I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction

Yang Li, Xiaogen Zhou, Wei Zheng, Robin Pearce, Chengxin Zhang, Eric W. Bell, Guijun Zhang, Yang Zhang

Published: 2022-08-04 DOI: 10.1038/s41596-022-00728-0

Multi-domain protein structure prediction

Deep-learning

I-TASSER-MTD

Automated pipeline

Function annotation

AI 解读

Supplementary information

Supplementary Information

Supplementary Figs. 1–19, Tables 1 and 2, Notes 1–6, Equations 1–25 and References.

Author Correction: Full-length sequencing of circular DNA viruses and extrachromosomal circular DNA using CIDER-Seq

Generation of heart-forming organoids from human pluripotent stem cells

Design and fabrication of wearable electronic textiles using twisted fiber-based threads

Activation of mechanoluminescent nanotransducers by focused ultrasound enables light delivery to deep-seated tissue in vivo

Chromatin accessibility profiling by ATAC-seq

Deoxyfluorination of phenols for chemoselective18F-labeling of peptides

Author Correction: cfSNV: a software tool for the sensitive detection of somatic mutations from cell-free DNA

Human primordial germ cell-like cells specified from resetting precursors develop in human hindgut organoids

An inexpensive semi-automated sample processing pipeline for cell-free RNA extraction

Application of CHyMErA Cas9-Cas12a combinatorial genome-editing platform for genetic interaction mapping and gene fragment deletion screening

查看全部

Sections

Figures

References

Supplementary information

Sali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).
Simons, K. T., Kooperberg, C., Huang, E. & Baker, D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J. Mol. Biol. 268, 209–225 (1997).
Xu, D. & Zhang, Y. Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins 80, 1715–1735 (2012).
Yang, J. et al. The I-TASSER Suite: protein structure and function prediction. Nat. Methods 12, 7–8 (2015).
Zhang, Y. & Skolnick, J. Scoring function for automated assessment of protein structure template quality. Proteins 57, 702–710 (2004).
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).
Marks, D. S. et al. Protein 3D structure computed from evolutionary sequence variation. PLoS ONE 6, e28766 (2011).
Mortuza, S. et al. Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions. Nat. Commun. 12, 5011 (2021).
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).
Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020).
Li, Y., Hu, J., Zhang, C., Yu, D.-J. & Zhang, Y. ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks. Bioinformatics 35, 4647–4655 (2019).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. & Moult, J. Critical assessment of methods of protein structure prediction (CASP)—Round XIV. Proteins 89, 1607–1617 (2021).
Chothia, C., Gough, J., Vogel, C. & Teichmann, S. A. Evolution of the protein repertoire. Science 300, 1701–1703 (2003).
Apic, G., Huber, W. & Teichmann, S. A. Multi-domain protein families and domain pairs: comparison with known structures and a random model of domain recombination. J. Struct. Funct. Genomics 4, 67–78 (2003).
Han, J.-H., Batey, S., Nickson, A. A., Teichmann, S. A. & Clarke, J. J. N. R. M. C. B. The folding and evolution of multidomain proteins. Nat. Rev. Mol. Cell Biol. 8, 319 (2007).
Zhou, X. G., Hu, J., Zhang, C. X., Zhang, G. J. & Zhang, Y. Assembling multidomain protein structures through analogous global structural alignments. Proc. Natl Acad. Sci. USA 116, 15930–15938 (2019).
Xu, D., Jaroszewski, L., Li, Z. & Godzik, A. AIDA: ab initio domain assembly for automated multi-domain protein structure prediction and domain–domain interaction prediction. Bioinformatics 31, 2098–2105 (2015).
Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).
Zhang, Y. & Skolnick, J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 33, 2302–2309 (2005).
Xue, Z., Xu, D., Wang, Y. & Zhang, Y. ThreaDom: extracting protein domain boundary information from multiple threading alignments. Bioinformatics 29, i247–i256 (2013).
Hong, S. H., Joo, K. & Lee, J. ConDo: protein domain boundary prediction using coevolutionary information. Bioinformatics 35, 2411–2417 (2019).
Zheng, W. et al. FUpred: detecting protein domains through deep-learning based contact map prediction. Bioinformatics 36, 3749–3757 (2020).
Wollacott, A. M., Zanghellini, A., Murphy, P. & Baker, D. Prediction of structures of multidomain proteins from structures of the individual domains. Protein Sci. 16, 165–175 (2007).
Zhang, C., Zheng, W., Freddolino, P. L. & Zhang, Y. MetaGO: predicting Gene Ontology of non-homologous proteins through low-resolution protein structure prediction and protein–protein network mapping. J. Mol. Biol. 430, 2256–2265 (2018).
Ramachandran, G. T. & Sasisekharan, V. in Advances in Protein Chemistry, 23 283–437 (Elsevier, 1968).
Yao, S. et al. NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information. Nucleic Acids Res. 49, W469–W475 (2021).
Piovesan, D. & Tosatto, S. C. INGA 2.0: improving protein function prediction for the dark proteome. Nucleic Acids Res. 47, W373–W378 (2019).
Koo, D. C. E. & Bonneau, R. Towards region-specific propagation of protein functions. Bioinformatics 35, 1737–1744 (2019).
Gligorijević, V. et al. Structure-based protein function prediction using graph convolutional networks. Nat. Commun. 12, 1–14 (2021).
Pearce, R. & Zhang, Y. Toward the solution of the protein structure prediction problem. J. Biol. Chem. 297, 100870 (2021).
Zheng, W. et al. Protein structure prediction using deep learning distance and hydrogen‐bonding restraints in CASP14. Proteins 89, 1734–1751 (2021).
Zheng, W. et al. Deep-learning contact-map guided protein structure prediction in CASP13. Proteins 87, 1149–1164 (2019).
Battey, J. N. et al. Automated server predictions in CASP7. Proteins 69 (Suppl.), 68–82 (2007).
Croll, T. I., Sammito, M. D., Kryshtafovych, A. & Read, R. J. Evaluation of template-based modeling in CASP13. Proteins 87, 1113–1127 (2019).
Zhang, Y. Template-based modeling and free modeling by I-TASSER in CASP7. Proteins 69 (Suppl.), 108–117 (2007).
Zhang, Y. I-TASSER: fully automated protein structure prediction in CASP8. Proteins 77 (Suppl.), 100–113 (2009).
Xu, D., Zhang, J., Roy, A. & Zhang, Y. Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement. Proteins 79 (Suppl.), 147–160 (2011).
Zhang, Y. Interplay of I-TASSER and QUARK for template-based and ab initio protein structure prediction in CASP10. Proteins 82 (Suppl.), 175–187 (2014).
Zhang, W. et al. Integration of QUARK and I-TASSER for ab initio protein structure prediction in CASP11. Proteins 84 (Suppl.), 76–86 (2016).
Zhang, C., Mortuza, S. M., He, B., Wang, Y. & Zhang, Y. Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12. Proteins 86 (Suppl.), 136–151 (2018).
Roy, A., Kucukural, A. & Zhang, Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat. Protoc. 5, 725–738 (2010).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition 770–778 (2016).
Zheng, W. et al. LOMETS2: improved meta-threading server for fold-recognition and structure-based function annotation for distant-homology proteins. Nucleic Acids Res. 47, W429–W436 (2019).
Wang, Y. et al. ThreaDomEx: a unified platform for predicting continuous and discontinuous protein domains by multiple-threading and segment assembly. Nucleic Acids Res. 45, W400–W407 (2017).
Li, Y. et al. Protein inter‐residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14. Proteins 89, 1911–1921 (2021).
Zhang, C., Freddolino, P. L. & Zhang, Y. COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information. Nucleic Acids Res. 45, W291–W299 (2017).
Sillitoe, I. et al. CATH: increased structural coverage of functional space. Nucleic Acids Res. 49, D266–D273 (2021).
Xu, Y., Xu, D. & Gabow, H. N. Protein domain decomposition using a graph-theoretic approach. Bioinformatics 16, 1091–1104 (2000).
Steinegger, M. & Söding, J. Clustering huge protein sequence sets in linear time. Nat. Commun. 9, 2542 (2018).
Steinegger, M., Mirdita, M. & Söding, J. Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold. Nat. Methods 16, 603–606 (2019).
Mitchell, A. L. et al. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 48, D570–D578 (2020).
Chen, I.-M. A. et al. The IMG/M data management and analysis system v. 6.0: new tools and advanced capabilities. Nucleic Acids Res. 49, D751–D763 (2021).
Mirdita, M. et al. Uniclust databases of clustered and deeply annotated protein sequences and alignments. Nucleic Acids Res. 45, D170–D176 (2017).
Suzek, B. E. et al. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926–932 (2015).
Zhang, C., Zheng, W., Mortuza, S., Li, Y. & Zhang, Y. DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins. Bioinformatics 36, 2105–2112 (2020).
Yan, R., Xu, D., Yang, J., Walker, S. & Zhang, Y. A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction. Sci. Rep. 3, 1–9 (2013).
Ekeberg, M., Hartonen, T. & Aurell, E. Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences. J. Comput. Phys. 276, 341–356 (2014).
Yang, J. et al. Improved protein structure prediction using predicted interresidue orientations. Proc. Natl Acad. Sci. USA 117, 1496–1503 (2020).
Thrun, S. in Advances in Neural Information Processing Systems 640–646 (Morgan Kaufmann Publishers, 1996).
Zheng, W., Zhang, C., Bell, E. W. & Zhang, Y. I-TASSER gateway: a protein structure and function prediction server powered by XSEDE. Future Gener. Comput. Syst. 99, 73–85 (2019).
Zhang, Y. I-TASSER server for protein 3D structure prediction. BMC Bioinforma. 9, 40 (2008).
Zheng, W. et al. Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell Rep. Methods 1, 100014 (2021).
Li, Y., Zhang, C., Bell, E. W., Yu, D. J. & Zhang, Y. Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13. Proteins 87, 1082–1091 (2019).
Li, Y. et al. Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks. PLOS Comput. Biol. 17, e1008865 (2021).
He, B., Mortuza, S., Wang, Y., Shen, H.-B. & Zhang, Y. NeBcon: protein contact map prediction using neural network training coupled with naïve Bayes classifiers. Bioinformatics 33, 2296–2306 (2017).
Zhang, Y. & Skolnick, J. SPICKER: a clustering approach to identify near‐native protein folds. J. Comput. Chem. 25, 865–871 (2004).
Huang, X., Pearce, R. & Zhang, Y. FASPR: an open-source tool for fast and accurate protein side-chain packing. Bioinformatics 36, 3758–3765 (2020).
Zhang, J., Liang, Y. & Zhang, Y. Atomic-level protein structure refinement using fragment-guided molecular dynamics conformation sampling. Structure 19, 1784–1795 (2011).
Roy, A., Yang, J. & Zhang, Y. COFACTOR: an accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Res. 40, W471–W477 (2012).
Yang, J., Roy, A. & Zhang, Y. BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions. Nucleic Acids Res. 41, D1096–D1103 (2012).
Zhou, X. G., Peng, C. X., Liu, J., Zhang, Y. & Zhang, G. J. Underestimation-assisted global-local cooperative differential evolution and the application to protein structure prediction. IEEE Trans. Evol. Comput. 24, 536–550 (2020).
Zhou, X. G. & Zhang, G. J. Abstract convex underestimation assisted multistage differential evolution. IEEE Trans. Cybern. 47, 2730–2741 (2017).
Zhou, X. G. & Zhang, G. J. Differential evolution with underestimation-based multimutation strategy. IEEE Trans. Cybern. 49, 1353–1364 (2018).
Yang, J., Wang, Y. & Zhang, Y. ResQ: an approach to unified estimation of B-factor and residue-specific error in protein structure prediction. J. Mol. Biol. 428, 693–701 (2016).
Glaeser, R. M. How good can cryo-EM become? Nat. Methods 13, 28–32 (2016).
Zhou, X. G. et al. Progressive assembly of multi-domain protein structures from cryo-EM density maps. Nat. Comput. Sci. 2, 265–275 (2022).
Mistry, J. et al. Pfam: the protein families database in 2021. Nucleic Acids Res. 49, D412–D419 (2021).
Lu, S. et al. CDD/SPARCLE: the conserved domain database in 2020. Nucleic Acids Res. 48, D265–D268 (2020).
Eickholt, J., Deng, X. & Cheng, J. DoBo: protein domain boundary prediction by integrating evolutionary signals and machine learning. BMC Bioinforma. 12, 1–8 (2011).
Tai, C. H., Lee, W. J., Vincent, J. J. & Lee, B. Evaluation of domain prediction in CASP6. Proteins 61, 183–192 (2005).
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).
Pearce, R. & Zhang, Y. Deep learning techniques have significantly impacted protein structure prediction and protein design. Curr. Opin. Struct. Biol. 68, 194–207 (2021).
Born, A., Henen, M. A. & Vögeli, B. Activity and affinity of Pin1 variants. Molecules 25, 36 (2020).
Born, A. et al. Reconstruction of coupled intra-and interdomain protein motion from nuclear and electron magnetic resonance. J. Am. Chem. Soc. 143, 16055–16067 (2021).
Chandonia, J.-M., Fox, N. K. & Brenner, S. E. SCOPe: manual curation and artifact removal in the structural classification of proteins—extended database. J. Mol. Biol. 429, 348–355 (2017).
Lam, S. D. et al. Gene3D: expanding the utility of domain assignments. Nucleic Acids Res. 44, D404–D409 (2015).
Yu, L. et al. Grammar of protein domain architectures. Proc. Natl Acad. Sci. USA 116, 3636–3645 (2019).
Chothia, C. & Lesk, A. M. The relation between the divergence of sequence and structure in proteins. EMBO J. 5, 823–826 (1986).
Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D. Biol. Crystallogr. 66, 486–501 (2010).
DiMaio, F. et al. Atomic-accuracy models from 4.5-Å cryo-electron microscopy data with density-guided iterative local refinement. Nat. Methods 12, 361–365 (2015).
Zhang, C. et al. Functions of essential genes and a scale-free protein interaction network revealed by structure-based function and interaction prediction for a minimal genome. J. Proteome Res. 20, 1178–1189 (2021).
Zhang, C., Wei, X., Omenn, G. S. & Zhang, Y. Structure and protein interaction-based gene ontology annotations reveal likely functions of uncharacterized proteins on human chromosome 17. J. Proteome Res. 17, 4186–4196 (2018).
Zhang, C., Lane, L., Omenn, G. S. & Zhang, Y. Blinded testing of function annotation for uPE1 proteins by I-TASSER/COFACTOR pipeline using the 2018–2019 additions to neXtProt and the CAFA3 challenge. J. Proteome Res. 18, 4154–4166 (2019).
Iyer, S., Subramanian, V. & Acharya, K. R. C9orf72, a protein associated with amyotrophic lateral sclerosis (ALS) is a guanine nucleotide exchange factor. PeerJ 6, e5815 (2018).
Skotnicová, P. et al. The cyanobacterial protoporphyrinogen oxidase HemJ is a new b-type heme protein functionally coupled with coproporphyrinogen III oxidase. J. Biol. Chem. 293, 12394–12404 (2018).
Hanson, R. M., Prilusky, J., Renjian, Z., Nakane, T. & Sussman, J. L. JSmol and the next‐generation web‐based representation of 3D molecular structure as applied to proteopedia. Isr. J. Chem. 53, 207–216 (2013).
Hiranuma, N. et al. Improved protein structure refinement guided by deep learning based accuracy estimation. Nat. Commun. 12, 1–11 (2021).
Guo, S.-S., Liu, J., Zhou, X. & Zhang, G. DeepUMQA: ultrafast shape recognition-based protein model quality assessment using deep learning. Bioinformatics 38, 1895–1903 (2022).
Xu, J. & Zhang, Y. How significant is a protein structure similarity with TM-score = 0.5? Bioinformatics 26, 889–895 (2010).
Ellson, J., Gansner, E.R., Koutsofios, E., North, S.C. & Woodhull, G. in Graph Drawing Software 127–148 (Springer, 2004).
Towns, J. et al. XSEDE: acceleratingscientific discovery. Comput. Sci. Eng. 16, 62–74 (2014).
Xu, J. Distance-based protein folding powered by deep learning. Proc. Natl Acad. Sci. USA 116, 16856–16865 (2019).
Källberg, M. et al. Template-based protein structure modeling using the RaptorX web server. Nat. Protoc. 7, 1511–1522 (2012).
Du, Z. et al. The trRosetta server for fast and accurate protein structure prediction. Nat. Protoc. 16, 5634–5651 (2021).
Lobley, A., Sadowski, M. I. & Jones, D. T. pGenTHREADER and pDomTHREADER: new methods for improved protein fold recognition and superfamily discrimination. Bioinformatics 25, 1761–1767 (2009).
Zimmermann, L. et al. A completely reimplemented MPI bioinformatics toolkit with a new HHpred server at its core. J. Mol. Biol. 430, 2237–2243 (2018).
Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10, 845–858 (2015).

I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction

Supplementary information

推荐阅读