Computer simulation of the Receptor–Ligand Interactions of Mannose Receptor CD206 in Comparison with the Lectin Concanavalin A Model

Zlotnikov, Igor D.; Kudryashova, Еlena V.

doi:10.1134/s0006297922010059

Cited by 16 publications

(40 citation statements)

References 75 publications

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“…The requirements imposed on the drug carrier include high affinity for the mannose receptor of macrophages. Relevant data on the affinity for mannose receptors of CD206 macrophages can be obtained using model experiments with lectin concanavalin A, because of a high correlation of carbohydrate binding capacity with the fourth domain (CRD4) of CD206 as was shown by in silico methods [ 17 ]. Therefore, it is necessary to have a robust, reproducible and accessible method for studying protein-receptor binding [ 8 , 18 ], which is generally complicated for structurally sophisticated ligands.…”

Section: Resultsmentioning

confidence: 99%

“…Recently we studied in silico the detailed mechanism of these interactions [ 17 ] ( Figure S14 ), using the interaction of trimannoside with ConA as the example. The “amide” atoms of the binding site are involved in complexation-Asn14 (N)-Man (O3), Asp16 (H)-Man (O5), Arg222 (H)-Man (O2).…”

Section: Resultsmentioning

confidence: 99%

“…Thus, changes in the amide II region are expected when the ligand binds to the protein. Moreover, carbonyl oxygen atoms of the Gly-peptide bond are also involved in complexation [ 17 ]. Experimental data on changes in the Amide I and Amide II regions (ConA spectrum) upon binding to the ligand HPCD-spermine-Man- 1 ( Figure 4 a) confirm the formation of point interactions of oxygen atoms of the carbonyl group and hydrogen or nitrogen atoms in the amide with ligand donors/acceptors.…”

Section: Resultsmentioning

confidence: 99%

“…Accordingly, in such cases, a model lectin ConA can be used, which is more suitable for screening purposes. The relevance of ConA as a CD206 model has been widely discussed in the literature and studied by us through computer modeling of 15 ligands [ 17 ]. We have previously shown the similarity of the structural organization of the binding sites in ConA and in CD206 (CRD4) with high correlation of the values of the free energies of complexation (r > 0.9).…”

Section: Introductionmentioning

confidence: 99%

“…ConA is an easily accessible lectin having a carbohydrate-binding domain structure similar to CD206 and a general similarity in carbohydrate-binding ability [ 8 , 17 , 18 , 19 , 20 , 21 ]. According to the literature, the CD206 domain shows a high similarity with mannan binding lectin A, which, in turn, is similar to ConA in terms of carbohydrate binding [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Spectroscopy Approach for Highly-Efficient Screening of Lectin-Ligand Interactions in Application for Mannose Receptor and Molecular Containers for Antibacterial Drugs

Zlotnikov

Kudryashova

2022

Pharmaceuticals

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Rational search of a ligand for a specific receptor is a cornerstone of a typical drug discovery process. However, to make it more “rational” one would appreciate having detailed information on the functional groups involved in ligand-receptor interaction. Typically, the 3D structure of a ligand-receptor complex can be built on the basis of time-consuming X-ray crystallography data. Here, a combination of FTIR and fluorescence methods, together with appropriate processing, yields valuable information about the functional groups of both the ligand and receptor involved in the interaction, with the simplicity of conventional spectrophotometry. We have synthesized the “molecular containers” based on cyclodextrins, polyethyleneimines (PEI) or spermine with mannose-rich side-chains of different molecular architecture (reticulated, star-shaped and branched) with variable parameters to facilitate delivery to alveolar macrophages. We have shown that synthetic mannose-rich conjugates are highly affine to the model mannose receptor ConA: Kd ≈ 10−5–10−7 M vs. natural ligand trimannoside (10−5 M). Further, it was shown that molecular containers effectively load levofloxacin (dissociation constants are 5·10−4–5·10−6 M) and the eugenol adjuvant (up to 15–80 drug molecules for each conjugate molecule) by including them in the cyclodextrins cavities, as well as by interacting with polymer chains. Promising formulations of levofloxacin and its enhancer (eugenol) in star-shaped and polymer conjugates of high capacity were obtained. UV spectroscopy demonstrated a doubling of the release time of levofloxacin into the external solution from the complexes with conjugates, and the effective action time (time of 80% release) was increased from 0.5 to 20–70 h. The synergy effect of antibacterial activity of levofloxacin and its adjuvants eugenol and apiol on Escherichia coli was demonstrated: the minimum effective concentration of the antibiotic was approximately halved.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Spectroscopy Approach for Highly-Efficient Screening of Lectin-Ligand Interactions in Application for Mannose Receptor and Molecular Containers for Antibacterial Drugs

Zlotnikov

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2022

Pharmaceuticals

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Applications of machine learning in computer-aided drug discovery

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Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.

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Discovery of Nanomolar Inhibitors for Human Dihydroorotate Dehydrogenase Using Structure-Based Drug Discovery Methods

Higgins,

Vibhute,

Bennett

et al. 2024

J. Chem. Inf. Model.

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We used a structure-based drug discovery approach to identify novel inhibitors of human dihydroorotate dehydrogenase (DHODH), which is a therapeutic target for treating cancer and autoimmune and inflammatory diseases. In the case of acute myeloid leukemia, no previously discovered DHODH inhibitors have yet succeeded in this clinical application. Thus, there remains a strong need for new inhibitors that could be used as alternatives to the current standard-of-care. Our goal was to identify novel inhibitors of DHODH. We implemented prefiltering steps to omit PAINS and Lipinski violators at the earliest stages of this project. This enriched compounds in the data set that had a higher potential of favorable oral druggability. Guided by Glide SP docking scores, we found 20 structurally unique compounds from the ChemBridge EXPRESS-pick library that inhibited DHODH with IC 50, DHODH values between 91 nM and 2.7 μM. Ten of these compounds reduced MOLM-13 cell viability with IC 50, MOLM-13 values between 2.3 and 50.6 μM. Compound 16 (IC 50, DHODH = 91 nM) inhibited DHODH more potently than the known DHODH inhibitor, teriflunomide (IC 50, DHODH = 130 nM), during biochemical characterizations and presented a promising scaffold for future hit-to-lead optimization efforts. Compound 17 (IC 50, MOLM-13 = 2.3 μM) was most successful at reducing survival in MOLM-13 cell lines compared with our other hits. The discovered compounds represent excellent starting points for the development and optimization of novel DHODH inhibitors.

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Computer simulation of the Receptor–Ligand Interactions of Mannose Receptor CD206 in Comparison with the Lectin Concanavalin A Model

Cited by 16 publications

References 75 publications

Spectroscopy Approach for Highly-Efficient Screening of Lectin-Ligand Interactions in Application for Mannose Receptor and Molecular Containers for Antibacterial Drugs

Spectroscopy Approach for Highly-Efficient Screening of Lectin-Ligand Interactions in Application for Mannose Receptor and Molecular Containers for Antibacterial Drugs

Applications of machine learning in computer-aided drug discovery

Discovery of Nanomolar Inhibitors for Human Dihydroorotate Dehydrogenase Using Structure-Based Drug Discovery Methods

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