Abstract
Protein-protein interactions coordinate functions within a cell making it fit to survive. Some of the proteins in this network of interactions act as hubs, interacting with many other proteins. A detailed understanding of the functioning of proteins, especially hub proteins, given its three-dimensional structure, is critical for drug development. Dynamics of proteins play a major role in these cases, as recognition of multiple ligands requires conformational diversity to form specific interactions to partner molecules. One hub-like protein is Cyclin-dependent kinases 2 or CDK2, whose primary function is to act as a cell-cycle checkpoint, and is particularly relevant to cancer progression. As a result, it has long been considered a drug target. Detailed analysis of its dynamics can help to understand how it binds to a drug molecule, or how it recognizes its partner protein Cyclin-E. Here, we show how two methods using molecular dynamics simulation (MD) and normal mode analysis (NMA) help us to address the above. We show that by using an advanced MD method, multicanonical MD, a drug molecule can be successfully docked into the pocket of CDK2 by fully exploring the conformational and configurational diversity. This enabled us to accurately estimate both the binding configuration and the affinity between the molecules. Next, we show the impact on the dynamics of Cyclin-E binding to CDK2 by using our custom implementation of NMA at the atomic level. We demonstrate the importance of insights gained into the dynamics of proteins via computational methods is crucial for understanding their function and behavior and for the development of drugs against cancer.
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References
Allen MP, Tildesley DJ (2017) Computer simulation of liquids, 2nd edn. Oxford University Press, Clarendon Press, NY, USA
Asghar U, Witkiewicz AK, Turner NC, Knudsen ES (2015) The history and future of targeting cyclin-dependent kinases in cancer therapy. Nat Rev Drug Discov 14:130–146. https://doi.org/10.1038/nrd4504
Atilgan AR, Durell SR, Jernigan RL et al (2001) Anisotropy of fluctuation dynamics of proteins with an elastic network model. Biophys J 80:505–515
Bekker G-J, Nakamura H, Kinjo AR (2016) Molmil: a molecular viewer for the PDB and beyond. J Chem 8:42–42. https://doi.org/10.1186/s13321-016-0155-1
Bekker G-J, Kamiya N, Araki M et al (2017) Accurate prediction of complex structure and affinity for a flexible protein receptor and its inhibitor. J Chem Theory Comput 13:2389–2399. https://doi.org/10.1021/acs.jctc.6b01127
Bekker G-J, Araki M, Oshima K et al (2019) Dynamic docking of a medium-sized molecule to its receptor by multicanonical MD simulations. J Phys Chem B 123:2479–2490. https://doi.org/10.1021/acs.jpcb.8b12419
Bekker G-J, Araki M, Oshima K et al (2020a) Exhaustive search of the configurational space of heat-shock protein 90 with its inhibitor by multicanonical molecular dynamics based dynamic docking. J Comput Chem 41:1606–1615. https://doi.org/10.1002/jcc.26203
Bekker G-J, Fukuda I, Higo J, Kamiya N (2020b) Mutual population-shift driven antibody-peptide binding elucidated by molecular dynamics simulations. Sci Rep 10:1406. https://doi.org/10.1038/s41598-020-58320-z
Bekker G-J, Kawabata T, Kurisu G (2020c) The biological structure model archive (BSM-arc): an archive for in silico models and simulations. Biophys Rev 12:371–375. https://doi.org/10.1007/s12551-020-00632-5
Chen P, Lee NV, Hu W et al (2016) Spectrum and degree of CDK drug interactions predicts clinical performance. Mol Cancer Ther 15:2273–2281. https://doi.org/10.1158/1535-7163.MCT-16-0300
Cornell WD, Cieplak P, Bayly CI et al (1995) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 117:5179–5197
Dasgupta B, Nakamura H, Kinjo AR (2011) Distinct roles of overlapping and non-overlapping regions of hub protein interfaces in recognition of multiple partners. J Mol Biol 411:713–727
Dasgupta B, Nakamura H, Kinjo AR (2013) Counterbalance of ligand- and self-coupled motions characterizes multispecificity of ubiquitin. Protein Sci 22:168–178
Dasgupta B, Kasahara K, Kamiya N et al (2014a) Specific non-local interactions are not necessary for recovering native protein dynamics. PLoS One 9:e91347. https://doi.org/10.1371/journal.pone.0091347
Dasgupta B, Nakamura H, Kinjo AR (2014b) Rigid-body motions of interacting proteins dominate multispecific binding of ubiquitin in a shape-dependent manner. Proteins 82:77–89
Dasgupta B, Nakamura H, Higo J (2016) Flexible binding simulation by a novel and improved version of virtual-system coupled adaptive umbrella sampling. Chem Phys Lett 662:327–332. https://doi.org/10.1016/j.cplett.2016.09.059
Dunbar JB, Smith RD, Damm-Ganamet KL et al (2013) CSAR data set release 2012: ligands, affinities, complexes, and docking decoys. J Chem Inf Model 53:1842–1852. https://doi.org/10.1021/ci4000486
Haliloglu T, Bahar I, Erman B (1997) Gaussian dynamics of folded proteins. Phys Rev Lett 79:3090–3093
Hamelberg D, Mongan J, McCammon JA (2004) Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J Chem Phys 120:11919–11929. https://doi.org/10.1063/1.1755656
Han J-DJ, Bertin N, Hao T et al (2004) Evidence for dynamically organized modularity in the yeast protein–protein interaction network. Nature 430:88–93. https://doi.org/10.1038/nature02555
Higo J, Ikebe J, Kamiya N, Nakamura H (2012) Enhanced and effective conformational sampling of protein molecular systems for their free energy landscapes. Biophys Rev 4:27–44
Higo J, Dasgupta B, Mashimo T et al (2015) Virtual-system-coupled adaptive umbrella sampling to compute free-energy landscape for flexible molecular docking. J Comput Chem 36:1489–1501
Higo J, Kasahara K, Dasgupta B, Nakamura H (2017) Enhancement of canonical sampling by virtual-state transitions. J Chem Phys 146:044104. https://doi.org/10.1063/1.4974087
Kamiya N, Watanabe YS, Ono S, Higo J (2005) AMBER-based hybrid force field for conformational sampling of polypeptides. Chem Phys Lett 401:312–317
Kamiya N, Yonezawa Y, Nakamura H, Higo J (2008) Protein-inhibitor flexible docking by a multicanonical sampling: native complex structure with the lowest free energy and a free-energy barrier distinguishing the native complex from the others. Proteins 70:41–53. https://doi.org/10.1002/prot.21409
Kar G, Gursoy A, Keskin O (2009) Human cancer protein-protein interaction network: a structural perspective. PLoS Comput Biol 5:e1000601. https://doi.org/10.1371/journal.pcbi.1000601
Kim PM, Lu LJ, Xia Y, Gerstein MB (2006) Relating three-dimensional structures to protein networks provides evolutionary insights. Science 314:1938–1941
Kinjo AR, Bekker G-J, Suzuki H et al (2017) Protein Data Bank Japan (PDBj): updated user interfaces, resource description framework, analysis tools for large structures. Nucleic Acids Res 45:D282–D288. https://doi.org/10.1093/nar/gkw962
Kinjo AR, Bekker GJ, Wako H et al (2018) New tools and functions in data-out activities at Protein Data Bank Japan (PDBj). Protein Sci 27:D453–D460. https://doi.org/10.1002/pro.3273
MacKerell AD, Bashford D, Bellott M et al (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102:3586–3616. https://doi.org/10.1021/jp973084f
McCurdy SR, Pacal M, Ahmad M, Bremner R (2017) A CDK2 activity signature predicts outcome in CDK2-low cancers. Oncogene 36:2491–2502. https://doi.org/10.1038/onc.2016.409
Meng EC, Pettersen EF, Couch GS et al (2006) Tools for integrated sequence-structure analysis with UCSF chimera. BMC Bioinformatics 7:339. https://doi.org/10.1186/1471-2105-7-339
Nakajima N, Nakamura H, Kidera A (1997) Multicanonical ensemble generated by molecular dynamics simulation for enhanced conformational sampling of peptides. J Phys Chem B 101:817–824
Noguti T, Gō N (1983a) A method of rapid calculation of a second derivative matrix of conformational energy for large molecules. J Phys Soc Jpn 52:3685–3690. https://doi.org/10.1143/JPSJ.52.3685
Noguti T, Gō N (1983b) Dynamics of native globular proteins in terms of dihedral angles. J Phys Soc Jpn 52:3283–3288
Okamoto Y (2004) Generalized-ensemble algorithms: enhanced sampling techniques for Monte Carlo and molecular dynamics simulations. J Mol Graph Model 22:425–439. https://doi.org/10.1016/j.jmgm.2003.12.009
Schneider P, Walters WP, Plowright AT et al (2020) Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 19:353–364. https://doi.org/10.1038/s41573-019-0050-3
Shapiro GI (2006) Cyclin-dependent kinase pathways as targets for cancer treatment. J Clin Oncol 24:1770–1783. https://doi.org/10.1200/JCO.2005.03.7689
Taylor IW, Linding R, Warde-Farley D et al (2009) Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol 27:199–204. https://doi.org/10.1038/nbt.1522
Tirion M (1996) Large amplitude elastic motions in proteins from a single-parameter, atomic analysis. Phys Rev Lett 77:1905–1908
Van Eerden J, Briels WJ, Harkema S, Feil D (1989) Potential of mean force by thermodynamic integration: molecular-dynamics simulation of decomplexation. Chem Phys Lett 164:370–376. https://doi.org/10.1016/0009-2614(89)85222-4
Wako H, Endo S (2013) Normal mode analysis based on an elastic network model for biomolecules in the Protein Data Bank, which uses dihedral angles as independent variables. Comput Biol Chem 44:22–30. https://doi.org/10.1016/j.compbiolchem.2013.02.006
Wako H, Endo S, Nagayama K, Gō N (1995) FEDER/2: program for static and dynamic conformational energy analysis of macro-molecules in dihedral angle space. Comput Phys Commun 91:233–251. https://doi.org/10.1016/0010-4655(95)00050-P
Wako H, Kato M, Endo S (2004) ProMode: a database of normal mode analyses on protein molecules with a full-atom model. Bioinformatics (Oxford, England) 20:2035–2043
Wilson EB, Decius JC, Cross PC (1955) Molecular vibrations: the theory of infrared and Raman vibrational spectra. McGraw-Hill, New York, USA
Yasuo N, Sekijima M (2019) Improved method of structure-based virtual screening via interaction-energy-based learning. J Chem Inf Model 59:1050–1061. https://doi.org/10.1021/acs.jcim.8b00673
Acknowledgments
This work was performed in part under the Cooperative Research Program of the Institute for Protein Research, Osaka University, CR-19-05 and CR-20-05 to N.K. and was supported by the Grand-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (JP20H03229 and JP20K15758).
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Dasgupta, B., Bekker, GJ., Kamiya, N. (2022). Dynamical Methods to Study Interaction in Proteins Facilitating Molecular Understanding of Cancer. In: Chakraborti, S., Ray, B.K., Roychoudhury, S. (eds) Handbook of Oxidative Stress in Cancer: Mechanistic Aspects. Springer, Singapore. https://doi.org/10.1007/978-981-15-9411-3_149
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DOI: https://doi.org/10.1007/978-981-15-9411-3_149
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