## 3 Results
### Molecular Docking
#### 3.1.2 Virtual Screening  
An in silico analysis investigated the interaction between the database of bioactive mushroom compounds and the P-pg (PDB: 7A6E) structure. The initial objective was to perform a virtual screening to identify the compounds with the best-predicted affinity for P-gp, highlighting the most promising P-gp inhibitor candidates. The mushroom compound database, as detailed in Section 2, was used for screening [21; 22; 23]. The predicted binding energy values, calculated using the AutoDock VINA docking software, were used as a selection criterion, with lower values indicating greater predicted affinity between the compounds and the P-pg protein. The binding energies of the bioactive compounds ranged from \(-\)13.19 to 0.07 kcal/ mol (Table S3, Supplementary Materials). With a binding energy of \(-\)10.78 kcal/ mol, the well-known and potent P-gp inhibitor tariquidar was used as a positive control and screening reference. The compounds with the highest binding energies compared to the positive control were (22\(E\),245)-ergosta-4,22-dien-3-one (\(-\)12.31 kcal/mol), lanostanoid 4 (\(-\)13.19 kcal/mol), and lanostanoid 3 (\(-\)12.67 kcal/mol). Analyzing the complete database of 211 compounds, 59 showed lower (more negative) predicted binding energies than the tariquidar used as a control, indicating a better predicted binding affinity to the P-gp protein (Table 1). Other compounds, such as ergosterols, also showed higher affinity, with ergosterol showing a predicted binding energy of \(-\)12.31 kcal/mol, well below the value obtained for tariquidar of \(-\)10.78 kcal/mol. Regarding the molecular contact surface (Con.Surf[A2]), the control tariquidar stood out with a larger area (633.75 A2) compared to the other compounds from the database. This value is due to its large and bulk structure (Figure 5). From the mushroom database, some compounds, such as oificimalonic acids C (554.09 A2) and oificimalonic acids E (550.17 A2), also presented large areas, albeit still lower than that of tariquidar. On the other hand, molecules such as ergosta-4,6,8(14),22-tetra-3-one (367.21 A2) and (22_E_)-19-noregosta-5,7,9,22-tetra-3\(\beta\)-ol (384.84 A2) exhibited more compact surfaces. Figure 4: Redocking of the isolated tariquidar with the P-gp protein (PDB: 7A6E). (**A**) shows the redocking ligand in green and the co-crystallized ligand of the 7A6E protein in pink. (**B**) illustrates the interaction area, highlighting residues distributed across different protein domains and their contributions to molecular interactions. \begin{table}
\begin{tabular}{c l c c} \hline \hline
**Compound** & **Name** & **Predicted Binding Energy [kcal/mol]** & **Con.Surf [Å2]** \\ \hline
**121** & Lanostanoid 4 & \(-\)13.19 & 470.73 \\
**120** & Lanostanoid 3 & \(-\)12.67 & 495.39 \\
**4** & (22\(E\),245)-Ergosta-4,22-dien-3-one & \(-\)12.31 & 461.12 \\
**177** & Ergosterol & \(-\)12.27 & 462.35 \\
**68** & Inoscavin A & \(-\)12.22 & 445.33 \\
**7** & (22\(E\))-19-Noregosta-5,7,9,22-tetraen-3\(\beta\)-ol & \(-\)12.15 & 384.84 \\
**11** & Officimalonic acids D & \(-\)12.09 & 565.94 \\
**1** & +E4:F38(3\(\beta\),5a,8a,22\(E\),245)-5,8-Epidioxyergosta-6,9(11),22-trien-3-ol & \(-\)12.07 & 448.19 \\
**21** & 9,11-Dehydroergosterol peroxide & \(-\)12.04 & 449.10 \\
**10** & (22\(E\))-Ergosta-5,7,22-trien-3\(\beta\)-ol & \(-\)12.01 & 457.70 \\
**9** & (22\(E\))-5a,6a-Eposeryergosta-8,14,22-triene-3\(\beta\),7\(\beta\)-diol & \(-\)11.98 & 447.32 \\
**3** & 3\(\beta\),5a,8a,22\(E\))-5,8-Epidioxyergosta-6,22-dien-3-yl\(\beta\)-D-glucopyranoside & \(-\)11.93 & 506.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Compounds with better binding energies. Figure 5: Best mushroom-derived bioactive compounds interacting with the P-gp protein (PDB: 7A6E), evaluated in comparison with the ratiquidar control. (**1a,2a,3a,4a,5a,6a**) illustrate the compounds compared to the control, while (**1b,2b,3b,4b,5b,6b**) show only the isolated compounds.