LassoPred: a tool to predict the 3D structure of lasso peptides
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Abstract
Lasso peptides (LaPs), characterized by their entangled slipknot-like structures, are a large class of ribosomally synthesized and post-translationally modified peptides (RiPPs), with examples functioning as antibiotics, enzyme inhibitors, and molecular switches. Despite thousands of LaP sequences predicted by bioinformatics, only around 50 distinct LaPs have been structurally characterized in the past 30 years. Existing computational tools, such as AlphaFold2, AlphaFold3 and ESMfold, fail to accurately predict LaP structures due to their irregular scaffold featuring a lariat knot-like fold and the presence of an isopeptide bond. To address this challenge, we developed LassoPred, designed with a classifier to annotate the ring, loop, and tail of an LaP sequence and a constructor to build a 3D structure. Leveraging LassoPred, we predict the 3D structures for 4749 unique LaP core sequences, creating the largest in silico-predicted lasso peptide structure database to date. LassoPred is publicly available through a web interface ( https://lassopred.accre.vanderbilt.edu/ ) and a command-line tool, supporting future structure-function relationship studies and aiding in the discovery of functional lasso peptides for chemical and biomedical applications.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
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 · see citing works
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems
Darden, T., York, D., & Pedersen, L. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464397 · Cited by 29,638 · see citing works
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879 · see citing works
Ryckaert, J., Ciccotti, G., & Berendsen, H. (1977) · Journal of Computational Physics · doi:10.1016/0021-9991(77)90098-5 · Cited by 19,525 · see citing works
Development and testing of a general amber force field
Wang, J.-M., Wolf, R., Caldwell, J., Kollman, P., & Case, D. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20035 · Cited by 18,318 · see citing works
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861 · see citing works
A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules
Cornell, W. D., Cieplak, P., Bayly, C. I., Gould, I. R., Merz, K. M., Ferguson, D. M., Spellmeyer, D. C., Fox, T., Caldwell, J. W., & Kollman, P. A. (1995) · Journal of the American Chemical Society · doi:10.1021/ja00124a002 · Cited by 13,111 · see citing works
ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB
Maier, J., Martinez, C., Kasavajhala, K., Wickstrom, L., Hauser, K., & Simmerling, C. L. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00255 · Cited by 10,971 · see citing works
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.708428 · Cited by 7,243 · see citing works
PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data.
Roe, D., & Cheatham, T. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400341p · Cited by 6,969 · see citing works
SWISS-MODEL: an automated protein homology-modeling server
Schwede, T. F. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg520 · Cited by 5,592 · see citing works
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
Structure and Dynamics of the TIP3P, SPC, and SPC/E Water Models at 298 K
Mark, P., & Nilsson, L. (2001) · The Journal of Physical Chemistry A · doi:10.1021/jp003020w · Cited by 4,756 · see citing works
Gradient boosting machines, a tutorial
Natekin, A., & Knoll, A. (2013) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2013.00021 · Cited by 3,887 · see citing works
Peterson, L. E. (2009) · Scholarpedia · doi:10.4249/scholarpedia.1883 · Cited by 2,462 · see citing works
Scoring function for automated assessment of protein structure template quality
Zhang, Y., & Skolnick, J. (2004) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20264 · Cited by 2,392 · see citing works
Template-based protein structure modeling using the RaptorX web server
Källberg, M., Wang, H., Wang, S., Peng, J., Wang, Z., Lu, H. J., & Xu, J. (2012) · Nature Protocols · doi:10.1038/nprot.2012.085 · Cited by 1,576 · see citing works
A new genome-mining tool redefines the lasso peptide biosynthetic landscape
Tietz, J. I., Schwalen, C. J., Patel, P. S., Maxson, T., Saint‐Vincent, P. M. B., Tai, H.-C., Zakai, U. I., & Mitchell, D. A. (2017) · Nature Chemical Biology · doi:10.1038/nchembio.2319 · Cited by 490 · see citing works
Lasso Peptides: An Intriguing Class of Bacterial Natural Products
Hegemann, J. D., Zimmermann, M., Xie, X., & Marahiel, M. A. (2015) · Accounts of Chemical Research · doi:10.1021/acs.accounts.5b00156 · Cited by 395 · see citing works
Microcin 25, a novel antimicrobial peptide produced by Escherichia coli
Salomón, R. A., & Farı́as, R. N. (1992) · Journal of Bacteriology · doi:10.1128/jb.174.22.7428-7435.1992 · Cited by 336 · see citing works
Lasso peptides: structure, function, biosynthesis, and engineering
Maksimov, M. O., Pan, S. J., & Link, A. J. (2012) · Natural Product Reports · doi:10.1039/c2np20070h · Cited by 277 · see citing works
Self-citationSaProt: Protein Language Modeling with Structure-aware VocabularyPreprint
Su, J., Han, C., Zhou, Y., Shan, J., Zhou, X., & Yuan, F. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.01.560349 · Cited by 251 · see citing works
Knappe, T. A., Linne, U., Zirah, S., Rebuffat, S., Xie, X., & Marahiel, M. A. (2008) · Journal of the American Chemical Society · doi:10.1021/ja802966g · Cited by 238 · see citing works
Lin, Y., Pan, D., Li, J., Zhang, L., & Shao, X. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4978807 · Cited by 197 · see citing works
AlphaFold predictions of fold-switched conformations are driven by structure memorization
Chakravarty, D., Schafer, J. W., Chen, E. A., Thole, J. F., Ronish, L. A., Lee, M., & Porter, L. L. (2024) · Nature Communications · doi:10.1038/s41467-024-51801-z · Cited by 159 · see citing works
Precursor-centric genome-mining approach for lasso peptide discovery
Maksimov, M. O., Pelczer, I., & Link, A. J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1208978109 · Cited by 147 · see citing works
Self-citationLariatins, Antimycobacterial Peptides Produced by Rhodococcus sp. K01−B0171, Have a Lasso Structure
Iwatsuki, M., Tomoda, H., Uchida, R., Gouda, H., Hirono, S., & Ōmura, S. (2006) · Journal of the American Chemical Society · doi:10.1021/ja056780z · Cited by 139 · see citing works
Knappe, T. A., Manzenrieder, F., Mas‐Moruno, C., Linne, U., Sasse, F., Kessler, H., Xie, X., & Marahiel, M. A. (2011) · Angewandte Chemie International Edition · doi:10.1002/anie.201102190 · Cited by 134 · see citing works
Systematic Structure-Activity Analysis of Microcin J25
Pavlova, O., Mukhopadhyay, J., Sineva, E. V., Ebright, R. H., & Severinov, K. (2008) · Journal of Biological Chemistry · doi:10.1074/jbc.m803995200 · Cited by 132 · see citing works
A simple and accurate algorithm for path integral molecular dynamics with the Langevin thermostat
Liu, J., Li, D.-Z., & Liu, X. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4954990 · Cited by 129 · see citing works
Structural mechanism of transcription inhibition by lasso peptides microcin J25 and capistruin
Braffman, N. R., Piscotta, F. J., Hauver, J., Campbell, E. A., Link, A. J., & Darst, S. A. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1817352116 · Cited by 125 · see citing works
Self-citationA broad-spectrum lasso peptide antibiotic targeting the bacterial ribosome
Jangra, M., Travin, D. Y., Aleksandrova, E. V., Kaur, M., Darwish, L., Koteva, K. K., Klepacki, D., Wang, W., Tiffany, M., Sokaribo, A. S., et al. (2025) · Nature · doi:10.1038/s41586-025-08723-7 · Cited by 124 · see citing works
Dynamic covalent self-assembly of mechanically interlocked molecules solely made from peptides
Schröder, H. V., Zhang, Y., & Link, A. J. (2021) · Nature Chemistry · doi:10.1038/s41557-021-00770-7 · Cited by 121 · see citing works
Self-citationWeber, W., Fischli, W., Hochuli, E., KUPFER, E., & Weibel, E. K. (1991) · The Journal of Antibiotics · doi:10.7164/antibiotics.44.164 · Cited by 121 · see citing works
Genome mining for lasso peptides: past, present, and future
Cheung‐Lee, W. L., & Link, A. J. (2019) · Journal of Industrial Microbiology & Biotechnology · doi:10.1007/s10295-019-02197-z · Cited by 103 · see citing works
Self-citationEnzymatic Reconstitution and Biosynthetic Investigation of the Lasso Peptide Fusilassin
DiCaprio, A. J., Firouzbakht, A., Hudson, G. A., & Mitchell, D. A. (2018) · Journal of the American Chemical Society · doi:10.1021/jacs.8b09928 · Cited by 93 · see citing works
Structural basis for hijacking siderophore receptors by antimicrobial lasso peptides
Mathavan, I., Zirah, S., Mehmood, S., Choudhury, H. G., Goulard, C., Li, Y., Robinson, C. V., Rebuffat, S., & Beis, K. (2014) · Nature Chemical Biology · doi:10.1038/nchembio.1499 · Cited by 93 · see citing works
Rational Improvement of the Affinity and Selectivity of Integrin Binding of Grafted Lasso Peptides
Hegemann, J. D., Simone, M. D., Zimmermann, M., Knappe, T. A., Xie, X., Leva, F. S. D., Marinelli, L., Novellino, E., Stefan Zahler, A., Kessler, H., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm5004478 · Cited by 89 · see citing works
Heterologous and in Vitro Reconstitution of Fuscanodin, a Lasso Peptide from Thermobifida fusca
Koos, J. D., & Link, A. J. (2018) · Journal of the American Chemical Society · doi:10.1021/jacs.8b10724 · Cited by 79 · see citing works
Self-citationCell-Free Biosynthesis to Evaluate Lasso Peptide Formation and Enzyme–Substrate Tolerance
Si, Y., Kretsch, A. M., Daigh, L. M., Burk, M. J., & Mitchell, D. A. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c01452 · Cited by 75 · see citing works
Lasso Peptide Benenodin-1 Is a Thermally Actuated [1]Rotaxane Switch
Zong, C., Wu, M. J., Qin, J. Z., & Link, A. J. (2017) · Journal of the American Chemical Society · doi:10.1021/jacs.7b04830 · Cited by 75 · see citing works
Self-citationAn Experimental and Computational Investigation of Spontaneous Lasso Formation in Microcin J25
Ferguson, A. L., Zhang, S., Dikiy, I., Panagiotopoulos, A. Z., Debenedetti, P. G., & Link, A. J. (2010) · Biophysical Journal · doi:10.1016/j.bpj.2010.08.073 · Cited by 70 · see citing works
Self-citationLasso peptides: chemical approaches and structural elucidation
Martín-Gómez, H., & Tulla‐Puche, J. (2018) · Organic & Biomolecular Chemistry · doi:10.1039/c8ob01304g · Cited by 68 · see citing works
Self-Assembly of Catenanes from Lasso Peptides
Allen, C. D., & Link, A. J. (2016) · Journal of the American Chemical Society · doi:10.1021/jacs.6b09454 · Cited by 65 · see citing works
Self-citationFactors Governing the Thermal Stability of Lasso Peptides
Hegemann, J. D. (2019) · ChemBioChem · doi:10.1002/cbic.201900364 · Cited by 64 · see citing works
Constantine, K. L., Friedrichs, M., Detlefsen, D. J., NISHIO, M., TSUNAKAWA, M., FURUMAI, T., OHKUMA, H., OKI, T., Hill, S. E., Bruccoleri, R., et al. (1995) · Journal of Biomolecular NMR · doi:10.1007/bf00211754 · Cited by 59 · see citing works
Katahira, R., Shibata, K., Yamasaki, M., Matsuda, Y., & Yoshida, M. (1995) · Bioorganic & Medicinal Chemistry · doi:10.1016/0968-0896(95)00122-w · Cited by 59 · see citing works
Cellulonodin-2 and Lihuanodin: Lasso Peptides with an Aspartimide Post-Translational Modification
Cao, L., Beiser, M., Koos, J. D., Orlova, M., Elashal, H. E., Schröder, H. V., & Link, A. J. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c05017 · Cited by 55 · see citing works
Self-citationThermal Unthreading of the Lasso Peptides Astexin-2 and Astexin-3
Allen, C. D., Chen, M. Y., Trick, A. Y., Le, D. T., Ferguson, A. L., & Link, A. J. (2016) · ACS Chemical Biology · doi:10.1021/acschembio.6b00588 · Cited by 54 · see citing works
Self-citationWilliams‐Noonan, B. J., Kamboukos, A., Todorova, N., & Yarovsky, I. (2023) · Chemical Physics Reviews · doi:10.1063/5.0142302 · Cited by 52 · see citing works
AlphaFold predicts the most complex protein knot and composite protein knots
Brems, M. A., Runkel, R., Yeates, T. O., & Virnau, P. (2022) · Protein Science · doi:10.1002/pro.4380 · Cited by 50 · see citing works
Zimmermann, M., Hegemann, J. D., Xie, X., & Marahiel, M. A. (2014) · Chemical Science · doi:10.1039/c4sc01428f · Cited by 50 · see citing works
Peptidase Activation by a Leader Peptide-Bound RiPP Recognition Element
Kretsch, A. M., Gadgil, M. G., DiCaprio, A. J., Barrett, S. E., Kille, B. L., Si, Y., Zhu, L., & Mitchell, D. A. (2023) · Biochemistry · doi:10.1021/acs.biochem.2c00700 · Cited by 38 · see citing works
Liu, T., Ma, X., Yu, J., Yang, W., Wang, G., Wang, Z., Ge, Y., Song, J., Han, H., Zhang, W., et al. (2021) · Chemical Science · doi:10.1039/d1sc02695j · Cited by 37 · see citing works
Protein Engineering in Ribosomally Synthesized and Post-translationally Modified Peptides (RiPPs)
Do, T., & Link, A. J. (2022) · Biochemistry · doi:10.1021/acs.biochem.1c00714 · Cited by 35 · see citing works
Self-citationLasso peptide, a highly stable structure and designable multifunctional backbone
Zhao, N., Pan, Y., Cheng, Z., & Liu, H. (2016) · Amino Acids · doi:10.1007/s00726-016-2228-x · Cited by 34 · see citing works
Lasso Peptides: Exploring the Folding Landscape of Nature’s Smallest Interlocked Motifs
da Hora, G. C. A., Oh, M., Mifflin, M. C., Digal, L., Roberts, A. G., & Swanson, J. M. J. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.3c10126 · Cited by 32 · see citing works
Thokkadam, A., Do, T., Ran, X., Brynildsen, M. P., Yang, Z. J., & Link, A. J. (2023) · ACS Central Science · doi:10.1021/acscentsci.2c01487 · Cited by 32 · see citing works
Self-citationBratovanov, E. V., Ishida, K., Heinze, B., Pidot, S. J., Stinear, T. P., Hegemann, J. D., Marahiel, M. A., & Hertweck, C. (2019) · ACS Chemical Biology · doi:10.1021/acschembio.9b00805 · Cited by 30 · see citing works
Substrate interactions guide cyclase engineering and lasso peptide diversification
Barrett, S. E., Song, Y., Jordan, P., Brunson, J. K., Gordon-Nunez, J., da Cruz, G. C. M., Rosario, C. J., Okada, B. K., Anderson, K., Pires, T. A., et al. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01727-w · Cited by 29 · see citing works
LassoHTP: A High-Throughput Computational Tool for Lasso Peptide Structure Construction and Modeling
Juarez, R. J., Jiang, Y., Tremblay, M., Shao, Q., Link, A. J., & Yang, Z. J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00945 · Cited by 25 · see citing works
Self-citationCloacaenodin, an Antimicrobial Lasso Peptide with Activity against Enterobacter
Carson, D. V., Patiño, M., Elashal, H. E., Cartagena, A. J., Zhang, Y., Whitley, M. E., So, L., Kayser-Browne, A., Earl, A. M., Bhattacharyya, R. P., et al. (2022) · ACS Infectious Diseases · doi:10.1021/acsinfecdis.2c00446 · Cited by 21 · see citing works
Self-citationRubach, P., Sikora, M., Jarmolinska, A. I., Perlinska, A. P., & Sulkowska, J. I. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae443 · Cited by 16 · see citing works
Everything AlphaFold tells us about protein knots
Perlinska, A. P., Sikora, M., & Sulkowska, J. I. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168715 · Cited by 13 · see citing works
Cha, Y., Kim, W., Park, Y., Kim, M., Son, Y., & Park, W. (2023) · Journal of Phycology · doi:10.1111/jpy.13412 · Cited by 12 · see citing works
The Shuttling Cascade in Lasso Peptide Benenodin‐1 is Controlled by Non‐Covalent Interactions
Schröder, H. V., Stadlmeier, M., Wühr, M., & Link, A. J. (2021) · Chemistry - A European Journal · doi:10.1002/chem.202103615 · Cited by 12 · see citing works
Self-citationYang, Z. J., Hajlasz, N., & Kulik, H. J. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c00762 · Cited by 12 · see citing works
Self-citationda Hora, G. C. A., Oh, M., Nguyen, J. D. M., & Swanson, J. M. J. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c08492 · Cited by 9 · see citing works
Heterologous production of new lasso peptide koreensin based on genome mining
Fuwa, H., Hemmi, H., Kaweewan, I., Kozaki, I., Honda, H., & Kodani, S. (2020) · The Journal of Antibiotics · doi:10.1038/s41429-020-00363-5 · Cited by 9 · see citing works
Shihoya, W., Akasaka, H., Jordan, P. A., Lechner, A., Okada, B. K., Costa Machado da Cruz, G., Sano, F. K., Tanaka, T., Kawahara, R., Chaudhari, R., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57960-x · Cited by 8 · see citing works
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1 of the 74 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Progress in Molecular Dynamics Simulations of Mechanically Interlocked Polymers
Wang, Y., Liu, G., Deng, J., & Yan, X. (2025) · Macromolecules · doi:10.1021/acs.macromol.5c02674 · Cited by 12 · see citing works
Progress and Challenges in the Biosynthesis and Chemical Synthesis of Lasso Peptides
Khatri, B., Lay, T., & Craik, D. J. (2026) · Journal of the American Chemical Society · doi:10.1021/jacs.5c10593 · Cited by 9 · see citing works
LassoESM a tailored language model for enhanced lasso peptide property prediction
Mi, X., Barrett, S. E., Mitchell, D. A., & Shukla, D. (2025) · Nature Communications · doi:10.1038/s41467-025-63412-3 · Cited by 7 · see citing works
Biosynthesis, Structure, and Antibiotic Properties of Gelatinamin A, a Triculamin‐Like Lasso Peptide
Svenningsen, T., Merrild, A., Wang, F., Hansen, F. D., Johnson, T. G., Iwase, R., Viennet, T., Larsen, M. K., Link, A. J., & Tørring, T. (2026) · ChemBioChem · doi:10.1002/cbic.70378 · Cited by 3 · see citing works
Self-citationHarnessing artificial intelligence for antimicrobial discovery and optimization
Clements, A. E., Fieldhouse, M. R., & Walker, A. S. (2026) · Current Opinion in Microbiology · doi:10.1016/j.mib.2026.102724 · Cited by 3 · see citing works
Lasso Peptide Syanodin I: Loop-Pulling Two-Step Thermal Unthreading Mechanism
Santos-Fernandez, M., Fouque, K. J. D., Karki, U., Blond, A., Zirah, S., Chapagain, P. P., Hegemann, J. D., & Fernandez-Lima, F. A. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.5c04118 · Cited by 2 · see citing works
Tangled Tail of Mechanically Interlocked Peptides
Johnson, T. G., & Link, A. J. (2026) · Journal of the American Chemical Society · doi:10.1021/jacs.6c05395 · Cited by 2 · see citing works
Self-citationWang, T., Huang, W., Yan, H., Zhao, S., Sun, R., Guo, Y., & Li, Y. (2026) · Environments · doi:10.3390/environments13030147 · Cited by 1 · see citing works
Yu, L., Zhang, J., Han, Y.-H., Zeng, Z., Chen, Q., Ke, W., & Cao, W. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.150111
Predicting lasso peptide structure with LassoPred
Ouyang, X., Ran, X., Zhu, D., & Yang, Z. J. (2026) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2026.01.037
Self-citationMahdy, A., Alam-ElDein, K. M., Amin, I., Mohamed, H. H., Abuelhaded, K., Hamdy, M., Elhemiely, A. A., El-Zahraa, F., Ibrahim, A. K., Gadelmawla, M. H. A., et al. (2026) · The Journal of Antibiotics · doi:10.1038/s41429-026-00954-8
- Authors
- Ouyang, X., Ran, X., Xu, H., Al-Abssi, R., Zhao, Y.-L., Link, A. J., & Yang, Z. J.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Vanderbilt University (US)
- Princeton University (US)
- Corresponding author
- Zhao, Yi-Lei
- Link, A. James
- Yang, Zhongyue J.
- Venue
- Nature Communications
- Published
- 2025-07-01
- Volume
- 16
- Issue
- 1
- Pages
- 5497-5497
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC12214591
- OpenAlex
- W4411891308
- Cited by
- 11
- References
- 74
- Citations by year
- 2026: 8 · 2025: 3
- FWCI
- 3.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Vanderbilt University
- National Natural Science Foundation of China (31970041)
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (GM107036)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Health Sciences › Medicine › Pharmacology › Microbial Natural Products and Biosynthesis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
- Keywords
- lasso peptides, 3D structure prediction, ribosomally synthesized and post-translationally modified peptides, isopeptide bonds, structure annotation, structure-function relationship, peptide antibiotics, web-based prediction tool
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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 ›
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