Computational Investigations on the Natural Small Molecule as an Inhibitor of Programmed Death Ligand 1 for Cancer Immunotherapy

Several therapeutic monoclonal antibodies approved by the FDA are available against the PD-1/PD-L1 (programmed death 1/programmed death ligand 1) immune checkpoint axis, which has been an unprecedented success in cancer treatment. However, existing therapeutics against PD-L1, including small molecule inhibitors, have certain drawbacks such as high cost and drug resistance that challenge the currently available anti-PD-L1 therapy. Therefore, this study presents the screening of 32,552 compounds from the Natural Product Atlas database against PD-L1, including three steps of structure-based virtual screening followed by binding free energy to refine the ideal conformation of potent PD-L1 inhibitors. Subsequently, five natural compounds, i.e., Neoenactin B1, Actinofuranone I, Cosmosporin, Ganocapenoid A, and 3-[3-hydroxy-4-(3-methylbut-2-enyl)phenyl]-5-(4-hydroxybenzyl)-4-methyldihydrofuran-2(3H)-one, were collected based on the ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiling and binding free energy (>−60 kcal/mol) for further computational investigation in comparison to co-crystallized ligand, i.e., JQT inhibitor. Based on interaction mapping, explicit 100 ns molecular dynamics simulation, and end-point binding free energy calculations, the selected natural compounds were marked for substantial stability with PD-L1 via intermolecular interactions (hydrogen and hydrophobic) with essential residues in comparison to the JQT inhibitor. Collectively, the calculated results advocate the selected natural compounds as the putative potent inhibitors of PD-L1 and, therefore, can be considered for further development of PD-L1 immune checkpoint inhibitors in cancer immunotherapy.


Introduction
Cancer is a severe concern faced by researchers worldwide due to the increase in the number of patients and its capacity to shorten the human lifespan. The International Agency for Research on Cancer (IARC) estimated~19.3 million new cancer cases and almost 10 million cancer deaths in their Global Cancer Statistics (GLOBOCAN) 2020 report, in which one-half of the new cases and more than 58% of cancer deaths occurred in Asian countries [1]. Although conventional treatment strategies, surgery, radiotherapies, and chemotherapy have shown massive progress in treating cancer within the past centuries, PD-L1 inhibitors and reflected by the number of growing publications and patents [36,37]. However, none of the reported small molecules have progressed into clinical trials to date [38]. Thus, finding natural products with the ability to inhibit PD-L1 can lead to the development of a novel small molecule-based cancer immunotherapy. Additionally, molecular docking simulations were recently applied to identify the potential terphenylbased small-molecule inhibitors against PD-L1 protein to disturb the PD-1/PD-L1 signaling pathway [38]. Therefore, in this study, we have utilized the multi-step virtual screening protocol linked with binding free energy and ADMET/pose filtering to identify the putative natural compounds as PD-L1 inhibitors for cancer immunotherapy.

Multi-Step Virtual Screening and Pose Filtration
Multi-step virtual screening of the ligands against PD-L1 was performed using a virtual screening workflow in the Schrödinger suite 2020-4 [40]. Initially, the pre-processing of the PD-L1 protein as receptor was performed using the PRIME tool [41,42] and protein preparation wizard [43] in the Maestro-Schrödinger suite 2020-4 [40]. Briefly, the cocrystallized water molecules were removed from the protein structure, which may obstruct the ligand interaction with the protein, while polar hydrogen atoms were added based on the hybridization of carbon atoms followed by protein structure refinement under default parameters using the Protein preparation wizard. Following, the key residues, viz. Phe 19 , Ile 54 , Tyr 56 , Met 115 , Ile 116 , Ala 121 , Asp 122 , Tyr 123 , Lys 124 , and Arg 125 of Chain A and Tyr 56 , Gln 66 , Met 115 , Ile 116 , Ser 117 , Ala 121 , and Asp 122 of chain B, in PD-L1 structure showing interactions with the co-crystalized ligand, i.e., JQT inhibitor, were considered for docking grid generation to perform the multi-step structure-based virtual screening under default parameters using GLIDE v8.9 tool in the Maestro-Schrödinger suite 2020-4 [44].
Likewise, 32,552 natural compounds were prepared as ligands under default parameters using LigPrep module tool in the Schrödinger suite 2020-4 [45]. Briefly, in the virtual screening workflow tool, all the ligands were filtered for the ADME (absorption, distribution, metabolism, and excretion) and drug-likeness criteria using the QikProp tool [46]. Following, the filtered ligands were virtually screened via three subsequent steps, including (i) high throughput virtual screening (HTVS), (ii) standard precision (SP) screening, and (iii) extra precision (XP) screening protocol, where only 10% of the top screened ligands from the first step were considered in next successive step to collect the most potent ligands against the PD-L1 protein. Finally, the post-process binding free energy calculations, based on molecular mechanics generalized Born surface area (MM/GBSA) method, were performed on the screened poses under default parameters with OPLS (Optimized Potentials for Liquid Simulations)-3e force field in Prime MM/GBSA module of Maestro-Schrödinger suite 2020-4 [40,42]. The binding free energy calculation was conducted on the screened poses to distinguish the most suitable docked conformations of the natural compounds with the PD-L1 protein for further computational analysis. The equations, used to cal-culate binding free energy and the associated energy dissociation components for each protein-ligand system, are described in the mathematical Equations (1)- (3).
In the above-mentioned equations, ∆G Bind and ∆G Com denote the binding free energy, or Gibbs free energy, and the total free energy of a docked complex containing protein and ligand, respectively. The sum of the protein and ligand in their free state is denoted by the ∆G Rec + ∆G Lig . The ∆G Bind can also be computed from enthalpy (∆H) and entropy (−T∆S) for the whole system under consideration using the second law of thermodynamics (Equation (1)). Here, in this study, the entropy contributing to the net ∆G Bind was not calculated for the protein-ligand complexes due to the unavailability of expensive computational calculations. In addition, the entropy seems to have a low contribution to the net ∆G Bind for similar systems, as reported earlier [47][48][49][50][51]. Therefore, ∆G Bind of the protein-ligand complex is designated equivalent to ∆H only, which is expressed as the sum of solvation free (∆G Sol ) and molecular mechanical (∆E MM ) energy (Equation (2)). Usually, ∆E MM constitutes the intermolecular, electrostatic, and van der Waals interactions energies represented as ∆E Int , ∆E Ele , and ∆E vdW , respectively, whereas the ∆G Sol constitutes polar and non-polar energies represented as ∆G Pol and ∆E Nonpol , respectively, for the whole system. Hence, the ∆G Bind for each docked protein-ligand complex was calculated using Prime MM/GBSA module under default parameters using the Prime MM/GBSA module of Maestro-Schrödinger suite 2020-4, as reported earlier [52,53].
Moreover, the crystal structure of PD-L1 with JQT inhibitor in the protein crystal structure was also docked in the protein pocket under similar conditions and used as a reference complex for the comparative analysis with the docked complexes of PD-L1 with screened natural compounds. The intermolecular interaction analysis of the docked complexes was extracted at 4 Å around the ligand in the binding pocket of the PD-L1 protein under default parameters of the Maestro-Schrödinger suite 2020-4. All 2D and 3D images of both ligand and receptor were rendered using the free academic version of the Maestro v12.6 tool of Schrödinger suite 2020-4 [54].

Molecular Dynamic Simulation
The best-docked poses of PD-L1 with natural compounds were studied for their dynamic stability and intermolecular interaction profiling as a function of 100 ns interval under explicit solvent molecular dynamics (MD) simulation on a Linux environment over HP Z2 Microtower workstation using the free academic version of Desmond v5.6 [55] module in Maestro-Schrödinger suite 2018-4 [56]. Herein, each complex was placed in the center of the orthorhombic grid box (10 Å × 10 Å × 10 Å) solvated with TIP4P (transferable intermolecular potential 4 points) to collect maximum solvation effects of the natural solvent. Additionally, the whole system was neutralized using the counter sodium and chlorine ions while placed at 20 Å around the docked ligand within the binding pocket of the PD-L1 protein. Moreover, 0.15 M salt was added to the system to mimic the physiological conditions for the docked complex using the system building tool. Furthermore, the complete system was subjected to 2000 steps and a 1.0 kcal/mol/Å convergence threshold for the initial minimization using minimization tool. Eventually, the unrestrained 100 ns MD simulation trajectories were generated for each complex under a normal temperature and pressure (NPT) ensemble at 300 K with a 10 ps step size under default parameters with Optimized Potentials for Liquid Simulations (OPLS)-2005 force field, and later produced trajectories were analyzed using the simulation interaction diagram (SID) tool in free academic Desmond v5.6 module with Maestro-Schrödinger suite 2018-4 interface [56].

End-Point Binding Free Energy Calculation
The end-point binding free energy calculations were conducted on the complete 100 ns MD simulation trajectory of each docked complex under the OPLS-3e force field using the Prime MM/GBSA module [40,42], as described earlier under section, 'Multi-step virtual screening and pose filtration'. Herein, snapshots collected at every 10 ps were treated for the removal of explicit solvent and ions molecules, and computed binding free energy for each complex is depicted as a function of 100 ns and as the mean with standard error.

Virtual Screening and ADMET Analysis
Virtual screening techniques are commonly used in the drug discovery pipelines to identify a ligand with considerable binding affinity with a receptor from the large compound databases and evaluate the ligand binding energy in terms of scoring functions [57][58][59]. However, accuracy remains a major limitation contributed by the least conformational sampling of ligand and reliability of approximate scoring functions implemented in virtual screening applications. This may result in the collection of false-positive and false-negative hits, which then require rigorous assessment before further computational analysis [59][60][61]. In this context, the refinement of the generated poses using binding free energy calculations have been demonstrated as an ideal method to distinguish the positive hits and ranking of cognate ligands identified using virtual screening applications [62][63][64]. Notably, MM/GBSA method has been reported to accurately assess the binding free energy between protein and small-molecule ligands [47,65,66].

Docking Pose Validation and Interaction Analysis
Initially, the co-crystallized ligand, i.e., JQT inhibitor, was docked in the targeted binding pocket of the PD-L1 protein followed by the selection of an ideal conformation based on binding free energy (−63.98 kcal/mol) ( Table 1). To validate the selected pose of the reference complex, docked conformation of the JQT inhibitor was aligned to cocrystallized conformation in the PD-L1 structure (PBD ID: 6R3K) using the Structure superimpose tool in the Maestro-Schrödinger suite 2020.4 ( Figure 2). Interestingly, docked conformation showed absolute matching with 0.217 Å root mean square deviation (RMSD) aligned on the native conformation of the JQT inhibitor in the crystal structure of the PD-L1 protein. These results support the considered binding pocket in the identification of putative natural compounds as inhibitors of PD-L1 protein; hence, the respective docked poses were considered for further computational analysis. The molecular interaction analysis for the protein-ligand docked complex is employed to determine the effectiveness of the compound against a target in the structurebased drug discovery approaches [72]. Thus, molecular docked poses of the selected five natural compounds, i.e., Neoenactin B1, Actinofuranone I, Cosmosporin A, Ganocapenoid A, and 3-[3-hydroxy-4-(3-methylbut-2-enyl)phenyl]-5-(4-hydroxybenzyl)-4-methyldihydrofuran-2(3H)-one, as putative inhibitors and the JQT inhibitor as the reference ligand were studied for the residual interactions at 4 Å radius around the docked ligand in the binding pocket of the PD-L1 protein ( Table 2).
The  The molecular interaction analysis for the protein-ligand docked complex is employed to determine the effectiveness of the compound against a target in the structure-based drug discovery approaches [72]. Thus, molecular docked poses of the selected five natural compounds, i.e., Neoenactin B1, Actinofuranone I, Cosmosporin A, Ganocapenoid A, and 3-[3hydroxy-4-(3-methylbut-2-enyl)phenyl]-5-(4-hydroxybenzyl)-4-methyldihydrofuran-2(3H)one, as putative inhibitors and the JQT inhibitor as the reference ligand were studied for the residual interactions at 4 Å radius around the docked ligand in the binding pocket of the PD-L1 protein ( Table 2).
The However, no H-bond formation was observed in the reference complex, i.e., PD-L1-JQT inhibitor ( Table 2, Supporting Information Figure S1). Additionally, only PD-L1-Neoenactin B1 complex was noted for salt-bridge formation with B:Asp 61 residue in comparison to PD-L1-JQT inhibitor complex (A:Lys 124 ). Moreover, PD-L1-Ganocapenoid A and PD-L1-3-[3hydroxy-4-(3-methylbut-2-enyl)phenyl]-5-(4-hydroxybenzyl)-4-methyldihydrofuran-2(3 H)one complexes exhibited π-π stacking interactions at A:Tyr 56 residue in comparison to PD-L1-JQT inhibitor complex, which showed both π-π stacking (B:Tyr 56 ) and π-cation stacking interactions (A:Lys 124 ). Additionally, the docked natural compounds and the JQT inhibitor in the binding pocket of PD-L1 were also observed for other residual interactions, involving hydrophobic, polar, positive, and negative interactions (Table 2, Figure 3, Supporting Information Figure S1). Notably, interacting residues with the natural compounds were also noted in the interaction map of the reference complex (residues are highlighted as 'bold text' in Table 1), indicating the natural compounds have relatively occupied the same binding pocket as the reference ligand. Thereof, based on the binding energy profiles (docking scores and binding free energy) in association with the observed intermolecular interactions of the docked natural compounds against PD-L1-JQT inhibitor complex, the docked natural compounds in the binding pocket of PD-L1 are suggested to have substantial stability by comparison to the JQT inhibitor, and may contribute to inhibition of the PD-1/PD-L1 signaling pathway, as reported for the JQT inhibitor [38]. Table 2. Intermolecular interaction profiles for the docked natural compounds conformation with active residues in the binding pocket of the PD-L1 protein.
Initially, the last poses of the 100 ns MD simulations were recovered and compared with respective docked poses for molecular contacts analysis to assure the residence of the docked natural compounds as ligand in the considered binding pocket of the PD-L1 protein via constant or similar number of residual interactions and studied for conformational changes in both proteins and ligands (Table 3, Figure 4). Notably, all the last poses of the docked natural compounds with PD-L1 receptor after 100 ns MD simulation were observed for consistent residual interactions in comparison to the respective docked poses, supporting the stability of all the docked natural compounds in the binding pocket of PD-L1 receptor in comparison to the reference complex (Table 3). Moreover, 3D surface analysis of the last poses from the 100 ns MD simulation reveals substantial conformational changes in the protein structure docked with natural compounds against the PD-L1 protein docked with the JQT inhibitor, suggesting the potential of docked natural compounds to significantly disturb the native conformational of the PD-L1 protein ( Figure 4). Furthermore, the MD simulation trajectories of the respective docked complexes were statistically analyzed in terms of root mean square deviation (RMSD), root mean square fluctuation (RMSF), and protein-ligand interaction fraction mapping to understand the dynamic stability of the docked complexes as a function of 100 ns MD simulation interval.

RMSD and RMSF Analysis
Initially, RMSD values were computed from the respective docked poses of PD-L1natural compounds as a function of 100 ns simulation interval and analyzed in comparison to the RMSD values of apo-protein and PD-L1-JQT inhibitor complex ( Figure 5, Supporting Information Figure S2). In all the docked complexes of PD-L1 with natural compounds, substantial deviations (>3.5 Å) were observed in the protein throughout 100 ns MD simulation, except in PD-L1-Ganocapenoid A complex, where equilibrium in the protein RMSD (~4 Å) was noted after 40 ns until the end of the 100 ns MD simulation, while PD-L1 protein exhibited higher deviations (>4.8 Å) in PD-L1-Neoenactin B1 and PD-L1-Actinofuranone I docked complexes during the simulation interval. However, PD-L1 docked with the JQT inhibitor showed high deviation (~4 Å) within the first 10 ns interval followed by a state of global minima (~3 Å) until the end of simulation, while apo-PD-L1 receptor was also noted for consistent deviations (<4.2 Å) on several occasions without a state of equilibrium during the MD simulation interval. These observations suggested that, unlike the JQT inhibitors, docked natural compounds, particularly Neoenactin B1 and Actinofuranone I, may promote the strong global conformational changes in the PD-L1 protein.
These observations were further supported by the calculated RMSF values (>2.5 Å) for the docked PD-L1 with Neoenactin B1 and Actinofuranone I natural compounds in comparison to apo-protein (<2.5 Å) and protein docked with the JQT inhibitor (<2.5 Å) (Supporting Information Figures S2 and S3).

RMSD and RMSF Analysis
Initially, RMSD values were computed from the respective docked poses of PD-L1natural compounds as a function of 100 ns simulation interval and analyzed in comparison to the RMSD values of apo-protein and PD-L1-JQT inhibitor complex ( Figure 5, Supporting information Figure S2). In all the docked complexes of PD-L1 with natural compounds, substantial deviations (>3.5 Å ) were observed in the protein throughout 100 ns MD simulation, except in PD-L1-Ganocapenoid A complex, where equilibrium in the protein RMSD (~4 Å ) was noted after 40 ns until the end of the 100 ns MD simulation, while PD-L1 protein exhibited higher deviations (>4.8 Å ) in PD-L1-Neoenactin B1 and PD-L1-Actinofuranone I docked complexes during the simulation interval. However, PD-L1 docked with the JQT inhibitor showed high deviation (~4 Å ) within the first 10 ns interval followed by a state of global minima (~3 Å ) until the end of simulation, while apo-PD-L1 receptor was also noted for consistent deviations (<4.2 Å ) on several occasions without a state of equilibrium during the MD simulation interval. These observations suggested that, unlike the JQT inhibitors, docked natural compounds, particularly Neoenactin B1 and Actinofuranone I, may promote the strong global conformational changes in the PD-L1 protein. These observations were further supported by the calculated RMSF values (>2.5 Å ) for the docked PD-L1 with Neoenactin B1 and Actinofuranone I natural compounds in comparison to apo-protein (<2.5 Å ) and protein docked with the JQT inhibitor (<2.5 Å ) (Supporting information Figures S2 and S3).  Likewise, protein-fit ligand RMSD analysis indicated substantial global minima (<3 Å) for all the docked natural compounds throughout the 100 ns MD simulation interval, except for the Cosmosporin A and 3-[3-hydroxy-4-(3-methylbut-2-enyl)phenyl]-5-(4hydroxybenzyl)-4-methyldihydrofuran-2(3H)-one compounds, which showed <2 Å RMSD as observed for the JQT inhibitor (<2 Å) ( Figure 5). Moreover, computed ligand RMSF also reveals the acceptable values (<3 Å) for all the docked natural compounds with PD-L1 against the JQT inhibitor (Supporting Information Figure S4), suggesting the substantial stability of the docked ligand with the protein. Collectively, RMSD and RMSF analysis of the docked complexes suggested the considerable stability of the docked ligands with the protein, while docked natural compounds were noted to induce substantial conformational changes in the PD-L1 protein structure that may results in the inhibition of PD-L1 protein with its receptor in the PD-1/PL-1 pathway.

Protein-Ligand Interaction Mapping
To further access the stability of the docked complexes in terms of intermolecular interactions as a function of simulation interval, protein-ligand contact maps, including H-bonding, hydrophobic interactions, ionic interactions, and water bridge formation, were extracted from the respective 100 ns MD simulation trajectories ( Figure 6). Notably, all the docked natural compounds showed considerable molecular contacts with the active residues in the binding pocket of the PD-L1 protein during the simulation interval in comparison to the reference complex, i.e., PD-L1-JQT inhibitor; the interacting residues were also noted in the initially docked poses (Table 2).
In the PDL1-Neoenactin B1 complex (Figure 5a), B:Asp 61 (which formed two H-bonds in docked complex) exhibited substantial H-bond formation for~100% of the interaction fraction, while B:Tyr 56 (which formed single H-bond in docked complex) and A:Tyr 123 (noted for both H-bond and hydrophobic interactions in docked complex) were noted for hydrophobic interactions with docked ligands for~20% of the total interaction fraction during the simulation interval. Additionally, B:Glu 58 (which depicted negative residual interaction in docked complex) showed~50% and~15% of total interaction fractions in water bridge formation and ionic bond formations, respectively, during the 100 ns simulation interval.
Likewise, in the PDL1-Actinofuranone I complex (Figure 5b), A:Asp 122 (which showed both H-bond and negative residual interaction in docked complex) residue contributed in~90% of the interaction fraction for H-bond formation, in addition to water bridge formation (~60% interaction fraction) during the 100 ns simulation interval. Moreover, A:Tyr 56 (which exhibited both H-bond and hydrophobic interaction in docked complex) and A:Tyr 123 (which displayed hydrophobic interaction in docked complex) showed~30% of the interaction fraction in hydrophobic interactions, A:Arg 125 (observed for intermolecular interaction during MD simulation only) exhibited water bridge formation (~50% interaction fraction), and B:Tyr 56 (which showed both H-bond and hydrophobic interaction in docked complex) formed H-bond (~70% of the interaction fraction) during total simulation interval.
Additionally, protein-ligand contact analysis of the PDL1-Cosmosporin A complex showed substantial contribution of A:Tyr 56 , B:Tyr 56 , and B:Ala 121 (all three residues also showed hydrophobic interactions in docked complex) in hydrophobic interaction (~50% of the interaction fraction); A:Tyr 123 (which displayed H-bond and hydrophobic interactions in docked complex) and B:Asp 122 (which displayed H-bond formation in docked pose) noted 90% and~85% of interaction fraction in H-bond formation, respectively; and A:Arg 125 (observed for interaction during MD simulation only) presented water bridge formation (~55% of the interaction fraction) during the 100 ns MD simulation interval (Figure 5c).  Furthermore, analysis of the PD-L1-Ganocapenoid A complex showed substantial contribution of A:Tyr 56 (noted for π-π stacking interaction in docked complex) and A:Gln 66 (distinguished for polar interaction in docked complex) in H-bond formation for~65% and~55% interaction fraction, respectively; B:Asp 73 (detected for interaction during MD simulation only) exhibits water bridge formation (~55% interaction fraction), and B:Tyr 123 (which displayed hydrophobic interaction in docked pose) was noted for hydrophobic interactions (~85% interaction fraction) during the total simulation interval (Figure 5d).
Whilst Additionally, molecular contact formation between the receptor and docked natural compounds were also logged at~30% of the total simulation interval, indicating substantial contribution of H-bonding and hydrophobic interactions in comparison to the reference complex (Supporting Information Figure S5). Altogether, collected interaction profiles as a function of simulation interval indicate a substantial contribution of H-bonds and hydrophobic interactions in the dynamic stability of PD-L1-natural compounds complexes during the 100 ns MD simulations. Hence, based on 100 ns MD simulation trajectories analysis, the selected docked complexes can be arranged in order of stability, i.e., PDL1-Neoenactin B1, PDL1-Cosmosporin A, PDL1-Actinofuranone I, PDL1-Ganocapenoid A, and PDL1-3-[3hydroxy-4-(3-methylbut-2-enyl)phenyl]-5-(4 hydroxybenzyl)methyldihydrofuran an-2(3H)one, in comparison to the reference complex, viz. PD-L1-JQT inhibitor.

End-Point Binding Free Energy Analysis
In addition, to understand the macromolecular system at atomic level, the application of MD simulation is also helpful to decipher the hidden or undetected states of system under consideration [73][74][75]. Thus, as end-point free energy methods [76][77][78], MD simulations in combination with binding free energy methods have been comprehensively used in structure-based drug design to determine the thermodynamic properties of the macromolecular system, including stability, affinity, and free energy decomposition analysis [75]. Therefore, the most well-known end-point free energy MM/GBSA method, which provides an ideal balance between accuracy and computational efficiency, was utilized on the complete 100 ns MD simulation trajectories to calculate the end-point binding free energy for each PD-L1-ligand complex (Supporting Information Table S2, Figure 7).
Initially, calculated binding free energy for the complete 100 ns simulation trajectories of the docked PD-L1 with natural compounds were analyzed in comparison to the reference complex. Although PD-L1-natural compounds showed prominent binding free energy between −60 to −90 kcal/mol against PD-L1-JQT inhibitor (−90 to −130 kcal/mol), some conformations also exhibited higher energy >−90 kcal/mol during the simulation interval. As expected, these conformations represent the most stable poses of the ligand compounds with the PD-L1 protein (Figure 7). These observations suggested that docked natural compounds may exhibit higher binding affinities than the predicted binding affinities using MM/GBSA method.
Furthermore, average binding free energy values for each simulated complex were computed along with energy decomposition components to assess the favorable and unfavorable energy terms to the net biding free energy of the system and compared with the respective energy terms computed on docked poses (Figure 7). Interestingly, all the PD-L1-natural compound complexes showed significant increment in the binding free energy (>−80 kcal/mol) in comparison to the respective docked poses (>−60 kcal/mol), except PD-L1-Neoenactin B1 complex (−73.55 ± 7.62 kcal/mol). Similarly, PD-L1-JQT inhibitor also showed considerable hike in the binding free energy (−112.91 ± 8.33 kcal/mol) after the MD simulation against docked pose (−96.7 kcal/mol). Among the PD-L1 natural compounds, PD-L1-3-[3hydroxy-4-(3-methylbut-2-enyl)phenyl]-5-(4hydroxybenzyl)methyldihydrofuran-2(3H)-one docked complex (−87.21 ± 4.11 kcal/mol) and PD-L1-Neoenactin B1 complex (−73.55 ± 7.62 kcal/mol) were marked for the highest and lowest end-point binding free energy, respectively, after the 100 ns MD simulation (Figure 7), whereas the end-point binding free energy of the docked complex PD-L1-Actinofuranone I was −81.07 ± 6.41 kcal/mol, followed by −83.96 ± 4.51 kcal/mol for PD-L1-Cosmosporin A, and −84.56 ± 6.36 kcal/mol for PD-L1-Ganocapenoid A (Figure 7). Furthermore, the computation of dissociation energy components for each complex before and after MD simulation revealed the favorable contribution of ∆G Bind Lipo (Lipophilic) and ∆G Bind vdW (Van der Waals interaction) energies to the net stability of docked complexes, whereas ∆G Bind Solv GB (Generalized Born electrostatic solvation energy) substantially contributed to the instability of the respective docked complexes (Figure 7). Notably, after the 100 ns MD simulation, a decrement in the ∆G Bind Solv GB was noted and no substantial difference was observed in the ∆G Bind Lipo energy terms. Similar energy dissociation components were previously noted to contribute to the stability of the docked complexes of the PD-L1 protein [79,80]. Furthermore, reduction in net ligand strain energy was also noted following 100 ns MD simulation in each complex in comparison to the respective docked poses, suggesting the favorable contribution to the protein-ligand complex stability after 100 ns MD simulation ( Figure 7). Hence, from the comparative binding free energy analysis of docked poses and MD simulation trajectories values, natural compounds are endorsed as potential hit candidates in comparison to the reference compounds, i.e., JQT inhibitor, for the development of PD-L1 inhibitors for cancer immunotherapy.

Conclusions
Programmed death ligand-1 (PD-L1) is a potential target for the suppression of cancer progression. The development of anti-cancer compounds using natural compounds by inhibiting the PD-L1 protein can be a turning point in the field of cancer immunotherapy. In this study, five natural products, i.e., Neoenactin B1, Actinofuranone I, Cosmosporin A, Ganocapenoid A, and 3-[3-hydroxy-4-(3-methylbut-2-enyl)phenyl]-5-(4hydroxybenzyl)methyldihydrofuran-2(3H)-one, are identified as potential candidates for the PD-L1 protein inhibition, with substantial drug-likeness, docking energy (>−10 kcal/mol), and MM/GBSA binding free energy (>−60 kcal/mol). In addition, the intermolecular interaction profiling of the docked poses and 100 ns molecular dynamic simulation trajectory analysis promotes the substantial contribution of H-bonding and hydrophobic interactions in the stability of docked natural compounds with the PD-L1 protein. Furthermore, calculated net binding free energy on each simulation trajectory supports the stability of the docked complexes in comparison to the reference complex and advocates the selected natural compounds as potent candidates for the development of PD-L1 inhibitors. Overall, the computational investigation of the natural compounds as PD-L1 inhibitors provides a positive endorsement for the selected natural compounds in the eventual designing and development of an effective small molecule-based anti-PD-L1 agent, which may provide PD-L1 inhibition at low concentration, to disturb the PD-1/PD-L1 signaling pathway for the cancer immunotherapy.
Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/life12050659/s1, Table S1: ADMET prediction for the screened five natural compounds as PD-L1 inhibitor, Table S2: Calculated net binding free energy for the selected docked poses of PD-L1-natural compounds snap shots from the last 10 ns interval of 100 ns MD simulation, Figure S1: (a) 3D and (b) 2D interaction poses of the reference complex, i.e., PD-L1-JQT inhibitor. In 2D interaction maps, green line (π-π stacking), red line (π-cation stacking), red-violet line (salt-bridge), red color residues (negative), violet color residues (positive), green color residues (hydrophobic), and blue color residues (polar) exhibits the intermolecular interactions at a radius of 4 Å around the ligand in the respective docked complexes, Figure S2

Data Availability Statement:
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.