Abstract
The association and dissociation of proteins and ligands are crucial in biophysics for potential drug development [Baron and McCammon, Annu. Rev. Phys. Chem. 64, 151–175 (2013)]. However, identifying and characterizing the reaction pathways for these rare events has been a long-standing challenge. Molecular dynamics (MD) simulations are limited in exploring biophysical processes on experimental timescales, so ligand transport processes through complex transient tunnels formed by proteins during dynamics are often simulated using enhanced sampling MD [Rydzewski and Nowak, Phys. Life Rev. 22–23, 58–74 (2017)]. Erroneously identified ligand binding pathways can affect thermodynamic and kinetic characteristics calculated from MD trajectories. A system that has the potential to be a therapeutic target for neurodegenerative diseases is prolyl oligopeptidase (PREP). This is due to its involvement in promoting protein aggregation and disrupting cellular function through affecting protein–protein interactions (PPI). The recent discovery of a new type of PREP inhibitor that targets PPI raises important questions about the diversity of ligand binding pathways in PREP and their impact on protein dynamics [Pätsi et al., J. Med. Chem. 67, 5421–5436 (2024); Kilpeläinen et al., J. Med. Chem. 66, 7475–7496 (2023); and Walczewska-Szewc et al., Phys. Chem. Chem. Phys. 24, 4366–4373 (2022)]. In this article, using results from enhanced sampling MD, we visually present how the binding process in PREP depends on subtle changes in inhibitors, which could be crucial in treating neurodegenerative disorders.
METHODS
The molecular systems were constructed in CHARMM-GUI using the 3DDU PDB structure,1,2,5,6 prepared with Schrödinger preparation wizard. The structures of three ligands (KYP2047, HUP46, and HUP55) were optimized using ORCA7 and docked using Glide, Schrödinger. System equilibration was performed in Gromacs 2021.38 combined with PLUMED 2.8.9,10 Ligand access pathways were calculated using the MAZE module for PLUMED.11,12 In each MD simulation, the centers of mass of the ligands were biased toward solvent with a constant velocity in the directions calculated by minimizing a loss function exp(-x)/x using simulated annealing, where x are ligand–protein pairwise distances. VMD13 was used to generate STL meshes from trajectory data, and Blender 3.0.1 was used to create the final graphics (Fig. 1).
FIG. 1.
Enhanced sampling methods enable the identification of ligand access and exit pathways that may not initially be evident when exploring static experimental structures alone. Here, we performed several MD simulations using a method for biasing the positions of ligands along transient protein tunnels, which is implemented in the MAZE module11 of PLUMED, to compute dominant unbinding pathways for three inhibitors of PREP. The figure depicts a snapshot of the PREP protein structure represented as a transparent volume. Colored paths trace the trajectories of ligands as they navigate from their binding sites to their exit points from the protein. Three analyzed ligands are highlighted: KYP2047 (the most well-known), HUP46, and HUP55 (inhibitors targeting PPI functions).3,4 These ligands, represented in orange, blue, and cyan, respectively, exhibit distinct exit site preferences, which suggest that they have different energy barriers along the unbinding pathways and, thus, possibly unbind from PREP on diverse timescales. These results have the potential to be explored further and be of experimental importance.
ACKNOWLEDGMENTS
We acknowledge Polish high-performance computing infrastructure PLGrid for awarding this project access to the LUMI supercomputer, owned by the EuroHPC Joint Undertaking, hosted by CSC (Finland) and the LUMI consortium through pllneuromd.
AUTHOR DECLARATIONS
Conflict of Interest
The authors have no conflicts to disclose.
Author Contributions
Katarzyna Walczewska-Szewc: Data curation (lead); Formal analysis (lead); Visualization (lead); Writing – original draft (equal). Jakub Rydzewski: Methodology (lead); Writing – original draft (equal).
DATA AVAILABILITY
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
- 1. Baron R. and McCammon J. A., Annu. Rev. Phys. Chem. 64, 151–175 (2013). 10.1146/annurev-physchem-040412-110047 [DOI] [PubMed] [Google Scholar]
- 2. Rydzewski J. and Nowak W., Phys. Life Rev. 22–23, 58–74 (2017). 10.1016/j.plrev.2017.03.003 [DOI] [PubMed] [Google Scholar]
- 3. Pätsi H. T., Kilpeläinen T. P., Jumppanen M., Uhari-Väänänen J., Wielendaele P. V., De Lorenzo F., Cui H., Auno S., Saharinen J., Seppälä E., Sipari N., Savinainen J., De Meester I., Lambeir A.-M., Lahtela-Kakkonen M., Myöhänen T. T., and Wallén E. A. A., J. Med. Chem. 67, 5421–5436 (2024). 10.1021/acs.jmedchem.3c01993 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Kilpeläinen T. P., Pätsi H. T., Svarcbahs R., Julku U. H., Eteläinen T. S., Cui H., Auno S., Sipari N., Norrbacka S., Leino T. O., Jäntti M., Myöhänen T. T., and Wallén E. A. A., J. Med. Chem. 66, 7475–7496 (2023). 10.1021/acs.jmedchem.3c00235 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Walczewska-Szewc K., Rydzewski J., and Lewkowicz A., Phys. Chem. Chem. Phys. 24, 4366–4373 (2022). 10.1039/D1CP05238A [DOI] [PubMed] [Google Scholar]
- 6. Haffner C. D., Diaz C. J., Miller A. B., Reid R. A., Madauss K. P., Hassell A., Hanlon M. H., Porter D. J. T., Becherer J. D., and Carter L. H., Bioorg. Med. Chem. Lett. 18, 4360–4363 (2008). 10.1016/j.bmcl.2008.06.067 [DOI] [PubMed] [Google Scholar]
- 7. Neese F., Wennmohs F., Becker U., and Riplinger C., J. Chem. Phys. 152, 224108 (2020). 10.1063/5.0004608 [DOI] [PubMed] [Google Scholar]
- 8. Abraham M. J., Murtola T., Schulz R., Páll S., Smith J. C., Hess B., and Lindahl E., SoftwareX 1, 19–25 (2015). 10.1016/j.softx.2015.06.001 [DOI] [Google Scholar]
- 9. Tribello G. A., Bonomi M., Branduardi D., Camilloni C., and Bussi G., Comput. Phys. Commun. 185, 604–613 (2014). 10.1016/j.cpc.2013.09.018 [DOI] [Google Scholar]
- 10. Bonomi M., Bussi G., Camilloni C., Tribello G. A., Banáš P., Barducci A., Bernetti M., Bolhuis P. G., Bottaro S., Branduardi D., Capelli R., Carloni P., Ceriotti M., Cesari A., Chen H., Chen W., Colizzi F., De S., De La Pierre M., Donadio D., Drobot V., Ensing B., Ferguson A. L., Filizola M., Fraser J. S., Fu H., Gasparotto P., Gervasio F. L., Giberti F., Gil-Ley A., Giorgino T., Heller G. T., Hocky G. M., Iannuzzi M., Invernizzi M., Jelfs K. E., Jussupow A., Kirilin E., Laio A., Limongelli V., Lindorff-Larsen K., Löhr T., Marinelli F., Martin-Samos L., Masetti M., Meyer R., Michaelides A., Molteni C., Morishita T., Nava M., Paissoni C., Papaleo E., Parrinello M., Pfaendtner J., Piaggi P., Piccini G., Pietropaolo A., Pietrucci F., Pipolo S., Provasi D., Quigley D., Raiteri P., Raniolo S., Rydzewski J., Salvalaglio M., Sosso G. C., Spiwok V., Šponer J., Swenson D. W. H., Tiwary P., Valsson O., Vendruscolo M., Voth G. A., and White A., Nat. Methods 16, 670–673 (2019). 10.1038/s41592-019-0506-8 [DOI] [PubMed] [Google Scholar]
- 11. Rydzewski J., Comput. Phys. Commun. 247, 106865 (2020). 10.1016/j.cpc.2019.106865 [DOI] [Google Scholar]
- 12. Rydzewski J. and Valsson O., J. Chem. Phys. 150, 221101 (2019). 10.1063/1.5108638 [DOI] [PubMed] [Google Scholar]
- 13. Humphrey W., Dalke A., and Schulten K., J. Mol. Graph. 14, 33–38 (1996). 10.1016/0263-7855(96)00018-5 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.