The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
SWISS-MODEL: homology modelling of protein structures and complexes
Open accessAbstract
Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
BLAST+: architecture and applications
Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-421 · Cited by 22,074
OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
Eastman, P., Swails, J., Chodera, J. D., McGibbon, R. T., Zhao, Y., Beauchamp, K. A., Wang, L.-P., Simmonett, A. C., Harrigan, M. P., Stern, C. D., et al. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005659 · Cited by 2,748
HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
Remmert, M., Biegert, A., Hauser, A., & Söding, J. (2012) · Nature Methods · doi:10.1038/nmeth.1818 · Cited by 2,288
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
Mariani, V., Biasini, M., Barbato, A., & Schwede, T. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt473 · Cited by 1,138
Self-citationShapovalov, M. V., & Dunbrack, R. L. (2011) · Structure · doi:10.1016/j.str.2011.03.019 · Cited by 1,000
Sequence co-evolution gives 3D contacts and structures of protein complexes
Hopf, T. A., Schärfe, C. P. I., Rodrigues, J. P. G. L. M., Green, A. G., Kohlbacher, O., Sander, C., Bonvin, A. M. J. J., & Marks, D. S. (2014) · eLife · doi:10.7554/elife.03430 · Cited by 515
NGL Viewer: a web application for molecular visualization
Rose, A. S., & Hildebrand, P. W. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv402 · Cited by 462
Templates are available to model nearly all complexes of structurally characterized proteins
Kundrotas, P. J., Zhu, Z., Janin, J., & Vakser, I. A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1200678109 · Cited by 179
Research works printed but not linked: 60
- Fuller, 2009, Predicting druggable binding sites at the protein-protein interface, Drug Discov. Today, 14, 155, 10.1016/j.drudis.2008.10.009 · DOI · Paper page
- Nim, 2016, Pooled screening for antiproliferative inhibitors of protein-protein interactions, Nat. Chem. Biol., 12, 275, 10.1038/nchembio.2026 · DOI
- Dutta, 2005, Large macromolecular complexes in the Protein Data Bank: a status report, Structure, 13, 381, 10.1016/j.str.2005.01.008 · DOI
- Marsh, 2015, Structure, dynamics, assembly, and evolution of protein complexes, Annu. Rev. Biochem., 84, 551, 10.1146/annurev-biochem-060614-034142 · DOI
- Tramontano, 2017, The computational prediction of protein assemblies, Curr. Opin. Struct. Biol., 46, 170, 10.1016/j.sbi.2017.10.006 · DOI
- Pazos, 1997, Correlated mutations contain information about protein-protein interaction, J. Mol. Biol., 271, 511, 10.1006/jmbi.1997.1198 · DOI · Paper page
- Morris, 2008, Molecular docking, Methods Mol. Biol., 443, 365, 10.1007/978-1-59745-177-2_19 · DOI
- Chaudhury, 2011, Benchmarking and analysis of protein docking performance in Rosetta v3.2, PLoS One, 6, e22477, 10.1371/journal.pone.0022477 · DOI · Paper page
- Kurkcuoglu, 2018, Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2, J. Computer-aided Mol. Des., 32, 175, 10.1007/s10822-017-0049-y · DOI
- Peterson, 2018, Modeling the assembly order of multimeric heteroprotein complexes, PLoS Comput. Biol., 14, e1005937, 10.1371/journal.pcbi.1005937 · DOI
- Janin, 2005, Assessing predictions of protein-protein interaction: the CAPRI experiment, Protein Sci., 14, 278, 10.1110/ps.041081905 · DOI
- Janin, 2013, The targets of CAPRI rounds 20–27, Proteins, 81, 2075, 10.1002/prot.24375 · DOI
- Rodrigues, 2015, Information-driven structural modelling of protein-protein interactions, Methods Mol. Biol., 1215, 399, 10.1007/978-1-4939-1465-4_18 · DOI
- Geng, 2017, Information-driven, ensemble flexible peptide docking using HADDOCK, Methods Mol. Biol., 1561, 109, 10.1007/978-1-4939-6798-8_8 · DOI
- Peterson, 2017, Improved performance in CAPRI round 37 using LZerD docking and template-based modeling with combined scoring functions, Proteins, 86, 311
- Zhang, 2010, Protein interface conservation across structure space, Proc. Natl. Acad. Sci. U.S.A., 107, 10896, 10.1073/pnas.1005894107 · DOI · Paper page
- Matthews, 2001, Identification of potential interaction networks using sequence-based searches for conserved protein-protein interactions or “interologs”, Genome Res., 11, 2120, 10.1101/gr.205301 · DOI
- Aloy, 2003, The relationship between sequence and interaction divergence in proteins, J. Mol. Biol., 332, 989, 10.1016/j.jmb.2003.07.006 · DOI
- Szilagyi, 2014, Template-based structure modeling of protein-protein interactions, Curr. Opin. Struct. Biol., 24, 10, 10.1016/j.sbi.2013.11.005 · DOI
- Yu, 2004, Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs, Genome Res., 14, 1107, 10.1101/gr.1774904 · DOI
- Bertoni, 2017, Modeling protein quaternary structure of homo- and hetero-oligomers beyond binary interactions by homology, Scientific Rep., 7, 10480, 10.1038/s41598-017-09654-8 · DOI · Paper page
- Lafita, 2018, Assessment of protein assembly prediction in CASP12, Proteins, 86, 247, 10.1002/prot.25408 · DOI
- Peitsch, 1996, ProMod and Swiss-Model: Internet-based tools for automated comparative protein modelling, Biochem. Soc. Trans., 24, 274, 10.1042/bst0240274 · DOI · Paper page
- Guex, 1997, SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling, Electrophoresis, 18, 2714, 10.1002/elps.1150181505 · DOI · Paper page
- Schwede, 2003, SWISS-MODEL: an automated protein homology-modeling server, Nucleic Acids Res., 31, 3381, 10.1093/nar/gkg520 · DOI
- Arnold, 2006, The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling, Bioinformatics, 22, 195, 10.1093/bioinformatics/bti770 · DOI
- Bordoli, 2012, Automated protein structure modeling with SWISS-MODEL Workspace and the Protein Model Portal, Methods Mol. Biol., 857, 107, 10.1007/978-1-61779-588-6_5 · DOI
- Biasini, 2014, SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information, Nucleic Acids Res., 42, W252, 10.1093/nar/gku340 · DOI · Paper page
- Benkert, 2011, Toward the estimation of the absolute quality of individual protein structure models, Bioinformatics, 27, 343, 10.1093/bioinformatics/btq662 · DOI
- Berman, 2002, The Protein Data Bank, Acta Crystallogr. D, Biol. Crystallogr., 58, 899, 10.1107/S0907444902003451 · DOI
- The UniProt, C., 2017, UniProt: the universal protein knowledgebase, Nucleic Acids Res., 45, D158, 10.1093/nar/gkw1099 · DOI · Paper page
- Biasini, 2013, OpenStructure: an integrated software framework for computational structural biology, Acta Crystallogr. D, Biol. Crystallogr., 69, 701, 10.1107/S0907444913007051 · DOI
- Bienert, 2017, The SWISS-MODEL Repository—new features and functionality, Nucleic Acids Res., 45, D313, 10.1093/nar/gkw1132 · DOI · Paper page
- de Beer, 2014, PDBsum additions, Nucleic Acids Res., 42, D292, 10.1093/nar/gkt940 · DOI · Paper page
- Velankar, 2016, PDBe: improved accessibility of macromolecular structure data from PDB and EMDB, Nucleic Acids Res., 44, D385, 10.1093/nar/gkv1047 · DOI
- Dawson, 2017, CATH: an expanded resource to predict protein function through structure and sequence, Nucleic Acids Res., 45, D289, 10.1093/nar/gkw1098 · DOI
- Grosdidier, 2011, SwissDock, a protein-small molecule docking web service based on EADock DSS, Nucleic Acids Res., 39, W270, 10.1093/nar/gkr366 · DOI · Paper page
- Marcatili, 2014, Antibody modeling using the prediction of immunoglobulin structure (PIGS) web server [corrected], Nat. Protoc., 9, 2771, 10.1038/nprot.2014.189 · DOI
- Messih, 2014, Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies, Bioinformatics, 30, 2733, 10.1093/bioinformatics/btu194 · DOI
- Lepore, 2017, PIGSPro: prediction of immunoGlobulin structures v2, Nucleic Acids Res., 45, W17, 10.1093/nar/gkx334 · DOI
- Chothia, 1987, Canonical structures for the hypervariable regions of immunoglobulins, J. Mol. Biol., 196, 901, 10.1016/0022-2836(87)90412-8 · DOI · Paper page
- Morea, 1998, Conformations of the third hypervariable region in the VH domain of immunoglobulins, J. Mol. Biol., 275, 269, 10.1006/jmbi.1997.1442 · DOI · Paper page
- Tramontano, 1990, Framework residue 71 is a major determinant of the position and conformation of the second hypervariable region in the VH domains of immunoglobulins, J. Mol. Biol., 215, 175, 10.1016/S0022-2836(05)80102-0 · DOI
- Sali, 1993, Comparative protein modelling by satisfaction of spatial restraints, J. Mol. Biol., 234, 779, 10.1006/jmbi.1993.1626 · DOI · Paper page
- Xu, 2005, Rapid protein Side-Chain packing via tree decomposition, 423, 10.1007/11415770_32 · DOI
- Krivov, 2009, Improved prediction of protein side-chain conformations with SCWRL4, Proteins, 77, 778, 10.1002/prot.22488 · DOI · Paper page
- Mackerell, 2004, Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations, J. Comput. Chem., 25, 1400, 10.1002/jcc.20065 · DOI · Paper page
- Read, 2007, Assessment of CASP7 predictions in the high accuracy template-based modeling category, Proteins, 69, 27, 10.1002/prot.21662 · DOI
- Elcock, 2001, Identification of protein oligomerization states by analysis of interface conservation, Proc. Natl. Acad. Sci. U.S.A., 98, 2990, 10.1073/pnas.061411798 · DOI
- Capra, 2007, Predicting functionally important residues from sequence conservation, Bioinformatics, 23, 1875, 10.1093/bioinformatics/btm270 · DOI
- Schwede, 2009, Outcome of a workshop on applications of protein models in biomedical research, Structure, 17, 151, 10.1016/j.str.2008.12.014 · DOI
- Kim, 2004, Protein structure prediction and analysis using the Robetta server, Nucleic Acids Res., 32, W526, 10.1093/nar/gkh468 · DOI · Paper page
- Yang, 2011, Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates, Bioinformatics, 27, 2076, 10.1093/bioinformatics/btr350 · DOI
- Kallberg, 2012, Template-based protein structure modeling using the RaptorX web server, Nat. Protoc., 7, 1511, 10.1038/nprot.2012.085 · DOI · Paper page
- Aliverti, 2001, Biochemical and crystallographic characterization of ferredoxin-NADP(+) reductase from nonphotosynthetic tissues, Biochemistry, 40, 14501, 10.1021/bi011224c · DOI
- Kurisu, 2001, Structure of the electron transfer complex between ferredoxin and ferredoxin-NADP(+) reductase, Nat. Struct. Biol., 8, 117, 10.1038/84097 · DOI
- Shinohara, 2017, Structural basis for the isotype-specific interactions of ferredoxin and ferredoxin: NADP(+) oxidoreductase: an evolutionary switch between photosynthetic and heterotrophic assimilation, Photosynth. Res., 134, 281, 10.1007/s11120-016-0331-1 · DOI
- Morales, 2000, Crystallographic studies of the interaction between the ferredoxin-NADP+ reductase and ferredoxin from the cyanobacterium Anabaena: looking for the elusive ferredoxin molecule, Acta Crystallogr. D, Biol. Crystallogr., 56, 1408, 10.1107/S0907444900010052 · DOI
- Duarte, 2012, Protein interface classification by evolutionary analysis, BMC Bioinformatics, 13, 334, 10.1186/1471-2105-13-334 · DOI
- Kimata-Ariga, 2013, Concentration-dependent oligomerization of cross-linked complexes between ferredoxin and ferredoxin-NADP+ reductase, Biochem. Biophys. Res. Commun., 434, 867, 10.1016/j.bbrc.2013.04.033 · DOI
Cited by
A new coronavirus associated with human respiratory disease in China
Wu, F., Zhao, S., Yu, B., Chen, Y.-M., Wang, W., Song, Z.-G., Hu, Y., Tao, Z.-W., Tian, J.-H., Pei, Y.-Y., et al. (2020) · Nature · doi:10.1038/s41586-020-2008-3 · Cited by 11,688
Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., Wang, W., Song, H., Huang, B., Zhu, N., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30251-8 · Cited by 11,907
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,549
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025
G3BP1 is a tunable switch that triggers phase separation to assemble stress granules
Yang, P., Mathieu, C., Kolaitis, R.-M., Zhang, P., Messing, J., Yurtsever, U., Yang, Z., Wu, J., Li, Y., Pan, Q., et al. (2020) · Cell · doi:10.1016/j.cell.2020.03.046 · Cited by 1,249
Generalized biomolecular modeling and design with RoseTTAFold All-Atom
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al. (2024) · Science · doi:10.1126/science.adl2528 · Cited by 978
NINJ1 mediates plasma membrane rupture during lytic cell death
Kayagaki, N., Kornfeld, O. S., Lee, B. L., Stowe, I. B., O’Rourke, K., Li, Q., Sandoval, W., Yan, D., Kang, J., Xu, M., et al. (2021) · Nature · doi:10.1038/s41586-021-03218-7 · Cited by 857
Advances in protein structure prediction and design
Kuhlman, B., & Bradley, P. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0163-x · Cited by 806
Loss of furin cleavage site attenuates SARS-CoV-2 pathogenesis
Johnson, B. A., Xie, X., Bailey, A. L., Kalveram, B., Lokugamage, K. G., Muruato, A., Zou, J., Zhang, X., Juelich, T., Smith, J. K., et al. (2021) · Nature · doi:10.1038/s41586-021-03237-4 · Cited by 694
Cerutti, G., Guo, Y., Zhou, T., Gorman, J., Lee, M., Rapp, M., Reddem, E. R., Yu, J., Bahna, F., Bimela, J., et al. (2021) · Cell Host & Microbe · doi:10.1016/j.chom.2021.03.005 · Cited by 519
Phospholipase iPLA2β averts ferroptosis by eliminating a redox lipid death signal
Sun, W.-Y., Tyurin, V. A., Mikulska-Ruminska, K., Shrivastava, I. H., Anthonymuthu, T. S., Zhai, Y.-J., Pan, M.-H., Gong, H.-B., Lu, D.-H., Sun, J., et al. (2021) · Nature Chemical Biology · doi:10.1038/s41589-020-00734-x · Cited by 337
In-cell architecture of an actively transcribing-translating expressome
O’Reilly, F. J., Xue, L., Graziadei, A., Sinn, L., Lenz, S., Tegunov, D., Blötz, C., Singh, N., Hagen, W. J. H., Cramer, P., et al. (2020) · Science · doi:10.1126/science.abb3758 · Cited by 284
On the origin and evolution of SARS-CoV-2
Singh, D., & Yi, S. V. (2021) · Experimental & Molecular Medicine · doi:10.1038/s12276-021-00604-z · Cited by 281
Recurrent emergence and transmission of a SARS-CoV-2 spike deletion H69/V70
Kemp, S. A., Meng, B., Ferriera, I. A., Datir, R., Harvey, W. T., Papa, G., Lytras, S., Collier, D. A., Mohamed, A., Gallo, G., et al. (2020) · doi:10.1101/2020.12.14.422555 · Cited by 252
A bispecific CAR-T cell therapy targeting BCMA and CD38 in relapsed or refractory multiple myeloma
Mei, H., Li, C., Jiang, H., Zhao, X., Huang, Z., Jin, D., Guo, T., Kou, H., Liu, L., Tang, L., et al. (2021) · Journal of Hematology & Oncology · doi:10.1186/s13045-021-01170-7 · Cited by 222
Tuning charge density of chimeric antigen receptor optimizes tonic signaling and CAR-T cell fitness
Chen, J., Qiu, S., Li, W., Wang, K., Zhang, Y., Yang, H., Liu, B., Li, G., Li, L., Chen, M., et al. (2023) · Cell Research · doi:10.1038/s41422-023-00789-0 · Cited by 195
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
AlphaFold heralds a data-driven revolution in biology and medicine
Thornton, J. M., Laskowski, R. A., & Borkakoti, N. (2021) · Nature Medicine · doi:10.1038/s41591-021-01533-0 · Cited by 185
Visualizing translation dynamics at atomic detail inside a bacterial cell
Xue, L., Lenz, S., Zimmermann-Kogadeeva, M., Tegunov, D., Cramer, P., Bork, P., Rappsilber, J., & Mahamid, J. (2022) · Nature · doi:10.1038/s41586-022-05255-2 · Cited by 184
Structures of rhodopsin in complex with G-protein-coupled receptor kinase 1
Chen, Q., Plasencia, M., Li, Z., Mukherjee, S., Patra, D., Chen, C.-L., Klose, T., Yao, X.-Q., Kossiakoff, A. A., Chang, L., et al. (2021) · Nature · doi:10.1038/s41586-021-03721-x · Cited by 152
CG2AT2: An enhanced fragment-based approach for serial multi-scale molecular dynamics simulations
Vickery, O. N., & Stansfeld, P. J. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00295 · Cited by 150
Jespersen, M. C., Mahajan, S., Peters, B., Nielsen, M., & Marcatili, P. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.00298 · Cited by 148
The structure of human thyroglobulin
Coscia, F., Taler-Verčič, A., Chang, V. T., Sinn, L., O’Reilly, F. J., Izoré, T., Renko, M., Berger, I., Rappsilber, J., Turk, D., et al. (2020) · Nature · doi:10.1038/s41586-020-1995-4 · Cited by 147
ARAF mutations confer resistance to the RAF inhibitor belvarafenib in melanoma
Yen, I., Shanahan, F., Lee, J., Hong, Y. S., Shin, S. J., Moore, A. R., Sudhamsu, J., Chang, M. T., Bae, I., Dela Cruz, D., et al. (2021) · Nature · doi:10.1038/s41586-021-03515-1 · Cited by 138
Metelli, A., Wu, B. X., Riesenberg, B., Guglietta, S., Huck, J. D., Mills, C., Li, A., Rachidi, S., Krieg, C., Rubinstein, M. P., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aay4860 · Cited by 139
Disruption of Amino Acid Homeostasis by Novel ASCT2 Inhibitors Involves Multiple Targets
Bröer, A., Fairweather, S., & Bröer, S. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.00785 · Cited by 124
Human germline heterozygous gain-of-function STAT6 variants cause severe allergic disease
Sharma, M., Leung, D., Momenilandi, M., Jones, L. C. W., Pacillo, L., James, A. E., Murrell, J. R., Delafontaine, S., Maimaris, J., Vaseghi-Shanjani, M., et al. (2023) · Journal of Experimental Medicine · doi:10.1084/jem.20221755 · Cited by 125
Gómez de San José, N., Massa, F., Halbgebauer, S., Oeckl, P., Steinacker, P., & Otto, M. (2022) · Journal of Neural Transmission · doi:10.1007/s00702-021-02411-2 · Cited by 112
Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al. (2023) · doi:10.1101/2023.10.09.561603 · Cited by 106
Barbosa, S., Greville-Heygate, S., Bonnet, M., Godwin, A., Fagotto-Kaufmann, C., Kajava, A. V., Laouteouet, D., Mawby, R., Wai, H. A., Dingemans, A. J. M., et al. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.01.018 · Cited by 104
Killer, M., Wald, J., Pieprzyk, J., Marlovits, T. C., & Löw, C. (2021) · Science Advances · doi:10.1126/sciadv.abk3259 · Cited by 94
Large language models improve annotation of prokaryotic viral proteins
Flamholz, Z. N., Biller, S. J., & Kelly, L. (2024) · Nature Microbiology · doi:10.1038/s41564-023-01584-8 · Cited by 92
Castro, C. N., Rosenzwajg, M., Carapito, R., Shahrooei, M., Konantz, M., Khan, A., Miao, Z., Groß, M., Tranchant, T., Radosavljevic, M., et al. (2020) · Journal of Experimental Medicine · doi:10.1084/jem.20192275 · Cited by 91
Architecture and mechanism of metazoan retromer:SNX3 tubular coat assembly
Leneva, N., Kovtun, O., Morado, D. R., Briggs, J. A. G., & Owen, D. J. (2021) · Science Advances · doi:10.1126/sciadv.abf8598 · Cited by 90
Structural basis for membrane attack complex inhibition by CD59
Couves, E. C., Gardner, S., Voisin, T. B., Bickel, J. K., Stansfeld, P. J., Tate, E. W., & Bubeck, D. (2023) · Nature Communications · doi:10.1038/s41467-023-36441-z · Cited by 85
Lee, K., Thwin, A. C., Nadel, C. M., Tse, E., Gates, S. N., Gestwicki, J. E., & Southworth, D. R. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.07.023 · Cited by 76
Pony, P., Rapisarda, C., Terradot, L., Marza, E., & Fronzes, R. (2020) · Nature Communications · doi:10.1038/s41467-020-15214-y · Cited by 82
PDBe-KB: collaboratively defining the biological context of structural data
PDBe-KB consortium, Varadi, M., Anyango, S., Armstrong, D., Berrisford, J., Choudhary, P., Deshpande, M., Nadzirin, N., Nair, S. S., Pravda, L., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab988 · Cited by 82
Structural basis for the oligomerization-facilitated NLRP3 activation
Yu, X., Matico, R. E., Miller, R., Chauhan, D., Van Schoubroeck, B., Grauwen, K., Suarez, J., Pietrak, B., Haloi, N., Yin, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45396-8 · Cited by 78
Building structural models of a whole mycoplasma cell
Maritan, M., Autin, L., Karr, J., Covert, M. W., Olson, A. J., & Goodsell, D. S. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167351 · Cited by 75
Biased cytochrome P450-mediated metabolism via small-molecule ligands binding P450 oxidoreductase
Jensen, S. B., Thodberg, S., Parween, S., Moses, M. E., Hansen, C. C., Thomsen, J., Sletfjerding, M. B., Knudsen, C., Del Giudice, R., Lund, P. M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22562-w · Cited by 64
Novel machine learning approaches revolutionize protein knowledge
Bordin, N., Dallago, C., Heinzinger, M., Kim, S., Littmann, M., Rauer, C., Steinegger, M., Rost, B., & Orengo, C. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.11.001 · Cited by 72
Fonager, J., Bennedbæk, M., Bager, P., Wohlfahrt, J., Ellegaard, K. M., Ingham, A. C., Edslev, S. M., Stegger, M., Sieber, R. N., Lassauniere, R., et al. (2022) · Eurosurveillance · doi:10.2807/1560-7917.es.2022.27.10.2200181 · Cited by 78
Beuming, T., Martín, H., Díaz-Rovira, A. M., Díaz, L., Guallar, V., & Ray, S. S. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00796 · Cited by 61
PAM-flexible genome editing with an engineered chimeric Cas9
Zhao, L., Koseki, S. R. T., Silverstein, R. A., Amrani, N., Peng, C., Kramme, C., Savic, N., Pacesa, M., Rodríguez, T. C., Stan, T., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41829-y · Cited by 59
Molecular features underlying differential SHP1/SHP2 binding of immune checkpoint receptors
Xu, X., Masubuchi, T., Cai, Q., Zhao, Y., & Hui, E. (2021) · eLife · doi:10.7554/elife.74276 · Cited by 59
Structural features of nucleosomes in interphase and metaphase chromosomes
Arimura, Y., Shih, R. M., Froom, R., & Funabiki, H. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.08.010 · Cited by 59
Zhang, Z., Morgan, C. E., Bonomo, R. A., & Yu, E. W. (2021) · mBio · doi:10.1128/mbio.01031-21 · Cited by 58
METTL17 is an Fe-S cluster checkpoint for mitochondrial translation
Ast, T., Itoh, Y., Sadre, S., McCoy, J. G., Namkoong, G., Wengrod, J. C., Chicherin, I., Joshi, P. R., Kamenski, P., Suess, D. L. M., et al. (2024) · Molecular Cell · doi:10.1016/j.molcel.2023.12.016 · Cited by 58
Zng1 is a GTP-dependent zinc transferase needed for activation of methionine aminopeptidase
Pasquini, M., Grosjean, N., Hixson, K. K., Nicora, C. D., Yee, E. F., Lipton, M., Blaby, I. K., Haley, J. D., & Blaby-Haas, C. E. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110834 · Cited by 55
Structural basis for Parkinson’s disease-linked LRRK2’s binding to microtubules
Snead, D. M., Matyszewski, M., Dickey, A. M., Lin, Y. X., Leschziner, A. E., & Reck-Peterson, S. L. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00863-y · Cited by 54
Krauspe, V., Fahrner, M., Spät, P., Steglich, C., Frankenberg-Dinkel, N., Maček, B., Schilling, O., & Hess, W. R. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2012277118 · Cited by 54
Leander, M., Liu, Z., Cui, Q., & Raman, S. (2022) · eLife · doi:10.7554/elife.79932 · Cited by 54
Cryo-EM structure of the cytosolic AhR complex.
Wen, Z., Zhang, Y., Zhang, B., Hang, Y., Xu, L., Chen, Y., Xie, Q., Zhao, Q., Zhang, L., Li, G., et al. (2023) · Structure · doi:10.1016/j.str.2022.12.013 · Cited by 51
Toledo, P. F. S., Viteri Jumbo, L. O., Rezende, S. M., Haddi, K., Silva, B. A., Mello, T. S., Della Lucia, T. M. C., Aguiar, R. W. S., Smagghe, G., & Oliveira, E. E. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.137328 · Cited by 52
Kumar, S., Karuppanan, K., & Subramaniam, G. (2022) · doi:10.1101/2022.02.11.480029 · Cited by 223
Barbarin-Bocahu, I., & Graille, M. (2022) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798322002157 · Cited by 52
Zhang, W., Roy Burman, S. S., Chen, J., Donovan, K. A., Cao, Y., Shu, C., Zhang, B., Zeng, Z., Gu, S., Zhang, Y., et al. (2022) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.008 · Cited by 51
Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50
Barh, D., Tiwari, S., Rodrigues Gomes, L. G., Ramalho Pinto, C. H., Andrade, B. S., Ahmad, S., Aljabali, A. A. A., Alzahrani, K. J., Banjer, H. J., Hassan, S. S., et al. (2023) · Inflammation · doi:10.1007/s10753-022-01734-w · Cited by 50
Structural basis for lipid transfer by the ATG2A–ATG9A complex
(2024) · doi:10.1038/s41594-024-01376-6 · Cited by 50
Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection
Lu, H., Li, F., Yuan, L., Domenzain, I., Yu, R., Wang, H., Li, G., Chen, Y., Ji, B., Kerkhoven, E. J., et al. (2021) · Molecular Systems Biology · doi:10.15252/msb.202110427 · Cited by 44
Peng, Y., Lu, Z., Pan, D., Shi, L.-D., Zhao, Z., Liu, Q., Zhang, C., Jia, K., Li, J., Hubert, C. R. J., et al. (2023) · The ISME Journal · doi:10.1038/s41396-023-01491-0 · Cited by 48
Design of a Cereblon construct for crystallographic and biophysical studies of protein degraders
Kroupova, A., Spiteri, V. A., Rutter, Z. J., Furihata, H., Darren, D., Ramachandran, S., Chakraborti, S., Haubrich, K., Pethe, J., Gonzales, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52871-9 · Cited by 47
Rapid development of an updated mRNA vaccine against the SARS-CoV-2 Omicron variant
Zhang, N.-N., Zhang, R.-R., Zhang, Y.-F., Ji, K., Xiong, X.-C., Qin, Q.-S., Gao, P., Lu, X.-S., Zhou, H.-Y., Song, H.-F., et al. (2022) · Cell Research · doi:10.1038/s41422-022-00626-w · Cited by 47
MOSPD2 is an endoplasmic reticulum–lipid droplet tether functioning in LD homeostasis
Zouiouich, M., Di Mattia, T., Martinet, A., Eichler, J., Wendling, C., Tomishige, N., Grandgirard, E., Fuggetta, N., Fromental-Ramain, C., Mizzon, G., et al. (2022) · Journal of Cell Biology · doi:10.1083/jcb.202110044 · Cited by 40
Three-step docking by WIPI2, ATG16L1, and ATG3 delivers LC3 to the phagophore
Rao, S., Skulsuppaisarn, M., Strong, L. M., Ren, X., Lazarou, M., Hurley, J. H., & Hummer, G. (2024) · Science Advances · doi:10.1126/sciadv.adj8027 · Cited by 40
Cross-phyla protein annotation by structural prediction and alignment
Ruperti, F., Papadopoulos, N., Musser, J. M., Mirdita, M., Steinegger, M., & Arendt, D. (2023) · Genome Biology · doi:10.1186/s13059-023-02942-9 · Cited by 38
Coincident rapid expansion of two SARS-CoV-2 lineages with enhanced infectivity in Nigeria
Ozer, E. A., Simons, L. M., Adewumi, O. M., Fowotade, A. A., Omoruyi, E. C., Adeniji, J. A., Dean, T. J., Zayas, J., Bhimalli, P. P., Ash, M. K., et al. (2021) · doi:10.1101/2021.04.09.21255206 · Cited by 34
Chen, Z.-P., Xu, D., Wang, L., Mao, Y.-X., Li, Y., Cheng, M.-T., Zhou, C.-Z., Hou, W.-T., & Chen, Y. (2022) · Nature Communications · doi:10.1038/s41467-022-30974-5 · Cited by 37
Challenges in bridging the gap between protein structure prediction and functional interpretation
Varadi, M., Tsenkov, M., & Velankar, S. (2025) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26614 · Cited by 36
Evolution of high-molecular-mass hyaluronic acid is associated with subterranean lifestyle
Zhao, Y., Zheng, Z., Zhang, Z., Xu, Y., Hillpot, E., Lin, Y. S., Zakusilo, F. T., Lu, J. Y., Ablaeva, J., Biashad, S. A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43623-2 · Cited by 34
Villalta, A., Schmitt, A., Estrozi, L. F., Quemin, E. R., Alempic, J.-M., Lartigue, A., Pražák, V., Belmudes, L., Vasishtan, D., Colmant, A. M., et al. (2022) · eLife · doi:10.7554/elife.77607 · Cited by 34
Next Generation Protein Structure Predictions and Genetic Variant Interpretation
Diwan, G. D., Gonzalez-Sanchez, J. C., Apic, G., & Russell, R. B. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167180 · Cited by 30
Rieske head domain dynamics and indazole-derivative inhibition of Candida albicans complex III
Di Trani, J. M., Liu, Z., Whitesell, L., Brzezinski, P., Cowen, L. E., & Rubinstein, J. L. (2022) · Structure · doi:10.1016/j.str.2021.08.006 · Cited by 32
Varadi, M., Nair, S., Sillitoe, I., Tauriello, G., Anyango, S., Bienert, S., Borges, C., Deshpande, M., Green, T., Hassabis, D., et al. (2022) · GigaScience · doi:10.1093/gigascience/giac118 · Cited by 32
Self-citationWang, X., Clément, R., Roger, M., Bauzan, M., Mazurenko, I., Poulpiquet, A. D., Ilbert, M., & Lojou, E. (2019) · Journal of the American Chemical Society · doi:10.1021/jacs.9b03268 · Cited by 32
CryoEM structure of the outer membrane secretin channel pIV from the f1 filamentous bacteriophage
Conners, R., McLaren, M., Łapińska, U., Sanders, K., Stone, M. R. L., Blaskovich, M. A. T., Pagliara, S., Daum, B., Rakonjac, J., & Gold, V. A. M. (2021) · Nature Communications · doi:10.1038/s41467-021-26610-3 · Cited by 32
High-throughput deep learning variant effect prediction with Sequence UNET
Dunham, A. S., Beltrao, P., & AlQuraishi, M. (2023) · Genome Biology · doi:10.1186/s13059-023-02948-3 · Cited by 31
Basis for Accurate Protein p K a Prediction with Machine Learning
Cai, Z., Liu, T., Lin, Q., He, J., Lei, X., Luo, F., & Huang, Y. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00254 · Cited by 31
Structure and activation of the RING E3 ubiquitin ligase TRIM72 on the membrane
Park, S. H., Han, J., Jeong, B.-C., Song, J. H., Jang, S. H., Jeong, H., Kim, B. H., Ko, Y.-G., Park, Z.-Y., Lee, K. E., et al. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-01111-7 · Cited by 31
Porter, J. R., Meller, A., Zimmerman, M. I., Greenberg, M. J., & Bowman, G. R. (2020) · eLife · doi:10.7554/elife.55132 · Cited by 31
The Charged Linker Modulates the Conformations and Molecular Interactions of Hsp90
López, A., Elimelech, A. R., Klimm, K., & Sattler, M. (2021) · ChemBioChem · doi:10.1002/cbic.202000699 · Cited by 30
Nemergut, M., Marques, S. M., Uhrik, L., Vanova, T., Nezvedova, M., Gadara, D. C., Jha, D., Tulis, J., Novakova, V., Planas-Iglesias, J., et al. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00620-9 · Cited by 30
Molecular mechanism of phospholipid transport at the bacterial outer membrane interface
Yeow, J., Luo, M., & Chng, S.-S. (2023) · Nature Communications · doi:10.1038/s41467-023-44144-8 · Cited by 30
SURFMAP: A Software for Mapping in Two Dimensions Protein Surface Features
Schweke, H., Mucchielli, M.-H., Chevrollier, N., Gosset, S., & Lopes, A. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01269 · Cited by 30
Mahmoud, D. E., Faraag, A. H. I., & Abu El-Wafa, W. M. (2021) · World Journal of Microbiology and Biotechnology · doi:10.1007/s11274-021-03158-4 · Cited by 26
Kusada, H., Arita, M., Tohno, M., & Tamaki, H. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.858263 · Cited by 29
Extracellular loop 2 of G protein–coupled olfactory receptors is critical for odorant recognition
Yu, Y., Ma, Z., Pacalon, J., Xu, L., Li, W., Belloir, C., Topin, J., Briand, L., Golebiowski, J., & Cong, X. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.102331 · Cited by 28
Catalytic cycling of human mitochondrial Lon protease
Mohammed, I., Schmitz, K. A., Schenck, N., Balasopoulos, D., Topitsch, A., Maier, T., & Abrahams, J. P. (2022) · Structure · doi:10.1016/j.str.2022.06.006 · Cited by 27
Levin, G., Yasmin, M., Liveanu, V., Burstein, C., Hanna, R., Kleifeld, O., Simanowitz, M. C., Meir, A., Tadmor, Y., Hirschberg, J., et al. (2023) · The Plant Journal · doi:10.1111/tpj.16241 · Cited by 27
Unique structural features govern the activity of a human mitochondrial AAA+ disaggregase, Skd3
Cupo, R. R., Rizo, A. N., Braun, G. A., Tse, E., Chuang, E., Gupta, K., Southworth, D. R., & Shorter, J. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111408 · Cited by 25
Jiang, D., Xi, B., Tan, W., Chen, Z., Wei, J., Hu, M., Lu, X., Chen, D., Cai, H., & Du, H. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae547 · Cited by 23
Türkaydin, B., Schewe, M., Riel, E. B., Schulz, F., Biedermann, J., Baukrowitz, T., & Sun, H. (2024) · Nature Communications · doi:10.1038/s41467-024-48823-y · Cited by 23
Gorman, B. R., Francis, M., Nealon, C. L., Halladay, C. W., Duro, N., Markianos, K., Genovese, G., Hysi, P. G., Choquet, H., Afshari, N. A., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06046-3 · Cited by 23
Identifying carbohydrate-active enzymes of Cutaneotrichosporon oleaginosus using systems biology
Fuchs, T., Melcher, F., Rerop, Z. S., Lorenzen, J., Shaigani, P., Awad, D., Haack, M., Prem, S. A., Masri, M., Mehlmer, N., et al. (2021) · Microbial Cell Factories · doi:10.1186/s12934-021-01692-2 · Cited by 22
Structural transitions modulate the chaperone activities of Grp94
Amankwah, Y. S., Fleifil, Y., Unruh, E., Collins, P., Wang, Y., Vitou, K., Bates, A., Obaseki, I., Sugoor, M., Alao, J. P., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2309326121 · Cited by 22
Metadata
- Authors
- Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., de Beer, T. A. P., Rempfer, C., Bordoli, L., Lepore, R., & Schwede, T.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-05-21
- Volume
- 46
- Issue
- W1
- Pages
- W296-W303
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2804822363
- Cited by
- 13,067
- References
- 11
- FWCI
- 345.61
- Type
- journal article
- Language
- en
- Open access
- gold
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.