The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Employing steered MD simulations for effective virtual screening: Active pharmacophore search by dynamic corrections to target MKK3-MYC interactions
Open accessPublished version of a preprint
Abstract
The protein-protein interaction (PPI) between mitogen-activated protein kinase kinase 3 (MKK3), and MYC is a crucial regulator of oncogenic signaling, particularly in triple-negative breast cancer (TNBC). Despite its clinical significance, effective small molecule inhibitors targeting this interaction remain elusive. In this study, we employed a comprehensive in silico approach integrating dynamic structure-based pharmacophore modeling, virtual screening, molecular docking, and molecular dynamics (MD) simulations to identify potential inhibitors disrupting the MKK3-MYC interaction. The pharmacophore-based screening of over 2 million compounds from ChemDiv and Enamine libraries led to the identification of 16,766 hits, which were further refined through docking and MD-based analyses. The top-ranked molecules underwent steered molecular dynamics (sMD) simulations to evaluate the mechanical stability of their binding interactions, followed by binding free energy calculations (MM/GBSA) to assess their affinity. Notably, several hit compounds exhibited stronger binding affinities and mechanical stability compared to the reference inhibitor SGI-1027, with Z332428622, 4476-2273, and 4292-0516 emerging as the most promising candidates. The lead compounds demonstrated stable interactions with key residues at the interface of MKK3 and MYC, suggesting their potential as novel modulators of MYC-driven malignancies. These findings provide a strong computational foundation for further experimental validation and offer promising candidates for targeted therapy development in MYC-dependent cancers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Density-functional thermochemistry. III. The role of exact exchange
Becke, A. D. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464913 · Cited by 103,767
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
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 30,546
A smooth particle mesh Ewald method
Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Lee, H., & Pedersen, L. G. (1995) · The Journal of Chemical Physics · doi:10.1063/1.470117 · Cited by 22,514
SWISS-MODEL: homology modelling of protein structures and complexes
Waterhouse, A. M., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., de Beer, T., Rempfer, C., Bordoli, L., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky427 · Cited by 13,364
CHARMM‐GUI: A web‐based graphical user interface for CHARMM
Jo, S., Kim, T., Iyer, V. G., & Im, W. (2008) · Journal of Computational Chemistry · doi:10.1002/jcc.20945 · Cited by 9,284
Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., Sanschagrin, P. C., & Mainz, D. T. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm051256o · Cited by 7,012
Nosé–Hoover chains: The canonical ensemble via continuous dynamics
Martyna, G. J., Klein, M. L., & Tuckerman, M. E. (1992) · The Journal of Chemical Physics · doi:10.1063/1.463940 · Cited by 6,009
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 4,251
Evans, D. J., & Holian, B. L. (1985) · The Journal of Chemical Physics · doi:10.1063/1.449071 · Cited by 3,644
OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins
Harder, E. D., Damm, W., Maple, J. R., Wu, C., Reboul, M., Xiang, J. Y., Wang, L., Lupyan, D., Dahlgren, M. K., Knight, J. L., et al. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00864 · Cited by 3,123
Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease
Bianchini, G., Balko, J. M., Mayer, I. A., Sanders, M. E., & Gianni, L. (2016) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2016.66 · Cited by 3,079
The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes
van Zundert, G. C. P., Rodrigues, J. P. G. L. M., Trellet, M., Schmitz, C., Kastritis, P. L., Karaca, E., Melquiond, A. S. J., van Dijk, M., de Vries, S. J., & Bonvin, A. M. J. J. (2016) · Journal of Molecular Biology · doi:10.1016/j.jmb.2015.09.014 · Cited by 2,785
Liu, Y., Yang, X., Gan, J., Chen, S., Xiao, Z. J., & Cao, Y. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac394 · Cited by 2,255
Shelley, J. C., Cholleti, A., Frye, L. L., Greenwood, J. R., Timlin, M. R., & Uchimaya, M. (2007) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-007-9133-z · Cited by 2,091
Reflecting on 25 years with MYC
Meyer, N., & Penn, L. Z. (2008) · Nature Reviews Cancer · doi:10.1038/nrc2231 · Cited by 1,575
OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules
Roos, K., Wu, C., Damm, W., Reboul, M., Stevenson, J., Lu, C. T., Dahlgren, M. K., Mondal, S., Chen, W., Wang, L., et al. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b01026 · Cited by 1,252
Dixon, S. L., Smondyrev, A. M., Knoll, E. H., Rao, S. N., Shaw, D. E., & Friesner, R. A. (2006) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-006-9087-6 · Cited by 1,242
The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling
Li, J., Abel, R., Zhu, K., Cao, Y., Zhao, S., & Friesner, R. A. (2011) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.23106 · Cited by 1,159
High-Resolution Comparative Modeling with RosettaCM
Song, Y., DiMaio, F., Wang, R. Y., Kim, D. E., Miles, C., Brunette, T., Thompson, J., & Baker, D. A. (2013) · Structure · doi:10.1016/j.str.2013.08.005 · Cited by 1,102
The MYC oncogene — the grand orchestrator of cancer growth and immune evasion
Dhanasekaran, R., Deutzmann, A., Mahauad‐Fernandez, W. D., Hansen, A. S., Gouw, A. M., & Felsher, D. W. (2021) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-021-00549-2 · Cited by 1,084
CB-Dock: a web server for cavity detection-guided protein–ligand blind docking
Liu, Y., Grimm, M., Dai, W., Hou, M.-C., Xiao, Z. J., & Cao, Y. (2019) · Acta Pharmacologica Sinica · doi:10.1038/s41401-019-0228-6 · Cited by 940
Nedeljković, M., & Damjanović, A. (2019) · Cells · doi:10.3390/cells8090957 · Cited by 921
Signal Transduction and Targeted Therapy
(2019) · Signal Transduction and Targeted Therapy · doi:10.1038/41392.2059-3635 · Cited by 616
Triple-negative breast cancer: epidemiological considerations and recommendations
Boyle, P. (2012) · Annals of Oncology · doi:10.1093/annonc/mds187 · Cited by 593
MYC as a target for cancer treatment
Duffy, M. J., O’Grady, S., Tang, M., & Crown, J. P. (2021) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2021.102154 · Cited by 538
MYC Deregulation in Primary Human Cancers
Kalkat, M., Melo, J. D., Hickman, K. A., Lourenco, C., Redel, C., Resetca, D., Tamachi, A., Tu, W. B., & Penn, L. Z. (2017) · Genes · doi:10.3390/genes8060151 · Cited by 469
Xu, J., Chen, Y., & Olopade, O. I. (2010) · Genes & Cancer · doi:10.1177/1947601910378691 · Cited by 407
Epik: p K a and Protonation State Prediction through Machine Learning
Johnston, R. C., Yao, K., Kaplan, Z., Chelliah, M., Leswing, K., Seekins, S., Watts, S., Calkins, D., Elk, J. C., Jerome, S. V., et al. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00044 · Cited by 402
Triple-negative breast cancer: is there a treatment on the horizon?
Yao, H., He, G., Yan, S., Chen, C., Song, L., Rosol, T. J., & Deng, X. (2016) · Oncotarget · doi:10.18632/oncotarget.12284 · Cited by 394
MYC protein interactors in gene transcription and cancer
Lourenco, C., Resetca, D., Redel, C., Lin, P., MacDonald, A. S., Ciaccio, R., Kenney, T. M. G., Wei, Y., Andrews, D. W., Sunnerhagen, M., et al. (2021) · Nature reviews. Cancer · doi:10.1038/s41568-021-00367-9 · Cited by 353
FitDock: protein–ligand docking by template fitting
Yang, X., Liu, Y., Gan, J., Xiao, Z. J., & Cao, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac087 · Cited by 348
MYC: a multipurpose oncogene with prognostic and therapeutic implications in blood malignancies
Ahmadi, S. E., Rahimi, S., Zarandi, B., Chegeni, R., & Safa, M. (2021) · Journal of Hematology & Oncology · doi:10.1186/s13045-021-01111-4 · Cited by 227
Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer
Ensenyat-Méndez, M., Llinàs‐Arias, P., Orozco, J. I. J., Íñiguez-Muñoz, S., Salomon, M. P., Sesé, B., DiNome, M. L., & Marzese, D. M. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.681476 · Cited by 221
Bragina, M. E., Daina, A., Perez, M. A. S., Michielin, O. A., & Zoete, V. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23020811 · Cited by 159
Luo, L., Tang, H., Li, L., Li, N., Jia, X., Zhang, Z., Wang, X., Shi, L., Yin, J., Qiu, N., et al. (2018) · Oncogene · doi:10.1038/s41388-018-0396-8 · Cited by 146
Dasmahapatra, U., Kumar, C. K., Das, S., Prathima, T. S., Murali, P., Isaac, A. E., Ramanathan, K., Balamurali, M. M., & Chanda, K. (2022) · Frontiers in Chemistry · doi:10.3389/fchem.2022.991369 · Cited by 131
Wallace, I. M., Blackshields, G., & Higgins, D. G. (2005) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2005.04.002 · Cited by 126
Kaynak, B., Bahar, İ., & Doruker, P. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.020 · Cited by 66
Challenges and Opportunities in Developing Targeted Therapies for Triple Negative Breast Cancer
Chapdelaine, A. G., & Sun, G. (2023) · Biomolecules · doi:10.3390/biom13081207 · Cited by 59
The leucine zippers of the HLH-LZ proteins Max and c-Myc preferentially form heterodimers
Muhle‐Goll, C., Nilges, M., & Pastore, A. (1995) · Biochemistry · doi:10.1021/bi00041a035 · Cited by 51
Tang, M., O’Grady, S., Crown, J. P., & Duffy, M. J. (2022) · Breast Cancer Research and Treatment · doi:10.1007/s10549-022-06673-6 · Cited by 39
Ivanov, A. A., González-Pecchi, V., Khuri, L. F., Niu, Q., Wang, Y., Xu, Y., Bai, Y., Mo, X., Prochownik, E. V., Johns, M. A., et al. (2017) · Oncogene · doi:10.1038/onc.2017.180 · Cited by 36
İş, Y. S., Durdağı, S., Aksoydan, B., & Yurtsever, M. (2018) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.8b00095 · Cited by 35
Self-citationCell Communication and Signaling
Perbal, B. (2003) · Cited by 32
Kanan, T., Kanan, D., Erol, İ., Yazdi, S., Stein, M., & Durdağı, S. (2018) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2018.09.014 · Cited by 32
Self-citationShakhpazyan, N. K., Михалева, Л. М., Bedzhanyan, A. L., Sadykhov, N. K., Midiber, K. Y., Konyukova, A. K., Kontorshchikov, A. S., Maslenkina, K. S., & Orekhov, A. N. (2023) · Biomedicines · doi:10.3390/biomedicines11092411 · Cited by 27
Haist, M., Stege, H. M., Kuske, M., Bauer, J., Klumpp, A., Grabbe, S., & Bros, M. (2023) · Cancer and Metastasis Reviews · doi:10.1007/s10555-023-10097-z · Cited by 25
ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy
Shi, X.-X., Wang, Z.-Z., Wang, F., Hao, G., & Yang, G. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad348 · Cited by 24
Ikram, S., Ahmad, J., & Durdağı, S. (2019) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2019.107462 · Cited by 22
Self-citationDiscovery of the first chemical tools to regulate MKK3-mediated MYC activation in cancer
Yang, X., Fan, D., Troha, A. H., Ahn, H. M., Qian, K., Liang, B., Du, Y., Fu, H., & Ivanov, A. A. (2021) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2021.116324 · Cited by 20
Hybrid In Silico and TR-FRET-Guided Discovery of Novel BCL-2 Inhibitors
Şahin, K., Orhan, M. D., Avşar, T., & Durdağı, S. (2021) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.0c00210 · Cited by 19
Self-citationMYC as a therapeutic target for the treatment of triple-negative breast cancer.
Foley, S. A., Castell, A., Kavanagh, E. L., Synnott, N. C., Crown, J. P., Larsson, L., & Duffy, M. J. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.2019.37.15_suppl.e12550 · Cited by 16
Zajac, K. K., Malla, S., Babu, R. J., Raman, D., & Tiwari, A. K. (2023) · Cancer Reports · doi:10.1002/cnr2.1779 · Cited by 13
Ikram, S., Ahmad, F., Ahmad, J., & Durdağı, S. (2021) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2021.107876 · Cited by 10
Self-citationPotent novel inhibitors against hepatitis C virus NS3 (HCV NS3 GT-3a) protease domain
Ikram, S., Ahmad, J., Rehman, I.-U., & Durdağı, S. (2020) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2020.107727 · Cited by 8
Self-citationSayyah, E., Oktay, L., Tunç, H., & Durdağı, S. (2024) · ChemMedChem · doi:10.1002/cmdc.202300644 · Cited by 6
Self-citationGhazy, S., Oktay, L., & Durdağı, S. (2024) · TURKISH JOURNAL OF BIOLOGY · doi:10.55730/1300-0152.2686 · Cited by 6
Self-citationDurdağı, S., Oktay, L., Erdemoğlu, E., Tolu, İ., Yumak, Y., Ozcan, A., Acar, E., Buyukkilic, S., & Olkan, A. (2021) · TURKISH JOURNAL OF BIOLOGY · doi:10.3906/biy-2106-61 · Cited by 5
Self-citationTriple negative breast cancer: emerging light on the horizon—a narrative review
Bharaj, U. K., Lohmann, A. E., & Blanchette, P. (2021) · Precision Cancer Medicine · doi:10.21037/pcm-20-75 · Cited by 4
Early Unfolding Response of a Stable Protein Domain to Environmental Changes
Perera, L., Darden, T. A., Duke, R. E., Venkateswarlu, D., & Pedersen, L. G. (2004) · The Journal of Physical Chemistry A · doi:10.1021/jp048385l · Cited by 3
Oktay, L., Sayyah, E., & Durdağı, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.26.550723 · Cited by 2
Self-citation
Metadata
- Authors
- Ulug, M. E., Ikram, S., Sayyah, E., & Durdağı, S.
- Affiliations
- Bahçeşehir University (TR)
- Corresponding author
- Durdağı, Serdar
- Venue
- International Journal of Biological Macromolecules
- Publisher
- Elsevier BV
- Published
- 2025-05
- Volume
- 310
- Issue
- Pt 2
- Pages
- 142602
- ISSN
- 0141-8130 (ISSN-L)
- OpenAlex
- W4408967716
- Cited by
- 3
- References
- 62
- Citations by year
- 2026: 2 · 2025: 1
- FWCI
- 1.38
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- International Journal of Biological Macromolecules (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Istanbul Kalkinma Ajansi
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Health Sciences › Medicine › Pharmacology (medical) › Cholinesterase and Neurodegenerative Diseases
- Keywords
- protein-protein interaction inhibitors, virtual screening, pharmacophore modeling, steered molecular dynamics, binding free energy, triple-negative breast cancer, MYC-driven cancer, small molecule inhibitors, binding affinity
- MeSH terms
- Pharmacophore, Humans, Protein Binding, Proto-Oncogene Proteins c-myc, Protein Kinase Inhibitors, MAP Kinase Kinase 3, Molecular Dynamics Simulation, Molecular Docking Simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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 ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
