, M. J. Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins. J. Mol. Biol. 213, 859–883 (1990).
18.Šali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).
19.Roy, A., Kucukural, A. & Zhang, Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat. Protocols 5, 725–738 (2010).
20.Altschuh, D., Lesk, A. M., Bloomer, A. C. & Klug, A. Correlation of co-ordinated amino acid substitutions with function in viruses related to tobacco mosaic virus. J. Mol. Biol. 193, 693–707 (1987).
21.Shindyalov, I. N., Kolchanov, N. A. & Sander, C. Can three-dimensional contacts in protein structures be predicted by analysis of correlated mutations? Protein Eng. 7, 349–358 (1994).
22.Weigt, M., White, R. A., Szurmant, H., Hoch, J. A. & Hwa, T. Identification of direct residue contacts in protein–protein interaction by message passing. Proc. Natl Acad. Sci. USA 106, 67–72 (2009).
23.Marks, D. S. et al. Protein 3D structure computed from evolutionary sequence variation. PLoS ONE 6, e28766 (2011).
24.Jones, D. T., Buchan, D. W. A., Cozzetto, D. & Pontil, M. PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics 28, 184–190 (2012).
25.Moult, J., Pedersen, J. T., Judson, R. & Fidelis, K. A large-scale experiment to assess protein structure prediction methods. Proteins 23, ii–iv (1995).
26.Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. & Moult, J. Critical assessment of methods of protein structure prediction (CASP)-round XIII. Proteins 87, 1011–1020 (2019).
27.Zhang, Y. & Skolnick, J. Scoring function for automated assessment of protein structure template quality. Proteins 57, 702–710 (2004).
28.Tu, Z. & Bai, X. Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1744–1757 (2010).
29.Carreira, J., Agrawal, P., Fragkiadaki, K. & Malik, J. Human pose estimation with iterative error feedback. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 4733–4742 (2016).
30.Mirabello, C. & Wallner, B. rawMSA: end-to-end deep learning using raw multiple sequence alignments. PLoS ONE 14, e0220182 (2019).
31.Huang, Z. et al. CCNet: criss-cross attention for semantic segmentation. In Proc. IEEE/CVF International Conference on Computer Vision 603–612 (2019).
32.Hornak, V. et al. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65, 712–725 (2006).
33.Zemla, A. LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Res. 31, 3370–3374 (2003).
34.Mariani, V., Biasini, M., Barbato, A. & Schwede, T. lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics 29, 2722–2728 (2013).
35.Xie, Q., Luong, M.-T., Hovy, E. & Le, Q. V. Self-training with noisy student improves imagenet classification. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 10687–10698 (2020).
36.Mirdita, M. et al. Uniclust databases of clustered and deeply annotated protein sequences and alignments. Nucleic Acids Res. 45, D170–D176 (2017).

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