Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2021.11.12.465807

Inhibition of Interactions Between NDPK-B and NDPK-C in Presence of Graphene Oxide

Zaznaev, A.·Macwan, I.
View original source ↗Export metadata ↓

Abstract

During a heart failure, higher amount of nucleoside diphosphate kinase (NDPK) enzyme in the sarcolemma membrane inhibits the synthesis of second messenger cyclic adenosine monophosphate (cAMP), which is required for the regulation of the calcium ion balance for normal functioning of the heart. In a dependent pathway, NDPK normally phosphorylates the stimulatory guanosine diphosphate, GDP(s), to a guanosine triphosphate, GTP(s), on the heterotrimeric (, {beta} and {gamma} subunits) guanine nucleotide binding protein (G protein), resulting in the stimulation of the cAMP formation. In case of a heart failure, an increased quantity of NDPK also reacts with the inhibitory GDP(i), which is converted to a GTP(i), resulting in the inhibition of the cAMP formation. Typically, the {beta}{gamma} dimer of the G protein binds with hexameric NDPK-B/C complex and receives the phosphate at the residue His266 from residue His118 of NDPK-B. It is known that NDPK-C is required for NDPK-B to phosphorylate the G protein. In this work, the interactions between NDPK-B and NDPK-C are quantified in the presence and absence of graphene oxide (GO) as well as those between NDPK-B and GO through stability analysis involving hydrogen bonds, center of mass (COM), root mean square deviation (RMSD), and salt bridges, and energetics analysis involving van der Waals (VDW) and electrostatic energies. Furthermore, the role of water molecules at the interface of NDPK-B and NDPK-C as well as between NDPK-B and GO is investigated to understand the nature of interactions. It is found that the adsorption of NDPK-B on GO triggers a potential conformational change in the structure of NDPK-B, resulting in a diminished interaction with NDPK-C. This is confirmed through a reduced center of mass (COM) distance between NDPK-B and GO (from 40 A{square} to 30 A{square}) and an increased COM distance between NDPK-B and NDPK-C (from 50 A{square} to 60 A{square}). Furthermore, this is also supported by fewer salt bridges between NDPK-B and NDPK-C, and an increased number of hydrogen bonds formed by the interfacial water molecules. As NDPK-C is crucial to be complexed with NDPK-B for successful interaction of NDPK-B with the G protein, this finding shows that GO can suppress the interactions between NDPK-B/C and G proteins, thereby providing an additional insight into the role of GO in the heart failure mechanism. AUTHOR SUMMARYWe report a novel computational understanding of the interactions between the enzymes NDPK-B and NDPK-C with GO as a potential inhibitor to such interactions and its implications. These types of interactions can play influential roles in many biochemical processes including those that take place during heart failure. A second messenger called cAMP is needed for proper cardiac contraction through the actions of NDPK-B/NDPK-C. It is needed to study the interactions between NDPK-B and NDPK-C to control the synthesis of cAMP. Towards this end, GO is tested through molecular simulations to understand the interactions between NDPK-B and NDPK-C. Influencing or modifying such enzyme active sites has been very less explored and, in this work, the molecular simulations suggest that GO is able to interact with the active site of NDPK-B to provide a sustained cAMP synthesis for longer duration. We found that conformational changes within NDPK-B and NDPK-C influence the interactions between them and such conformational changes are found to be governed by their adsorption on GO. Finally, we found the role of interfacial water molecules between NDPK-B and GO to be crucial in maintaining the interface between them.

Source connections

Explore related subjects

bioengineeringPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zaznaev, A., Macwan, I.. 2021-11-13. Inhibition of Interactions Between NDPK-B and NDPK-C in Presence of Graphene Oxide. https://doi.org/10.1101/2021.11.12.465807

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Single-Component Photo-Crosslinked Lipoic Acid Functionalized Mucin Hydrogels: Dynamic Mucus Mimetics for Antiviral Protection

Native mucus protects epithelial surfaces through viscoelastic networks of disulfide-linked mucins. However, commercially available mucins often fail to reconstitute comparable mechanical and barrier properties, while synthetic and hybrid mucus mimetics require polymeric crosslinkers that may alter mucus-like composition. Inspired by the native architecture, we directly functionalized porcine gastric and bovine submaxillary mucins with -lipoic acid to develop a single-component, photo-crosslinkable hydrogels. Upon UV irradiation (365 nm), the modified mucins rapidly form disulfide-crosslinked networks without added photo-initiator/ polymeric crosslinkers, two orders of magnitude faster as compared with PEG-SH approaches. The resulting mucin-lipoic acid (MUC-LA) hydrogels exhibit mucus-relevant properties, including tunable viscoelasticity, redox responsiveness, self-healing, and size-dependent restrictions on nanoparticle transport. In transwell-based infection models, MUC-LA hydrogels (2-3%) mimicking the healthy airway mucus (G' {approx} 10-20 Pa) suppress epithelial infection by SARS-CoV-2 and influenza A virus. Crosslinked MUC-LA provides greater protection against SARS-CoV-2 than the non-crosslinked, whereas both formulations suppress influenza A infection, suggesting distinct contributions from network formation and mucin-associated interactions. These findings establish a dynamic, mucus-mimetic platform integrating rapid photo-gelation, controllable mechanics, selective transport, and antiviral barrier functionality. MUC-LA hydrogels offer opportunities to investigate mucosal transport and host-pathogen interactions, with potential applications in drug delivery, and as biomaterials.

bioengineering↗
bioRxiv2026

Population-level emulation enables fast Bayesian calibration of cardiac digital twins

Purpose: Patient-specific cardiac digital twins have the potential to guide individualized therapy, but generating these in a routine clinical workflow remains challenging. This is especially true when the twins must carry quantified uncertainty: Bayesian calibration requires thousands of simulations per patient, and the emulators that reduce this cost are typically trained one patient at a time. We aimed to train a single emulator across a patient cohort, so that a new patient can be calibrated in minutes without new simulations. Methods: We trained a population-level Gaussian process emulator whose inputs included a 12-dimensional latent encoding of the clinical features of each patient, so that a single emulator represents the whole cohort. We compared spending simulation budgets on resampling the real cohort with augmenting the cohort with digital chimeras, synthetic patients interpolated between real patients. Seven parameters of a closed-loop heart and circulation model were calibrated by Markov chain Monte Carlo to ten clinical biomarkers in 26 patients with left bundle branch block, each using an emulator trained without that patient, and the calibrated posteriors were compared against the full simulator. Results: A single emulator, trained without the held-out patient, calibrated the digital twin of each patient in 59.7-63.0 s per patient, versus 15 h with the simulator. Resampling the real cohort was the more efficient training strategy, bringing every output to R2 greater than or equal to 0.9, which augmentation did not achieve for the same simulation budget. For held-out patients, both strategies performed comparably (median leave-one-out normalized root-mean-square error 11.0% vs. 10.7%; p = 0.745), and error rose with distance from the cohort centroid (Spearman rho = +0.406). The calibrated models showed no cohort-wide bias (median standardized residual -0.12 against the clinical targets), and 95% predictive intervals contained 78.5% of targets, with the largest misfit in systolic blood pressure. Conclusion: Removing the need for new simulations for every new patient is a meaningful step toward uncertainty-aware cardiac digital twins that fit within a routine clinical workflow. Because the simulator, surrogate and sampler are interchangeable, the approach used here can be applied to other (cardiac) models and cohorts.

bioengineering↗
bioRxiv2026

Multi-Structure Segmentation of Type B Aortic Dissection from CT Images with U-Net

Type B aortic dissection (TBAD) is a life-threatening condition in which a tear in the intimal layer separates the aortic wall into a true and a false lumen. Assessment of the lumen, the intimal flap, and false-lumen thrombosis from CT images is important for risk analysis and treatment planning. This work applied U-Net-based deep neural networks to the automatic segmentation of these structures in TBAD. We collected 46 CT scans from 20 patients, with voxel-wise annotations of three structures: the aortic lumen, the intimal flap, and the thrombus. We explored four models: two convolutional networks, nnU-Net 2D and 3D, and two transformer-based architectures, TransUNet 2D and 3D. We observed that (1) for both nnU-Net and TransUNet, the 3D models outperformed their 2D counterparts on all three structures. The difference was small for the aortic lumen but large for the thin flap and the thrombus. The best model, nnU-Net 3D, reached a DSC of 93.05% for the lumen, 50.22% for the flap, and 69.94% for the thrombus. (2) The intimal flap was the most difficult structure for all models. Even the best 3D models showed large thrombus boundary errors in a few scans. These results provide a baseline for developing more advanced models for accurate segmentation of these structures in TBAD.

bioengineering↗