
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39169082
69850
10.1038/s41598-024-69850-1
Article
Structure-based drug-development study against fibroblast growth factor receptor 2: molecular docking and Molecular dynamics simulation approaches
Shamsi Anas anas.shamsi18@gmail.com

1
Khan Mohd Shahnawaz 2
Yadav Dharmendra Kumar 3
Shahwan Moyad 14
Furkan Mohammad 5
Khan Rizwan Hasan 6
1 https://ror.org/01j1rma10 grid.444470.7 0000 0000 8672 9927 Center for Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, UAE
2 https://ror.org/02f81g417 grid.56302.32 0000 0004 1773 5396 Department of Biochemistry, College of Science, King Saud University, KSA, Riyadh, Saudi Arabia
3 https://ror.org/03ryywt80 grid.256155.0 0000 0004 0647 2973 Gachon Institute of Pharmaceutical Science and Department of Pharmacy, College of Pharmacy, Gachon University, Incheon, Republic of Korea
4 https://ror.org/01j1rma10 grid.444470.7 0000 0000 8672 9927 Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
5 https://ror.org/03kw9gc02 grid.411340.3 0000 0004 1937 0765 Department of Biochemistry, Aligarh Muslim University, Aligarh, India
6 https://ror.org/03kw9gc02 grid.411340.3 0000 0004 1937 0765 Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
21 8 2024
21 8 2024
2024
14 194393 4 2024
9 8 2024
© The Author(s) 2024, corrected publication 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Developing new therapeutic strategies to target specific molecular pathways has become a primary focus in modern drug discovery science. Fibroblast growth factor receptor 2 (FGFR2) is a critical signaling protein involved in various cellular processes and implicated in numerous diseases, including cancer. Existing FGFR2 inhibitors face limitations like drug resistance and specificity issues. In this study, we present an integrated structure-based bioinformatics analysis to explore the potential of FGFR2 inhibitors-like compounds from the PubChem database with the Tanimoto threshold of 80%. We conducted a structure-based virtual screening approach on a dataset comprising 2336 compounds sourced from the PubChem database. Primarily, the selection of promising compounds was based on several criteria, such as drug-likeness, binding affinities, docking scores, and selectivity. Further, we conducted all-atom molecular dynamics (MD) simulations for 200 ns, followed by an essential dynamics analysis. Finally, a promising FGFR2 inhibitor with PubChem CID:507883 (1-[7-(1H-benzimidazol-2-yl)-4-fluoro-1H-indol-3-yl]-2-(4-benzoylpiperazin-1-yl)ethane-1,2-dione) was screened out from the study. This compound indicates a higher potential for inhibiting FGFR2 than the control inhibitor, Zoligratinib. The identified compound, CID:507883 shows >80% structural similarity with Zoligratinib. ADMET analysis showed promising pharmacokinetic potential of the screened compound. Overall, the findings indicate that the compound CID:507883 may have promising potential to serve as a lead candidate against FGFR2 and could be further exploited in therapeutic development.

Keywords

Fibroblast growth factor receptor 2
FGFR2 inhibitors
Drug discovery
Virtual screening
Molecular dynamics simulations
Subject terms

Computational biology and bioinformatics
Drug discovery
Structural biology
http://dx.doi.org/10.13039/501100019286 Ajman University http://dx.doi.org/10.13039/501100002383 King Saud University RSP2024R352 Khan Mohd Shahnawaz issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Fibroblast growth factor receptor 2 (FGFR2) is a member of the receptor tyrosine kinase family that plays a pivotal role in various cellular processes such as proliferation, differentiation, and tissue development1. These signals are crucial for the development and maintenance of various tissues and organs throughout the body2. FGFR2 is composed of three main domains: an extracellular ligand-binding domain, a transmembrane domain, and an intracellular tyrosine kinase domain3. The extracellular domain of FGFR2 binds specifically to FGF ligands that trigger a cascade of intracellular events that ultimately lead to the activation of downstream signaling pathways4. Upon ligand binding, FGFR2 undergoes dimerization, where two receptor molecules come together and form a complex5. This dimerization triggers the autophosphorylation of specific tyrosine residues within the intracellular domain of FGFR2, thereby catalyzes the activation of its tyrosine kinase activity.

Activated FGFR2 phosphorylates a variety of downstream signaling proteins, including adaptor molecules and transcription factors6. These pathways can regulate gene expression, cell growth, cell differentiation, and cell survival7. Dysregulation of FGFR2 signaling has been implicated in various diseases, such as cancer, skeletal abnormalities, craniosynostosis, and other developmental disorders8,9. Understanding the structure, function, and regulation of FGFR2 is crucial for unraveling its role in development, disease, and potential therapeutic interventions. Given its significance in both normal physiology and disease pathogenesis, FGFR2 has garnered considerable attention as a potential therapeutic target10,11. Notable examples of FGFR2 inhibitors include Erdafitinib, Infigratinib, Zoligratinib, and AZD454712. These inhibitors work by blocking the activity of FGFR2, which is often found in excess in certain cancers, thereby hindering tumor growth. However, despite their importance, these inhibitors face limitations in terms of efficacy due to concerns like drug resistance and lack of specificity13. Studies showed that cancer cells adapt and stop responding to these inhibitors. Lack of specificity is another limitation of these inhibitors where they also affect non-target cells, leading to side effects. As a result, there is a demanding necessity to develop novel inhibitors that are highly potent and can effectively address these limitations.

Conventional drug discovery strategies follow a laborious path of identifying and developing novel small molecules14. This process is known to be time-consuming, resource-intensive, and plagued by high failure rates during clinical trials. However, drug repositioning and developments and improvements of existing drugs present an attractive alternative approach15. It involves the exploration of existing drugs and drug-like compounds to improve their efficacy, and selectivity to uncover new therapeutic indications. By advancing approved drugs with established safety profiles, it bypasses much of the early stages of drug development, reducing both the time and resources required16. It allows us to build upon existing knowledge of a compound's pharmacology, side effects, and dosing, which can significantly speed up the development timeline.

Bioinformatics is crucial for drug development, employing computational methods and analyzing vast structural data17. Publicly accessible biological data have fueled the development of novel bioinformatics tools and algorithms. These resources help identify potential drug-target interactions, predict drug efficacy, and assess off-target effects. By integrating diverse computational approaches and conducting large-scale omics data analysis, bioinformatics plays a pivotal role in this process. It empowers us to harness the wealth of biological knowledge available and expedite the discovery of new therapeutic applications for known drugs. In this study, we present an integrated bioinformatics study aimed at exploiting the drug development approach against FGFR2. We have leveraged the publicly available PubChem database and cutting-edge computational techniques to identify existing FGFR2 inhibitors-like compounds that potentially modulate the FGFR2 activity. Our study encompasses multiple steps, including FGFR2 known inhibitors acquisition and downloading their structural analogs from the PubChem database18 with a Tanimoto threshold of 80%. It also involves molecular docking, interaction, and PASS analyses, and molecular dynamics (MD) simulations followed by principal component analysis (PCA) and free energy landscapes (FELs). Overall, we present an integrated bioinformatics study that harnesses the power of structure-based drug development to identify novel therapeutic candidates against FGFR2-related complex diseases.

Material and methods

Computer-aided virtual screening harnesses sophisticated computational algorithms and molecular simulations to search extensive compound repositories and predict their interactions with a specific target protein19. To conduct this study, several bioinformatics tools were utilized, including MGL AutoDock Tools20, InstaDock21, and Discovery Studio Visualizer22. For visualization purposes, PyMOL23, VMD (Visual Molecular Dynamics)24, and Discovery Studio Visualizer were employed. Online resources like RCSB Protein Data Bank25, PubChem18, SwissADME26, pkCSM 27, and CarcinoPred-EL28 were accessed to retrieve, evaluate, and analyze the data.

Preparation of target

The atomic coordinates of FGFR2 crystal structure were downloaded from the Protein Data Bank (PDB ID: 6LVK, resolution 2.29 Å) and subsequently processed in AutoDock Tools29. This PDB structure was chosen based on its resolution and wild-type nature. The other higher-resolution structures were mutants with some missing residues. The co-crystallized ligand present in the coordinates file was removed during this preprocessing step. Following that, the structure underwent refinement and energy minimization using SwissPDB Viewer30. This refinement procedure improved the structure's optimization and reduced its overall energy for subsequent molecular docking investigations. The processed protein structure was subsequently saved as a PDB file within MGL AutoDock Tools. This step was essential for precise atom type assignment for the molecular docking procedure. Following this, the structure was integrated into InstaDock v1.2 for subsequent processing of docking-based screening.

Library preparation

A few FGFR2 inhibitors are presently under evaluation in clinical trials. Prominent examples of these inhibitors include Erdafitinib, Infigratinib, Zoligratinib, and AZD4547. These specific inhibitors were chosen as reference molecules for library preparation. In order to facilitate the virtual screening process, a pre-processed version of the PubChem library was downloaded based on a Tanimoto threshold of 80% in conjunction with Erdafitinib, Infigratinib, Zoligratinib, and AZD4547. To curate an appropriate compound selection for virtual screening, we employed the Lipinski Rule of Five filters31. The Lipinski Rule of Five comprises specific criteria that evaluate the drug-like attributes of compounds. By employing these filters, a total of 2336 compounds met the criteria and remained available for subsequent molecular docking (Table 1). The process of filtering and screening played a pivotal role in the virtual screening procedure which ensured that the compounds included in the study possessed the essential properties requisite for potential drug development.Table 1 Reference inhibitors and the number of similar compounds downloaded from the PubChem database.

S. No.	Reference inhibitor	Number of compounds after RO5 with 80% Tanimoto threshold	
1.	Erdafitinib	574	
2.	Infigratinib	231	
3.	Zoligratinib	807	
4.	AZD4547	724	
Total	2336	

Molecular docking

Molecular docking plays a fundamental role in drug discovery to get a deep understanding of the complex molecular interactions underpinning vital biological processes32. This powerful technique delivers deeper insights into how molecules interact with each other, which in turn aids in the identification and development of potential drugs33. In our study, we conducted molecular docking experiments involving 2336 compounds to identify novel inhibitors for FGFR2. For the execution of structure-based virtual screening, we employed preprocessed structures of all the small molecules and the receptor in the PDBQT file format. InstaDock and AutoDock Vina34 were employed for this purpose as they markedly improve the precision of binding mode predictions. The virtual screening process involved a structurally blind search that allowed the compounds to freely move and explore their binding site(s) on FGFR2. The docking protocol was validated through a retrospective procedure35. Here, the co-crystallized EVC was redocked into the FGFR2 binding site. The analysis revealed that upon redocking, the docked EVC occupied an identical position within the FGFR2 binding site as observed in its co-crystallized state (Supplementary Figure S1). This identity between the docked and co-crystallized EVC highlights the precision and reliability of our docking protocol.

Using their binding affinities and scoring function, we identified the top 10 hits characterized by the lowest energy scores. The lower energy scores are indicative of stronger binding affinities for the FGFR2 binding pocket. Additionally, we conducted a thorough examination of the interactions between these selected compounds and FGFR2. Our primary goal was to pinpoint compounds with enhanced specificity for the FGFR2 binding pocket. Specifically, we select the compounds whose binding conformations closely resembled the binding pose of the co-crystalized molecule and exhibited consistent interactions with the residues within the FGFR2 binding pocket. By employing these methods, we aimed to identify and prioritize potential inhibitors for FGFR2 that possess favorable binding characteristics and show promise for further development as therapeutic agents.

Visualization and evaluation

The docked conformations of each ligand screened with FGFR2 were visualized using various molecular visualization tools, including PyMOL, Discovery Studio Visualizer, and LigPlot+36. These software applications provide exceptional visual representations of proteins and chemical compounds that offer animated three-dimensional and two-dimensional renderings. Beyond visualization, these tools enable a wide range of measurements, including bond lengths, distances between the protein and ligand.

MD simulations

MD simulations were conducted on FGFR2, FGFR2-CID:507883, and FGFR2-Zoligratinib at a temperature of 300 K, using the GROMACS 2020β37 software with the GROMOS96 54A7 force field38 at the molecular mechanics level. The topology and force-field parameters for the selected molecules were generated through the ATB online server39. The generated topologies for CID:507883 and Zoligratinib were combined by merging the topology files created by the pdb2gmx module of GROMACS for FGFR2. We impeccably integrated the additional ligand atoms into the complex's topology files, ensuring that all parameters for these ligands were appropriately incorporated into the system's topology. The free FGFR2, FGFR2-CID:507883, and FGFR2-Zoligratinib were immersed in a cubic box with dimensions of 1 nm. This was achieved using the gmx editconf module to set the boundary conditions. The protein was further solvated using the Simple Point Charge (spc216) solvent model40. The SPC solvent model is one of the most used water models due to its simplicity, efficiency, and compatibility with the GROMACS simulations. It contains pre-equilibrated SPC water molecules which can save computational time as the initial configuration is already reasonably close to equilibrium. To neutralize the systems and maintain a physiological concentration (0.15 M), Na+ and Cl− ions were added to the free FGFR2 and its complexes using the gmx genion module. In the input parameters files, we specified "Energygrps" to focus on analyzing interactions between FGFR2 and CID:507883, as well as interactions between FGFR2 and Zoligratinib. All three systems were energy minimized utilizing 1500 steps of the steepest descent algorithm. During the subsequent equilibration phase of 1000 picoseconds (ps), the temperature was gradually increased of all systems from 0 to 300 K. The resultant trajectories of 200 ns were analyzed using various GROMACS utilities. The graphical presentations were prepared using VMD24 and XMGRACE 5.141.

Principal component analysis

PCA is a useful technique employed in MD simulation to study the atomic motion of protein and protein-ligand complexes42. The methodology employed in this study involves the computation of eigenvectors (EVs) derived from the covariance matrix of atomic coordinates. To gain a deeper understanding of atomic motion, PCA was applied to both the unbound FGFR2 and its complexes with selected compounds. The projection of all the systems was drawn onto the first two eigenvectors (EVs), i.e., EV1 and EV2. These projections serve to visually represent the amplitude and direction of atomic movements occurring within the system under investigation. By focusing primarily on EV1 and EV2, the generated plots highlighted the maximum extent of atomic motions within the protein-ligand complex. Visual representations of EV1 and EV2 allowed us to intuitively grasp the maximum atomic displacements occurring in FGFR2.

Result and discussion

Molecular docking

Molecular docking is a crucial technique in the drug discovery process to do an in-depth examination of the molecular-level interaction between a small molecule and its target protein. This process plays a pivotal role in advancing the development of novel and efficacious medications43. The output files generated through molecular docking contained valuable information, including affinity scores, and docked poses for each compound screened. In the docking screening, it was found that several compounds demonstrated a robust binding affinity to the FGFR2 binding pocket that highlights them as noteworthy contenders for further investigation as potential FGFR2 inhibitors.

Hits selection and drug-ability assessment

A molecular docking screening process was conducted on a pool of 2336 compounds, resulting in the identification of 10 hits that displayed a binding affinity score of − 12.4 kcal/mol to − 11.6 kcal/mol with FGFR2. Initially, these compounds showed appreciable docking values, higher than the reference inhibitors. Table 2 showcases the top 10 hits from the docking analysis and the reference inhibitors, along with their respective docking scores. These chosen compounds demonstrated incredible binding potential towards FGFR2, as evidenced by docking scores between −12.4 kcal/mol to −11.6 kcal/mol, with a ligand efficiency of >0.30 kcal/mol/non-H atom. All these selected hits exhibited a superior affinity for FGFR2 compared to the control molecules, Erdafitinib, Infigratinib, Zoligratinib, and AZD4547. This highlights the exceptional binding capabilities of the selected molecules.Table 2 List of selected compounds and their docking parameters with FGFR2.

S. No.	Compound ID (PubChem)	Binding Free Energy (kcal/mol)	pKi	Ligand Efficiency (kcal/mol/non-H atom)	Torsional Energy	
1.	137049257	−12.4	9.09	0.3647	0.6226	
2.	23646206	−12.3	9.02	0.3237	1.8678	
3.	135497783	−12.1	8.87	0.3559	1.2452	
4.	137049172	−11.9	8.73	0.3606	0.6226	
5.	135497869	−11.7	8.58	0.3441	1.2452	
6.	507883	−11.7	8.58	0.3162	1.2452	
7.	71525524	−11.7	8.58	0.3774	1.2452	
8.	71526169	−11.7	8.58	0.3774	1.2452	
9.	138500078	−11.7	8.58	0.325	1.5565	
10.	137049340	−11.6	8.51	0.3625	0.6226	
11.	Zoligratinib	−9.7	7.11	0.3593	1.2452	
12.	AZD4547	−9.0	6.6	0.2647	2.1791	
13.	Erdafitinib	−8.5	6.23	0.2576	2.8017	
14.	Infigratinib	−8.4	6.16	0.2211	2.1791	

To avoid the Pan-assay interference compounds (PAINS)44 in the top 10 hits obtained from the FGFR2 molecular docking study, additional evaluations were conducted using PAINS filter. Further, the compounds' physicochemical properties were thoroughly examined using various models to assess their bioavailability and drug-like attributes (Supplementary Table S1). Moreover, we assessed the ADMET for the top 10 hits using the SwissADME and pkCSM web tools. This assessment leads to the identification of one compound, PubChem CID:507883 (1-[7-(1H-benzimidazol-2-yl)-4-fluoro-1H-indol-3-yl]-2-(4-benzoylpiperazin-1-yl)ethane-1,2-dione) that displayed no toxic patterns, and showing better properties than the reference drugs (Supplementary Table S1). Compound CID:23646206 also exhibits similar pharmacokinetic characteristics to CID:507883; however, it was noted to function as an OCT2 substrate. Consequently, it might engage in competitive interactions with other substrates during transport, potentially modifying their pharmacokinetic profiles. Such interactions could result in fluctuations in drug concentrations and efficacy, heightening the likelihood of adverse effects. Consequently, only one compound with CID:507883 appeared as a promising candidate for further development as an FGFR2 inhibitor. The summarized results of this analysis can be found in Table 3. Here, CID:507883 was identified as a promising candidate warranting further investigation.Table 3 ADMET properties of the selected compound CID:507883 and the reference drug Zoligratinib.

Property	Attribute	Optimal reference value	Predicted values	Unit (Categorical/numerical)	
CID:507883	Zoligratinib	
Absorption	Water solubility	−1 to −5	−2.892	−2.917	log mol/L	
Caco2 permeability	>0.90	1.402	0.656	log Papp in 10−6 cm/s	
Intestinal Absorption (human)	>30	90.886	90.476	% Absorbed	
Skin Permeability	>−2.5	−2.735	−2.735	log Kp	
P-glycoprotein substrate	Yes/No	Yes	Yes	Yes/No	
P-glycoprotein I inhibitor	Yes/No	Yes	No	Yes/No	
P-glycoprotein II inhibitor	Yes/No	Yes	Yes	Yes/No	
Distribution	VDss (human)	−1.5 to 0.45	0.068	0.044	log L/kg	
Fraction unbound (human)	Vary	0.337	0.027	Fu	
BBB permeability	>0.3 readily cross

<−1 poorly cross

	−0.772	−1.216	log BB	
CNS permeability	>−2 can penetrate

<−3 unable to penetrate

	−2.373	−2.364	log PS	
Metabolism	CYP2D6 substrate	Yes/No	No	No	Yes/No	
CYP3A4 substrate	Yes/No	Yes	No	Yes/No	
CYP1A2 inhibitor	Yes/No	Yes	Yes	Yes/No	
CYP2C19 inhibitor	Yes/No	Yes	Yes	Yes/No	
CYP2C9 inhibitor	Yes/No	Yes	Yes	Yes/No	
CYP2D6 inhibitor	Yes/No	No	No	Yes/No	
CYP3A4 inhibitor	Yes/No	Yes	Yes	Yes/No	
Excretion	Total Clearance	0-10	0.778	1.041	log ml/min/kg	
Renal OCT2 substrate	Yes/No	No	Yes	Yes/No	
Toxicity	AMES toxicity	No	No	Yes	Yes/No	
Max. tolerated dose (human)	≤0.477 low

≥0.477 high

	0.385	0.461	log mg/kg/day	
hERG I inhibitor	No	No	No	Yes/No	
hERG II inhibitor	No	Yes	Yes	Yes/No	
Oral Rat Acute Toxicity (LD50)	<50	2.484	2.48	mol/kg	
Oral Rat Chronic Toxicity (LOAEL)	Vary	2.829	1.979	log mg/kg_bw/day	
Hepatotoxicity	No	No	Yes	Yes/No	
Skin Sensitization	No	No	No	Yes/No	
T. Pyriformis toxicity	≥−0.5 is toxic	0.285	0.285	log ug/L	
Minnow toxicity	<−0.3 is toxic	1.527	1.685	log mM	

Visualization and evaluation

The FGFR2 structure chosen for screening purposes consists of a protein kinase domain (amino acids 481-770). Within this domain, certain residues, such as L487-V495, K417, E565-A567, and N571, hold great significance due to their ATP-binding motifs45. Our investigation revealed that these residues engage in various close interactions with CID:507883 and Zoligratinib, which can potentially impact the catalytic activity of FGFR2 (Fig. 1). The selected compound CID:507883 and the reference drug Zoligratinib bind at the same location, with similar orientations, and establish several close interactions comparable to the co-crystal ligand 1,3,5-triazine derivative, EVC in PDB ID: 6LVK. Interaction patterns of the screened compounds from the top ten hits and three remaining inhibitors, AZD4547, Erdafitinib, and Infigratinib are shown in Supplementary Figure S2. They also showed various similar interactions toward FGFR2 as CID:507883. Compound CID:507883 resides deep within the FGFR2 cavity, demonstrating close interactions with the binding residues (Fig. 1A). Notably, the ATP-binding residue Leu487 forms a hydrogen bond with CID:507883, complemented by various van der Waals interactions (Fig. 1B i). Furthermore, surrounding residues, including Arg630 and Asp644, contribute significant interactions to secure both ligands within the FGFR2 cavity (Fig. 1B). Surface representations vividly depict that the compounds are positioned within the internal cavity, displaying a specific binding affinity to the catalytic pocket of FGFR2 (Fig. 1C).Figure 1 Interaction plots. (A) Presentation of binding mode of CID:507883 and the reference drug Zoligratinib with FGFR2. (B) Magnified cartoon representation of FGFR2 binding pocket complex with (i) CID:507883 and (ii) Zoligratinib. (C) Surface representation of FGFR2 complex with (i) CID:507883 and (ii) Zoligratinib. CID:507883: green element, Zoligratinib: yellow element. The angle and distance cut-off for hydrogen bonds between donor and acceptor were set to 3.5 Å and 150-180°, respectively.

Overall, this analysis revealed that CID:507883 and Zoligratinib exhibit a remarkable degree of closeness in their interactions. These interactions primarily involve the formation of hydrogen bonds and van der Waals forces, which play crucial roles in stabilizing the binding between the compounds and FGFR2. Figure 2 illustrates the spatial arrangement of these interactions, providing visual evidence of the closeness and specificity of the compound-protein binding. Notably, both CID:507883 and Zoligratinib were found to interact with several common residues of FGFR2. The analysis identified that CID:507883 and Zoligratinib establish several intimate interactions with the ATP-binding motif within the catalytic domain of FGFR2 (Fig. 2A, B). Both compounds establish several close interactions comparable to the co-crystal ligand EVC in PDB ID: 6LVK (Fig. 2C).Figure 2 2D interaction plots of compound (A) CID:507883. (B) Zoligratinib, and (C) Co-crystalized inhibitor EVC toward FGFR2.

Previous studies on FGFR2 inhibition have identified the same key residues highlighted in our study46. A de novo design and development study aimed at developing a selective FGFR2 inhibitor found that their compounds interacted with the ATP-binding residues of the protein47. Similarly, a virtual screening study investigating Gefitinib-like compounds as potential therapeutic candidates against FGFR2 reported a similar set of interactions48. These interactions are of significant interest since the ATP-binding motif is responsible for facilitating the catalytic activity of FGFR2. By forming close associations with this motif, CID:507883 has the potential to modulate or inhibit the catalytic activity of the FGFR2 kinase domain. Therefore, the presence of compound CID:507883 may hold great promise in the context of preventing diseases associated with aberrant FGFR2 activity. Overall, these findings shed light on the molecular interactions between CID:507883 and Zoligratinib with the FGFR2 which opens up new possibilities for the drug development process.

MD simulations

The interaction between a compound and a protein's binding pocket can trigger substantial conformational alterations in the protein's structure49. To evaluate the stability of protein structures, one fundamental property is the root mean square deviation (RMSD). In this study, the average RMSD values were determined as 0.33 nm for FGFR2, 0.42 nm for FGFR2-CID:507883, and 0.35 nm for FGFR2-Zoligratinib (Table 4). The RMSD plot indicated that the binding of compound CID:507883 slightly increase the backbone RMSD, but overall stabilized FGFR2 structure without any major peak (Fig. 3A). The orientation of CID:507883 in the binding pocket of FGFR2 demonstrated a stable distribution throughout the 200 ns MD simulation (Fig. 3B). In the case of the FGFR2-Zoligratinib complex, the initial 50 ns of the MD trajectories exhibited somewhat unstable fluctuations in the binding of Zoligratinib within the active pocket of FGFR2. This behavior could be attributed to the specific orientation of Zoligratinib within the active pocket of FGFR2. However, as the simulation progressed, the system eventually reached a stable equilibrium, albeit with minor RMSD fluctuations in the FGFR2 binding pocket. In summary, the analysis of RMSD values revealed minimal fluctuations in all systems, suggesting negligible structural deviations after ligand binding. Table 4 The average values of systematic parameters obtained after 200 ns MD simulations.

System	Average RMSD (nm)	Average RMSF (nm)	Average rGyr (nm)	Average SASA (nm2)	
FGFR2	0.33	0.12	1.94	152.02	
FGFR2-CID:507883	0.42	0.15	1.97	151.74	
FGFR2-Zoligratinib	0.35	0.17	1.98	156.78	

Figure 3 Dynamics of FGFR2 upon ligands binding. (A) RMSD plot as a function of time. (B) Backbone RMS fluctuations in FGFR2 upon ligands binding.

Additionally, we assessed the root mean square fluctuation (RMSF) of FGFR2 to gauge the average fluctuation of all residues throughout the simulation period following the binding of compounds (Fig. 3B). The RMSF plot unveiled residual fluctuations occurring in different regions of the FGFR2 protein structure. However, these fluctuations were observed to be slightly increase upon the binding of CID:507883 and Zoligratinib. Notably, it was observed that the binding of Zoligratinib resulted in higher residual fluctuations compared to CID:507883. In summary, the examination of RMSF values indicated that there were no notable fluctuations seen in the residues, except in the loop regions, especially R580-T599. This finding implies that the binding of the compounds had a minimal impact on the overall protein structure, indicating little to no conformational changes upon their interaction.

The stability of the protein within the biological system was evaluated through the computation of the radius of gyration (rGyr). The average rGyr values were determined for three different states: free FGFR2, FGFR2 bound to CID:507883, and FGFR2 bound to Zoligratinib, resulting in values of 1.94 nm, 1.97 nm, and 1.98 nm, respectively (Table 4). The rGyr plot provided valuable insights into the conformational dynamics of FGFR2 under these conditions. In its free form, FGFR2 exhibited a tightly packed and well-defined conformation. However, when bound to Zoligratinib, FGFR2 experienced higher structural deviations compared to both its free state and when bound to CID:507883 (Fig. 4A). This increase in rGyr values can be attributed to the occupancy of intramolecular space within FGFR2 by the ligands, particularly Zoligratinib. Ovearll, the analysis of rGyr values indicated that while both CID:507883 and Zoligratinib binding had an impact on the conformation of FGFR2. Zoligratinib exhibited a more pronounced effect that caused slightly higher structural deviations and reduced compactness compared to the other conditions.Figure 4 (A) Time evolution of radius of gyration (rGyr) values during 200 ns of MD simulation. (B) Solvent Accessible Surface Area (SASA) as a function of time. Black, red, and aqua represent values obtained from FGFR2, FGFR2-CID:507883 and FGFR2-Zoligratinib complexes respectively.

The solvent accessible surface area (SASA) is the surface area of a protein that is accessible to a solvent50. During the simulations, the average SASA values were calculated for the FGFR2, FGFR2-CID:507883, and FGFR2-Zoligratinib complexes. The average SASA values were determined to be 152.02 nm2, 151.74 nm2, and 156.78 nm2 for FGFR2, FGFR2-CID:507883, FGFR2-Zoligratinib, respectively (Table 3). Notably, no significant changes were observed in the SASA values due to the binding of the compounds (Figure 4B). This analysis indicates that the folding state of the protein, FGFR2, remains stable upon compounds binding with minimal conformational changes.

Hydrogen bonding

Hydrogen bonds are pivotal for assessing the stability and directionality of protein and protein-ligand interactions. To further evaluate the structural integrity and stability of protein folding, the presence of intramolecular hydrogen bonding within a 0.35 nm distance was investigated in three different systems: free FGFR2, FGFR2 bound to CID:507883, and FGFR2 bound to Zoligratinib, during the simulations. This analysis played a critical role in validating the stability of the docked complexes and aimed to assess the persistence of hydrogen bonds between FGFR2 and the ligands over time (Fig. 5). Remarkably, throughout the simulations, no significant changes were observed in the average hydrogen bonding patterns resulting from the binding of the compounds (Fig. 5A). The findings suggest that the docked complexes maintained their stability, and the intramolecular hydrogen bonds between FGFR2 and both CID:507883 and Zoligratinib remained relatively constant during the simulation period. This consistency in hydrogen bonding indicates that the interactions between the protein and the ligands were robust and enduring (Fig. 5B). This supports the overall stability of the protein-ligand complexes throughout the MD simulation.Figure 5 Dynamics of hydrogen bonding. (A) Formation of intramolecular hydrogen bonds within FGFR2 as a function of time. (B) The average number of hydrogen bonds distribution and their probability. Formation of intermolecular hydrogen bonds between FGFR2 and (C) CID:507883 and (D) Zoligratinib. Black, red, and aqua represent the time-evolution of hydrogen bonds formed within FGFR2, FGFR2-CID:507883, and FGFR2-Zoligratinib complexes, respectively.

Furthermore, we assessed the stability of ligand-FGFR2 interactions by quantifying the hydrogen bonds formed between ligands CID:507883 and Zoligratinib with FGFR2 throughout the simulation. Over the entire simulation duration, both ligands were consistently engaged with FGFR2 through 1-2 enduring hydrogen bonds, highlighting the durability and strength of the ligand-protein interactions (Fig. 5C, D). These bonds formed up to three at some time during the simulation. These results validate the interaction patterns identified during molecular docking, reinforcing the accuracy and reliability of the docking predictions. The intermolecular interactions observed after 200 ns of MD simulations were analyzed. The interactions from the representative pose of the final snapshot at 200 ns revealed that the hits exhibited similar interactions with only slight variations compared to those obtained from docking studies (Supplementary Figure S3). The IUPAC names of CID:507883 and Zoligratinib, along with their structural features are shown in Table 5.Table 5 The IUPAC names and structural features of CID:507883 and Zoligratinib.

S. No.	Compound	IUPAC name	Molecular structure	
1.	CID:507883	1-[7-(1H-benzimidazol-2-yl)-4-fluoro-1H-indol-3-yl]-2-(4-benzoylpiperazin-1-yl)ethane-1,2-dione		
2.	Zoligratinib	[5-amino-1-(2-methyl-3H-benzimidazol-5-yl)pyrazol-4-yl]-(1H-indol-2-yl)methanone		

Principal component analysis

PCA is a method utilized to analyze the overall expansion of a protein under different simulation conditions51. It provides insights into the flexibility and dynamics of the protein. In this study, PCA was performed using the gmx covar module to calculate the dynamics of FGFR2 in relation to the protein backbone. Notably, the eigenvalues for the free FGFR2 protein were relatively lower compared to the complexes, indicating an increase in random fluctuations upon binding of the compounds. Elevated eigenvalues indicate a more pronounced expansion of FGFR2, signifying reduced compactness of the complexes. Figure 6 illustrates the multidimensional atomic covariance matrix, which captures the covariances between each pair of atoms. Additionally, we utilized the gmx anaeig module to project the MD trajectory onto specific eigenvector values. The 2D projections of the trajectories on these eigenvectors revealed overlap between FGFR2, FGFR2-CID:507883, and FGFR2-Zoligratinib complexes.Figure 6 Two-dimensional projections of PCA trajectories on both eigenvectors showed conformational landscapes of FGFR2 and its complexes with CID:507883 and Zoligratinib.

Gibbs free energy landscape

The FELs were computed based on the projections of the first (PC1) and second (PC2) eigenvectors. The resulting color-coded FELs are depicted in Fig. 7. These landscapes examine the fluctuation direction of the two systems for all Cα atoms in the structures of FGFR2, FGFR2-CID:507883, and FGFR2-Zoligratinib complexes. The corresponding free energy contour map indicates lower energy with deeper blue, as observed in FGFR2, FGFR2-CID:507883, and FGFR2-Zoligratinib. Notably, FGFR2, FGFR2-CID:507883, and FGFR2-Zoligratinib complexes displayed 1-2 stable conformational states. Comparing the full views of FELs among these complexes, it was observed that FGFR2-CID:507883 covered larger ranges of PC1 and PC2, indicating a more rugged free energy surface than FGFR2-Zoligratinib (Fig. 7B, C). Overall, FGFR2 showed 2 free energy wells in 2-3 basins, while FGFR2-CID:507883, and FGFR2-Zoligratinib exhibited 2-3 stable conformational states in 3-4 basins. The structural snapshots of FGFR2 and its docked complexes were extracted from the global minima following the simulations. Analysis of these structures revealed that FGFR2 did not undergo significant structural alterations compared to its initial state (Fig. 7, lower panels). Overall, the analysis of essential dynamics indicated no significant changes in the structural stability of FGFR2 upon compounds binding. This suggests strong stability of the FGFR2-compounds complexes.Figure 7 The free energy landscape plots obtained during 200 ns MD simulation for (A) FGFR2, (B) FGFR2-CID:507883, (C) FGFR2-Zoligratinib. Lower panels showed the structural snapshots of FGFR2, and its docked complexes fetched from the global minima after the simulations.

Conclusions

The search for specific inhibitors targeting FGFR2 has shown promise, as the development of kinase inhibitors for treating various diseases continues to advance. Following a thorough examination of binding affinities, crucial interactions with active site residues, and predicted activities, we have pinpointed CID:507883 as a promising candidate for FGFR2 inhibition. This compound has undergone various evaluations for drug-likeness, ADMET properties, and MD simulations. Docking analysis demonstrated that CID:507883 binds to the same pocket as known FGFR2 inhibitors, exhibiting enhanced binding affinities and sharing several common interactions with vital residues within the ATP-binding pocket. Subsequent MD simulation of CID:507883 and the reference inhibitor Zoligratinib in complex with FGFR2 demonstrated their ability to effectively close the catalytic cavity, inducing distinct conformational changes in the protein. These findings indicate that the identified compound CID:507883 has a strong potential to modulate FGFR2 activity and serve as a promising therapeutic agent. In summary, our investigation has led to the identification of a novel compound CID:507883, structurally based on Zoligratinib that exhibits favorable binding properties, forms critical interactions, and induces conformational changes in FGFR2. These findings highlight the potential of CID:507883 as a putative therapeutic agent for targeting FGFR2 and modulating its activity in the treatment of relevant diseases such as cancer.

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-69850-1.

Acknowledgements

The authors acknowledge the generous support from the Research Supporting project (RSP2024R434) by the King Saud University, Riyadh, Kingdon of Saudi Arabia.

Author contributions

A.S, M.S.K. and R.H.K. designed the experiments, carried out the experiments, analyzed the data and wrote the paper. A.S., M.F, D.K.Y., and M.S. contributed to the data analysis and aided in writing-reviewing and editing the manuscript.

Data availability

All data generated or analyzed during this study are included in this published article.

Competing interests

The authors declare no competing interests.

The original online version of this Article was revised: The original version of this Article contained an error in the Acknowledgements section. It now reads: “The authors acknowledge the generous support from the Research Supporting project (RSP2024R434) by the King Saud University, Riyadh, Kingdon of Saudi Arabia.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Change history

9/18/2024

A Correction to this paper has been published: 10.1038/s41598-024-72768-3
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