1 Background

Everyone agrees that cancer is one of the leading causes of mortality worldwide and the most important public health concern [1]. Membrane proteins and other cell components are constantly being broken down by a diverse range of proteases, including hydrolases and cathepsins, which are found in lysosomes, which are cellular units [2]. Two members of this class, lysosome-associated membrane proteins 1 and 2 (LAMP-1 and LAMP-2) [3], have molecular weights (about 90–120 kDa) and comparable structures [4]. They are lysosomal membrane glycoproteins. The lysosomal membrane proteins LAMP-1 and LAMP-2 are thought to make up around half of all the proteins in the lysosome membrane [5]. The pathophysiology of Alzheimer's disease is linked to lysosomal disruptions in multiple studies [6]. LAMP-1 has been shown to carry alpha 1,2-fucosylated Lewis Y (LeY) antigens in breast cancer cells. Nevertheless, nothing is now known about the precise biological roles that LeY plays in LAMP-1 [7]. Research has shown that the presence of intracellular LAMP-1 is necessary for the process of micropinocytosis, fusion, and internalization of the Lassa virus (LASV) into the cell environment [8, 9]. So far, the presence of LAMP has been observed in human bone marrow mast cells and the leukemic human mast cell line 1 (HMC-1) [10]. It is worth noting that the expression of LAMP was found to be elevated in the mast cells of individuals diagnosed with indolent mastocytosis [11]. The reasons behind this are ambiguous. The LAMP plays a crucial role in eradicating intruding microorganisms [12]. Although the role of LAMPs expressed on the surface of peripheral blood lymphocytes has not been previously investigated [13], it has been demonstrated that these proteins facilitate the attachment of tumor cells to vascular endothelial selectins [14]. A cutting-edge therapeutic anti-cancer tactic for eliminating cancer cells that might otherwise be resistant to apoptosis is to target lysosomes [15]. Treatment options of this kind are desperately needed for brain tumors, particularly glioblastoma, which is the most common and deadly kind [16]. LAMP-1's transmembrane and cytoplasmic domains include crucial targeting information [17].

The field of medicinal chemistry relies heavily on pyrazole derivatives, which are particularly valuable as skeletons for the discovery of novel bioactive agents due to their strong pharmacological properties, including anti-inflammatory [18], protein glycation inhibitors [19], antimicrobial [20], anti-bacterial [21], depressant inhibitors [22], fungal inhibitors [23], anti-oxidant [24], anticancer [25, 26], antitubercular [27], and malaria inhibitor [28] and a host of other activities. Anti-cancer medications' main goal is to destroy cancer cells without harming healthy cells in the process. Unfortunately, several side effects, including endometrial cancer and drug resistance, are associated with the anti-cancer medications currently in use. Abraxane and Everolimus are currently being used as therapies for lysosomal storage disorders [29], and they appear promising, However, several negative effects, including hepatotoxicity and some pharmacokinetic drawbacks, severely restrict their clinical use; therefore, more research into novel compounds that function as LAMP-1 inhibitors is necessary.

Over the past few decades, computational modeling has grown in importance in the biochemical and biomolecular sciences [30,31,32,33,34,35]. This has to do with major advancements in software and methodology, as well as the incredible technological innovations in computing hardware and productive cross-disciplinary collaborations [36,37,38]. It is possible to screen virtual libraries containing several million chemicals in search of possible novel inhibitors. Large biomolecular complex simulations can be performed on time frames of microseconds or even milliseconds. Protein structures can be predicted as accurately as X-ray crystallography with high resolution. Therefore, the search for and development of new anti-cancer drugs with enhanced tumor selectivity, safety, and efficacy is imperative [39]. Molecular docking, molecular dynamics simulations, and ADMET investigations are now essential techniques for creating plans for preventing cancer resistance [40]. For the pyrazole derivatives, molecular docking research was performed utilizing two distinct LAMP receptors, 4AKM and 5GV0. One benefit of computational research is that it can predict a compound's biological function before it is synthesized or characterized, which saves time and money [41]. Consequently, the goal of this work is to utilize the strong anti-tumor properties of certain pyrazole derivatives as alternative anti-cancer drug-like chemicals by conducting computational and in-silico anti-cancerous investigations on them via molecular docking, ADMET, molecular dynamics simulations, and bind free energy calculations.

2 Materials and methods

The PubChem database was used to gather a set of twenty (20) pyrazole compounds as presented in Table 1. The pyrazole derivatives and standard drugs were fully optimized using a semi-empirical parameterization 7 (PM7) on MOPAC software [42] after modeling and carrying out a conformation search using the molecular mechanics force field (MMFF94) with Avogadro software to find the most stable conformers (lowest energy configuration). Its consistency with experimental results led to the selection of the theory's level [43].

Table 1 Chemical compositions and the investigated compound's binding affinities
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2.1 Protein preparation and molecular docking

As a result, before completing the docking and MD simulation, the crystal structures of LAMP were prepared using the AutoDock software, the MGLTools v1.5.7 [44] package, and the BIOVIA Discovery Studio 2020 client software. This helped to prevent errors. AutoDock Vina v1.2.1 [45] was used to achieve highly accurate molecular docking of the pyrazole derivatives and conventional medicines with LAMPs [46]. AutoSite v1.1, a grid-based docking program, was used to determine the binding mechanism of each inhibitor [47]. Receptor = 4akm.pdbqt (exhaustiveness = 8, center_x = 28.5752, center_y = 6.8082, center_z = 89.7584, size_x = 52.4129394519, size_y = 39.96890975, size_z = 32.9072632599) and receptor = 5gv0.pdbqt (exhaustiveness = 8, center_x = 11.4112, center_y = 15.0973, center_z = 34.0506, size_x = 40.1485080004, size_y = 43.4703913069, size_z = 50.0944208527) were the chosen grid sizes. It offers the binding affinity necessary for the complex to develop between conventional medications, pyrazole derivatives, and receptors.

2.2 Drug-likeness and pharmacokinetics ADMET prediction

To identify potential therapeutic candidates, synthetic accessibility, Lipinski rule-based filters, and an in-silico examination of adsorption, distribution, metabolism, and excretion are used to assess compounds' drug-likeness. The first step in the development of drugs is to determine the level of toxicity of the substances under investigation [48]. This study assesses the drug-likeness and ADME/T of the selected compounds using the online admetlab 2.0 [49] and pkcsm [50].

2.3 Molecular dynamics simulations

By helping the docking mechanisms and predicting the affinities, molecular dynamics simulations may help us comprehend and enhance drug-like processes. The molecular recognition between the ligand and the LAMP protein was ascertained by molecular docking data, which were used to choose the best-docked ligands with the highest negative binding affinity for MD simulations. The MD simulations were run using the University of Illinois at Urbana-CAMP program, NAMD v2.14, for 100 ns [51]. Complexes generated by the System Builder tool were optimized and minimized using a solvent model with a cubic box selected as TIP3P and the CHARMM36m force field. Through docking investigations and preprocessing with the CHARMM-GUI server, this procedure yielded the initial step of the molecular dynamic simulation of protein–ligand complexes [52]. Water molecules were introduced to the models to neutralize them at 310 K temperature and 1 atm pressure. To replicate physiological conditions, counterions such as 0.15 M salt (NaCl) were also supplied. Ultimately, the trajectories were stored for examination at 10-ps intervals, and to evaluate the stability of the simulations, the root mean square deviation (RMSD) of the ligand–protein over time was employed. Hydrogen bonding, gyration of radius (RoG), solvent-accessible surface area (SASA), and root-mean-square fluctuation (RMSF) are all included. The structural conformation characterization and visualization of the MD trajectories were performed using Visual Molecular Dynamics (VMD) and a related Tcl script.

2.4 Binding free energy calculations

Next, two computationally sound approaches were employed to estimate the binding free energy (△G) based on the findings of the MD simulation: Generalised Molecular Dynamics After performing molecular docking simulations, the free energies of binding (\({\Delta G}_{binding}\)) for potential ligand–protein target complexes were computed using Born (GB) and Poisson-Boltzmann (PB) surface area techniques under implicit solvent conditions [53]. The ligand–protein complex’s △G was calculated with the MolAlCal (MM/GBSA) module [54] and the NAMD café 1.0 plugin package (MM/PBSA). This △G is defined as follows [55]:

$${\Delta G}_{binding}={\Delta G}_{com}-({\Delta G}_{rec}+{\Delta G}_{lig})$$
(1)
$${\Delta G}_{com/rec/lig}=\Delta H-T\Delta S$$
(2)
$$\Delta H= {\Delta E}_{gas}+{\Delta G}_{sol}$$
(3)
$${E}_{gas}={E}_{int}+{E}_{vdw}+{E}_{ele}$$
(4)
$${\Delta G}_{sol}={\Delta G}_{PB/GB}+{\Delta G}_{NP}$$
(5)
$${\Delta G}_{NP}=\gamma SASA+\beta$$
(6)

The formula denotes the total free energy of the protein–ligand complex as ΔGcom, the total binding energy of the protein in solvents as ΔGrec, and the total binding energy of the ligand in solvents as ΔGlig. The van der Waals energy (ΔEvdw) and electrostatic energy (ΔEele) may be used to calculate the interaction energy of protein and ligand in the gas phase, or ΔMM. The free energy of solvation, denoted by ΔGsol, may be found by calculating the polar solvation energy, ΔGGB, and the non-polar solvation energy, ΔGSA. Entropy contribution, denoted by the symbol TΔS, is typically disregarded due to its high computational overhead and little effect on the outcome. We determined the binding free energy and then broke down the energy into each residue to identify the important amino acids that significantly influence it.

2.5 Cluster and trajectory analysis

Pronk et al., [56] applied the g_cluster tool with a cut-off radius of 2 Å to the whole MD production trajectory to apply the GROMACS clustering protocol. VEGAZZ software (http://www.vegazz.net/) was used to convert the DCD trajectory files (obtained via NAMD) to an XTC format usable by GROMACS. The trjconv command was utilized to renumber the trajectory frames in the final XTC file that was used for further clustering because, during the trajectory format conversion, all frame numbers in the XTC file format were turned to 0 by default. A typical conformation was identified as the middle frame of the cluster that displayed the greatest number of frames after the trajectory's frames were clustered using the g_cluster tool with an RMSD threshold of 0.11 nm. A 2D interaction diagram between the ligand and the protein was once more produced using the retrieved representative structure and the trjconv function to convert it into pdb format. This was then fed into the Discovery Studio program.

3 Results and discussion

Each compound's docking affinities were expressed in kcal/mol. The select compounds are shown in Table 1 based on their binding affinities to the target proteins of LAMP-1. It was discovered that the well-docked molecules, such as ligands CID 44555488, 44,555,489, 45,487,637, 45,487,645, and CID 45487667, had the highest average binding affinities. The chosen ligands and the conventional drugs will be contrasted.

3.1 Drug-like action and pharmacodynamics results

Chemical interactions, post-receptor effects, and receptor binding are all part of pharmacodynamics, which is the term used to describe the effects of a medicine on the body. A summary of the estimated binding affinities of the best ligands and the two standard medicines docked into the binding site of the LAMP receptors appears in Table 1 and Fig. 1 through Fig. 4. The affinity for a receptor indicates how strongly a medication binds to it. It is helpful to perform in silico studies to forecast the orientation and binding affinity at the receptor's active site with in vitro anticancer activity. Figure 1 shows the 2D and 3D complex LAMP-1 (PDB id 4AKM)-ligand (CID 44555488) with the dominance of carbon-hydrogen bond, electrostatic, and hydrophobic interactions. There are hydrophobic interactions with key residues such as ARG235 (alkyl and pi-alkyl), GLY275 (amide-pi stacked), and THR276 (pi-sigma). The amino acid ARG235 at a distance of 4.74 Å contributes to the electrostatic (pi-cation) interaction. Meanwhile, the residues LEU236 (6.23 Å) and PHE363 (5.04 and 7.59 Å) contribute to the carbon-hydrogen bond interactions. The pharmacodynamics interaction of ligand CID 45487645 inside the binding cavity of the LAMP-1 (PDB id 4AKM) was analyzed (Fig. 2). The pharmacodynamics interaction revealed that the ligand CID 45487645 formed two hydrogen bonds with the protein. It was observed that the oxygen atom of the side-chain of PHE363 LAMP-1 formed a conventional hydrogen bond with the hydrogen of the ligand CID 45487645. This conventional hydrogen bond has a bond length of 5.20 Å. The carbon atom of ligand CID 45487645 interacts with the hydrogen of ASP364 of the LAMP-1 (PDB id 4AKM) target. This carbon-hydrogen bond has a bond length of 4.37 Å. Several amino acids found in the active site of LAMP-1 (PDB id 4AKM), including THR276 and ARG235, are responsible for the hydrophobic and electrostatic interactions in the protein–ligand complex, respectively. The residues PRO112, TYR181, TYR81, PRO74, and TRP84 were conserved throughout docking simulations.

Fig. 1
figure 1

The ligands CID 44555488 are posing interactions with the LAMP− 1 active site by 2D and 3D docking (PDB id: 4AKM)

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Fig. 2
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The ligand CID 45487645 poses interactions with the LAMP− 1 4AKM active site

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Between the oxygen atoms of the carbonyl group and the amino acids GLN343, PHE329, GLN330, VAL327, and LYS323, the Abraxane forms five conventional hydrogen bonds with nuclear distances of 4.57 Å, 5.79 Å, 3.85 Å, 4.56 Å, and 5.96 Å, in that order. Additionally, as shown in Fig. 3, it forms a pi-alkyl-type chemical connection with the amino acid VAL327 at a distance of 4.91 Å. As shown in Fig. 4, the ligand (Everolimus) forms an alkyl (hydrophobic interactions) chemical bond with the amino acids VAL312 and ARG259 at a distance of 4.58 Å and 6.24 Å, respectively, as well as a carbon-hydrogen bond between the oxygen atom and the amino acid GLY287 at a nuclear distance of 4.17 Å and between the compound's nitrogen atom and the amino acid THR311 at a nuclear distance of 6.21 Å.

Fig. 3
figure 3

The active site of LAMP− 1 (PDB id: 4AKM) is where Abraxane engages in docking interactions

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Fig. 4
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The active site of LAMP− 1 (PDB id: 4AKM) is where Everolimus engages in docking interactions

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The outer vestibule of LAMP-1 (PDB id: 5GV0) was docked with the two chosen pyrazole derivatives (CID: 44,555,488 and 45,487,645). Table 1, Figs. 5, 6 display the binding affinities and binding interaction of the two favorable postures that were produced. The highest scoring position that was chosen for additional examination displays the ligand 44,555,488 bonded between the β and γ subunit helices. At this site, ligand 44,555,488 establishes a favorable conventional hydrogen bond with the residues THR234 (5.25 Å) another favorable hydrophobic interaction with LEU242 and ILE309, and a miscellaneous pi-sulfur interaction with MET328 (Fig. 5). The pharmacodynamics calculations showed that ligand CID 45487645 entered into the binding site of LAMP-1 (PDB ID: 5GV0) and produced carbon and pi-donor hydrogen bonds with SER308 (5.52 Å) and LYS337 (5.40 Å), respectively. The ligand also produces some hydrophobic interactions with PHE307 (pi-pi stacked) and VAL304 (pi-alkyl) as presented in Fig. 6.

Fig. 5
figure 5

Ligands CID 44555488 engage with the LAMP− 1 active site by 2D docking (PDB id: 5GV0)

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Fig. 6
figure 6

Ligands CID 45487645 engage with the LAMP− 1 active site by 2D docking (PDB id: 5GV0)

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Further, in silico molecular docking was carried out to estimate the binding affinity of the standard drugs and to demonstrate the protein–ligand pharmacodynamics mechanism. The binding affinity values observed for Abraxane (- 6.3 kcal/mol) and Everolimus (- 6.3 kcal/mol) against receptor PDB 5GV0 were presented in Table 1. Abraxane was able to bind to the receptor (PDB 5GV0) by creating four hydrophobic contacts in the target protein's active region with LEU236, VAL304, and PHE307, respectively (Fig. 7). Everolimus and residues inside the 5GV0 pocket of LAMP-1 are shown to interact by van der Waals interactions, conventional hydrogen bonds, and carbon-hydrogen bonds in Fig. 8. With residue ASN270, three conventional hydrogen bond interactions were found, while the carbon-hydrogen bond interactions were observed with THR209 and ASN272. Other residues, such as ASP207, ALA246, ARG273, ASP253, PHE247, SER250, ASN252, and ASN248 participated in van der Waals interactions.

Fig. 7
figure 7

Abraxane (A) and Everolimus (B) interact with the LAMP− 1 active site through 2D docking (PDB id: 5GV0)

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Fig. 8
figure 8

Everolimus interacts with the LAMP− 1 5GV0 receptor active site through docking interaction

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3.2 The physicochemical properties and pharmacokinetics results

The length, strength, and start of a drug's action are all determined by its pharmacokinetics. The pharmacokinetic behavior of the selected drug-like compounds and the standard drugs are summarized in Fig. 9 (upper limit and lower limit) linking these processes. Additionally shown are the computed compound characteristics (blue lines) for the chosen compounds. According to Lipinski et al. [57], an orally active molecule must not exceed any two of the following ranges of physicochemical parameters: molecular weight (MW) ≤ 500, partition coefficient of a solute between octanol and water (Log P) ≤ 5, number of hydrogen donors (nHDs) ≤ 5, and number of hydrogen acceptors (nHAs) ≤ 10. According to Veber et al. [58], effective and selective criteria for oral bioavailability include the total polar surface area (TPSA) and the number of rotatable bonds (nRoT) values of no more than 140 Å2 and 10, respectively. Furthermore, according to Ugbe et al. [59, 60], Log S is one of the crucial factors that supports the developmental actions of pharmaceuticals taken orally. Compound CID 45487645 conformed with Veber's rule and the rule of five (RO5) in this investigation. Compounds CID 44555488 and 45,487,645 may be soluble in water, according to the Log S values. Thus, the data suggest that compounds 45,487,645 and 44,555,488 have a reasonable chance of passing through the membrane with good permeability and absorption. Additionally, molecules CID 44555488 and 45,487,645 have synthetic accessibility scores (SA) of 2.207, indicating that their molecular fragment would be accessible. The chosen compounds' physicochemical characteristics are enumerated in Table 2.

Fig. 9
figure 9

The bioavailability radar charts of A ligand CID 44555488, B ligand CID 45487645, (C) Abraxane, and (D) Everolimus as the selected lead compounds

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Table 2 The molecular and physicochemical properties of the selected compounds
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In Table 3, the chosen drugs' pharmacokinetic characteristics are listed. The data imply that, although both compounds were anticipated to be P-substrates and to have the capacity to obstruct the blood–brain barrier (BBB), compounds CID 44555488 and 45,487,645 may absorb rather effectively. The majority of CYP450 isozymes, such as CYP2C19, CYP2C9, and CYP3A4, were inhibited by both drugs, while only CYP2D6 isozymes were unaffected. The Ames test for mutagenicity. The mutagenic effect has a close relationship with the carcinogenicity, and it is the most widely used assay for testing the mutagenicity of compounds. A positive test indicates that the compound is mutagenic and therefore may act as a carcinogen. According to the toxicity profile, none of the chemicals had a high potential for amnesia. This finding suggests that a more thorough in-vitro evaluation of these chemicals' safety is required.

Table 3 Selected pharmacokinetic properties of the selected compounds
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The following formula was used to determine the absorption percentage (ABS%) [61, 62]:

$$ABS\%=109-(0.345\times TPSA)$$
(7)

According to Edache al. [63] the drug's superior oral bioavailability, distribution, and circulation may be inferred from its high ABS% (> 50%). Due to their large total polar surface areas (TPSA) of 221.29 and 204.66 Å2, respectively, Abraxane and Everolimus were found to have ABS%s of 32.65 and 38.39 (< 50%), although TPSA ≤ 140 Å2 was suggested to be optimum [64]. Except for standards, all remaining inhibitors had ABS% values over 50%, which is a good indicator that the ligands were well chosen and had appropriate oral bioavailability.

3.3 Molecular dynamic (MD) simulations results

The behavior of the projected complex was examined in the context of MD simulations for the chosen ligands-bound LAMP-1 in explicit aqueous solution, running for 100 ns, to account for protein flexibility and stability [65]. The MD simulations for the two selected compounds (CID 44555488 and CID 45487645) and the standard drug (Everolimus) are analyzed (Fig. 10) to investigate the dynamic stability of both systems and to verify the validity of the sampling procedure. The 100 ns MD trajectory was used to determine the RMSD values for each frame, which were then displayed versus time (Fig. 10A, B). The estimated RMSD value derived from the MD trajectory indicates likely alterations that occur in the geometric orientation of a protein structure. A globular protein exhibits greater stability when its RMSD is smaller than 3 Å. Each ligand oscillates at a smaller RMSD, based on the average RMSD. The RMSD for Everolimus, CID 44555488, and CID 45487645 complex systems had maximum RMSDs of 1.2761 Å, 1.1120 Å, and 1.0947, respectively, with ligand CID 45487645 with LAMP-1 4AKM having the greatest RMSD and Everolimus with LAMP-1 4AKM complex having the lowest (Fig. 10A). The ligand CID 44555488 with LAMP-1 5GV0 system has the lowest RMSD of 1.0038 Å, followed by ligand CID 45487645 complex with an RMSD of 1.0162 Å, while Everolimus complex has a RMSD value of 1.0210 Å (Fig. 10B). Every system oscillates with an RMSD of under 3.0 Å. Upon closer inspection, Fig. 10A, B show that none of the possible systems that were identified experienced sudden changes in their RMSD profiles.

Fig. 10
figure 10figure 10figure 10

The complexes' hydrogen bond numbers during a 100 ns MD simulation are listed from left to right: A and B RMSD, C and D RoG, E and F RMSF, G and H SASA, and I and J

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During the simulation, the protein structural compactness was measured using the radius of gyration (RoG). This investigation made it easier to determine if the interacting molecules got along well with one another, whether they maintained their equilibrium, and whether they weren't high-energy molecules that would have caused the system to become extremely unstable. Whereas a smaller RoG suggests a stable and equilibrium system, a larger RoG number indicates a very unstable system. The ligand complexes for Everolimus, CID 44555488, and CID 45487645, as seen in Fig. 10C, D, all the complexes exhibited distinct RoG patterns and experienced oscillations in their RoG values during the simulation period. The average RoG value was 16.08 Å for the ligand CID 44555488-4AKM and 16.62 Å for ligand CID 44555488-5GVO complex, while ligand CID 45487645 against 4AKM and 5GVO are 15.998 and 16.774 Å, respectively. The standard drug against the receptors has RoG values of 16.097 and 16.562 Å. The RoG value ligand CID 45487645 with 4AKM is the smallest, meaning it is more stable than the standard drug, however, the RoG values for the ligand CID 45487645-5GVO complex remained higher than the rest 5GVO complexes throughout the simulation. When one or both ends of the LAMP-1 receptor-binding domain were bound and unbound, the RoG value fluctuated during the simulation.

The root-means-square-fluctuations (RMSF) were also investigated to evaluate the flexibility of the amino acid residues (Figs. 10E, F). The ligand CID 44555488 complex system displays a comparatively more stable fluctuation pattern, with just slight changes between residues 25 and 100, according to the findings of the RMSF study (Fig. 10E). The average residual fluctuation for LAMP-1 4AKM and LAMP-1 5GV0 are 0.118 and 0.5644 Å. In contrast, the ligand CID 45487645 against 4AKM showed erratic variations, with an average RMSF of 0.6974 Å (Fig. 10E). According to Edache et al. [56], large MD systems frequently exhibit such significant volatility patterns. A significant variation is seen between residues 20–160 and a few smaller fluctuations at other areas for the ligand CID 45487645-5GVO complex, which is a similarly big system. The average residual fluctuation of this system is 0.6496 Å (Fig. 10F). The RMSF values of the complex relative to the reference compound during the trajectory of the MD simulations showed one major fluctuation peaks exceeding 1.5 Å were observed at residue 155 against LAMP-1 4AKM receptor (Fig. 10E), and two major fluctuation peaks exceeding 1.5 Å were observed at residue 13 and 162 against LAMP-1 5GV0 receptor (Fig. 10F), while relative to the remainders of the residues they were quite weak and fluctuated with values less than 1.3 Å. The average residual fluctuations against the receptors are 0.6887 and 0.6496 Å, respectively.

Understanding the happenings in the protein–ligand interface may be gained from the solvent-accessible surface area (SASA) parameter, which is computed from the MD trajectory. A lower SASA value indicates that the protein or ligand is exposed to the solventless when ligand binding to a protein is regarded as a solvent replacement strategy. In other words, the ligand is buried well within the binding pocket. An illustration was created to comprehend how SASA changed throughout time. Based on the 100 ns atomistic MD trajectory, Figs. 10G, H show that the SASA values of the ligand CID 45487645 against the 4AKM (9596.86 Å2) system are less than those of the ligand CID 44555488-4AKM protein (10,113.21 Å2) and the standard drug (9718.33 Å2). This crucial discovery clearly shows that ligand CID 45487645 is inside the receptor-binding pocket during the 100 ns MD simulation. In Fig. 10H, ligand CID 44555488 with LAMP-1 5GV0 SASA value (9902.80 Å2) is less than the SASA value (10,249.18 Å2) ligand CID 45487645 with LAMP-1 5GV0 and the standard drugs with SASA value of 9946.74 Å2. This shows that ligand CID 44555488 is inside the receptor-binding pocket.

3.4 Hydrogen bonding (HB) analysis

To confirm the specificity of the complexes for the LAMP-1 receptors resulting from hydrogen bonding-driven biological processes, the hydrogen bonds were examined. Determining the receptor-ligand interaction under dynamic settings over time requires an understanding of the HB interaction. This is also required to fulfill a functional role and to generate stable connections that allow intermolecular communication over a prolonged length of time. The number of hydrogen bonds stabilizing protein–ligand complexes was monitored during the 100 ns molecular dynamics experiment (Fig. 10I, J). With ligand CID 44555488, the mean number of hydrogen bonds in PDB ids 4AKM and 5GV0 was 23.955 and 17.988, respectively, while with ligand CID 45487645, it was 21.668 and 23.03. The typical hydrogen bond values for the standard drug are 19.352 and 21.822. The greatest HB is found for ligand CID 44555488 against the 4AKM receptor while the highest HB is found for ligand CID 45487645 against the 5GV0 receptor.

The location and orientation of the ligand in the newly added binding site altered after the MD simulation (Figs. 11, 12, and 13), and this significant finding suggests the practical use of MD simulations following ligand docking in the binding site. Exploratory runs of MD simulations on the complex between the ligand CID 44555488 and the receptors under study (PDB id 4AKM) revealed that all but THR276, GLY275, PHE363, PHE365, LEU236, ASP364, ARG225, CYS274, LYS296, ASP368, and ASP367 had their residues determined by docking altered. Additionally, new residues, such as ASN232, CYS237, and GLU366, are located close to the ligand and may engage in interaction (Fig. 11). The complex formed by the ligand CID 44555488 and the receptor (PDB id 5GV0) also showed that a few more amino acid residues, including LEU303, ALA302, SER336, and ILE233, were added near the ligand, replacing the remaining amino acid residues found by docking at THR242 (Fig. 12), except for ILE309, MET338, LEU236, THR234, SER308, PHE307, VAL304, and TYR235. The dynamic conformational variations of the ligand CID 44555488 complexes, as shown in Figs. 11 and 12, indicate that the simulation is well-equilibrated because, throughout the course of the simulation, the protein's RMSD fluctuations are concentrated around the thermal mean structure. This is because small globular proteins can tolerate conformational changes of any order between 1–3 Å. A distance cut-off of 4 Å was used in the contactFreq.tcl script in VMD to calculate the number of interactions between the receptors and the ligand under analysis. The relationship between the ligand CID 44555488 and certain residues was expressed as contact frequency. It was thought that residues with high contact frequency would operate as energy barriers to substrate mobility. For every receptor-ligand interaction, Table 4 displays the amino acid residues with greater contact frequencies %.

Fig. 11.
figure 11

2D and 3D binding interaction of ligand CID 44555488 against 4AKM receptor after 100 ns MD simulations

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Fig. 12.
figure 12

2D and 3D binding interaction of ligand CID 44555488 against 5GV0 receptor after 100 ns MD simulations

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Fig. 13.
figure 13

2D and 3D binding interaction of ligand CID 45487645 against 4AKM receptor after 100 ns MD simulations

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Table 4 Receptor-ligand contact frequencies
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The ligand CID 45487645 with LAMP-1 4AKM contact has one hydrogen bond interaction with LYS296, a pi-anion (electrostatics) interaction with ASP364, three hydrophobic interactions with residues ARG235, CYS237, and CYS274, as shown in Fig. 13. The ligand CID 45487645 with LAMP-1 5GV0 receptor binding complex exhibited three hydrogen-bonding contacts (LEU236, LYS337, and MET338), and one carbon-hydrogen bond with TYR235. Additionally, the complex had three hydrophobic interactions (pi-pi T-shaped and pi-alkyl) corresponding to these residues VAL304, PHE307, and MET338 as displayed in Fig. 14.

Fig. 14.
figure 14

2D and 3D binding interaction of ligand CID 45487645 against 5GV0 receptor after 100 ns MD simulations

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The Everolimus (standard drug) against the 4AKM receptor has one carbon-hydrogen bond interaction with residue ALA267 as presented in Fig. 15. The interactions between the standard drug (Everolimus) against the 5GV0 receptor active site are shown in Fig. 16. ASN248 created a conventional hydrogen bond and carbon-hydrogen bond with the enzyme. Additionally, amino acid PHE247 formed a hydrophobic bond with the standard drug.

Fig. 15.
figure 15

2D and 3D binding interaction of Everolimus against 4AKM receptor after 100 ns MD simulations

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Fig. 16.
figure 16

2D and 3D binding interaction of Everolimus against 5GV0 receptor after 100 ns MD simulations

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3.5 Binding free energy (BFE) analysis

The binding free energies of the simulated complexes were determined using the MM/PBSA and MM/GBSA techniques to obtain more insight into the molecular interactions between the chosen drug-like compounds and the residues of the LAMP-1 active site.

3.6 MM/GBSA calculations analysis

The binding free energies and energy terms for the standard complexes are compiled in Table 5. The outcome showed that there was excellent interaction between the standard drug complexes. The MM/GBSA results revealed that the Van der Waals (VDW) interaction energy contributed significantly to the physical and chemical components of the standard drug. The total free binding (MM/GBSA) energies against LAMP-1 4AKM and LAMP-1 5GV0 receptors are -6.1578 ± 0.0922 kcal/mol and -5.1316 ± 0.0624 kcal/mol, respectively. The Everolimus complexes are energetically stable and strongly bind to the receptors.

Table 5 The binding free energies (kcal/mol) of Everolimus complexes were determined using the MM/GBSA technique
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Table 6 displays the binding free energy (MM/GBSA) estimate for ligands CID 44555488 and CID 45487645 as provided by MolAICal. The ligand–protein binding is energetically stable and strongly binds to the receptor (ligand CID 45487645_4AKM, ligand CID 45487645_5GV0, and ligand CID 44555488_4AKM, ligand CID 44555488_5GV0, respectively), as indicated by the negative values of the estimated binding free energy (MM/GBSA) for both complexes, which are -26.3567, -22.1835 kcal/mol and -18.8081, -21.9022 kcal/mol. The BFE calculation findings indicated that the ligands and receptors were stably bound, which provided support for the conformational dynamics and molecular docking studies. The ligand complexes were found to have the highest binding free energy than the standard drug.

Table 6 MD simulations' binding free energies (kcal/mol) for the complexes determined using the MM/GBSA technique
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3.7 MM/PBSA calculations analysis

The MM/PBSA method was used to carry out further computations to ascertain the drugs' binding free energy with the receptors. Stable protein–ligand binding is indicated by a very negative binding free energy. The binding free energy of the standard (Everolimus) drug obtained from the MMPBSA method is summarized in Table 7. The data expressed different terms in MMPBSA free energy estimation was collected from the final 100 frames of the trajectory. The Van der Waals energy (Vdw), electrostatic energy (Elec), polar solvation energy (Pol), SA energy (SA), and binding free energy (\({\Delta G}_{bind}\)) were calculated and listed in Table 7. The results showed that the binding free energies for Everolimus with LAMP-1 4AKM and Everolimus with LAMP-1 5GV0 complex structure were -2.1289 ± 4.3001 and -4.7284 ± 3.1730 kcal/mol, respectively.

Table 7 The binding energies for Everolimus combined with LAMP− 1 receptors by MM/PBSA calculation
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Tabulated here are the outcomes for the ligands CID 44555488 and CID 45487645 complexes (Table 8). Complex binding free energy analysis identifies internal energy in the gas phase and van der Waals interactions as critical to ligand binding against PDB 4AKM. The LAMP-1 4AKM systems for the ligands CID 44555488 and CID 45487645 exhibit binding energies of 1.2811 and 1.7905 kJ/mol, respectively. Furthermore, Table 8 shows that ligand CID 45487645's BFE was shown to be much lower than that of ligand CID 44555488 system. Each ligand seems to bind steadily to the LAMP-1 5GV0 protein, according to the MM/PBSA-based binding-energy profile of the discovered ligands. Compared to ligand CID 44555488 with LAMP-1 5GV0 coupled system, it is evident that the BFE of ligand CID 45487645 with LAMP-1 5GV0 was determined to be significantly negative. After further analysis of the BFE of ligand CID 45487645 with LAMP-1 5GV0, it was discovered that the non-polar interactions and the internal energy in the gas phase are crucial for the ligand binding to the receptor. When binding, the hydrophobic groups of inhibitors are buried due to the non-polar solvation interactions, which are primarily caused by the van der Waals interaction and the non-polar solvation effect. The conventional gas phase energy for \({\Delta E}_{ele}\) and \({\Delta E}_{vdw}\), respectively, is -13.5015 and -27.8963 kcal/mol, indicating that electrostatic contact is unable to overcome the higher polar energy barrier. The results showed that the BFE for the standard drug against the LAMP-1 4AKM receptor is better than the selected ligands. whereas the ligands BFE against the LAMP-1 5GV0 is far better than Everolimus. The negative values represented the binding interaction while the positive values represented the opposite interaction. Data on other metrics computed from the MD trajectory, such as RMSD, RMSF, RoG, and SASA, further corroborate this assertion and provide a direction for further research.

Table 8 Using the MM/PBSA approach, binding free energies (kcal/mol) for the complexes were determined for the MD simulations
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3.8 Cluster analysis and distance plot

The interaction analysis data from MD simulations of the LAMP-1 enzyme (5GV0) with the chosen molecules, as determined by contact frequency calculations (Table 4) and MM/PBSA (Table 8), is discussed in this section. These data highlight the significance of conserved residues. Figure 17 provides a summary of the interactions seen in the representative structure of ligand–protein complexes derived from MD trajectory cluster analysis. It has been discovered that the ligand CID 44555488 with LAMP-1 5GV0 forms no hydrogen bonds with the enzyme and only three hydrophobic (pi-alkyl) contacts (Fig. 17A). A carbon-hydrogen bond interaction with TYR235 and two conventional hydrogen bonds, MET338 and LUE236 are found inside the ligand CID 45487645 with LAMP-1 5GV0 (Fig. 17B). Over the course of the simulation trajectory, it was discovered that both of the hydrogen bonds found in the cluster analysis were stable. The distance map between the respective atoms (Fig. 18) was analyzed to confirm this fact once more. Distance study shows that the carbonyl (-CO) group of ligand CID 45487645 and the -NH group of MET338 are still relatively near to one another. The hydrogen bond between the ligand and MET338 is 7.29 Å at the start of the MD simulation. The -NH group of MET338 does, however, become closer to the ligand as the simulation progresses, reaching 3.61 Å, 3.01 Å, and 3.64 Å at 30 ns, 40 ns, and 65.8 ns, respectively. Nevertheless, over the simulation's 70–100 ns, hydrogen bonding with MET338 was found to be 1.88 Å (Fig. 18A).

Fig. 17.
figure 17

2D cluster interactions between ligand CID (A) 44,555,488 and B 45,487,645 with LAMP− 1 5GV0

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Fig. 18
figure 18

The plot shows the distance between atom pairs that form hydrogen bonds during the MD simulation trajectory between ligand CID 45487645 and MET338 (A) and LEU236 (B)

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The Ligand CID 45487645 experiment predicted the formation of hydrogen bonds between LEU236 and its fluorobenzene ring substituent (Fig. 17B). Based on the distance analysis of these hydrogen bonds, at the start of the simulation, LEU236 forms a hydrogen bond at 5.14 Å. The hydrogen bonds are 4.91 Å, 4.79 Å, and 5.73 Å at 18 ns, 33 ns, and 70 ns. Similarly, over the simulation's 95–100 ns, residue LEU236 and ligand CID 45487645 displayed a persistent hydrogen bond contact at 3.25 Å (Fig. 18B).

4 Conclusion

Efficient anti-tumor options for cancer therapy were identified by applying systematic in silico research to twenty pyrazole derivatives as Lysosome-associated membrane protein 1 (LAMP-1) inhibitors. These compounds, ligand CID 44555488 and ligand CID 45487645 are shown to have stable interactions and high binding affinity scores on average based on the docking result. These two chemicals were used to test the in-silico ADME characteristic. It is clear from the ADME property result and Lipinski's contribution that the chosen compounds have a high drug-likeness score. In contrast to computationally costly molecular mechanics/dynamics and solvation effects-based classical energy estimates of ligand-target complexes (e.g., MM-GBSA and MM-PBSA), ES-Screen outperforms them and is far more efficient. Consequently, molecular docking simulations are better suited for virtual screenings with a large throughput. On the other hand, the MM-GBSA/MMPBSA techniques are more restricted to tiny libraries of chemical congeners or a small portion of the virtual chemical library (for example, the top 1% produced from docking). These criteria are very valuable in that they aid in the creation of new inhibitors with great potential for use in pharmaceutical research. This concept serves as a basis that will eventually need validation using in vitro experiments.