Computational design of new drugs for binding ⍺-amylase as candidates for treating diabetes

doi:10.21203/rs.3.rs-1892617/v1

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Computational design of new drugs for binding ⍺-amylase as candidates for treating diabetes

https://doi.org/10.21203/rs.3.rs-1892617/v1

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Type-2 diabetes is a pandemic metabolic disorder affecting almost 200 million people globally, and this figure is expected to rise gradually to 642 million by 2040. α-amylase is a digestive enzyme that causes postprandial hyperglycemia and blood glucose levels rise. Inhibiting the α-amylase enzyme, it could help to reduce hyperglycemia, obesity, and overweight conditions by reducing postprandial levels of glucose.

In the present study, the identification of potential α-amylase inhibitors is explored as a potential strategy for treating type-2 diabetes mellitus. A new computationally driven approach aimed at identifying novel α-amylase inhibitors based on molecular docking was carried out. The interactions of potential drugs with the enzyme’s active site were investigated and compared with the contacts established by acarbose (a reference drug for α-amylase inhibition) in the crystallographic structure 1B2Y. For this active site characterization, both molecular docking and molecular dynamics simulations were performed, and the residues involved in the α-amylase–acarbose complex were considered to analyse the potential drug's interaction with the enzyme. Two potential α-amylase inhibitors (AN-153I105594 and AN-153I104845) have been selected following this computational strategy. Both compounds established a large number of interactions with key binding site α-amylase amino acids and obtained a comparable docking score with respect to the reference drug (acarbose).

Protein binding

diabetes

database screening

molecular dynamics

docking

acarbose

Diabetes mellitus is one of the most common non-communicable metabolic disorders categorized by type-1 or insulin-dependent diabetes, type-2 or non-insulin dependent diabetes mellitus (NIDDM) and gestational diabetes mellitus¹. Amongst these, type-2 diabetes is a pandemic and has affected almost 200 million people globally, and this figure is likely to rise gradually to 642 million by 2040. It happens when the pancreas stops using insulin effectively, which means the body’s cells lose the ability to respond to insulin’s efforts to drive glucose into the cells, a condition called insulin resistance leads to a type of metabolic disorder in which the patient has an increased level of sugar (glucose) in blood, which can lead to potential complications that include blindness, heart attacks, strokes, kidney damage, lower limb amputation, and nerve damage. The management of blood glucose in patients affected by diabetes mellitus is complex because of personal factors, such as exercise and diet, and the non-specific mode of action associated with oral antihyperglycemic².

In the present scenario, enzyme inhibitors can be explored as alternative targets due to their high degree of substrate specificity. Amylase is a digestive enzyme, mainly produced by the salivary glands and pancreas, serving both endocrine and exocrine functions. Among the different types of amylase enzymes, one major category of enzyme is pancreatic α-amylase, which is a calcium metalloenzyme that aids digestion by breaking down polysaccharide molecules into smaller ones, such as glucose and maltose. In addition, the enzyme causes postprandial hyperglycemia and blood glucose levels to rise. By performing the act of inhibiting α-amylase enzyme, it helps in reducing hyperglycemia, obesity and problems such as overweight conditions by reducing postprandial levels of glucose.

α-Amylase is the major form of amylase found in humans and other mammals. It is an enzyme that hydrolyses α-bonds of large, α-linked polysaccharides, such as starch and glycogen, yielding shorter chains thereof, dextrins, and maltose.³ As α-amylase is an important target for the treatment of diabetes mellitus and the development of new drugs, scientists are showing great interest in this enzyme.⁴ In this regard, 3D computer-aided drug design methods, such as molecular docking and molecular dynamics (MD), take into account ligand-receptor simulation systems.⁵ Molecular docking analysis models the interaction between a molecule and a protein at the atomic level. This information permits us to characterise the behaviour of molecules in the binding sites of target proteins.⁶

An essential computational tool for analyzing the time evolution of biological structures is molecular dynamics (MD). The stability and conformation of macromolecules, as well as their host-guest complexes with smaller compounds (i.e. drugs), can be better understood by MD simulations. Moreover, the role of molecular flexibility, solvent, and counter-ions can be assessed, as they are not taken into account in the rigid molecular docking calculations.

In the present study, receptor-based methods are combined in order to design new, potential α-amylase inhibitors for the control and reduction of blood sugar levels and the treatment of type II diabetes.

Computational strategy to identify novel α-amylase inhibitors

In order to identify novel α-amylase inhibitors for the treatment of type-2 diabetes mellitus, we depicted a structure-based drug design strategy to select potential candidates to inhibit α-amylase. For this purpose, pancreatic α-amylase co-crystallized with the reference antidiabetic drug acarbose was retrieved from the Protein Data Bank (PDB: 1B2Y).⁷ Steps followed were:

Virtual high-throughput screening: a molecular docking screening was performed in order to identify potential α-amylase inhibitors. Three different commercial databases were considered: Academic and Natural Compounds collection (from Specs)⁸ and Natprod collection (from Discovery Systems, Inc.).⁹ From over 380,000 molecules, 16 potential α-amylase inhibitors were selected.
Validation of the binding site: the magnitude and nature of interaction with the reference drug was investigated. From blind and site-specific docking, as well as molecular dynamics simulations, enzyme residues interacting with the acarbose could be tracked, and used as references in the next step. The drug stability in the binding pocked could also be verified. Acarbose was selected as a reference antidiabetic drug in order to check if there is a similarity between the experimental location of α-amylase and the calculated location of the candidate inhibitors.
Candidate drugs interaction with α-amylase: a blind and site-specific docking analysis was performed in order to check whether these 16 potential α-amylase inhibitors will interact preferentially with the reported binding site with therapeutic activity.

For the tasks above, molecular docking and molecular dynamics calculations were performed as described below.

Molecular Docking

Molecular docking studies play a major role in determining whether millions of synthesised compounds are effective drug substances. The calculation of the interaction of a chemical compound on a protein structure allows the prediction of different binding modes. Since the docking process brings powerful insights for explaining the enzyme-ligand relationship, it plays an important role in enzyme inhibition studies⁶. For the present study, the target protein, human pancreatic α-amylase (1B2Y)⁷, was retrieved from the RCSB Protein Data Bank.¹⁰ Docking calculations were performed using AutoDockVina¹¹ and Schrödinger¹² software suite molecular modeling packages (version 2021-3), using default parameters unless otherwise reported.

Two types of docking calculations were performed: site-specific docking and blind docking. Site-specific docking focuses on a reduced region of the protein that is selected as the preferred binding active site, so that all Monte Carlo moves are centered in this region. Here, the binding site was defined by the crystallographic position of the co-crystallized antidiabetic drug (acarbose). Blind docking considers the whole protein as potential sites for binding, and allows obtaining, as a result, a shortlist (called "modes") of the potential active sites within the protein for the ligand. If some of these modes correspond to the active site of known drugs with demonstrated therapeutic action, it is an additional indication that the molecule being tested could be a good candidate with the same potential therapeutic action.

In addition to all these important and useful features of molecular docking studies, they should be supported by molecular dynamics. The molecule, which is buried inside the protein structure, is also interacting with the solvent. The thermal effects on the kinetic energy of the molecule and on the protein conformation could lead to an activated jump of the molecule to between different adsorption sites, that can be studied using molecular dynamics calculations.

Molecular Dynamics Simulations

According to the literature, there is good agreement between computational and experimental measurements of macromolecular dynamics.¹³ Since receptor dynamics and flexibility play a key role in their interaction with candidate drugs, Molecular Dynamics simulations can be a powerful tool for drug discovery studies. To complement the docking study and further evaluate the changes in enzyme and drug conformations, MD simulations were performed with pmemd.cuda software from the AMBER16 simulation package.¹⁴ The α-amylase was described by the ff99SB-ILDN force field,¹⁵ since it presents good agreement with experiments when in conjunction with TIP4P-Ew¹⁶ water model.^17,18 General Amber Force Field (GAFF)¹⁹ was considered to model drug molecules, as it is compatible with Amber force fields such as f99¹⁹. An all atom modelling of the system was considered, where hydrogen atoms for the macromolecule and drug were added with pdb4amber program, from Ambertools package. Enzyme-ligand complexes were solvated with TIP4P-Ew water molecules, considering a solvation shell of at least 10.0 Å with respect to any solute atom. Na⁺ and Cl^- ions were added to the simulation box to neutralise the system and to simulate a saline solution (0.15 M), as previously reported in the literature.²⁰ AM1-BCC partial charges^21,22 were calculated for the enzyme-acarbose complexes.

First, energy minimization of the simulation boxes were performed, followed by temperature and pressure equilibration of the systems. The temperature was gradually increased until 298.15 K during an MD simulation of 100 ps at the NVT ensemble. Then, the thermalized system was simulated in the NPT ensemble for 250 ps and at 1 bar. Temperature and pressure control were ensured by Langevin thermostat²³ and Monte Carlo barostat²⁴ for orthogonal boxes, respectively. Next, the production run was performed, with trajectories being computed over 135 ns. Every MD simulation considered a time step of 2.0 fs, which was possible due to constraints applied to bonds containing hydrogen atoms, by the SHAKE algorithm.²⁵ To avoid finite-size effects, periodic boundary conditions were considered. Finally, a cutoff of 9.0 Å was set for the nonbonded interactions, while electrostatic interactions were treated with particle mesh Ewald (PME)²⁶ method.

The hydrogen bonds (HB) established during the simulations, mass-weighted Root Mean Square Deviation (RMSD), mass-weighted radius of gyration (R_g), gyration tensor and 3D histogram of drug conformations were computed from trajectories by cpptraj²⁷ program, available in the AmberTools. Additional analyses are included in Section S2 of Supplementary Information (SI).

We outline here the strategy to identify novel α-amylase inhibitors with antidiabetes activity by using two different approaches based on molecular docking studies. First, we performed a virtual high-throughput screening from which a potential list of α-amylase inhibitors was identified. Then, we performed a validation of the binding site for α-amylase antidiabetic activity. Then, molecular dynamics simulations were performed in order to identify and check the stability of the ligand-α-amylase interaction determined by molecular docking.

Finally, once the active binding site has been validated, potential anti-diabetic compounds were docked (blind and site-specific docking) with α-amylase so that the amino acid interactions and docking score values are reported. Only molecules with both low docking scores and the same amino acid interactions pattern with α-amylase as acarbose will be selected as molecules with potential therapeutic antidiabetes activity.

Virtual screening of potential α-amylase inhibitors

A site-specific molecular docking study was performed for reference α-amylase inhibitor (acarbose) as well as over 380,000 compounds from three different commercial databases with Schrödinger software²⁸ and using the OPLS3e force field.²⁹ This software allows performing high-performance ligand-receptor docking to accelerate structure-based drug design. This is made possible by Schrödinger because it offers different ranges of speed/accuracy options: from the HTVS (high-throughput virtual screening) mode to the XP (extra precision) mode, where a more extensive sampling and advanced scoring is performed. The receptor grid for this HTVS was generated considering the position of the co-crystallized reference antidiabetic drug acarbose in the α-amylase reported structure (PDB: 1B2Y). Using the docking score as the only criteria (which will be refined later), 16 compounds (Table 1) were selected as promising candidates for antidiabetic activity. These compounds (Figure S1, SI) present a docking score value lower than -7.95 kcal/mol, which corresponds to the acarbose (reference drug).

Table 1. High-throughput site-specific docking score values (Schrödinger software) of potential α-amylase inhibitors across three databases of compounds. For reference, the value of acarbose is also included.

Compound	Docking score	Database
Acarbose	-7.95	Reference
Hematoporphyrin	-9.36	Discovery Systems, Inc.
Octopamine	-9.01	Discovery Systems, Inc.
AN-153I105594	-8.95	Specs
Kynuramine	-8.83	Discovery Systems, Inc.
Diptrobin A	-8.69	Discovery Systems, Inc.
AN-153I104161	-8.63	Specs
AN-153I100678	-8.62	Specs
AN-153I101592	-8.53	Specs
AN-153I103354	-8.43	Specs
Hederacoside C	-8.40	Discovery Systems, Inc.
Danuorobicin	-8.33	Discovery Systems, Inc.
AN-153KC12612	-8.23	Specs
AN-153I104845	-8.15	Specs
Geneticin	-8.11	Discovery Systems, Inc.
AN-153I103073	-8.10	Specs
AN-153I100720	-8.02	Specs

Αlpha-amylase binding site validation: blind docking vs. site-specific docking

The structure of the α-amylase co-crystallized with the reference antidiabetic drug acarbose (PDB:1B2Y) contains specific amino acids that interact preferentially with acarbose and are related to its pharmacologic activity. A heavy benchmark for the software should be to test if a blind docking calculation of acarbose in α-amylase is able to find the specific site of interaction as found in the crystallized structure. For this task, we have used AutodockVina. Water and other additional molecules present in the crystallographic structure were removed from α-amylase for the calculations. The enzyme’s polar hydrogens were explicitly considered, and Kollman partial charges³⁰ were calculated. For the drug candidates, Gasteiger partial charges³¹ were defined and non‐polar hydrogen atoms were added. Finally, the complexes were subsequently analysed and ranked based on their binding affinity. The interaction profiles were utilised to evaluate the interactions between the proteins and the compounds. The history files for the selected docking poses were stored and visualized using Biovia Discovery Studio Visualizer software³²(version 21.1).

The results are shown in Table 2 and Figure 1. In the calculations, only the 9 most stable locations will be selected, called from ‘mode 1’ to ‘mode 9’, according to the notation employed by AutoDock-Vina software, with ‘mode 1’ being the most stable and the others corresponding to increasingly weaker compound-protein interactions. ‘Mode 5’, among the most stable, is reasonably close to the experimental location of acarbose. Given the structural complexity of proteins, finding the active site among the 9 most stable can be considered a sufficiently accurate result, and this has been the case using AutodockVina when exploring the binding sites of acarbose in α-amylase.

Table 2. Acarbose blind docking score values on α-amylase using AutoDockVina. For reference, the site-specific docking score is also included. ‘Mode 5’ corresponds, very approximately, to the same location than that for the site-specific docking, which in turns corresponds to the active site experimentally found (PDB: 1B2Y)⁷.

Mode	Docking score (kcal/mol)
Site-specific docking	-10.4
1	-8.6
2	-8.5
3	-8.1
4	-7.6
5	-7.6
6	-7.6
7	-7.5
8	-7.3
9	-7.3

An analysis of specific interactions between α-amylase and acarbose in Mode 5 (blind docking) is shown in Figure 2. Docking of the compounds to the enzyme revealed a network of non-covalent interactions such as hydrogen bonds, pi-sigma, pi‐pi (T-shaped and stacked conformations) and pi‐alkyl interactions. Mode 5 of acarbose is close to the experimental location that defines the active site with the therapeutic activity that should be the target for potential new drugs. Site-specific docking, by giving a location closer to the crystallized location (Figure 3), allows establishing a more accurate estimation of the relevant interactions between α-amylase and acarbose, and they are included as reference in Table 3. A comparison with other compounds will allow an assessment of their potential therapeutic activity.

The partial overlap of the Mode 5 configuration (from blind docking) in the actual binding site of the enzyme is confirmed by comparing the interacting residues depicted in Figures 2 and 3. Among the 12 residues interacting with the site-specific docking geometry (Figure 3, Table 3), 6 were detected in the mode 5 geometry (HIS-305, TRP-59, GLN-63, HIS-299, TYR-62 and GLU-233).

Αlpha-amylase binding site validation: MD simulations of stability of α-amylase-acarbose complex.

Molecular dynamics simulations were carried out in order to evaluate the stability of acarbose blind docking modes, when compared to the crystallographic structure. Furthermore, by taking into account a flexible system, solvated and with counter ions, the binding site and interactions established can be compared with the results from the previous section. Four simulations were considered: containing only the enzyme and the enzyme with acarbose (Figure S1a, SI) at three different initial positions: experimental (PDB:1BY2)⁷, mode 5 from blind docking (Figure 2), and mode 1 from site-specific docking (Figure 3).

Movies of the MD simulations can be accessed by the QR codes in Figure S2 (SI) and they illustrate that, structurally, the three acarbose locations simulated do not lead to significant changes in the enzyme, as shown in Figure 4a by the small RMSD values of the enzyme (between 1.5 and 2.0 Å), which is analogous to the RMSD considering only the enzyme in solution. Likewise, the average radius of gyration gives very similar values, 23.1 ± 0.1 Å for the co-crystallized geometry, and 23.3 ± 0.1 Å for mode 1 and mode 5 from blind docking, and enzyme without drug.

Concerning drug dynamics, acarbose stays bound to the enzyme throughout all sampling conformations, close to its initial configuration over the 135 ns simulation. While small changes in drug position were observed during the simulation of acarbose in its co-crystallized geometry and blind docking mode 5 (Figures 4b, 5a, 5b), the mode 1 from blind docking presented a wider mobility (Figures 4b, 5c).

Acarbose geometry from 1B2Y crystallographic structure is composed by a disubstituted amine, containing a disaccharide and a trisaccharide group (Figure S1a, SI). Although all acarbose chains are initially in contact with the enzyme surface in the initial configuration of the ‘mode 1’ simulation (Figure S3a, SI), the disaccharide moiety presented considerable mobility, being completely solvated after the first 16 ns of simulation (Figure S3b, SI), corresponding to the first significant change observed in the drug RMSD plot (yellow curve of Figure 4b). Nonetheless, the solvation of this disaccharide group is probably not the preferred state, as in the remaining time simulated, such a group re-established interactions with the enzyme surface, but with conformations different from the original (Figure S3c, SI).

Considering the co-crystallized structure as reference, by relaxing the acarbose structure in α-amylase binding pocket during the site-specific docking, the number of hydrogen bonds increases from 6 to 11 (Figures 6a and 6b, respectively). When the co-crystallized structure is simulated through MD (Figure 6c), a higher number of different HBs is observed (involving 19 residues), as a logical consequence of adding flexibility to the system. Even so, the main interactions (with color tending to magenta in Figure 6c) correspond to the residues already accounted for in the site-specific docking geometry (such as ASP-300, GLU-233, THR-163, and GLN-63). Thus, MD findings support the docking results that will be considered in the following section as the reference interactions.

Since acarbose therapeutic effect seems to suggest a relatively strong binding to α-amylase, a stable active site can be expected from the molecular dynamics simulations. This is in agreement with the binding free energy in solution obtained from the MD trajectories (Section S2 of the SI). For the simulation starting in the co-crystallized geometry, acarbose binding energy is the most stabilizing (-84.2 ± 7.4 kcal/mol), confirming that the preferential adsorption site of acarbose is near to the described active site. On the other hand, the acarbose binding free energies obtained from the simulations starting in mode 5 and mode 1 are -53.2 ± 5.6 kcal/mol and -23.9 ± 10.3 kcal/mol, respectively. The lower stabilisation of the latter mode is directly connected to the acarbose conformations adopted during this simulation, as previously discussed (Figure 5c). These results show the importance of relaxing the structure and considering the flexibility in order to understand the dynamic interactions between acarbose and α-amylase.

In summary, although it is considerably important that various modes from blind docking analysis of a drug are found near the enzyme active site, these MD results reinforce the importance of using site-specific docking as a criteria for the energy and the interactions between enzyme residues and candidate drugs.

Molecular docking analysis of potential antidiabetic compounds with α-amylase: blind and site-specific docking approach.

Molecular docking analysis of potential α-amylase inhibitors determined in a section above will be expanded in this section. Using both blind (best mode #1) and site-specific docking (using the experimental location 1B2Y), the main interactions of acarbose with the amino acid residues of α-amylase have been determined. The calculations have been performed in AutoDockVina software, considering the AD4 scoring function, which uses potentials derived from early versions of AMBER force field³³. Consequently, the following results were described by models compatible with those used in the MD simulations. Amino acid interactions and docking scores with α-amylase of the 16 potential antidiabetic compounds are reported in Table 3, which also contains the reference values for acarbose that were calculated previously. If we analyse both docking scores and the relevant amino acid interactions, two candidates stand out from the rest, AN-153I105594 and AN-153I104845.

AN-153I105594 presents a large number of interactions with the relevant amino acids of α-amylase amino acids and a favourable docking score (Table 3), both in site-specific (-10.8 kcal/mol; LEU165, HIS305, TRP59, TYR151, TYR62) and blind docking (-10.3 kcal/mol; LEU165, HIS305, TRP59, TYR151). This is shown in Figure 7. Seven of the nine modes whose geometry is analysed are very close to the targeted specific site(the active side of the protein). AN-153I104845 also presents a large number of interactions with the relevant amino acids of α-amylase amino acids and a favourable docking score (Table 3), both in site-specific (-8.1 kcal/mol; GLY306, HIS305, GLN63, TRP59, TYR62, GLU233) and blind docking (-7.4 kcal/mol; THR163, ASP300, HIS305, LEU165). This is shown in Figure 8. All nine modes whose geometry is analysed are very close to the targeted specific site. This brings them to the fore as drugs, since both molecules are more likely to bind to the protein.

Table 3. Results of site-specific and blind docking of potential anti-diabetic compounds with α-amylase using AutoDock Vina. The compound location in site-specific docking was defined by the experimental location of acarbose in α-amylase (1B2Y). Relevant amino acid interactions with α-amylase are specified, and those common to acarbose are highlighted in bold. Docking scores are in kcal/mol. Types of interactions are explained in Table S2, Supplementary Information.

Compound	Site-specific docking		Blind docking
Compound	Docking score	Amino Acid interaction	Docking score	Amino Acid interaction
Acarbose	-10.4	HB: ASP-300, ARG-195, GLY-306, GLU-233, GLN-63, HIS-299, HIS-305, TYR-62, THR-163, TYR-151, TRP-59 Alkyl: LEU-165	-8.6	HB: ARG-252, ARG-421, GLY-403, TRP-280, HIS-331, PRO-332, PRO-405 Alkyl: PRO-4
AN-153I105594	-10.8	Pi-Alkyl: LEU-162, LEU-165, HIS-305, TYR-151 Alkyl: LYS-200 Pi-Pi: TYR-62 HB+Alkyl: HIS-201 Alkyl+Pi-Alkyl: ILE-235 Pi-Alkyl+Pi-Pi: TRP-59	-10.3	Pi-Alkyl: TRP-59, HIS-101, ALA-198, LEU-162, HIS-305 Alkyl: LEU-165 Pi-Sigma: ILE-235 Pi-Cation+Pi-Pi: HIS-201 Pi-Alkyl+HB: LYS-200 Pi-Alkyl+Pi-Pi: TYR-151
AN-153I101592	-10.6	Pi-Cation: HIS-305, HIS-201 Pi-Alkyl: ALA-307 Pi-Pi: TYR-151 Pi-Alkyl+Pi-Sigma: LEU-162	-13.3	HB: HIS-201, GLN-63 Pi-Alkyl: LEU-162, VAL-107 Pi-Pi: TYR-62, TRP-59 Pi-Anion: ASP-197 Pi-Sigma: ILE-51 HB+Pi-Pi: TYR-151 Pi-Alkyl+Pi-Sigma: ILE-235
Daunorubicin	-9.3	Pi-Alkyl: LYS-200, ALA-198 HB: THR-163 Acceptor-Acceptor: GLU-233: Pi-Sigma: LEU-162 HB+Pi-Sigma: HIS-305 Alkyl+Pi-Sigma: ILE-235 Pi-Cation+Pi-Pi: HIS-201	-8.8	HB: GLN-63, HIS-101 Alkyl: LEU-162 Pi-Alkyl+Pi-Pi: TRP-59
Hematoporphyrin	-9.2	HB: ASP-197, HIS-299, HIS-201, GLU-233 Pi-Alkyl: TYR-151, HIS-305 Alkyl: ILE-235 Donor-Donor: THR-163, ARG-195 Pi-Alkyl+Pi-Sigma: TRP-59, TYR-62 Pi-Sigma+Alkyl: LEU-162	-7.0	HB: THR-6, ASN-5, ARG-10 Pi-Alkyl: PHE-335 Pi-Anion: ASP-402 Pi-Cation: ARG-252
Hederacoside C	-9.1	HB: ASP-300, ASP-356, GLU-233, GLY-306, GLY-304, ASN-352, TRP-357, HIS-305 Pi-Alkyl: TRP-59, TRP-58 Alkyl: LEU-165 Pi-Alkyl+Pi-Sigma: TYR-62	-9.6	HB: GLU-282, ARG-421, ASP-402 Pi-Alkyl: PHE-406 Alkyl: ARG-398 HB+Alkyl: PRO-332
AN-153I104161	-8.1	Pi-Alkyl: ALA-198, LYS-200 HB: HIS-101 Pi-Sigma: ILE-235 Salt Bridge: GLU-233 Attractive Charge: ASP-300 Pi-Pi: TYR-62 Pi-Cation+Pi-P: HIS-201 HB+Attractive Charge+Pi-Anion: ASP-197	-7.7	Pi-Alkyl: LEU-162, LYS-200 Pi-Sigma: ILE-235 Attractive Charge: ASP-197 HB: GLY-306 Attractive Charge+Pi-Anion: ASP-300 Pi-Cation+Pi-Pi: HIS-201 Salt Bridge+Charge Attractive: GLU-233

Table 3. (Continuation) Results of site-specific and blind docking of potential anti-diabetic compounds with α-amylase using AutoDock Vina. The compound location in site-specific docking was defined by the experimental location of acarbose in α-amylase (1B2Y). Relevant amino acid interactions with α-amylase are specified, and those common to acarbose are highlighted in bold. Docking scores are in kcal/mol. Types of interactions are explained in Table S2, Supplementary Information.

Compound	Site-specific docking		Blind docking
Compound	Docking score	Amino Acid interaction	Docking score	Amino Acid interaction
AN-153I104845	-8.1	Pi-Alkyl: ALA-307, ALA-198, LYS-200 HB: GLN-63, GLY-306, GLU-233 Pi-Pi: TYR-62 Pi-Alkyl+Pi-Sigma: ILE-235 Pi-Pi+Pi-Sulfur: HIS-305: Pi-Sulfur+HB: TRP-59 Pi-Pi+Pi-Cation+Pi-Donor: HIS-201	-7.4	HB: GLU-240, HIS-305, THR-163, ASP-300, ASP-197 Pi-Sigma: LEU-162 Pi-Alkyl: LEU-165 Pi-Sulfur+Pi-Cation: HIS-201
Geneticin	-7.8	HB: ASP-197, ASP-300, GLU-233 Alkyl: LEU-165 Pi-Alkyl: HIS-305 HB+Pi-Alkyl: HIS-101 Pi-Alkyl+Pi-Sigma+HB: TRP-59	-7.7	HB: GLU-233, ASP-197, TYR-151, ASP-300 Alkyl: LEU-162, LEU-165 Donor-Donor: GLN-63, GLY-306
AN-153KC12612	-7.8	Pi-Pi: TYR-151, TRP-58,TYR-62 Pi-Alkyl: LEU-162, ALA-198 Salt Bridge: GLU-233 Pi-Sigma: ILE-235 Pi-Pi+Pi-Donor: HIS-201 HB+Attractive Charge: ASP-197 Attractive Charge+Pi-Anion: ASP-300 Pi-Alkyl+HB: LYS-200	-5.6	Pi-Donor: SER-132 Attractive Charge: ASP-135 HB: LYS-172, PRO-130 Pi-Pi: TYR-174
AN-153I103354	-7.2	HB: ASP-197, GLU-233, GLY-306, ASP-300 Alkyl: ALA-198, ILE-235 Pi-Alkyl: LEU-165, TYR-151 Pi-Pi: TRP-59 Alkyl+Pi-Alkyl: LEU-162 Pi-Pi+Pi-Cation: HIS-305	-5.4	HB: ARG-252, SER-289
Diprotin A	-7.0	HB: HIS-299, ASP-197, GLU-233 Alkyl: ALA-198, LEU-162 Pi-Alkyl: TRP-59, HIS-305	-6.7	HB: GLN-63 Pi-Sigma: TRP-59 Alkyl: ILE-235, ALA-198 LEU-162 Pi-Alkyl: TYR-62, HIS-201, TRP-59
AN-153I103073	-7.0	Pi-Alkyl: TRP-58, TYR-62 HB: GLN-63, THR-163 Pi-Pi: TRP-59 Alkyl+Pi-Alkyl: LEU-162	-7.9	Pi-Alkyl: TYR-62, TRP-58, LEU-165, VAL-107, ILE-51 HB: GLN-63 Pi-Pi: TRP-59 Pi-Alkyl+Pi-Donor: TYR-52
AN-153I100720	-6.8	Attractive Charge: ASP-356 Pi-Pi: TRP-59 Donor-Donor: GLN-63	-6.6	HB: ASP-300, THR-163, GLN-63 Positive-Positive: HIS-305 HB+Pi-Pi+Pi-Cation: TRP-59
Octopamine	-5.7	HB: ASP197, ASP-300, GLU-233 Pi-Pi: TYR-62	-5.0	HB: ASP-300, ASP-197, TRP-59 Pi-Pi: TYR-62 Donor-Donor: ARG-195
Kynuramine	-5.6	HB: ASP-197, GLU-233 Pi-Anion: ASP-300 Pi-Pi: TYR-62 Donor-Donor: ARG-195	-5.5	Pi-Pi+HB: TYR-62
AN-153I100678	-5.0	HB: GLY-309, GLN302, ILE-312, ALA-310, ASN-301 Positive-Positive: ARG-346, ARG-267	-4.9	HB: GLY-309, GLN-302, ILE312, ALA-310, ASN-301 Salt Bridge: ASP-317 Positive-Positive: ARG-346

A combination of structure-based drug design computational techniques, molecular docking and molecular dynamics methodologies, have been employed in order to study the potential amino acid interactions with α-amylase. By analysing key amino acid interactions between acarbose and the target enzyme (PDB:1B2Y), we can identify those relevant to developing the same antidiabetic pharmacological activity as the reference antidiabetic drug. In order to find suitable candidates to exert this antidiabetic activity, a high-throughput docking screening (over 380,000 compounds) is performed. Sixteen molecules have been selected as potential α-amylase inhibitors based on docking scores (acarbose co-crystallized binding site reported in literature). In order to test if some of these molecules would interact favourably with different binding sites within α-amylase, we performed a blind docking approach. From an overall of sixteen previously reported compounds, two of them (AN-153I105594 and AN-153I104845) are predicted to interact with the active site as the most favourable binding site. Finally, the most important interactions of these two molecules with the amino acids of α-amylase resulted to be reasonably similar to those described for acarbose, and hence they can be labelled as potential α-amylase inhibitors such as reference drug, acarbose.

Acknowledgements

We thank ASIC-UPV, SGAI-CSIC and CESGA for computational facilities.

Funding

We thank the Science and Technology Commission of Generalitat Valenciana (GVA) for funding through Prometeo 2021/077 and predoctoral fellowship GRISOLIAP/2019/084.

We declare no conflict of interest

Authors contributions

MA and MG performed the docking calculations; AM performed the molecular dynamics calculations and supervised all force fields employed; MG and GS made a substantial contribution to the concept and design of the study; all authors contributed to the writing of the manuscript.

Supplementary Information

Chemical structure and names of the 16 compounds selected by the Virtual high-throughput screening (Section S1), additional details concerning the molecular dynamics simulations such as movies, snapshots, and binding free energy calculation (Section S2), and description of the different types of protein-ligand interactions (Section S3).

Data availability

The datasets generated in the current study are available from the corresponding author on reasonable request.

Abchir O, Daoui O, Belaidi S, Ouassaf M, Qais FA, ElKhattabi S, Belaaouad S, Chtita, S (2022) Design of novel benzimidazole derivatives as potential α-amylase inhibitors using QSAR, pharmacokinetics, molecular docking, and molecular dynamics simulation studies. J Mol Model 28(4):106. https://doi.org/10.1007/s00894-022-05097-9
Duhan M, Singh R, Devi M, Sindhu J, Bhatia R, Kumar A, Kumar P (2021) Synthesis, molecular docking and QSAR study of thiazole clubbed pyrazole hybrid as α-amylase inhibitor. J Biomol Struct 39:91-107. https://doi.org/10.1080/07391102.2019.1704885
Tomasik P, Horton D (2012) Enzymatic conversions of starch. Adv Carbohydr Chem Biochem 68:59-436. https://doi.org/10.1016/B978-0-12-396523-3.00001-4
Kaur N, Kumar V, Nayak SK, Wadhwa P, Kaur P, Sahu SK (2021) Alpha‐amylase as molecular target for treatment of diabetes mellitus: A comprehensive review. Chem Biol Drug Des 98(4):539-560 https://doi.org/10.1111/cbdd.13909
Galvez-Llompart M, Ocello R, Rullo L, Stamatakos S, Alessandrini I, Zanni R, Tuñón I,Cavalli A, Candeletti S, Masetti Met al (2021) Targeting the JAK/STAT Pathway: A Combined Ligand-and Target-Based Approach. J Chem Inf Model 61(6):3091-3108. https://doi.org/10.1021/acs.jcim.0c01468
Meng XY, Zhang HX, Mezei M, Meng C (2011) Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des 7(2):146-157. https://doi.org/10.2174/157340911795677602
Nahoum V, Roux G, Anton V, Rouge P, Puigserver A, Bischoff H, Henrissat B, Payan F (2000) Crystal structures of human pancreatic α-amylase in complex with carbohydrate and proteinaceous inhibitors. Biochem J 346(1):201-208. https://doi.org/10.1042/bj3460201
Screening Compounds Collection. Specs. https://www.specs.net/index.php (accessed 2022-07-23).
The Natural Product Collection from MicroSource Discovery System Inc. http://www.msdiscovery.com/natprod.html (accessed 2022-07-23)
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne, PE (2000) The Protein Data Bank Nucleic Acids Research. 28: 235-242. http://www.rcsb.org/pdb/ https://doi.org/10.1093/nar/28.1.235
Trott O, Olson AJ (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2): 455-461. https://doi:10.1002/jcc.21334
Schrödinger software, release 2020-3, Schrödinger; LLC, New York, NY, USA (2020).
Durrant, JD, McCammon, JA (2011). Molecular dynamics simulations and drug discovery. BMC biol 9(1):1-9. https://doi.org/10.1186/1741-7007-9-71
Case DA, Betz RM, Cerutti DS et al. (2016) AMBER 2016, University of California, San Francisco.
Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis JL, Dror RO, Shaw DE (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 78(8):1950-1958. https://doi:10.1002/prot.22711
Horn, HW, Swope, WC, Pitera, JW, Madura JD, Dick TJ, Hura GL, Head-Gordon T (2004) Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew. J Chem Phys 120(20):9665-9678. https://doi:10.1063/1.1683075
Beauchamp, KA, Lin, YS, Das, R, Pande VS (2012) Are protein force fields getting better? A systematic benchmark on 524 diverse NMR measurements. J Chem Theory Comput 8(4): 1409-1414. https://doi:10.1021/ct2007814
Zhang H, Yin C, Jiang Y, van der Spoel, D (2018) Force field benchmark of amino acids: I. hydration and diffusion in different water models. J Chem Inf Model 58(5):1037-1052. https://doi:10.1021/acs.jcim.8b00026
Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174. https://doi.org/10.1002/jcc.20035
Singh AK, Rana HK, Singh V, Yadav TC, Varadwaj P, Pandey AK (2021) Evaluation of antidiabetic activity of dietary phenolic compound chlorogenic acid in streptozotocin induced diabetic rats: molecular docking, molecular dynamics, in silico toxicity, in vitro, and in vivo studies. Comput Biol Med, 134: 104462. https://doi:10.1016/j.compbiomed.2021.104462
Jakalian A, Bush BL, Jack DB, Bayly CI (2000) Fast, efficient generation of high‐quality atomic charges. AM1‐BCC model: I. Method. J Comput Chem 21(2):132-146. https://doi:10.1002/(SICI)1096-987X(20000130)21:2<132::AID-JCC5>3.0.CO;2-P
Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high‐quality atomic charges. AM1‐BCC model: II. Parameterization and validation. J Comput Chem, 23(16): 1623-1641.https://doi:10.1002/jcc.10128
Loncharich RJ, Brooks BR, Pastor RW (1992) Langevin dynamics of peptides: The frictional dependence of isomerization rates of N‐acetylalanyl‐N′‐methylamide. Biopolymers 32(5):523-535. https://doi:10.1002/bip.360320508
Åqvist J, Wennerström P, Nervall M, Bjelic S, Brandsdal BO (2004) Molecular dynamics simulations of water and biomolecules with a Monte Carlo constant pressure algorithm. Chem Phys Lett 384(4-6):288-294. https://doi:10.1016/j.cplett.2003.12.039
Ryckaert JP, Ciccotti G, Berendsen HJ (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23(3):327-341. https://doi:10.1016/0021-9991(77)90098-5
Darden T, York D, Pedersen L (1993) Particle mesh Ewald: An N⋅log (N) method for Ewald sums in large systems. J Chem Phys 98(12):10089-10092.https://doi:10.1063/1.464397
Roe DR, Cheatham III TE (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9(7):3084-3095. https://doi:10.1021/ct400341p
Schrödinger software, release 2020-3, Schrödinger; LLC, New York, NY, USA (2020).
Roos K, Wu C, Damm W, Reboul M, Stevenson JM, Lu C, Dahlgren MK, Mondal S, Chen W, Wang L, Abel R, Friesner RA, Harder ED (2019) OPLS3e: Extending force field coverage for drug-like small molecules. J Chem Theory Comput 15(3):1863-1874. https://doi.org/10.1021/acs.jctc.8b01026
Singh UC, Kollman PA (1984) An approach to computing electrostatic charges for molecules. J Comput Chem 5:129-145. https://doi.org/10.1002/jcc.540050204
Gasteiger J, Marsili M (1980) Iterative Partial Equalization of Orbital Electronegativity - a Rapid Access to Atomic Charges. Tetrahedron 36(22):3219-3228. https://doi.org/10.1016/0040-4020(80)80168-2v
BIOVIA, Dassault Systèmes, Discovery Studio Visualizer Software, Version 4.0. San Diego: Dassault Systèmes, 2021.
Eberhardt J, Santos-Martins D, Tillack AF, Forli S (2021) AutoDock Vina 1.2.0: New docking methods, expanded force field, and python bindings. J Chem Inf Model 61(8):3891-3898. https://doi.org/10.1021/acs.jcim.1c00203

No competing interests reported.

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Computational design of new drugs for binding ⍺-amylase as candidates for treating diabetes

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