Figure 6 shows the binding mode of sLex to the E- and P-selectin. in the pathogenesis of inflammatory diseases by mediating leukocyte migration from the blood into the tissue. Thus, the selectins and selectin ligands represent a promising therapeutic target for the treatment of COVID-19. In this paper, potential pan-selectin inhibitors were identified employing a virtual screening using a docking procedure. For this purpose, the Asinex and ZINC databases of ligands, including approved drugs, biogenic compounds and glycomimetics, altogether 923,602 compounds, were screened against the P-, L- and E-selectin. At first, the experimentally confirmed inhibitors were docked into all three selectins carbohydrate recognition domains to assess the suitability of the screening procedure. Finally, based on the evaluation of ligands binding, we propose 10 purchasable pan-selectin inhibitors to develop COVID-19 therapeutics. values allow for a straightforward comparison of the inhibitors within a single study, they cannot be used to compare inhibitors among the different studies due to the various experimental conditions. To deal with this issue, the logarithm of the ratio of online. Open in a separate window Fig. 2 Comparison of the results from docking with the SP algorithm with the experimental data for (A) E-selectin and (B) P-selectin. This figure is available in black and white in print and in color at online. Open in a separate window Fig. 5 (A) Comparison of the PF-3845 results from docking with the HTVS algorithm with the experimental data for E-selectin. The compound 26 from the paper by Calosso et?al. (2014)) was dropped by the docking algorithm. (B) Comparison of the docking score calculated at the XP level of precision with the experimental data for E-selectin. This figure is available in black and white in print and in color at online. For L-selectin, there are not enough experimental data available. However, based on the selectins structural similarity, with a 60C70% identity of the lectin-like domain and the same binding mode of sLex PF-3845 (Figure 6), a very similar correlation to those presented for E- and P-selectin can be assumed. Open in a separate window Fig. 6 Overview of E- and P-selectin interactions with sLeX in the binding PF-3845 site (Somers et?al. 2000; Woelke et?al. 2013). The tetrasaccharide sLeX binds in a Ca2+-dependent manner via interactions with fucose hydroxyls. The fucose residue is shown in yellow and the Ca2+ cation as a red sphere. This figure is available in black and white in print and in color at PF-3845 online. The Glide algorithm chooses the best pose based on the model energy scorethe parameter Glide Emodel, which is a combination of empirical GlideScore, molecular mechanics interaction energy (Coulombic and van der Waals LIN41 antibody interactions) and the ligand internal strain energy (Friesner et?al. 2004). However, Glide also calculates the CoulombCvan der Waals interaction energy score, referred to as Glide Energy. When applied to the selectins, we find that Glide Energy is the best of these parameters at reproducing the experimental activity. The comparison of the docking score to E-selectins experimental data is shown in Figure 5B for illustration. The good correlation obtained with the molecular mechanic PF-3845 Glide Energy scoring function corresponds with the findings of Woelke et?al. that the interactions between selectins and their ligands are mostly electrostatic (Woelke et?al. 2013). To shed some light onto the possible concerns and issues of the selected docking algorithms, the comparison presented in Figure 1A needs to be discussed in more detail. The linear fit (online. Equation (2) was later used to predict the experimental activity relative to the activity of sLex, allowing to put the proposed inhibitors into the context of known/tested selectin antagonists. The set of inhibitors used to verify the method covers the values of log(ratio) ranging from ?3 to 4 4.5. Thus, the applicability of some of the proposed inhibitors is limited. Screening of the ligand databases The previously verified methods were used for docking of compounds from the Assinex and ZINC databases. At first, for efficient screening of compounds, the compounds from selected databases were docked using the SP algorithm. The distributions of predicted binding energies with the SP algorithm are shown in Figure 3. The maximum of the distribution is located around ?30?kcal/mol for all three selectins. Based on the data for SP docking algorithm presented in Figure 2, the majority of the compounds have a low or negligible affinity toward selectins. Hence, only a small portion of the compounds need to be docked at the XP level of precision. Open in a separate window Fig. 3 Distribution of Glide Energy of compounds from the Asinex and ZINC database for the different receptors predicted by the SP algorithm. This figure is available in black and white in print and in.