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Evolutionary adaptation of DHFR via expression of enzyme isoforms with various binding properties and dynamics behavior: a bioinformatics and computational study

  • Elahe Karimi , Emran Heshmati and Khosrow Khalifeh ORCID logo EMAIL logo
Published/Copyright: December 15, 2021
Biological Chemistry
From the journal Biological Chemistry Volume 403 Issue 7

Abstract

We compared the binding properties and dynamics of three experimentally reviewed isoforms of human dihydrofolate reductase (DHFR). The cytoplasmic variants including isoforms1 and 2 (iso1 and iso2) are produced by alternative splicing; while the mitochondrial form is located in the mitochondria. The iso1 as the canonical sequence contains 187 residues, and iso2 differs from the iso1, where it has 1–52 residues missing at the N-terminus of canonical sequence. Here, the structural models of the iso2 and mitochondrial forms were constructed by the MODELLER program using the crystal structure of the iso1 as the template. Bioinformatics analysis on ligand-bearing structures demonstrates that mitochondrial variant forms more stable complex with ligands compared with iso1 and 2, indicating their different binding properties. The root mean square fluctuation (RMSF) data suggest that C-terminus of iso1 contains two representative highly flexible fragments, while iso2 contains a highly flexible fragment at N-terminus end. Interestingly, both ends of mitochondrial variant have a degree of rigidity. Finally, the observation of differences in structural dynamics and binding properties predicts that the simultaneous existence of enzyme isoforms is a way to increase the speed of the enzyme maneuver in response to various environmental conditions. This prediction needs to be tested experimentally.

Keywords: binding; bioinformatics; dihydrofolate reductase; flexibility; isoform; molecular dynamics (MDs)
Corresponding author: Khosrow Khalifeh, Department of Biology, Faculty of Sciences, University of Zanjan, University Blvd., Zanjan 45371-38791, Iran; and Department of Biotechnology, Research Institute of Modern Biological Techniques, University of Zanjan, Zanjan, Iran, E-mail:

Funding source: University of Zanjan

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: Financial support for this work was provided by the research council of the University of Zanjan.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J. (1990). Basic local alignment search tool. J. Mol. Biol. 215: 403–410, https://doi.org/10.1016/s0022-2836(05)80360-2.Search in Google Scholar

Bailey, S.W. and Ayling, J.E. (2009). The extremely slow and variable activity of dihydrofolate reductase in human liver and its implications for high folic acid intake. Proc. Natl. Acad. Sci. U.S.A. 106: 15424–15429, doi:https://doi.org/10.1073/pnas.0902072106.Search in Google Scholar

Banka, S., Blom, H.J., Walter, J., Aziz, M., Urquhart, J., Clouthier, C.M., Rice, G.I., De Brouwer, A.P.M., Hilton, E., Vassallo, G., et al.. (2011). Identification and characterization of an inborn error of metabolism caused by dihydrofolate reductase deficiency. Am. J. Hum. Genet. 88: 216–226, doi:https://doi.org/10.1016/j.ajhg.2011.01.004.Search in Google Scholar

Barker, W.C., Garavelli, J.S., Huang, H., McGarvey, P.B., Orcutt, B.C., Srinivasarao, G.Y., Xiao, C., Yeh, L.S.L., Ledley, R.S., Janda, J.F ., et al.. (2000). The protein information resource (PIR). Nucleic Acids Res. 28: 41–44https://doi.org/10.1093/nar/28.1.41.Search in Google Scholar

Bateman, A., Martin, M.J., Orchard, S., Magrane, M., Agivetova, R., Ahmad, S., Alpi, E., Bowler-Barnett, E.H., Britto, R., Bursteinas, B., et al.. (2021). UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49: D480–D489, doi:https://doi.org/10.1093/nar/gkaa1100.Search in Google Scholar

Benkovic, S.J. and Hammes-Schiffer, S. (2003). A perspective on enzyme catalysis. Science 301: 1196–11202, doi:https://doi.org/10.1126/science.1085515.Search in Google Scholar

Berendsen, H.J.C., van der Spoel, D., and van Drunen, R. (1995). GROMACS: a message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 91: 43–56, https://doi.org/10.1016/0010-4655(95)00042-e.Search in Google Scholar

Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., and Bourne, P.E. (2000). The protein data bank. Nucleic Acids Res. 28: 235–242, https://doi.org/10.1093/nar/28.1.235.Search in Google Scholar PubMed PubMed Central

Darden, T., York, D., and 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.Search in Google Scholar

Djukic, A. (2007). Folate-Responsive Neurologic Diseases. Pediatr. Neurol 37: 387–397, doi:https://doi.org/10.1016/j.pediatrneurol.2007.09.001.Search in Google Scholar PubMed

Dolinsky, T.J., Nielsen, J.E., McCammon, J.A., and Baker, N.A. (2004). PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations. Nucleic Acids Res. 1: 665–667, https://doi.org/10.1093/nar/gkh381.Search in Google Scholar PubMed PubMed Central

Duff, M.R., Gabel, S.A., Pedersen, L.C., Derose, E.F., Krahn, J.M., Howell, E.E., and London, R.E. (2020). The structural basis for nonsteroidal anti-inflammatory drug inhibition of human dihydrofolate reductase. J. Med. Chem. 63: 8314–8324, doi:https://doi.org/10.1021/acs.jmedchem.0c00546.Search in Google Scholar PubMed PubMed Central

Felsenstein, J. (1989). PHYLIP - phylogeny inference package (version 3.2). Cladistics 5: 164–166, https://doi.org/10.1016/0168-9525(89)90204-7.Search in Google Scholar

Fiser, A. and Šali, A. (2003). MODELLER: generation and refinement of homology-based protein structure models. Methods Enzymol. 374: 461–491, https://doi.org/10.1016/s0076-6879(03)74020-8.Search in Google Scholar

Foloppe, N. and MacKerell, A.D. (2000). All-atom empirical force field for nucleic acids: I. Parameter optimization based on small molecule and condensed phase macromolecular target data. J. Comput. Chem. 21: 86–104, https://doi.org/10.1002/(sici)1096-987x(20000130)21:2<86::aid-jcc2>3.0.co;2-g.10.1002/(SICI)1096-987X(20000130)21:2<86::AID-JCC2>3.0.CO;2-GSearch in Google Scholar

Hess, B., Kutzner, C., Van Der Spoel, D., and Lindahl, E. (2008). GRGMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theor. Comput. 4: 435–447, https://doi.org/10.1021/ct700301q.Search in Google Scholar

Higgins, D.G., Thompson, J.D., and Gibson, T.J. (1996). Using CLUSTAL for multiple sequence alignments. Methods Enzymol. 266: 383–402, https://doi.org/10.1016/s0076-6879(96)66024-8.Search in Google Scholar

Howell, E.E. (2005). Searching sequence space: two different approaches to dihydrofolate reductase catalysis. ChemBioChem. 6: 590–600, doi:https://doi.org/10.1002/cbic.200400237.Search in Google Scholar

Kompis, I.M., Islam, K., and Then, R.L. (2005). DNA and RNA synthesis: antifolates. Chem. Rev. 105: 593–620, doi:https://doi.org/10.1021/cr0301144.Search in Google Scholar

Li, W., Cowley, A., Uludag, M., Gur, T., McWilliam, H., Squizzato, S., Park, Y.M., Buso, N., and Lopez, R. (2015). The EMBL-EBI bioinformatics web and programmatic tools framework. Nucleic Acids Res. 43: W580–W584, https://doi.org/10.1093/nar/gkv279.Search in Google Scholar

Liu, C.T., Hanoian, P., French, J.B., Pringle, T.H., Hammes-Schiffer, S., and Benkovic, S.J. (2013). Functional significance of evolving protein sequence in dihydrofolate reductase from bacteria to humans. Proc. Natl. Acad. Sci. U.S.A. 110: 10159–10164, doi:https://doi.org/10.1073/pnas.1307130110.Search in Google Scholar

Madeira, F., Park, Y.M, Lee, J., Buso, N., Gur, T., Madhusoodanan, N., Basutkar, P., Tivey, A.R.N., Potter, S.C., Finn, R.D, et al.. (2019). The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res. 2: W636–W641, https://doi.org/10.1093/nar/gkz268.Search in Google Scholar

Meng, E.C., Pettersen, E.F., Couch, G.S., Huang, C.C., and Ferrin, T.E. (2006). Tools for integrated sequence-structure analysis with UCSF Chimera. BMC Bioinf. 7: 1–10, https://doi.org/10.1186/1471-2105-7-339.Search in Google Scholar

Pettersen, E.F., Goddard, T.D., Huang, C.C., Couch, G.S., Greenblatt, D.M., Meng, E.C., and Ferrin, T.E. (2004). UCSF Chimera - a visualization system for exploratory research and analysis. J. Comput. Chem. 25: 1605–1612, https://doi.org/10.1002/jcc.20084.Search in Google Scholar

Sasso, S.P., Gilli, R.M., Sari, J.C., Rimet, O.S., and Briand, C.M. (1994). Thermodynamic study of dihydrofolate reductase inhibitor selectivity. Biochim. Biophys Acta. 1207: 74–79, doi:https://doi.org/10.1016/0167-4838(94)90053-1.Search in Google Scholar

Schweitzer, B.I., Dicker, A.P., and Bertino, J.R. (1990). Dihydrofolate reductase as a therapeutic target. Faseb. J. 4: 2441–2452, https://doi.org/10.1096/fasebj.4.8.2185970.Search in Google Scholar PubMed

Shen, M.-Y. and Sali, A. (2006). Statistical potential for assessment and prediction of protein structures. Protein Sci. 15: 2507–2524, https://doi.org/10.1110/ps.062416606.Search in Google Scholar PubMed PubMed Central

Thul, P.J., Akesson, L., Wiking, M., Mahdessian, D., Geladaki, A., Ait Blal, H., Alm, T., Asplund, A., Björk, L., Breckels, L.M., et al.. (2017). A subcellular map of the human proteome. Science 356: 1–22, doi:https://doi.org/10.1126/science.aal3321.Search in Google Scholar PubMed

Uhlén, M., Fagerberg, L., Hallström, B.M., Lindskog, C., Oksvold, P., Mardinoglu, A., Sivertsson, Å., Kampf, C., Sjöstedt, E., Asplund, A., et al.. (2015). Tissue-based map of the human proteome. Science 347: 394–405, doi:https://doi.org/10.1126/science.1260419.Search in Google Scholar PubMed

Uhlen, M., Zhang, C., Lee, S., Sjöstedt, E., Fagerberg, L., Bidkhori, G., Benfeitas, R., Arif, M., Liu, Z., Edfors, F., et al.. (2017). A pathology atlas of the human cancer transcriptome. Science 357: 1–11, https://doi.org/10.1126/science.aan2507.Search in Google Scholar PubMed

Wu, C.H., Yeh, L.S., Huang, H., Arminski, L., Castro-Alvear, J., Chen, Y., Hu, Z., Kourtesis, P., Ledley, R.S., Suzek, B.E ., et al.. (2003). The protein information resource. Nucleic Acids Res. 31: 345–347https://doi.org/10.1093/nar/gkg040.Search in Google Scholar PubMed PubMed Central

Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A.E., and Berendsen, H.J.C. (2005). GROMACS: fast, flexible, and free. J. Comput. Chem. 26: 1701–1718, https://doi.org/10.1002/jcc.20291.Search in Google Scholar PubMed

Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/hsz-2021-0346).

Received: 2021-08-17
Accepted: 2021-12-04
Published Online: 2021-12-15
Published in Print: 2022-06-27

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

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  5. Protein Structure and Function
  6. Evolutionary adaptation of DHFR via expression of enzyme isoforms with various binding properties and dynamics behavior: a bioinformatics and computational study
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https://doi.org/10.1515/hsz-2021-0346
Keywords for this article
binding; bioinformatics; dihydrofolate reductase; flexibility; isoform; molecular dynamics (MDs)

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. Chemerin – exploring a versatile adipokine
  4. Research Articles/Short Communications
  5. Protein Structure and Function
  6. Evolutionary adaptation of DHFR via expression of enzyme isoforms with various binding properties and dynamics behavior: a bioinformatics and computational study
  7. Cell Biology and Signaling
  8. SRPK1 promotes cell proliferation and tumor growth of osteosarcoma through activation of the NF-κB signaling pathway
  9. LINC00520 up-regulates SOX5 to promote cell proliferation and invasion by miR-4516 in human hepatocellular carcinoma
  10. Long non-coding RNA FAM66C regulates glioma growth via the miRNA/LATS1 signaling pathway
  11. CERKL alleviates ischemia reperfusion-induced nervous system injury through modulating the SIRT1/PINK1/Parkin pathway and mitophagy induction
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