*3.4. Alkaloids Identification and Quantification*

## 3.4.1. Equipment

The equipment used for the identification and quantification of the alkaloids was a GC-MS 6890N apparatus (Agilent Technologies, Santa Clara, CA, USA) coupled with MSD5975 inert XL operating in the electron ionization (EI) mode at 70 eV. A Sapiens-X5 MS column (30 m × 0.25 mm i.d., film thickness 0.25 μm) was used. The temperature gradient was as follows: 12 min at 100 ◦C, 100–180 ◦C at 15 ◦C/min, 180–300 ◦C at 5 ◦C/min and 10 min hold at 300 ◦C. The injector and detector temperatures were 250 and 280 ◦C, respectively, and the flow-rate of carrier gas (He) was 1 mL/min. Two mg of each alkaloid extract was dissolved in 1 mL of MeOH:CHCl3 (1:1, *v*/*v*) and 1 μL was injected using the splitless mode. Codeine (0.05 mg/mL) was used as an internal standard in all the samples.

#### 3.4.2. Alkaloids Identification

Amaryllidaceae alkaloids occurring in the samples were identified by comparison of the Rt, fragmentation patterns and data interpretation of the spectra. The database used was built using single alkaloids previously isolated and identified by spectroscopic and spectrometric methods (NMR, UV, CD, IR, MS) in the Natural Products Laboratory, Universidad de Barcelona, the NIST 05 Database and literature data [37–42].

#### 3.4.3. Alkaloid Quantification

To quantify the single constituents, a calibration curve of galanthamine (10, 20, 40, 60, 80 and 100 μg/mL) was used. The same amount of codeine (0.05 mg/mL) was added to each solution sample as an internal standard. The peak areas were manually obtained considering selected ions for each compound (usually the base peak of their MS, i.e., *m*/*z* at 286 for galanthamine, at 299 for codeine). The ratio between the values obtained for galanthamine and codeine in each solution was plotted against the corresponding concentration of galanthamine to obtain the calibration curve and its equation (y = 0.0224x − 0.2037; R2 = 0.9977). All data were standardized to the area of the internal standard (codeine) and the equation obtained for the calibration curve of galanthamine was used to calculate the amount of each alkaloid. Results are expressed as mg GAL, which was finally related to the alkaloid extract weight. As the peak area does not only depend on the corresponding alkaloid concentration but also on the intensity of the mass spectra fragmentation, the quantification is not absolute. However, the methodology is considered suitable to compare the specific alkaloid amount between samples [39,42].

#### *3.5. Molecular Docking*

The molecular docking simulations for the alkaloids identified in *Rhodophiala splendens*, the most promising species found in this study, were performed to investigate the binding mode into the active site of two different enzymes, namely *Torpedo californica* AChE (*Tc*AChE) and *h*BuChE: proteins with PDB codes 1DX6 [43] and 4BDS [44], respectively. The 3D-structures of the alkaloids were drawn using the Chemcraft program [45], and then submitted to a geometrical optimization procedure at PBE0 [46]/6-311+g\* [47] level of theory using the Gaussian 09 program [48]. All optimized alkaloids were confirmed as a minimum on the potential energy surface. The docking simulations for the set of optimized ligands were performed using the AutoDock v.4.2 program [49]. AutoDock combines a rapid energy evaluation through pre-calculated grids of affinity potentials with a variety of search algorithms to find suitable binding positions for a ligand on a given macromolecule. To compare the results from the docking simulations, the water molecules, cofactors, and ions were excluded from each X-ray crystallographic structure. Likewise, the polar hydrogen atoms of the enzymes were added, and the non-polar hydrogen atoms were merged. Finally, the enzyme was treated as a rigid body. The grid maps of interaction energy for various atom types with each macromolecule were calculated by the auxiliary program AutoGrid, choosing a grid box with dimensions of 70 × 70 × 70 Å around the active site, which was sufficiently large to include the most important residues of each enzyme. The docking searches for the best orientations of the ligands binding to the active site of each protein were performed using the Lamarckian Genetic Algorithm (LGA) [50]. The LGA protocol applied a population size of 2000 individuals, while 2,500,000 energy evaluations were used for the 50 LGA runs. The best conformations were chosen from the lowest docked energy solutions in the cluster populated by the highest number of conformations. The best docking complex solutions (poses) were analyzed according to the potential intermolecular interactions (ligand/enzyme), such as hydrogen bonding and the cation–π, π–π stacking.

**Author Contributions:** G.S.-H. and J.B. conceived and designed the experiments; L.R.T., N.C., E.H.O. and C.T. performed the experiments; L.R.T., J.B. and G.S.-H. analyzed the data; C.T. and G.S.-H. collected and processed the samples; G.S.-H., J.B., L.R.T. and E.H.O. wrote the paper.

**Funding:** This research was funded by PIEI-QUIM-BIO, Universidad de Talca, CCiTUB and Programa CYTED (416RT0511). L.R.T. and J.B. are members of the Research Group 2017-SGR-604 at the University of Barcelona.

**Acknowledgments:** L.R.T. is thankful to CAPES (Coordenação de Pessoal de Nível Superior-Bolsista CAPES, Processo 13553135) for a doctoral fellowship.

**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
