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I-TASSER: a unified platform for automated protein structure and function prediction
Open accessAbstract
The iterative threading assembly refinement (I-TASSER) server is an integrated platform for automated protein structure and function prediction based on the sequence-to-structure-to-function paradigm. Starting from an amino acid sequence, I-TASSER first generates three-dimensional (3D) atomic models from multiple threading alignments and iterative structural assembly simulations. The function of the protein is then inferred by structurally matching the 3D models with other known proteins. The output from a typical server run contains full-length secondary and tertiary structure predictions, and functional annotations on ligand-binding sites, Enzyme Commission numbers and Gene Ontology terms. An estimate of accuracy of the predictions is provided based on the confidence score of the modeling. This protocol provides new insights and guidelines for designing of online server systems for the state-of-the-art protein structure and function predictions. The server is available at http://zhanglab.ccmb.med.umich.edu/I-TASSER.
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Cites
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
I-TASSER server for protein 3D structure prediction
Zhang, Y. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-40 · Cited by 5,124
Self-citationComparative Protein Structure Modeling of Genes and Genomes
Martí-Renom, M. A., Stuart, A. C., Fiser, A., Sánchez, R., Melo, F., & Šali, A. (2000) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.29.1.291 · Cited by 3,169
A new approach to protein fold recognition
Jones, D. T., Taylort, W. R., & Thornton, J. M. (1992) · Nature · doi:10.1038/358086a0 · Cited by 1,221
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
Self-citationProtein structure prediction: when is it useful?
Zhang, Y. (2009) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2009.02.005 · Cited by 277
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Research works printed but not linked: 64
- The UniProt, C. The Universal Protein Resource (UniProt) 2009. Nucleic Acids Res. 37, D169–D174 (2008). · DOI · Paper page
- Berman, H.M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000). · DOI · Paper page
- Zhang, Y. Progress and challenges in protein structure prediction. Curr. Opin. Struct. Biol. 18, 342–348 (2008). · DOI · Paper page
- Bowie, J.U., Luthy, R. & Eisenberg, D. A method to identify protein sequences that fold into a known three-dimensional structure. Science 253, 164–170 (1991). · DOI · Paper page
- Liwo, A., Lee, J., Ripoll, D.R., Pillardy, J. & Scheraga, H.A. Protein structure prediction by global optimization of a potential energy function. Proc. Natl. Acad. Sci. USA 96, 5482–5485 (1999). · DOI
- 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). · DOI · Paper page
- Wu, S., Skolnick, J. & Zhang, Y. Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biol. 5, 17 (2007). · DOI · Paper page
- Jauch, R., Yeo, H.C., Kolatkar, P.R. & Clarke, N.D. Assessment of CASP7 structure predictions for template free targets. Proteins 69, 57–67 (2007). · DOI
- Zhang, Y., Kolinski, A. & Skolnick, J. TOUCHSTONE II: a new approach to ab initio protein structure prediction. Biophys. J. 85, 1145–1164 (2003). · DOI · Paper page
- Battey, J.N. et al. Automated server predictions in CASP7. Proteins 69, 68–82 (2007). · DOI
- Moult, J. et al. Critical assessment of methods of protein structure prediction-Round VII. Proteins 69 (Suppl 8): 3–9 (2007). · DOI · Paper page
- Kopp, J., Bordoli, L., Battey, J.N., Kiefer, F. & Schwede, T. Assessment of CASP7 predictions for template-based modeling targets. Proteins 69, 38–56 (2007). · DOI
- Das, R. et al. Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home. Proteins 69, 118–128 (2007). · DOI
- Zhang, Y. Template-based modeling and free modeling by I-TASSER in CASP7. Proteins 69, 108–117 (2007). · DOI · Paper page
- Zhou, H. et al. Analysis of TASSER-based CASP7 protein structure prediction results. Proteins 69 (Suppl 8): 90–97 (2007). · DOI
- Zhang, Y. I-TASSER: fully automated protein structure prediction in CASP8. Proteins 77, 100–113 (2009). · DOI · Paper page
- Cozzetto, D. et al. Evaluation of template-based models in CASP8 with standard measures. Proteins 77 (Suppl 9): 18–28 (2009). · DOI
- Ekins, S., Mestres, J. & Testa, B. In silico pharmacology for drug discovery: applications to targets and beyond. Br. J. Pharmacol. 152, 21–37 (2007). · DOI · Paper page
- Becker, O.M. et al. An integrated in silico 3D model-driven discovery of a novel, potent, and selective amidosulfonamide 5-HT1A agonist (PRX-00023) for the treatment of anxiety and depression. J. Med. Chem. 49, 3116–3135 (2006). · DOI
- Brylinski, M. & Skolnick, J. Q-Dock: low-resolution flexible ligand docking with pocket-specific threading restraints. J. Comput. Chem. 29, 1574–1588 (2008). · DOI
- Arakaki, A.K., Zhang, Y. & Skolnick, J. Large-scale assessment of the utility of low-resolution protein structures for biochemical function assignment. Bioinformatics 20, 1087–1096 (2004). · DOI
- Yue, P. & Moult, J. Identification and analysis of deleterious human SNPs. J. Mol. Biol. 356, 1263–1274 (2006). · DOI · Paper page
- Boyd, A. et al. A random mutagenesis approach to isolate dominant-negative yeast sec1 mutants reveals a functional role for domain 3a in yeast and mammalian Sec1/Munc18 proteins. Genetics 180, 165–178 (2008). · DOI
- Ye, Y., Li, Z. & Godzik, A. Modeling and analyzing three-dimensional structures of human disease proteins. Pac. Symp. Biocomput. 11, 439–450 (2006).
- Keedy, D.A. et al. The other 90% of the protein: assessment beyond the Calphas for CASP8 template-based and high-accuracy models. Proteins 77 (Suppl 9): 29–49 (2009). · DOI
- Tress, M., Ezkurdia, I., Grana, O., Lopez, G. & Valencia, A. Assessment of predictions submitted for the CASP6 comparative modeling category. Proteins 61 (Suppl 7): 27–45 (2005). · DOI
- Moult, J. Comparative modeling in structural genomics. Structure 16, 14–16 (2008). · DOI
- Tress, M. et al. Assessment of predictions submitted for the CASP7 domain prediction category. Proteins 69 (Suppl 8): 137–151 (2007). · DOI
- Malmstrom, L. et al. Superfamily assignments for the yeast proteome through integration of structure prediction with the gene ontology. PLoS Biol. 5, e76 (2007). · DOI
- Zhang, Y., Devries, M.E. & Skolnick, J. Structure modeling of all identified G protein-coupled receptors in the human genome. PLoS Comput. Biol. 2, e13 (2006). · DOI
- Lopez, G., Rojas, A., Tress, M. & Valencia, A. Assessment of predictions submitted for the CASP7 function prediction category. Proteins 69 (Suppl 8): 165–174 (2007). · DOI
- Brylinski, M. & Skolnick, J. A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation. Proc. Natl. Acad. Sci. USA 105, 129–134 (2008). · DOI · Paper page
- Roy, A., Srinivasan, N. & Gowri, V.S. Molecular and structural basis of drift in the functions of closely-related homologous enzyme domains: implications for function annotation based on homology searches and structural genomics. In Silico Biol. 9, S41–S55 (2009). · DOI
- Bork, P., Sander, C. & Valencia, A. Convergent evolution of similar enzymatic function on different protein folds: the hexokinase, ribokinase, and galactokinase families of sugar kinases. Protein Sci. 2, 31–40 (1993). · DOI · Paper page
- Zhang, Y. & Skolnick, J. Tertiary structure predictions on a comprehensive benchmark of medium to large size proteins. Biophys. J. 87, 2647–2655 (2004). · DOI
- Zhang, Y. & Skolnick, J. Automated structure prediction of weakly homologous proteins on a genomic scale. Proc. Natl. Acad. Sci. USA 101, 7594–7599 (2004). · DOI · Paper page
- Karplus, K., Barrett, C. & Hughey, R. Hidden Markov models for detecting remote protein homologies. Bioinformatics 14, 846–856 (1998). · DOI · Paper page
- McGuffin, L.J. & Jones, D.T. Improvement of the GenTHREADER method for genomic fold recognition. Bioinformatics 19, 874–881 (2003). · DOI · Paper page
- Wallner, B. & Elofsson, A. Pcons5: combining consensus, structural evaluation and fold recognition scores. Bioinformatics 21, 4248–4254 (2005). · DOI
- Soding, J. Protein homology detection by HMM-HMM comparison. Bioinformatics 21, 951–960 (2005). · DOI
- Rost, B., Yachdav, G. & Liu, J. The PredictProtein server. Nucleic Acids Res. 32, W321–W326 (2004). · DOI · Paper page
- Ginalski, K., Elofsson, A., Fischer, D. & Rychlewski, L. 3D-Jury: a simple approach to improve protein structure predictions. Bioinformatics 19, 1015–1018 (2003). · DOI · Paper page
- Fischer, D. 3D-SHOTGUN: a novel, cooperative, fold-recognition meta-predictor. Proteins 51, 434–441 (2003). · DOI
- Kim, D.E., Chivian, D. & Baker, D. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res. 32, W526–W531 (2004). · DOI · Paper page
- Kelley, L.A. & Sternberg, M.J. Protein structure prediction on the Web: a case study using the Phyre server. Nat. Protoc. 4, 363–371 (2009). · DOI · Paper page
- Jones, D.T. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999). · DOI
- Wu, S. & Zhang, Y. LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acids Res. 35, 3375–3382 (2007). · DOI · Paper page
- Shi, J., Blundell, T.L. & Mizuguchi, K. FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. J. Mol. Biol. 310, 243–257 (2001). · DOI · Paper page
- Wu, S. & Zhang, Y. MUSTER: improving protein sequence profile-profile alignments by using multiple sources of structure information. Proteins 72, 547–556 (2008). · DOI · Paper page
- Xu, Y. & Xu, D. Protein threading using PROSPECT: design and evaluation. Proteins 40, 343–354 (2000). · DOI
- Zhou, H. & Zhou, Y. Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments. Proteins 58, 321–328 (2005). · DOI · Paper page
- Zhou, H. & Zhou, Y. Single-body residue-level knowledge-based energy score combined with sequence-profile and secondary structure information for fold recognition. Proteins 55, 1005–1013 (2004). · DOI
- Zhang, Y., Kihara, D. & Skolnick, J. Local energy landscape flattening: parallel hyperbolic Monte Carlo sampling of protein folding. Proteins 48, 192–201 (2002). · DOI
- Zhang, Y., Hubner, I., Arakaki, A., Shakhnovich, E. & Skolnick, J. On the origin and completeness of highly likely single domain protein structures. Proc. Natl. Acad. Sci. USA 103, 2605–2610 (2006). · DOI · Paper page
- Chen, H. & Zhou, H.X. Prediction of solvent accessibility and sites of deleterious mutations from protein sequence. Nucleic Acids Res. 33, 3193–3199 (2005). · DOI
- Wu, S. & Zhang, Y. A comprehensive assessment of sequence-based and template-based methods for protein contact prediction. Bioinformatics 24, 924–931 (2008). · DOI
- Zhang, Y. & Skolnick, J. SPICKER: A clustering approach to identify near-native protein folds. J. Comput. Chem. 25, 865–871 (2004). · DOI · Paper page
- Zhang, Y. & Skolnick, J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 33, 2302–2309 (2005). · DOI · Paper page
- Li, Y. & Zhang, Y. REMO: a new protocol to refine full atomic protein models from C-α traces by optimizing hydrogen-bonding networks. Proteins 76, 665–676 (2009). · DOI
- Barrett, A.J. Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (NC-IUBMB). Enzyme Nomenclature. Recommendations 1992. Supplement 4: corrections and additions (1997). Eur. J. Biochem. 250, 1–6 (1997). · DOI
- Zhang, Y. & Skolnick, J. Scoring function for automated assessment of protein structure template quality. Proteins 57, 702–710 (2004). · DOI · Paper page
- Li, W., Zhang, Y. & Skolnick, J. Application of sparse NMR restraints to large-scale protein structure prediction. Biophys. J. 87, 1241–1248 (2004). · DOI
- Sali, A. & Blundell, T.L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993). · DOI · Paper page
- Betancourt, M.R. & Skolnick, J. Universal similarity measure for comparing protein structures. Biopolymers 59, 305–309 (2001). · DOI
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Sagaram, U. S., Pandurangi, R., Kaur, J., Smith, T. J., & Shah, D. M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018550 · Cited by 185
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Sterol and genomic analyses validate the sponge biomarker hypothesis
Gold, D. A., Grabenstatter, J., de Mendoza, A., Riesgo, A., Ruiz-Trillo, I., & Summons, R. E. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1512614113 · Cited by 145
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Weiss, H., Wester-Rosenloef, L., Koch, C., Koch, F., Baltrusch, S., Tiedge, M., & Ibrahim, S. (2012) · Endocrinology · doi:10.1210/en.2012-1296 · Cited by 52
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Pan, M., Neilson, M. P., Grunfeld, A. M., Cruz, P., Wen, X., Insall, R. H., & Jin, T. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2005754 · Cited by 36
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Doritchamou, J. Y. A., Morrison, R., Renn, J. P., Ribeiro, J., Duan, J., Fried, M., & Duffy, P. E. (2019) · Communications Biology · doi:10.1038/s42003-019-0704-z · Cited by 31
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Hayne, C. K., Lafferty, M. J., Eglinger, B. J., Kane, J. P., & Neher, S. B. (2017) · Biochemistry · doi:10.1021/acs.biochem.6b00945 · Cited by 25
Sen, N., Anishchenko, I., Bordin, N., Sillitoe, I., Velankar, S., Baker, D., & Orengo, C. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac187 · Cited by 25
Li, Y., Cheng, Y., Consolato, F., Schiano, G., Chong, M. R., Pietzner, M., Nguyen, N. Q. H., Scherer, N., Biggs, M. L., Kleber, M. E., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.157035 · Cited by 22
Bussières, S., Cantin, L., Desbat, B., & Salesse, C. (2012) · Langmuir · doi:10.1021/la203896n · Cited by 20
González-del Pozo, M., Fernández-Suárez, E., Bravo-Gil, N., Méndez-Vidal, C., Martín-Sánchez, M., Rodríguez-de la Rúa, E., Ramos-Jiménez, M., Morillo-Sánchez, M. J., Borrego, S., & Antiñolo, G. (2022) · npj Genomic Medicine · doi:10.1038/s41525-022-00286-0 · Cited by 21
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Barba, M., Di Pietro, L., Massimi, L., Geloso, M. C., Frassanito, P., Caldarelli, M., Michetti, F., Della Longa, S., Romitti, P. A., Di Rocco, C., et al. (2018) · Bone · doi:10.1016/j.bone.2018.04.013 · Cited by 19
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Milk Protein Adsorption on Metallic Iron Surfaces
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Beutler, M., Harnischfeger, J., Weber, M. H. W., Hahnel, S. R., Quack, T., Blohm, A., Ueberall, M. E., Timm, T., Lochnit, G., Rennar, G. A., et al. (2023) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2023.115179 · Cited by 10
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Liu, X., Zhang, Y., Wen, Z., Hao, Y., Banks, C. A. S., Cesare, J., Bhattacharya, S., Arvindekar, S., Lange, J. J., Xie, Y., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2318601121 · Cited by 9
Self-citationBruno, S., Bersani, M., Astore, S., Chiabotto, G., Barge, A., Binello, A., & Spyrakis, F. (2022) · Toxicology · doi:10.1016/j.tox.2022.153257 · Cited by 7
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Xu, J., Yan, Q., Song, C., Liang, J., Zhao, L., Zhang, X., Weng, Z., Xu, C., Liu, Q., Xu, S., et al. (2021) · Hereditas · doi:10.1186/s41065-021-00182-0 · Cited by 4
De Silva, M. R. P., Weeraman, J. W. J. K., Piyatissa, S., & Fernando, P. C. (2025) · BMC Plant Biology · doi:10.1186/s12870-025-06595-7 · Cited by 3
Jaiswal, A., Roy, R., Tamrakar, A., Singh, A. K., Kar, P., & Kodgire, P. (2023) · Scientific Reports · doi:10.1038/s41598-023-46448-7 · Cited by 3
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Odhiambo, C. A., Malico, A. A., & Williams, G. J. (2026) · RSC Advances · doi:10.1039/d6ra03129c · Cited by 1
Metadata
- Authors
- Roy, A., Kucukural, A., & Zhang, Y.
- Venue
- Nature Protocols
- Published
- 2010-03-25
- Volume
- 5
- Issue
- 4
- Pages
- 725-738
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W2037312364
- Cited by
- 6,170
- References
- 7
- FWCI
- 116.26
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
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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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.