INTRODUCTION:

Colorectal cancer is the third most incidence cancer in the world¹. The overexpression of epidermal growth factor receptor (EGFR) has been identified as a prominent factor in colorectal cancer, significantly correlating with poor prognosis. To combat this, anti-EGFR drugs like cetuximab and panitumumab are commonly employed in colorectal cancer treatment. Unfortunately, the effectiveness of anti-EGFR treatment is hampered by the prevalent issue of anti-EGFR resistance among colorectal cancer patients². One of the main causes of this resistance is attributed to the loss of PTEN function or the presence of the KRAS G12D mutation^3,4.

PTEN loss of function leads to hyperactivation of AKT, thereby resulting in constitutive activation of the PI3K-AKT signaling pathway⁵. On the other hand, the KRAS G12D mutation impairs GTP hydrolysis in KRAS, leading to the persistent activation of the RAS-MAPK signaling pathway⁶. Consequently, targeting AKT or KRAS G12D through the use of inhibitors represents a viable strategy to treat colorectal cancer patients with anti-EGFR resistance, primarily driven by PTEN loss or KRAS G12D mutation³.

Efforts have been made to develop AKT and KRAS G12D inhibitors. Some AKT inhibitor candidates showed limited efficacy in clinical trials^7–9, and selectivity remains a crucial concern in the development of AKT inhibitor. Similarly, the development of KRAS G12D inhibitors still faces challenge regarding its selective binding and their ability to target the KRAS G12D in its active, which these aspects are essential for ensuring a more favorable safety profile and inhibitory effects^10,11. In light of that, there is a need to discover new potential compounds with potent and selective inhibitory activities against AKT and KRAS G12D. Exploration of natural compounds is one among many strategies to identify prospective lead molecules for further development into potent inhibitors. This strategy has led to the discovery of several anticancer drugs such as Paclitaxel and Vinblastine which are developed from natural compounds of taxols and velbans, respectively ¹². This report suggests that the exploration of natural compounds derived from plants holds promise for discovering potential lead molecules in developing drugs for colorectal cancer treatment¹³. In the pursuit of discovering new potential compounds, molecular docking method can be performed to efficiently facilitate the identification of these compounds^14,15. Molecular docking is a computational method thats can predict suitable pair of small molecule and protein based on their binding strength¹⁶. According to that explanation, this study was conducted to identify potential natural compounds from Indonesian medicinal plants that possess inhibitory effects against AKT or KRAS G12D using a molecular docking approach.

MATERIALS AND METHODS:

Ligands Preparation:

The natural compounds were filtered using SWISSADME webserver (www.swissadme.ch/index.php)¹⁷. The filtration was done utilizing oral drug parameters, including molecular weight (200-500 g/mol), solubility (moderate-high), high GI absorption, and Lipinski`s rule of five. Molecules that comply with Lipinski’s rule of five are probably potentials to be developed as oral drugs due to their favorable bioavailability¹⁸. Ligands that met the oral drug parameters were then subjected to QSAR analysis to predict their feasibility as anticancer agents. This analysis was conducted using the antineoplastic indicator on Way2drug web tools (http://way2drug.com/passonline)¹⁹. Ligands that exceeded a Pa threshold of > 0.3 were expected to pose moderate bioactivities²⁰. The 2D structure of natural compounds that passed the selection was retrieved from PubChem (https://pubchem.ncbi.nlm.nih.gov/). The 3D conformation of the natural compounds was drawn on MarvinSketch by applying the dreiding force field as minimization energy. The file format was converted to pdbqt using AutoDock Tools.

Protein Structures Preparation:

The 3D protein structures of AKT1 (PDB ID: 6CCY), KRAS G12C (PDB ID: 6OIM), KRAS G12D (PDB ID: 6GJ8), and KRAS WT (PDB ID: 6MBT) were retrieved from Protein Data Bank (PDB) (www.rcsb.org). Protein structures from PDB may have some errors, therefore it necessitates to revise their structures and add hydrogen atoms before performing simulation docking^21,22. The refinement procedure to improve protein structure quality was done using PDB_REDO webserver (https://pdb-redo.eu/)²³. The water molecules and ligands co-crystallography in the protein structure were deleted using BIOVIA Discovery Studio Visualizer 2021. Polar hydrogen atoms were added to structure and the file format was converted to pdbqt using AutoDock Tools 1.5.6.

Binding Site Determination:

The identification of the binding site was assisted by POCASA webserver²⁴. The binding sites for docking in AKT1 and KRAS G12C structures were in the same location of the ligand co-crystallography binding sites. Therefore, the docking process validation was needed before other ligands docking²⁵. The docking process validation was carried out by re-docking the ligand within the determined binding site. The parameter of docking process validation is RMSD value. RMSD value < 2 Å indicates the procedure for docking is valid and can be used for other ligands docking²⁶. The RMSD value was calculated using BIOVIA Discovery Studio Visualizer 2021.

Molecular Docking Analyses and Visualizations:

The prepared ligands and protein structures were docked using AutoDock Vina²⁷. Docking simulations were conducted with grid box center settings for AKT1 (x = -11.5266, y = 16.7462, z = -32.2008); KRAS G12C (x = 3.710, y = -7.746, z = -2.207); KRAS G12D (x = 5.748, y = 15.454, z = -6.94); and KRAS WT (x = 1.243, y = -21.094, z = 51.391). The analysis of ligand interactions within binding site was done using 2D diagram generated by LigPlot+2.2²⁸. Ligplot+ is able to visualize hydrogen, hydrophobic, and sometimes covalent bonds ²⁹. The 3D visualization of ligand binding pose in the protein binding site was done using Chimera 1.15³⁰.

RESULTS AND DISCUSSIONS:

Filtration of Natural Compounds:

Choosing appropriate molecular descriptors is important for correctly predicting the drug-likeness of new compounds, thus making it a paramount consideration in drug design³¹. In this study, natural compounds were filtered by considering several oral drug parameters, such as molecular weight (200-500g/mol), solubility (moderate-high), high GI absorption, and Lipinski`s rule of five. Using these parameters in the filtration step can assist us to find the feasible potential compounds to be developed as oral drug¹⁷. In this study, we collected 240 plant species from the book of Atlas Tumbuhan Obat Indonesia (Volumes 1-6) and 80 plant species from 13 journal articles^32–44. The filtration results disclosed that 274 out of 1311 natural compounds from 320 Indonesian medicinal plant species met the criteria of oral drug and predicted to pose anticancer activity based on QSAR analysis, therefore they were used as ligands in molecular docking simulation. Additionally, the analysis highlighted the three predominant compound classes within the 274 selected compounds, namely flavonoid, terpenoid, and alkaloid (Table 1).

Validation of Molecular Docking Process:

The RMSD value of docking process validation in AKT1 and KRAS G12C were 1.88 Å and 0.44 Å, respectively. RMSD <2Å indicates the docking procedures on both proteins are valid and the grid box setting can be used for other ligands docking²⁶. The lower the RMSD value means the closer similarity of ligand conformation produced by docking simulation to the ligand conformation from the crystallography experiment⁴⁵. Superimposition of re-docked ligand conformation and ligand co-crystallography conformation is given in Figure 1.

Figure 1: Superimposition of re-docked ligand (red) and ligand co-crystallography (cyan) conformations (A. AKT1 (6CCY); B. KRAS G12C (6OIM))

AKT1 Docking Study:

In our study, the ATP binding pocket of AKT1 was used as the docking site for molecular docking simulation. The active form of AKT1 is found to be more numerous in cancer cells than normal cells as the effect of the constitutive activation of PI3K-AKT signaling pathway in cancer cells. Inhibitor that can target the ATP binding pocket of AKT1 is expected to have selective inhibition effect on the active form of AKT1, thus result in better safety because it will have lower toxic effect on normal cells⁷.

ATP binding pocket is located around G-loop, β-3, αC, hinge region, catalytic loop, and DFG motif of AKT1⁴⁶. There are 34 residues forming the ATP-binding pocket of AKT1 which are L156, G157, K158, G159, T160, F161, G162, K163, V164 (G-loop), A177, K179, L181 (β-3), E191, E198, L202, T211 (αC), M227, E228, Y229, A230, N231, G232, G233, E234 (hinge region), R273, D274, K276, E278, N279, M281 (catalytic loop), T291, D292, F293, G294 (DFG motif)^46,47. Among these residues, F161, K179, E191, T211, E228, A230, E234, E278, M281, and D292 are known to have frequent interactions with various AKT ATP-competitive inhibitors in ATP-binding pocket of AKT1^47,48. Therefore, these residues can be considered as important residues for ligand binding in the ATP-binding pocket of AKT1⁴⁹. These important residues will be written in bold in Table 3. A ligand that forms more interactions with the important residues may have more potent inhibitory effect on the target protein⁵⁰. Additionally, it is reported that ATP-binding pocket of AKT1 shares high homology with ATP-binding pocket of PKA. The sequence similarity between both ATP-binding pockets is 81%⁴⁶. Both ATP-binding pockets are only distinguished by three residues particularly T211, A230, and M281 in AKT1 that are substituted into V211, V230, and L281 in PKA⁵¹. Therefore, ligand interactions with T211, A230, and M281 in ATP-binding pocket of AKT1 is important to enhance ligand`s selectivity on targeting the ATP-binding pocket of AKT1⁵².

Two AKT ATP-competitive inhibitors (AZD5363 and GDC0068) that have entered clinical trials phase I and II for monotherapy were used as docking controls⁵³. The result of docking simulation revealed 12 natural compounds with best affinity score ranging from -9 kcal/mol up to -9.8 kcal/mol (Table 2). These affinity scores were lower than AZD5363 (-7.9kcal/mol) and GDC0068 (-8.1kcal/mol) (Table 2). This result suggested that the natural compounds in Table 2 had stronger binding to AKT1 compared to AZD5363 and GDC0068.

Table 2: Analysis results of docking affinity score in ATP-binding pocket of AKT1, ligands total interaction with 34 residues forming ATP-binding pocket of AKT1, and ligands interaction with 10 important residues in ATP-binding pocket of AKT1

Plant Species	Compounds	PubChem ID	Class of compound	Affinity (kcal/mol)	Total Interaction with residues in ATP-Binding Pocket	Total Interaction with Important Residues
-	AZD5363 (Control)	25227436	-	-7.9	35.29%	40%
-	GDC0068 (Control)	134692697	-	-8.1	38.24%	40%
Tribulus terrestris	Hecogenin	91453	Triterpenoid	-9.8	26.47%	60%
Caesalpinia pulcherrima	Neocaesalpin P	102286692	Diterpenoid	-9.7	23.53%	50%
Tribulus terrestris	Ruscogenin	441893	Triterpenoid	-9.5	26.47%	60%
Swieteniae macrophylla	7-deacetoxy-7-oxogedunin	71300386	Limonoid	-9.4	23.53%	40%
Erythrina variegata Erythrina fusca	Phaseollin	91572	Isoflavonoid	-9.2	35.29%	50%
Nelumbo nucifera	Roemerine	119204	Aporphine alkaloid	-9.2	29.41%	60%
Tinospora crispa	Columbin	188289	Diterpenoid	-9.1	26.47%	40%
Morinda citrifolia	Morindone	442756	Anthraquinone	-9.1	35.29%	60%
Aloe vera	Rhein	10168	Anthraquinone	-9.1	29.41%	40%
Nelumbo nucifera	Anonaine	160597	Aporphine alkaloid	-9	26.47%	30%
Alstonia scholaris	Porphyrin	66868	Tetrapyrrole	-9	35.29%	70%
Catharanthus roseus	Tetrahydroalstonine	72340	Indole alkaloid	-9	29.41%	50%

Through the 3D visualization, it can be seen that the binding poses of morindone and pophyrin in ATP-binding pocket of AKT1 are located deeper than AZD5363 (Figure 2-4). This deeper binding pose may play a role in causing morindone and porphyrin to form interactions with T211, A230, and M281. Based on this study results, morindone and porphyrin were found to be the most potential natural compounds to be developed as AKT inhibitors.

Figure 2: 3D and 2D visualizations of AZD5363 in ATP-binding pocket of AKT1

Figure 3: 3D and 2D visualizations of Morindone in ATP-binding pocket of AKT1

Figure 4: 3D and 2D visualizations of Porphyrin in ATP-binding pocket of AKT1

KRAS Docking Study:

To identify potential natural compounds with selective inhibition effects on KRAS G12D, we performed docking experiments using filtered natural compounds against KRAS G12D, KRAS G12C, and KRAS WT. The 3D protein structure of KRAS G12D in its active state was utilized since it has been reported that cancer cells with KRAS G12D mutation predominantly express KRAS in its active state. Consequently, targeting KRAS G12D in its active state is considered more favorable for achieving optimum inhibition of cancer cells with KRAS G12D mutation ⁵⁴. However, due to the unavailability of 3D protein structures of KRAS G12C and KRAS WT in their active states in the PDB database, we had to dock the filtered ligands onto KRAS G12C and KRAS WT in their inactive states. In order to ensure fairness in the prediction process for both states, we employed a strategy that targeted a binding site accessible in both the active and inactive states of KRAS. Our selection process considered multiple factors, including affinity score, ligand interactions with important residues within the defined binding site, and ligand interactions with specific mutation residues, to identify natural compounds with potential selective inhibition effects on KRAS G12D.

The docking site for ligand binding on KRAS was determined to be the allosteric site between the central β-sheet and α2 helix (Switch-II) as well as the α3 helix ⁵⁵. Within this allosteric site, two possible binding sites for ligands exist: the Switch-II pocket (SII-P) and the Switch-II groove (SII-G)^55,56. The availability of SII-P is dependent on the conformation of the Switch-II region in KRAS. SII-P is accessible only when Switch-II adopts an open conformation, which occurs when KRAS is in its inactive state. In contrast, when KRAS is in its active state, the conformation of Switch-II changes to a closed state, covering the SII-P binding site and rendering it unavailable^55,56. On the other hand, SII-G remains accessible regardless of the conformation of Switch-II, making it available in both the active and inactive states of KRAS⁵⁵. Targeting the
SII-G of KRAS in its active state, Zhang et al. (2020) and Gentile et al. (2017) successfully identified potential compounds that exhibited good inhibition effects on KRAS in its active state. Several important residues play a crucial role in ligand binding within SII-P and SII-G of KRAS, including V9, C12/D12, K16, T58, G60, Q61, E62, E63, R68, D69, M72, D92, H95, Y96, Q99, and R102^54,57,58, as written in bold in Table 5.

During this study, there were no reports of clinical trials of KRAS G12D inhibitors. Therefore, an KRAS G12C inhibitor named AMG-510 was used as docking control. AMG-510 is a KRAS G12C covalent inhibitor which has entered clinical trial phase I/II for monotherapy ⁵⁹. The inhibition effect of AMG-510 only works on KRAS G12C but not on KRAS WT or other KRAS mutants ⁶⁰. This specific inhibition effect is caused by the covalent interaction between AMG-510 with the mutation point of C12 in the KRAS G12C⁵⁶. Specific inhibition of AMG-510 toward KRAS G12C suggests that this compound will bind stronger on KRAS G12C than other KRAS variants. Hence, the affinity score of AMG-510 toward KRAS G12C, KRAS G12D, and KRAS WT can be used as cut-off score to select natural compounds with inhibitory effects on KRAS G12D. Furthermore, as the interaction with mutation point will result in specific inhibition effect, we decided only natural compounds forming interaction with D12 in the KRAS G12D but not with C12 in the KRAS G12C and G12 in the KRAS WT that could be considered as potential compounds having selective inhibition effect on KRAS G12D.

Table 4: Analysis results of ligands` docking affinity scores in SII-G of KRAS proteins and ligands total interaction with important residues in SII-G of KRAS variants

Plant Species	Compounds	PubChem ID	Class of compound	Affinity (kcal/mol)			Total interaction with important residues
Plant Species	Compounds	PubChem ID	Class of compound	KRAS G12C	KRAS G12D	KRAS WT	KRAS G12C	KRAS G12D	KRAS WT
-	AMG-510 (control)	-	-	-8.3	-6.2	-6.2	68.75%	31.25%	37.5%
Erythrina variegata Erythrina fusca	Phaseollin	91572	Isoflavonoid	-8.7	-7.5	-6.2	37.5%	56.25%	56.25%
Plumbago zeylanica	Chitranone	633072	Naphthaquinone	-8.6	-7.2	-6.4	50%	43.75%	37.5%
Rauvolfia serpentina	Ajmalicine	441975	Indole alkaloid	-8	-7.1	-6.3	43.75%	31.25%	37.5%
Erythrina fusca	Cristacarpin	126540	Isoflavonoid	-8.3	-6.9	-6.7	56.25%	50%	62.5%

Figure 5: 3D and 2D visualizations of AMG-510 in SII-G of KRAS G12D in active state

Figure 6: 3D and 2D visualizations of phaseollin in SII-G of KRAS G12D in active state

Table 5: Interaction profile between ligands and residues in SII-P/SII-G of KRAS variants

Compounds	Covalent Bonds			Hydrogen Bonds			Hydrophobic Bonds
Compounds	KRAS G12C	KRAS G12D	KRAS WT	KRAS G12C	KRAS G12D	KRAS WT	KRAS G12C	KRAS G12D	KRAS WT
AMG-510 (control)	C12	-	-	K16		Q61, K88, N86	P34, A59, G60, Q61, M72, R68, V103, E62, E63, Q99, H95, Y96, G10	A66, Q99, R68, E62, R102, H95	G60, Q99, Y96, D92, H95, A11
Phaseollin	-	-	-	-	D12, G60, K88	T58, Y71	E63, M72, V103, I100, Q99, Y96, H95, D92	E62, Y96, D92, R68, Q99, R102, H95, A11	R68, G60, Q99, H95, Q61, K88, D92, Y96, V9
Chitranone	-	-	-	-	E62, D12, Y96	G10, D92	G60, R68, T58, M72, E63, Q99, H95, Y96	Q61, G60, K88, D92, A11, H95, G10	A11, G12, Y96, R68, Q61, G60
Ajmalicine	-	-	-	C12	N86	-	G60, A59, K16, A11, M72, Q99, Y96, Q61	D12, A11, Y96, H95, D92, K88, E62	Y96, D92, H95, G12, P34, A59, Q61, G60, Y71
Cristacarpin	-	-	-	G60	D12, K88, D92, G60	Y71, T58, Y96	C12, G10, V9, T58, M72, I100, Q99, H95, Y96, Q61	H95, R68, Q99, Y96, E62, A11	D92, H95, Q99, Q61, R68, G10, V9, G60, A11

The 3D visualization of the binding poses of AMG-510 and phaseollin provided insights into why phaseollin exhibited a better affinity score and better interactions with important residues in SII-G of KRAS G12D in active state compared to AMG-510. It can be seen that AMG-510 does not adopt a proper binding pose in SII-G of KRAS G12D in active state (Figure 5), while phaseollin demonstrates a proper binding pose (Figure 6). The proper binding pose of phaseollin in SII-G of KRAS G12D in active state may contribute to its stronger binding affinity. Additionally, phaseollin formed interactions with G60, D69, D92, Y96, Q99, and D12 SII-G of KRAS G12D in active state, similar to the interaction profile of KD2, a selective KRAS G12D peptide inhibitor. This result suggests that phaseollin is potential to be developed as selective KRAS G12D inhibitor.

CONCLUSION:

In the docking study of AKT1, this study found morindone and porphyrin as potential compounds to be developed as AKT inhibitors. In the docking study of KRAS, this study found phaseollin was potential to be developed as KRAS G12D inhibitor. As this study relied solely on molecular docking simulations for predictions, a molecular dynamics analysis could be an avenue for forthcoming study to validate the findings.

CONFLICT OF INTEREST:

The authors have no conflicts of interest.

ACKNOWLEDGMENTS:

The first author sincerely would thank to LPDP (Indonesia Endowment Fund for Education), Ministry of finance, Republic Indonesia for providing financial support for this study. The authors are thankful to Riyanti Weni Syavitri from Biotechnology Department, School of Life Sciences and Technology, Bandung Institute of Technology, Indonesia for her contribution to provide us several data related to Indonesian medicinal plant species and the natural compounds they contain.

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Received on 04.07.2023 Modified on 22.01.2024

Research J. Pharm. and Tech 2024; 17(8):3777-3785.

DOI: 10.52711/0974-360X.2024.00587

Class of compound	Number of compounds
Flavonoid: 2'-hydroxychalcone, chalcone, chromene, dimethoxyflavon, dimethoxyflavone, flavan, flavanol, flavanone, flavon, flavone, flavonone, homo-isoflavan, homo-isoflavanon, isoflavonoid, methoxy flavonoid, o-methylated flavonoid, o-methylated flavonol, o-methylated isoflavone, o-methylated isoflavonoid, tetramethoxyflavone.	91
Terpenoid: abdane diterpenoid, diterpene trilactone, diterpenoid furanolactone, diterpenoid lactone, germacrane sesquiterpenoid, limonoid, menthane monoterpenoid, monoterpenoid, sesquiterpenoid, sesquiterpenoid lactone, terpene lactone, triterpenoid.	65
Alkaloid: aporphine alkaloids, furoquinoline alkaloids, indole alkaloids, isoquinoline alkaloids, pyridine alkaloids, pyrrolizidine alkaloids, pyrrolizidine-type alkaloids, quinoline alkaloids, rhoeadine alkaloids.	33
Quinone Anthraquinone, dihydroxyanthraquinone, hydroxyanthraquinone, naphthaquinone, naphthoquinone	19
Quassinoid	13
Phenol	8
Coumarin: 7-hydroxycoumarin, furanocoumarin	7
Xanthone	4
Lactone	3
Phenylpropanoid	4
Others	27

Compounds	Hydrogen Bonds	Hydrophobic Bonds
AZD5363 (Control)	E234	L181, G159, G162, K163, K179, E198, M227, D292, V164, M281, T291
Phaseollin	F161, G162	K158, G157, V164, E234, A177, M281, M227, T211, K179, G159
Morindone	E228, A230, T211, D292, K179, E198	Y229, L156, V164, F438, A177, M227, M281
Porphyrin	-	A230, T211, A177, E228, M281, M227, L156, K179, E234, F438, V164, D292, G157