Bioinfo Chem
System biology and Infochemistry | Online ISSN 3071-4826
1
Citations
13.7k
Views
32
Articles
Volume 2 Number 1 2020
REVIEWS (Open Access)
Molecular Dynamics Simulations in Cancer Drug Design: From Atomic-Level Insights to Precision Therapeutics: A Review
Shamsuddin Sultan Khan 1*
Bioinfo Chem 2 (1) 1-12 https://doi.org/10.25163/bioinformatics.2110737
Submitted: 10 January 2020 Revised: 04 March 2020 Accepted: 12 March 2020 Published: 14 March 2020
Abstract
There is, perhaps, a quiet shift underway in cancer drug discovery—one that is less about discovering new molecules and more about understanding how they truly behave within the living, moving architecture of biology. Traditional approaches, while powerful, often rely on static representations of proteins, inadvertently overlooking the dynamic nature of molecular interactions. This review explores how Molecular Dynamics (MD) simulations are beginning to reshape this perspective, offering a more fluid and time-resolved understanding of drug–target interactions. By synthesizing findings across key oncological systems—including the Bcl-2 family, the p53–MDM2/MDMX regulatory axis, and mutant TP53–BRCA1 complexes—this study highlights how MD enables the identification of transient conformations, cryptic binding pockets, and residue-level interaction patterns that are otherwise inaccessible. These insights appear particularly relevant in addressing challenges of selectivity and resistance, where small structural differences can have disproportionately large functional consequences. At the same time, integrating MD with complementary techniques such as MM-PBSA free energy calculations and fragment-based drug discovery frameworks offers a more comprehensive, multi-scale approach to therapeutic design. Yet, despite these advances, uncertainties remain—especially regarding sampling limitations and translational predictability. Taken together, this review suggests that MD simulations are not merely computational tools but evolving conceptual frameworks, gradually shifting drug discovery from static observation toward dynamic, precision-driven intervention.Keywords: Molecular Dynamics; Cancer Drug Design; Bcl-2 Family; Protein–Protein Interactions; Precision Therapeutics
References
Al-Khafaji, K., & Taskin Tok, T. (2020). Amygdalin as multi-target anticancer drug against targets of cell division cycle: Double docking and molecular dynamics simulation. Journal of Biomolecular Structure and Dynamics, 195, 105660.
Alonso, H., Bliznyuk, A. A., & Gready, J. E. (2006). Combining docking and molecular dynamic simulations in drug design. Medicinal Research Reviews, 26(5), 531–568.
Amaro, R. E., et al. (2008). Discovery of drug-like inhibitors of an essential RNA-editing ligase in Trypanosoma brucei. Proceedings of the National Academy of Sciences, 105(45), 17278–17283.
Azam, S. S., Abro, A., Tanvir, F., & Parvaiz, N. (2014). Identification of unique binding site and molecular docking studies for structurally diverse Bcl-xL inhibitors. Medicinal Chemistry Research, 23, 3765–3783.
Barakat, K., Mane, J., Friesen, D., & Tuszynski, J. (2010). Ensemble-based virtual screening reveals dual-inhibitors for the p53–MDM2/MDMX interactions. Journal of Molecular Graphics and Modelling, 28(6), 555–568.
Barakat, K., Mane, J., Friesen, D., & Tuszynski, J. (2010). Ensemble-based virtual screening reveals dual-inhibitors for the p53–MDM2/MDMX interactions. Journal of Molecular Graphics and Modelling, 28(6), 555–568.
Barelier, S., Pons, J., Marcillat, O., Lancelin, J. M., & Krimm, I. (2010). Fragment-based deconstruction of Bcl-xL inhibitors. Journal of Medicinal Chemistry, 53(6), 2577–2588.
Bekker, G. J., Fukuda, I., Higo, J., Fukunishi, Y., & Kamiya, N. (2021). Cryptic-site binding mechanism of medium-sized Bcl-xL inhibiting compounds elucidated by McMD-based dynamic docking simulations. Scientific Reports, 11(1), 1–15.
Bekker, G. J., Fukuda, I., Higo, J., Fukunishi, Y., & Kamiya, N. (2021). Cryptic-site binding mechanism of medium-sized Bcl-xL inhibiting compounds elucidated by McMD-based dynamic docking simulations. Scientific Reports, 11(1), 1–15.
Ding, K., Lu, Y., Nikolovska-Coleska, Z., Qiu, S., Ding, Y., Gao, W., ... & Wang, S. (2005). Structure-based design of potent non-peptide MDM2 inhibitors. Journal of the American Chemical Society, 127(29), 10130–10131.
Grasberger, B. L., Lu, T., Schubert, C., Parks, D. J., Carver, T. E., Koblish, H. K., ... & Bone, R. F. (2005). Discovery and cocrystal structure of benzodiazepinedione HDM2 antagonists that activate p53 in cells. Journal of Medicinal Chemistry, 48(4), 909–912.
Hernández-Rodríguez, M., et al. (2016). Current tools and methods in Molecular Dynamics (MD) for drug design. Current Medicinal Chemistry, 23(32), 1–18.
Hernández-Rodríguez, M., Rosales-Hernández, M. C., Mendieta-Wejebe, J. E., Martínez-Archundia, M., & Correa-Basurto, J. (2016). Current tools and methods in Molecular Dynamics (MD) for drug design. Current Medicinal Chemistry, 23(32), 1–18.
Kollman, P. A., Massova, I., Reyes, C., Kuhn, B., Huo, S., Chong, L., ... & Case, D. A. (2000). Calculating structures and free energies of complex molecules: Combining molecular mechanics and continuum models. Accounts of Chemical Research, 33(12), 889–897.
Lessene, G., Czabotar, P. E., Sleebs, B. E., Zobel, K., Lowes, K. N., Adams, J. M., ... & Fairbrother, W. J. (2013). Structure-guided design of a selective BCL-XL inhibitor. Nature Chemical Biology, 9, 390–397.
Mortier, J., Rakers, C., Bermudez, M., Murgueitio, M. S., Riniker, S., & Wolber, G. (2015). The impact of molecular dynamics on drug design: applications for the characterization of ligand-macromolecule complexes. Drug Discovery Today, 20(6), 686–702.
Oltersdorf, T., Elmore, S. W., Shoemaker, A. R., Armstrong, R. C., Augeri, D. J., Belli, B. A., ... & Rosenberg, S. H. (2005). An inhibitor of Bcl-2 family proteins induces regression of solid tumours. Nature, 435, 677–681.
Pinto, M., Orzaez, M. D. M., Delgado-Soler, L., Perez, J. J., & Rubio-Martinez, J. (2011). Rational design of new class of BH3-mimetics as inhibitors of the Bcl-xL protein. Journal of Chemical Information and Modeling, 51(5), 1242–1253.
Pinto, M., Orzaez, M. D. M., Delgado-Soler, L., Perez, J. J., & Rubio-Martinez, J. (2011). Rational design of new class of BH3-mimetics as inhibitors of the Bcl-xL protein. Journal of Chemical Information and Modeling, 51(5), 1242–1253.
Rew, Y., Sun, D., Gonzalez-Lopez De Turiso, F., Bartberger, M. D., Beck, H. P., Canon, J., ... & Olson, S. H. (2012). Structure-based design of novel inhibitors of the MDM2–p53 interaction. Journal of Medicinal Chemistry, 55(11), 4936–4954.
Sirous, H., Fassihi, A., Brogi, S., Campiani, G., & Saghaie, L. (2019). Integrated virtual screening and molecular dynamics simulation revealed promising drug candidates of p53-MDM2 interaction. Computational Biology and Chemistry, 83, 107105.
Souers, A. J., Leverson, J. D., Boghaert, E. R., Ackler, S. L., Catron, N. D., Chen, J., ... & Fairbrother, W. J. (2013). ABT-199 a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nature Medicine, 19, 202–208.
Stewart, M. L., Fire, E., Keating, A. E., & Walensky, L. D. (2010). The MCL-1 BH3 helix is an exclusive MCL-1 inhibitor and apoptosis sensitizer. Nature Chemical Biology, 6(8), 595–601.
Tiwari, S., Awasthi, M., Singh, S., Pandey, V. P., & Dwivedi, U. N. (2017). Modulation of interaction of mutant TP53 and wild type BRCA1 by alkaloids: A computational approach towards targeting protein-protein interaction. Journal of Biomolecular Structure and Dynamics, 36(10), 1–13.
Wakui, N., Yoshino, R., Yasuo, N., Ohue, M., & Sekijima, M. (2018). Exploring the selectivity of inhibitor complexes with Bcl-2 and Bcl-XL: A molecular dynamics simulation approach. Journal of Molecular Graphics and Modelling, 79, 166–174.
Yang, C. Y., & Wang, S. (2011). Hydrophobic binding hot spots of Bcl-xL protein-protein interfaces by cosolvent molecular dynamics simulation. ACS Medicinal Chemistry Letters, 2(4), 280–284.
You, W., Huang, Y. M., Kizhake, S., Natarajan, A., & Chang, C. E. A. (2016). Characterization of promiscuous binding of phosphor ligands to breast-cancer-gene 1 (BRCA1) C-terminal (BRCT). PLOS Computational Biology, 12(8), e1005057.
Recommended articles
Rethinking Antimicrobial Strategies: Integrating Microbiome Modulation and Next Generation Therapeutics to Combat Multidrug-Resistant Pathogens
Computational Elucidation of EGCG Interaction with PD-L1: A Natural Strategy for Immune Checkpoint Modulation in Melanoma
Targeting Mutant KRAS G12C with Berberine Derivatives: A Molecular Docking and Dynamics Approach in Colorectal Cancer
0
Save
Save
0
Citation
Citation
10
View
View
0
Share
Share