Mapping the underlying mechanisms of fibrinogen benzothiazole drug interactions using computational and experimental approaches

González-Durruthy, Michael; Scanavachi, Gustavo; Rial, Ramón; Liu, Zhen; Cordeiro, M. Natália D. S.; Itri, Rosângela; Ruso, Juan M.

doi:10.1016/j.ijbiomac.2020.07.044

Cited by 12 publications

(11 citation statements)

References 73 publications

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“…As previously cited, a FD ≈ 2 reveals a high variety of geometrical information and low self-similarity, while FD ≈ 1 represents little complexity and high self-similarity. On the other hand, the FD values obtained for FD B+BW and FD W+BW remain as unperturbed around 1.85 in all the cases, revealing high complexity of geometrical information [9,55].…”

Section: Modeling F0atpase Inhibition Induced By Swcntsmentioning

confidence: 86%

“…In order to quantify potential fractal geometrical perturbations, a fractal surface analysis was carried out to model changes-based perturbations in the geometric surface of the binding effector residues of the F0-ATPase under unbound and bound states (i.e., under SWCNT-pristine and SWCNT-COOH interactions) [9]. Several fractal dimensions (FDs, namely: D BW , D B+BW , and D W+BW ) were calculated using the box-counting methods from the LPRS maps previously obtained [55]. The Fractal Theory allows the mathematical modeling of the geometric complexity (across multiple scales) and self-similarity (scale-invariant structure) from non-Euclidean real or virtual objects (such as the tested SWCNT).…”

Section: Modeling F0atpase Inhibition Induced By Swcntsmentioning

confidence: 99%

“…Then, a topological fractal dimension near to 2 is categorized-like, high complexity (i.e., high variety of geometrical information after the docking interaction) and low self-similarity; in contrast, a topological fractal dimension closer to 1 informs about little complexity and high self-similarity after the docking interaction. Herein, the non-Euclidean geometrical patterns were included according to the fractal dimension, like FD BW , that describes the surface geometric perturbations in the border of the LPRS map fractal pattern [55]. The FD B+BW characterizes the surface geometric perturbations on the white background, and the FD W+BW characterizes the fractal perturbations pattern on the black background from the LPRS images calculated for each simulation condition, see Figure 5.…”

Section: Modeling F0atpase Inhibition Induced By Swcntsmentioning

confidence: 99%

“…The FD B+BW characterizes the surface geometric perturbations on the white background, and the FD W+BW characterizes the fractal perturbations pattern on the black background from the LPRS images calculated for each simulation condition, see Figure 5. Herein, the obtained FDs are related to the F0-ATPase surface and backbone non-Euclidean geometry [9,55]. FDs inform about how the F0-ATPase folding, packing density, solvent accessibility, and binding interaction properties could be perturbed under the presence of different ligands forming docking complexes (oligomycin A/F0-ATPase complex, SWCNT-pristine/F0-ATPase complex, and SWCNT-COOH/F0-ATPase complex).…”

Section: Modeling F0atpase Inhibition Induced By Swcntsmentioning

confidence: 99%

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New Mechanistic Insights on Carbon Nanotubes’ Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches

González-Durruthy

Concu

Ruso

et al. 2021

Biology

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Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition. To completely characterize the mechanistic effect triggering the toxicity, we have developed a new approach based on the combination of experimental and computational study, since the use of only one or few techniques may not fully describe the phenomena. To this end, the in vitro inhibition responses in submitochondrial particles (SMP) was combined with docking, elastic network models, fractal surface analysis, and Nano-QSTR models. In vitro studies suggest that inhibition responses in SMP of F0F1-ATPase enzyme were strongly dependent on the concentration assay (from 3 to 5 µg/mL) for both pristine and COOH single-walled carbon nanotubes types (SWCNT). Besides, both SWCNTs show an interaction inhibition pattern mimicking the oligomycin A (the specific mitochondria F0F1-ATPase inhibitor blocking the c-ring F0 subunit). Performed docking studies denote the best crystallography binding pose obtained for the docking complexes based on the free energy of binding (FEB) fit well with the in vitro evidence from the thermodynamics point of view, following an affinity order such as: FEB (oligomycin A/F0-ATPase complex) = −9.8 kcal/mol > FEB (SWCNT-COOH/F0-ATPase complex) = −6.8 kcal/mol ~ FEB (SWCNT-pristine complex) = −5.9 kcal/mol, with predominance of van der Waals hydrophobic nano-interactions with key F0-ATPase binding site residues (Phe 55 and Phe 64). Elastic network models and fractal surface analysis were performed to study conformational perturbations induced by SWCNT. Our results suggest that interaction may be triggering abnormal allosteric responses and signals propagation in the inter-residue network, which could affect the substrate recognition ligand geometrical specificity of the F0F1-ATPase enzyme in order (SWCNT-pristine > SWCNT-COOH). In addition, Nano-QSTR models have been developed to predict toxicity induced by both SWCNTs, using results of in vitro and docking studies. Results show that this method may be used for the fast prediction of the nanotoxicity induced by SWCNT, avoiding time- and money-consuming techniques. Overall, the obtained results may open new avenues toward to the better understanding and prediction of new nanotoxicity mechanisms, rational drug design-based nanotechnology, and potential biomedical application in precision nanomedicine.

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Section: Modeling F0atpase Inhibition Induced By Swcntsmentioning

confidence: 86%

Section: Modeling F0atpase Inhibition Induced By Swcntsmentioning

confidence: 99%

Section: Modeling F0atpase Inhibition Induced By Swcntsmentioning

confidence: 99%

Section: Modeling F0atpase Inhibition Induced By Swcntsmentioning

confidence: 99%

See 2 more Smart Citations

New Mechanistic Insights on Carbon Nanotubes’ Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches

González-Durruthy

Concu

Ruso

et al. 2021

Biology

Self Cite

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“…The main goal of these methods is to predict the nature and strength of interactions, to improve relevant biological data, to better understand all the factors involved in the occurrence of a given response, etc. [ 80 , 81 ]. Computational techniques involve characterization across multiple scales and levels of organization.…”

Section: Application Of Modern Computational Methods In Hap Based Materialsmentioning

confidence: 99%

Advanced Materials Based on Nanosized Hydroxyapatite

et al. 2021

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The development of new materials based on hydroxyapatite has undergone a great evolution in recent decades due to technological advances and development of computational techniques. The focus of this review is the various attempts to improve new hydroxyapatite-based materials. First, we comment on the most used processing routes, highlighting their advantages and disadvantages. We will now focus on other routes, less common due to their specificity and/or recent development. We also include a block dedicated to the impact of computational techniques in the development of these new systems, including: QSAR, DFT, Finite Elements of Machine Learning. In the following part we focus on the most innovative applications of these materials, ranging from medicine to new disciplines such as catalysis, environment, filtration, or energy. The review concludes with an outlook for possible new research directions.

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Biophysical Approaches for the Characterization of Protein-Metabolite Interactions

Thalhammer

Bröker

2022

Methods in Molecular Biology

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Mapping the underlying mechanisms of fibrinogen benzothiazole drug interactions using computational and experimental approaches

Cited by 12 publications

References 73 publications

New Mechanistic Insights on Carbon Nanotubes’ Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches

New Mechanistic Insights on Carbon Nanotubes’ Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches

Advanced Materials Based on Nanosized Hydroxyapatite

Biophysical Approaches for the Characterization of Protein-Metabolite Interactions

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