Multiscale Simulation of Phosphofructokinase-1 Assemblies: Capturing the Interplay between Specific and Transient Interactions
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články
PubMed
41247276
PubMed Central
PMC12670391
DOI
10.1021/acs.jpcb.5c05346
Knihovny.cz E-zdroje
- MeSH
- fosfofruktokinasa-1 * chemie metabolismus genetika MeSH
- lidé MeSH
- simulace molekulární dynamiky * MeSH
- vazba proteinů MeSH
- vodíková vazba MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- fosfofruktokinasa-1 * MeSH
Human phosphofructokinase-1 (PFK1) forms filaments and organizes into large-scale assemblies that are thought to play a key role in the spatial organization of glycolysis. However, the molecular interactions driving this assembly and the isoform-specific tendencies to form such structures remain poorly understood. In this work, we combine coarse-grained and all-atom molecular dynamics simulations to characterize interactions between PFK1 tetramers. Using the Martini and OPEPv7 coarse-grained force fields, we identify key regions mediating transient PFK1-PFK1 interactions and show that these include experimentally identified filament-forming interfaces. At the same time, we find that current coarse-grained models─optimized for nonspecific, transient contacts─lack the resolution to capture the specific side-chain interactions critical for filament stability, as revealed by previous experiments and our all-atom simulations. To address this, we propose enhancing the coarse-grained representation of filament-forming interfaces by introducing additional hydrogen-bonding terms for key residues. This modification improves filament stability and more accurately reproduces the effects of the filament-disrupting Asn-to-Thr mutation. Overall, our work provides a foundation for molecular-level modeling of glycolytic enzyme assemblies and offers a strategy to improve the accuracy of coarse-grained models in capturing the delicate interplay between specific and transient interactions in dynamic protein complexes.
Zobrazit více v PubMed
Morais M., Dias F., Teixeira A. L., Medeiros R.. MicroRNAs and altered metabolism of clear cell renal cell carcinoma: Potential role as aerobic glycolysis biomarkers. Biochim. Biophys. Acta Gen. Subj. 2017;1861:2175–2185. doi: 10.1016/j.bbagen.2017.05.028. PubMed DOI
Webb B. A., Forouhar F., Szu F.-E., Seetharaman J., Tong L., Barber D. L.. Structures of human phosphofructokinase-1 and atomic basis of cancer-associated mutations. Nature. 2015;523:111–114. doi: 10.1038/nature14405. PubMed DOI PMC
Compton J. A., Patrick W. M.. The more we learn, the more diverse it gets: structures, functions and evolution in the Phosphofructokinase Superfamily. Biochem. J. 2025;482:467–483. doi: 10.1042/BCJ20253024. PubMed DOI PMC
Trujillo J. L., Deal W. C.. Metabolic control and structure of glycolytic enzymes. 17. Pig liver phosphofructokinase: asymmetry properties, proof of rapid association-dissociation equilibriums, and effect of temperature and protein concentration on the equilibriums. Biochemistry. 1977;16:3098–3104. doi: 10.1021/bi00633a009. PubMed DOI
Foe L., Trujillo J.. Quaternary structure of pig liver phosphofructokinase. J. Biol. Chem. 1980;255:10537–10541. doi: 10.1016/S0021-9258(19)70497-9. PubMed DOI
Reinhart G. D., Lardy H. A.. Rat liver phosphofructokinase: use of fluorescence polarization to study aggregation at low protein concentration. Biochemistry. 1980;19:1484–1490. doi: 10.1021/bi00548a035. PubMed DOI
Webb B. A., Dosey A. M., Wittmann T., Kollman J. M., Barber D. L.. The glycolytic enzyme phosphofructokinase-1 assembles into filaments. J. Cell Biol. 2017;216:2305–2313. doi: 10.1083/jcb.201701084. PubMed DOI PMC
Lynch E. M., Hansen H., Salay L., Cooper M., Timr S., Kollman J. M., Webb B. A.. Structural basis for allosteric regulation of human phosphofructokinase-1. Nat. Commun. 2024;15:7323. doi: 10.1038/s41467-024-51808-6. PubMed DOI PMC
Tien M. Z., Meyer A. G., Sydykova D. K., Spielman S. J., Wilke C. O.. Maximum Allowed Solvent Accessibilites of Residues in Proteins. PLoS One. 2013;8:e80635–e80638. doi: 10.1371/journal.pone.0080635. PubMed DOI PMC
Wang M., Flaswinkel H., Joshi A., Napoli M., Masgrau-Alsina S., Kamper J. M., Henne A., Heinz A., Berouti M., Schmacke N. A.. et al. Phosphorylation of PFKL regulates metabolic reprogramming in macrophages following pattern recognition receptor activation. Nat. Commun. 2024;15:6438. doi: 10.1038/s41467-024-50104-7. PubMed DOI PMC
Sivadas A., McDonald E. F., Shuster S. O., Davis C. M., Plate L.. Site-specific crosslinking reveals Phosphofructokinase-L inhibition drives self-assembly and attenuation of protein interactions. Adv. Biol. Regul. 2023;90:100987–101013. doi: 10.1016/j.jbior.2023.100987. PubMed DOI PMC
Zhan H., Pal D. S., Borleis J., Deng Y., Long Y., Janetopoulos C., Huang C.-H., Devreotes P. N.. Self-organizing glycolytic waves tune cellular metabolic states and fuel cancer progression. Nat. Commun. 2025;16:5563. doi: 10.1038/s41467-025-60596-6. PubMed DOI PMC
Grünewald F., Punt M. H., Jefferys E. E., Vainikka P. A., König M., Virtanen V., Meyer T. A., Pezeshkian W., Gormley A. J., Karonen M.. et al. Martini 3 Coarse-Grained Force Field for Carbohydrates. J. Chem. Theory Comput. 2022;18:7555–7569. doi: 10.1021/acs.jctc.2c00757. PubMed DOI PMC
Timr S., Melchionna S., Derreumaux P., Sterpone F.. Optimized OPEP Force Field for Simulation of Crowded Protein Solutions. J. Phys. Chem. B. 2023;127:3616–3623. doi: 10.1021/acs.jpcb.3c00253. PubMed DOI PMC
Souza P. C. T., Alessandri R., Barnoud J., Thallmair S., Faustino I., Grünewald F., Patmanidis I., Abdizadeh H., Bruininks B. M. H., Wassenaar T. A.. et al. Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat. Methods. 2021;18:382–388. doi: 10.1038/s41592-021-01098-3. PubMed DOI PMC
Marrink S. J., Risselada H. J., Yefimov S., Tieleman D. P., de Vries A. H.. The MARTINI Force Field: Coarse Grained Model for Biomolecular Simulations. J. Phys. Chem. B. 2007;111:7812–7824. doi: 10.1021/jp071097f. PubMed DOI
Monticelli L., Kandasamy S. K., Periole X., Larson R. G., Tieleman D. P., Marrink S.-J.. The MARTINI Coarse-Grained Force Field: Extension to Proteins. J. Chem. Theory Comput. 2008;4:819–834. doi: 10.1021/ct700324x. PubMed DOI
de Jong D. H., Singh G., Bennett W. F. D., Arnarez C., Wassenaar T. A., Schäfer L. V., Periole X., Tieleman D. P., Marrink S. J.. Improved Parameters for the Martini Coarse-Grained Protein Force Field. J. Chem. Theory Comput. 2013;9:687–697. doi: 10.1021/ct300646g. PubMed DOI
Yesylevskyy S. O., Schäfer L. V., Sengupta D., Marrink S. J.. Polarizable Water Model for the Coarse-Grained MARTINI Force Field. PLoS Comput. Biol. 2010;6:1–17. doi: 10.1371/journal.pcbi.1000810. PubMed DOI PMC
Coronas L. E., Van T., Iorio A., Lapidus L. J., Feig M., Sterpone F.. Stability and deformation of biomolecular condensates under the action of shear flow. J. Chem. Phys. 2024;160:215101–215111. doi: 10.1063/5.0209119. PubMed DOI
Stark A. C., Andrews C. T., Elcock A. H.. Toward Optimized Potential Functions for Protein–Protein Interactions in Aqueous Solutions: Osmotic Second Virial Coefficient Calculations Using the MARTINI Coarse-Grained Force Field. J. Chem. Theory Comput. 2013;9:4176–4185. doi: 10.1021/ct400008p. PubMed DOI PMC
Vögele M., Holm C., Smiatek J.. Properties of the polarizable MARTINI water model: A comparative study for aqueous electrolyte solutions. J. Mol. Liq. 2015;212:103–110. doi: 10.1016/j.molliq.2015.08.062. DOI
Berg A., Kukharenko O., Scheffner M., Peter C.. Towards a molecular basis of ubiquitin signaling: A dual-scale simulation study of ubiquitin dimers. PLoS Comput. Biol. 2018;14:e1006589. doi: 10.1371/journal.pcbi.1006589. PubMed DOI PMC
Majumder A., Straub J. E.. Addressing the Excessive Aggregation of Membrane Proteins in the MARTINI Model. J. Chem. Theory Comput. 2021;17:2513–2521. doi: 10.1021/acs.jctc.0c01253. PubMed DOI PMC
Pommié C., Levadoux S., Sabatier R., Lefranc G., Lefranc M.. IMGT standardized criteria for statistical analysis of immunoglobulin V-REGION amino acid properties. J. Mol. Recognit. 2004;17:17–32. doi: 10.1002/jmr.647. PubMed DOI
Miclot T., Timr S.. Beyond contacts: the important role of the support region to distinguish stable and transient protein interfaces. bioRxiv. 2025:1–14. doi: 10.1101/2025.01.29.635419. DOI
Levy E. D.. A Simple Definition of Structural Regions in Proteins and Its Use in Analyzing Interface Evolution. J. Mol. Biol. 2010;403:660–670. doi: 10.1016/j.jmb.2010.09.028. PubMed DOI
Kastritis P. L., Rodrigues J. P., Folkers G. E., Boelens R., Bonvin A. M.. Proteins Feel More Than They See: Fine-Tuning of Binding Affinity by Properties of the Non-Interacting Surface. J. Mol. Biol. 2014;426:2632–2652. doi: 10.1016/j.jmb.2014.04.017. PubMed DOI
Sugita Y., Okamoto Y.. Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 1999;314:141–151. doi: 10.1016/S0009-2614(99)01123-9. DOI
Huang J., Rauscher S., Nawrocki G., Ran T., Feig M., de Groot B. L., Grubmüller H., MacKerell A. D.. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods. 2017;14:71–73. doi: 10.1038/nmeth.4067. PubMed DOI PMC
Tian C., Kasavajhala K., Belfon K. A. A., Raguette L., Huang H., Migues A. N., Bickel J., Wang Y., Pincay J., Wu Q.. et al. ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution. J. Chem. Theory Comput. 2020;16:528–552. doi: 10.1021/acs.jctc.9b00591. PubMed DOI
Pedersen K. B., Borges-Araújo L., Stange A. D., Souza P. C. T., Marrink S. J., Schiøtt B.. OLIVES: A Go̅-like Model for Stabilizing Protein Structure via Hydrogen Bonding Native Contacts in the Martini 3 Coarse-Grained Force Field. J. Chem. Theory Comput. 2024;20:8049–8070. doi: 10.1021/acs.jctc.4c00553. PubMed DOI
Jang S., Xuan Z., Lagoy R. C., Jawerth L. M., Gonzalez I. J., Singh M., Prashad S., Kim H. S., Patel A., Albrecht D. R.. et al. Phosphofructokinase relocalizes into subcellular compartments with liquid-like properties in vivo. Biophys. J. 2021;120:1170–1186. doi: 10.1016/j.bpj.2020.08.002. PubMed DOI PMC
Lynch E. M., Kollman J. M., Webb B. A.. Filament formation by metabolic enzymesA new twist on regulation. Curr. Opin. Cell Biol. 2020;66:28–33. doi: 10.1016/j.ceb.2020.04.006. PubMed DOI PMC
Campos M., Albrecht L. V.. Hitting the Sweet Spot: How Glucose Metabolism Is Orchestrated in Space and Time by Phosphofructokinase-1. Cancers. 2023;16:16. doi: 10.3390/cancers16010016. PubMed DOI PMC
Shegay P. V., Shatova O. P., Zabolotneva A. A., Shestopalov A. V., Kaprin A. D.. Moonlight functions of glycolytic enzymes in cancer. Front. Mol. Biosci. 2023;10:1076138. doi: 10.3389/fmolb.2023.1076138. PubMed DOI PMC
Poma A. B., Cieplak M., Theodorakis P. E.. Combining the MARTINI and Structure-Based Coarse-Grained Approaches for the Molecular Dynamics Studies of Conformational Transitions in Proteins. J. Chem. Theory Comput. 2017;13:1366–1374. doi: 10.1021/acs.jctc.6b00986. PubMed DOI
Hao M.-H.. Theoretical Calculation of Hydrogen-Bonding Strength for Drug Molecules. J. Chem. Theory Comput. 2006;2:863–872. doi: 10.1021/ct0600262. PubMed DOI
Xie N.-Z., Du Q.-S., Li J.-X., Huang R.-B.. Exploring Strong Interactions in Proteins with Quantum Chemistry and Examples of Their Applications in Drug Design. PLoS One. 2015;10:e0137113. doi: 10.1371/journal.pone.0137113. PubMed DOI PMC
Kloos M., Brüser A., Kirchberger J., Schöneberg T., Sträter N.. Crystal Structure of Human Platelet Phosphofructokinase-1 Locked in an Activated Conformation. Biochem. J. 2015;469(3):421–432. doi: 10.1042/BJ20150251. PubMed DOI
Humphrey W., Dalke A., Schulten K.. VMD: Visual molecular dynamics. J. Mol. Graphics Modell. 1996;14:33–38. doi: 10.1016/0263-7855(96)00018-5. PubMed DOI
Kroon, P. ; Grunewald, F. ; Barnoud, J. ; van Tilburg, M. ; Souza, P. ; Wassenaar, T. ; Marrink, S. . Martinize2 and Vermouth: Unified Framework for Topology Generation. arXiv:2212.01191. PubMed PMC
Páll, S. ; Abraham, M. J. ; Kutzner, C. ; Hess, B. ; Lindahl, E. . Tackling exascale software challenges in molecular dynamics simulations with GROMACS; Springer International Publishing, 2014; pp 3–27.
Abraham M. J., Murtola T., Schulz R., Páll S., Smith J. C., Hess B., Lindahl E.. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1–2:19–25. doi: 10.1016/j.softx.2015.06.001. DOI
Darden T., York D., Pedersen L.. Particle mesh Ewald: An N.log(N) method for Ewald sums in large systems. J. Chem. Phys. 1993;98:10089–10092. doi: 10.1063/1.464397. DOI
Bussi G., Donadio D., Parrinello M.. Canonical sampling through velocity rescaling. J. Chem. Phys. 2007;126:014101–014107. doi: 10.1063/1.2408420. PubMed DOI
Bernetti M., Bussi G.. Pressure control using stochastic cell rescaling. J. Chem. Phys. 2020;153:114107–114112. doi: 10.1063/5.0020514. PubMed DOI
Parrinello M., Rahman A.. Crystal Structure and Pair Potentials: A Molecular-Dynamics Study. Phys. Rev. Lett. 1980;45:1196–1199. doi: 10.1103/PhysRevLett.45.1196. DOI
Eastman P., Galvelis R., Peláez R. P., Abreu C. R. A., Farr S. E., Gallicchio E., Gorenko A., Henry M. M., Hu F., Huang J.. et al. OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. J. Phys. Chem. B. 2024;128:109–116. doi: 10.1021/acs.jpcb.3c06662. PubMed DOI PMC
Leontyev I. V., Stuchebrukhov A. A.. Polarizable Mean-Field Model of Water for Biological Simulations with AMBER and CHARMM Force Fields. J. Chem. Theory Comput. 2012;8:3207–3216. doi: 10.1021/ct300011h. PubMed DOI PMC
Lemak A. S., Balabaev N. K.. On The Berendsen Thermostat. Mol. Simul. 1994;13:177–187. doi: 10.1080/08927029408021981. DOI
Berendsen H. J. C., Postma J. P. M., van Gunsteren W. F., DiNola A., Haak J. R.. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 1984;81:3684–3690. doi: 10.1063/1.448118. DOI
Evans D. J., Holian B. L.. The Nose–Hoover thermostat. J. Chem. Phys. 1985;83:4069–4074. doi: 10.1063/1.449071. DOI
Izadi S., Anandakrishnan R., Onufriev A. V.. Building Water Models: A Different Approach. J. Phys. Chem. Lett. 2014;5:3863–3871. doi: 10.1021/jz501780a. PubMed DOI PMC
Kumar S., Rosenberg J. M., Bouzida D., Swendsen R. H., Kollman P. A.. THE weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J. Comput. Chem. 1992;13:1011–1021. doi: 10.1002/jcc.540130812. DOI