The Tn antigen is an epitope containing N-acetyl-D-galactosamine present in the extracellular matrix of some carcinoma cells in humans, and it is often used as a biomarker. Lectins are proteins capable of binding to carbohydrates and can be used as a molecular tool to recognize antigens and to differentiate cancer cells from normal cells. In this context, the present work aimed to characterize the interaction of Vatairea guianensis seed lectin with N-acetyl-D-galactosamine and the Tn antigen by molecular dynamics and molecular mechanics/Poisson–Boltzmann solvent-accessible surface area analysis. This study revealed new interacting residues not previously identified in static analysis of the three-dimensional structures of Vatairea lectins, as well as the configuration taken by the carbohydrate recognition domain, as it interacts with each ligand. During the molecular dynamics simulations, Vatairea guianensis lectin was able to bind stably to Tn antigen, which, as seen previously for other lectins, enables its use in cancer research, diagnosis, and therapy. This work further demonstrates the efficiency of bioinformatics in lectinology.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Pinho SS, Reis CA (2015) Glycosylation in cancer: mechanisms and clinical implications. Nat. Rev. Cancer 15:540–555. https://doi.org/10.1038/nrc3982
Tabassum DP, Polyak K (2015) Tumorigenesis: it takes a village. Nat. Rev. Cancer 15:473–483. https://doi.org/10.1038/nrc3971
Peumans WJ, Van Damme E (1995) Lectins as plant defense proteins. Plant Physiol. 109:347–352. https://doi.org/10.1104/pp.109.2.347
Ghazarian H, Idoni B, Oppenheimer SB (2011) A glycobiology review: carbohydrates, lectins and implications in cancer therapeutics. Acta Histochem. 113:236–247. https://doi.org/10.1016/j.acthis.2010.02.004
Hashim OH, Jayapalan JJ, Lee CS (2017) Lectins: an effective tool for screening of potential cancer biomarkers. PeerJ 2017:1–30. https://doi.org/10.7717/peerj.3784
Fu C, Zhao H, Wang Y et al (2016) Tumor-associated antigens: Tn antigen, sTn antigen, and T antigen. Hla 88:275–286. https://doi.org/10.1111/tan.12900
Poiroux G, Barre A, van Damme EJM et al (2017) Plant lectins targeting O-glycans at the cell surface as tools for cancer diagnosis, prognosis and therapy. Int. J. Mol 18. https://doi.org/10.3390/ijms18061232
Ju T, Otto VI, Cummings RD (2011) The Tn antigena-structural simplicity and biological complexity. Angew. Chem. Int. Ed. 50:1770–1791. https://doi.org/10.1002/anie.201002313
Julien S, Videira PA, Delannoy P (2012) Sialyl-Tn in cancer: (how) did we miss the target? Biomolecules 2:435–466. https://doi.org/10.3390/biom2040435
Lubkowski J, Durbin SV, Silva MCC et al (2017) Structural analysis and unique molecular recognition properties of a Bauhinia forficata lectin that inhibits cancer cell growth. FEBS J. 284:429–450. https://doi.org/10.1111/febs.13989
Kulkarni KA, Sinha S, Katiyar S et al (2005) Structural basis for the specificity of basic winged bean lectin for the Tn-antigen: a crystallographic, thermodynamic and modelling study. FEBS Lett. 579:6775–6780. https://doi.org/10.1016/j.febslet.2005.11.011
Babino A, Tello D, Rojas A et al (2003) The crystal structure of a plant lectin in complex with the Tn antigen. FEBS Lett. 536:106–110. https://doi.org/10.1016/S0014-5793(03)00037-1
Madariaga D, Martinez-Sáez N, Somovilla VJ et al (2015) Detection of tumor-associated glycopeptides by lectins: the peptide context modulates carbohydrate recognition. ACS Chem. Biol. 10:747–756. https://doi.org/10.1021/cb500855x
Sousa BL, Silva Filho JC, Kumar P et al (2015) High-resolution structure of a new Tn antigen-binding lectin from Vatairea macrocarpa and a comparative analysis of Tn-binding legume lectins. Int. J. Biochem. Cell Biol. 59:103–110. https://doi.org/10.1016/j.biocel.2014.12.002
Sousa BL, Silva-Filho JC, Kumar P et al (2016) Structural characterization of a Vatairea macrocarpa lectin in complex with a tumor-associated antigen: a new tool for cancer research. Int. J. Biochem. Cell Biol. 72:27–39. https://doi.org/10.1016/j.biocel.2015.12.016
Marques GFOGFO, Osterne VJSVJS, Almeida LMLM et al (2017) Contribution of the carbohydrate-binding ability of Vatairea guianensis lectin to induce edematogenic activity. Biochimie 140:58–65. https://doi.org/10.1016/j.biochi.2017.06.008
Silva HC, Bari AU, Rocha BAM et al (2013) Purification and primary structure of a mannose/glucose-binding lectin from Parkia biglobosa Jacq. seeds with antinociceptive and anti-inflammatory properties. J Mol Recognit 26. https://doi.org/10.1002/jmr.2289
Laskowski MW, MacArthur MW, Moss DS et al (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J. Appl. Crystallogr. 26:283–291. https://doi.org/10.1107/S0021889892009944
Benkert P, Biasini M, Schwede T (2011) Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 27:343–350. https://doi.org/10.1093/bioinformatics/btq662
Benkert P, Tosatto SCE, Schomburg D (2008) QMEAN: a comprehensive scoring function for model quality assessment. Proteins Struct. Funct. Genet. 71:261–277. https://doi.org/10.1002/prot.21715
Benkert P, Künzli M, Schwede T (2009) QMEAN server for protein model quality estimation. Nucleic Acids Res. 37:510–514. https://doi.org/10.1093/nar/gkp322
Jones G, Willett P, Glen RC et al (1997) Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 267:727–748. https://doi.org/10.1006/jmbi.1996.0897
Eldridge MD, Murray CW, Auton TR et al (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J. Comput. Aided Mol. Des. 11:425–445. https://doi.org/10.1023/A:1007996124545
Case DA, Ben-Shalom IY, Brozell SR et al (2018) AMBER 2018. University of California, San Francisco
Lee J, Cheng X, Swails JM et al (2016) CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J. Chem. Theory Comput. 12:405–413. https://doi.org/10.1021/acs.jctc.5b00935
Nosé S (1984) A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys. 81:511–519. https://doi.org/10.1063/1.447334
Hoover WG (1985) Canonical dynamics—equilibrium phase-space distributions. pdf. 31:1695–1697
Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J. Appl. Phys. 52:7182–7190. https://doi.org/10.1063/1.328693
Hess B, Bekker H, Berendsen HJC, Fraaije JGEM (1997) LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 18:1463–1472. https://doi.org/10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H
Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N·log(N) method for Ewald sums in large systems. J. Chem. Phys. 98:10089–10092. https://doi.org/10.1063/1.464397
Roe DR, Cheatham TE (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput. 9:3084–3095. https://doi.org/10.1021/ct400341p
Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J. Mol. Graph. 14(1):33–38. https://doi.org/10.1016/0263-7855(96)00018-5
Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discovery 10:449–461. https://doi.org/10.1517/17460441.2015.1032936
Miller BR, McGee TD, Swails JM et al (2012) MMPBSA.py: an efficient program for end-state free energy calculations. J. Chem. Theory Comput. 8:3314–3321. https://doi.org/10.1021/ct300418h
Xue J, Huang X, Zhu Y (2019) Using molecular dynamics simulations to evaluate active designs of cephradine hydrolase by molecular mechanics/Poisson-Boltzmann surface area and molecular mechanics/generalized Born surface area methods. RSC Adv. 9:13868–13877. https://doi.org/10.1039/c9ra02406a
David Martin helped with the English editing of the manuscript.
This research was supported by grants from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), and Fundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico (FUNCAP); Nascimento KS and Cavada BS are senior investigators of CNPq/Brazil.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
About this article
Cite this article
Cavada, B.S., Osterne, V.J.S., Pinto-Junior, V.R. et al. Molecular dynamics and binding energy analysis of Vatairea guianensis lectin: a new tool for cancer studies. J Mol Model 26, 22 (2020). https://doi.org/10.1007/s00894-019-4281-3
- Tn antigen
- Vatairea guianensis
- Molecular dynamics