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Large-scale discovery of protein interactions at residue resolution using co-evolution calculated from genomic sequences
Open accessAbstract
Increasing numbers of protein interactions have been identified in high-throughput experiments, but only a small proportion have solved structures. Recently, sequence coevolution-based approaches have led to a breakthrough in predicting monomer protein structures and protein interaction interfaces. Here, we address the challenges of large-scale interaction prediction at residue resolution with a fast alignment concatenation method and a probabilistic score for the interaction of residues. Importantly, this method (EVcomplex2) is able to assess the likelihood of a protein interaction, as we show here applied to large-scale experimental datasets where the pairwise interactions are unknown. We predict 504 interactions de novo in the E. coli membrane proteome, including 243 that are newly discovered. While EVcomplex2 does not require available structures, coevolving residue pairs can be used to produce structural models of protein interactions, as done here for membrane complexes including the Flagellar Hook-Filament Junction and the Tol/Pal complex.
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![Fig. 2 Model predicts interacting residues and interacting proteins with high precision. A Recall on the held-out fraction of the positive benchmark set (x-axis) and false positive rate on the held-out dataset of non-interacting complexes (y-axis) at a score threshold that gives the corresponding recall. Our logistic regression model (purple) reduces the false positive rate compared to the previous EV Complex score (blue). B Prediction of protein interaction is based on the prediction of interacting residues. Number of predicted interacting residue pairs for complexes inferred to interact (purple) or not inferred to interact (gray) based on our stringent protein complex prediction threshold. C Example performance on known interaction between ABC transporter permease and ATP binding subunit (UniProt IDs: Y1470_HAEIN and Y1471_HAEIN, PDB ID: 2NQ2 chains C and A [https://www.rcsb.org/structure/2NQ2]. D Example performance on known interaction between DNA primase PriS and PriL (UniProt IDs: PRIS_SULSO and PRIL_SULSO, PDB ID: 1ZT2 chain A and B [https://www.rcsb.org/structure/1ZT2]).](/paper-figures/8c99d2bf4a504b23915ac20b65474882/large-scale-discovery-of-protein-interactions-at-residue-resolution-using-co-figure-2.jpg)
![Fig. 3 Discovery of hundreds of new interactions in the E. coli membrane proteome. A We searched a high-value subset of the 10∧7 possible interactions in the E. coli proteome by searching membrane compartments with themselves and with adjacent membrane compartments. B We found 504 high-scoring protein interactions in the cell envelope, including 75 with structural characterization and 186 with previous experimental evidence (and no structural characterization). C–E 3D configurations of previously structurally characterized interactions are accurately predicted by molecular docking with inferred restraints. RMSDs calculated by comparison to known structures with PDB IDs 3RKO [https://www.rcsb.org/structure/3RKO], 2WU2 [https://www.rcsb.org/structure/2WU2], and 2HQS [https://www.rcsb.org/structure/2HQS], respectively. F–H Example of three docked models of newly resolved protein complexes: BamE/MltB, YajC/FtsI, and Lnt/MurJ. Evolutionarily coupled residues used as restraints in docking are shown in magenta and connected with solid lines.](/paper-figures/4a6f05b9624e471291ece061750a4d89/large-scale-discovery-of-protein-interactions-at-residue-resolution-using-co-figure-3.jpg)
![Fig. 4 Model of the flagellar hook-filament junction. A Schematic of the orientation of the bacterial flagellum. The proteins FlgL (green) and FlgK (blue) form two rings which create the junction between the hook and filament of the flagellum24. B Docked model of FlgL and FlgK using evolutionary couplings. PDB structures of homologous proteins from Salmonella typhimurium were used in docking (PDB IDs 2D4Y [https://www.rcsb.org/structure/2D4Y], and 2D4X [https://www.rcsb.org/structure/2D4X], respectively). Predicted interface residues are highlighted in purple. C A previously inferred model of the FlgK ring from Campylobacter jejuni24 was used to infer the structure of the entire hook-filament junction. Evolutionary coupled residues (purple) show the interface for FlgL ring insertion into the FlgK ring. By aligning our docked model to the C. jejuni ring, we show that an 11-mer ring of FlgL fits inside the FlgK structure.](/paper-figures/2f04c515d1b4411fa5d72558ae1a1263/large-scale-discovery-of-protein-interactions-at-residue-resolution-using-co-figure-4.jpg)
![Fig. 5 Atomic resolution model of the Tol-Pal complex. A Schematic of the proteins involved in the Tol-Pal complex2 (TolABR, CpoB, and Pal). Interactions with previously solved interfaces are shown in orange and interactions inferred by our method are shown in purple. B Complete model of the Tol-Pal complex inferred by aligning results of docked pairwise models. Note that CpoB is inferred to be a trimer in vivo but was docked as a monomer for modeling purposes C–F Residue resolution of TolB-Pal, TolB-TolA, TolR-TolB, and CpoB-TolB interfaces. The top 5 inferred interface contacts are shown in purple. Dashed lines indicate inferred contacts where one or more residues are missing from the solved structure. Structures used are 1TOL_A (TolA) [https://www.rcsb.org/structure/1TOL], 2HQS_A (TolB) [https://www.rcsb.org/structure/2HQS], 5BY4_A (TolR) [https://www.rcsb.org/structure/5BY4], 2HQS_H (Pal) [https://www.rcsb.org/structure/2HQS], and 2XDJ_A (CpoB) [https://www.rcsb.org/structure/2XDJ].](/paper-figures/4aeb569056cf42b881376a4f19038abe/large-scale-discovery-of-protein-interactions-at-residue-resolution-using-co-figure-5.jpg)
![Fig. 6 Predicted protein interactions in eukaryotic proteomes. A Distribution of eukaryotic sequences in concatenated sequence alignments. Shown in orange are alignments that passed our sequence diversity threshold, and in gray are those that did not. B Number of correctly predicted inter-protein ECs for eukaryotic-exclusive complexes above the sequence diversity threshold. Eukaryotic-exclusive complexes are defined as complexes whose concatenated sequence alignment is at least 90% eukaryotic sequences. Inter protein ECs are defined as correct if their minimum atom distance is <8 Å. C The human spliceosome proteins Prp38 and MFAP1 have a known interface correctly predicted by our method, and have a protein interaction detected. The inter-protein ECs above the 80% precision threshold used throughout the paper are shown in purple. Known protein structure (PDB ID: 5F5U, [https://www.rcsb.org/structure/5F5U]) is used to visualize the subunits. D Schematic of predicted interaction between Lsm5 and Prp38 with top 5 inter-protein ECs.](/paper-figures/72ac0543df0e4718b992c18782b93f63/large-scale-discovery-of-protein-interactions-at-residue-resolution-using-co-figure-6.jpg)
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“Because residues at protein-protein interfaces are expected to coevolve, the likelihood that any two proteins interact can be assessed by identifying and aligning the ortholog sequences of the two proteins in many different species, joining them to create paired multiple sequence alignments (pMSA), and then determining the extent to which changes in the sequences of orthologs for the first protein”
From Computed structures of core eukaryotic protein complexes (2021) · cites this paper as [10]
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Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320
STRING v10: protein–protein interaction networks, integrated over the tree of life
Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436
Inference of Macromolecular Assemblies from Crystalline State
Krissinel, E., & Henrick, K. (2007) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.05.022 · Cited by 10,007
UniProt: the Universal Protein knowledgebase
Apweiler, R. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh131 · Cited by 7,593
Accelerated Profile HMM Searches
Eddy, S. R. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002195 · Cited by 6,975
The Pfam protein families database in 2019
El-Gebali, S., Mistry, J., Bateman, A., Eddy, S. R., Luciani, A., Potter, S. C., Qureshi, M., Richardson, L. J., Salazar, G. A., Smart, A., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky995 · Cited by 4,602
Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry
Ho, Y., Gruhler, A., Heilbut, A. M., Bader, G. D., Moore, L., Adams, S., Millar, A., Taylor, P. P., Bennett, K. L., Boutilier, K., et al. (2002) · Nature · doi:10.1038/415180a · Cited by 3,573
HADDOCK: A Protein−Protein Docking Approach Based on Biochemical or Biophysical Information
Dominguez, C., Boelens, R., & Bonvin, A. M. J. J. (2003) · Journal of the American Chemical Society · doi:10.1021/ja026939x · Cited by 3,187
Towards a proteome-scale map of the human protein–protein interaction network
Rual, J.-F. M., Venkatesan, K., Hao, T., Hirozane-Kishikawa, T., Dricot, A., Li, N., Berriz, G. F., Gibbons, F. D., Dreze, M., Ayivi-Guedehoussou, N., et al. (2005) · Nature · doi:10.1038/nature04209 · Cited by 2,904
UniProt: the universal protein knowledgebase
The UniProt Consortium (2018) · Nucleic Acids Research · doi:10.1093/nar/gky092 · Cited by 2,043
Spliceosome Structure and Function
Will, C. L., & Luhrmann, R. (2011) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a003707 · Cited by 1,829
Federhen, S. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1178 · Cited by 1,436
Version 1.2 of the Crystallography and NMR system
Brünger, A. T. (2007) · Nature Protocols · doi:10.1038/nprot.2007.406 · Cited by 1,325
Kühlbrandt, W. (2014) · Science · doi:10.1126/science.1251652 · Cited by 1,325
Improved prediction of protein side‐chain conformations with SCWRL4
Krivov, G. G., Shapovalov, M. V., & Dunbrack, R. L. (2009) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22488 · Cited by 1,312
Interaction network containing conserved and essential protein complexes in Escherichia coli
Butland, G., Peregrín-Alvarez, J. M., Li, J., Yang, W., Yang, X., Canadien, V., Starostine, A., Richards, D., Beattie, B., Krogan, N., et al. (2005) · Nature · doi:10.1038/nature03239 · Cited by 1,201
The nature of the accessible and buried surfaces in proteins
Chothia, C. (1976) · Journal of Molecular Biology · doi:10.1016/0022-2836(76)90191-1 · Cited by 1,165
Protein 3D Structure Computed from Evolutionary Sequence Variation
Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R., & Sander, C. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028766 · Cited by 1,144
Self-citationKamisetty, H., Ovchinnikov, S., & Baker, D. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1314045110 · Cited by 672
Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models
Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M., & Aurell, E. (2013) · Physical Review E · doi:10.1103/physreve.87.012707 · Cited by 661
Ovchinnikov, S., Kamisetty, H., & Baker, D. (2014) · eLife · doi:10.7554/elife.02030 · Cited by 629
Xue, B., Dunker, A. K., & Uversky, V. N. (2012) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2012.675145 · Cited by 557
Similarity of phylogenetic trees as indicator of protein–protein interaction
Pazos, F., & Valencia, A. (2001) · Protein Engineering Design and Selection · doi:10.1093/protein/14.9.609 · Cited by 521
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
Self-citationThree-Dimensional Structures of Membrane Proteins from Genomic Sequencing
Hopf, T. A., Colwell, L. J., Sheridan, R., Rost, B., Sander, C., & Marks, D. S. (2012) · Cell · doi:10.1016/j.cell.2012.04.012 · Cited by 509
Self-citationGlobal Functional Atlas of Escherichia coli Encompassing Previously Uncharacterized Proteins
Hu, P., Janga, S. C., Babu, M., Díaz-Mejía, J. J., Butland, G., Yang, W., Pogoutse, O., Guo, X., Phanse, S., Wong, P., et al. (2009) · PLoS Biology · doi:10.1371/journal.pbio.1000096 · Cited by 387
Gerding, M. A., Ogata, Y., Pecora, N., Niki, H., & de Boer, P. D. (2007) · Molecular Microbiology · doi:10.1111/j.1365-2958.2006.05571.x · Cited by 380
Learning generative models for protein fold families
Balakrishnan, S., Kamisetty, H., Carbonell, J. G., Lee, S., & Langmead, C. J. (2011) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22934 · Cited by 316
The EVcouplings Python framework for coevolutionary sequence analysis
Hopf, T. A., Green, A. G., Schubert, B., Mersmann, S., Schärfe, C., Ingraham, J., Tóth-Petróczy, Á., Brock, K. P., Riesselman, A. J., Palmedo, P., et al. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty862 · Cited by 286
Self-citationSIFTS: Structure Integration with Function, Taxonomy and Sequences resource
Velankar, S. S., Dana, J. M., Jacobsen, J. O. B., van Ginkel, G., Gane, P. J., Luo, J., Oldfield, T. J., O’Donovan, C., Martin, M. J., & Kleywegt, G. J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1258 · Cited by 277
Protein interaction networks revealed by proteome coevolution
Cong, Q., Anishchenko, I., Ovchinnikov, S., & Baker, D. (2019) · Science · doi:10.1126/science.aaw6718 · Cited by 241
The binary protein-protein interaction landscape of Escherichia coli
Rajagopala, S. V., Sikorski, P., Kumar, A., Mosca, R., Vlasblom, J., Arnold, R., Franca-Koh, J., Pakala, S. B., Phanse, S., Ceol, A., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2831 · Cited by 233
Large-scale determination of previously unsolved protein structures using evolutionary information
Ovchinnikov, S., Kinch, L., Park, H., Liao, Y., Pei, J., Kim, D. E., Kamisetty, H., Grishin, N. V., & Baker, D. (2015) · eLife · doi:10.7554/elife.09248 · Cited by 222
Lloubès, R., Cascalès, E., Walburger, A., Bouveret, E., Lazdunski, C. J., Bernadac, A., & Journet, L. (2001) · Research in Microbiology · doi:10.1016/s0923-2508(01)01226-8 · Cited by 188
Gray, A. N., Egan, A. J. F., Veer, I. L. V., Verheul, J., Colavin, A., Koumoutsi, A., Biboy, J., Altelaar, M. A. F., Damen, M. J. A., Huang, K. C., et al. (2015) · eLife · doi:10.7554/elife.07118 · Cited by 183
Homma, M., DeRosier, D. J., & Macnab, R. M. (1990) · Journal of Molecular Biology · doi:10.1016/s0022-2836(05)80266-9 · Cited by 168
Inferring protein 3D structure from deep mutation scans
Rollins, N. J., Brock, K. P., Poelwijk, F. J., Stiffler, M. A., Gauthier, N. P., Sander, C., & Marks, D. S. (2019) · Nature Genetics · doi:10.1038/s41588-019-0432-9 · Cited by 166
Self-citation3D RNA and Functional Interactions from Evolutionary Couplings
Weinreb, C., Riesselman, A. J., Ingraham, J. B., Gross, T., Sander, C., & Marks, D. S. (2016) · Cell · doi:10.1016/j.cell.2016.03.030 · Cited by 159
Self-citationDetermining protein structures using deep mutagenesis
Schmiedel, J. M., & Lehner, B. (2019) · Nature Genetics · doi:10.1038/s41588-019-0431-x · Cited by 148
Inferring interaction partners from protein sequences
Bitbol, A., Dwyer, R. S., Colwell, L. J., & Wingreen, N. S. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1606762113 · Cited by 137
ComplexContact: a web server for inter-protein contact prediction using deep learning
Zeng, H., Wang, S., Zhou, T., Zhao, F., Li, X., Wu, Q., & Xu, J. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky420 · Cited by 137
Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G. L., Bhat, T. N., Weissig, H., Shindyalov, I. N., & Bourne, P. E. (2006) · International Tables for Crystallography · doi:10.1107/97809553602060000722 · Cited by 135
Structure of the peptidoglycan polymerase RodA resolved by evolutionary coupling analysis
Sjodt, M., Brock, K. P., Dobihal, G. S., Rohs, P. D. A., Green, A. G., Hopf, T. A., Meeske, A. J., Srisuknimit, V., Kahne, D. E., Walker, S., et al. (2018) · Nature · doi:10.1038/nature25985 · Cited by 135
Self-citationGlobal landscape of cell envelope protein complexes in Escherichia coli
Babu, M., Bundalovic-Torma, C., Calmettes, C., Phanse, S., Zhang, Q., Jiang, Y., Minić, Z., Kim, S., Mehla, J., Gagarinova, A. G., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.4024 · Cited by 130
Walburger, A., Lazdunski, C. J., & Corda, Y. (2002) · Molecular Microbiology · doi:10.1046/j.1365-2958.2002.02895.x · Cited by 123
Expansion of Biological Pathways Based on Evolutionary Inference
Li, Y., Calvo, S. E., Gutman, R., Liu, J. S., & Mootha, V. K. (2014) · Cell · doi:10.1016/j.cell.2014.05.034 · Cited by 122
Gueudré, T., Baldassi, C., Zamparo, M., Weigt, M., & Pagnani, A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1607570113 · Cited by 116
Allosteric β‐propeller signalling in TolB and its manipulation by translocating colicins
Bonsor, D. A., Hecht, O., Vankemmelbeke, M. N., Sharma, A., Krachler, A. M., Housden, N. G., Lilly, K. J., James, R., Moore, G. R., & Kleanthous, C. (2009) · The EMBO Journal · doi:10.1038/emboj.2009.224 · Cited by 95
Predicting functional linkages from gene fusions with confidence.
Marcotte, C. V., & Marcotte, E. M. (2002) · PubMed · Cited by 66
Feinauer, C., Szurmant, H., Weigt, M., & Pagnani, A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149166 · Cited by 60
Díaz-Mejía, J. J., Babu, M., & Emili, A. (2008) · FEMS Microbiology Reviews · doi:10.1111/j.1574-6976.2008.00141.x · Cited by 54
Malinverni, D., López, A. J., Rios, P. D. L., Hummer, G., & Barducci, A. (2017) · eLife · doi:10.7554/elife.23471 · Cited by 54
Inferring interaction partners from protein sequences using mutual information
Bitbol, A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006401 · Cited by 54
Rodriguez-Rivas, J., Marsili, S., Juan, D., & Valencia, A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611861114 · Cited by 36
Scaffolding in the Spliceosome via Single α Helices
Ulrich, A. K. C., Seeger, M., Schütze, T., Bartlick, N., & Wahl, M. C. (2016) · Structure · doi:10.1016/j.str.2016.09.007 · Cited by 27
Structure of FlgK reveals the divergence of the bacterial Hook-Filament Junction of Campylobacter
Bulieris, P. V., Shaikh, N. H., Freddolino, P. L., & Samatey, F. A. (2017) · Scientific Reports · doi:10.1038/s41598-017-15837-0 · Cited by 21
Research works printed but not linked: 1
- Marcotte, C. & Marcotte, E. Predicting functional linkages from gene fusion. Appl. Bioinf. 1, 1–8 (2002).
Cited by
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Yang, Z., Zeng, X., Zhao, Y. C., & Chen, R. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01381-z · Cited by 633
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Shin, J.-E., Riesselman, A. J., Kollasch, A. W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A. C., & Marks, D. S. (2021) · Nature Communications · doi:10.1038/s41467-021-22732-w · Cited by 320
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Metadata
- Authors
- Green, A. G., Elhabashy, H., Brock, K. P., Maddamsetti, R., Kohlbacher, O., & Marks, D. S.
- Affiliations
- Harvard University (US)
- Max Planck Institute for Developmental Biology (DE)
- Universitätsklinikum Tübingen (DE)
- Broad Institute (US)
- Corresponding author
- Kohlbacher, Oliver
- Marks, Debora S.
- ORCID
- Venue
- Nature Communications
- Published
- 2021-03-02
- Volume
- 12
- Issue
- 1
- Pages
- 1396-1396
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7925567
- OpenAlex
- W3133793061
- Cited by
- 114
- References
- 57
- Citations by year
- 2026: 14 · 2025: 17 · 2024: 24 · 2023: 25 · 2022: 21 · 2021: 13
- FWCI
- 6.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (DGE1144152)
- U.S. Department of Health & Human Services | National Institutes of Health (T15LM007092)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- protein-protein interaction, membrane protein complexes
- MeSH terms
- Amino Acids, Bacterial Proteins, Base Sequence, Escherichia coli, Eukaryotic Cells, Membrane Proteins, Protein Binding, Genome, Bacterial, Evolution, Molecular, Proteome, Protein Interaction Mapping, Molecular Docking Simulation
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